diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..fa5d2d310a4b0433746ddff7cf8f2269f0c46a74 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +images/comparison.png filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..198cbcaf6f04000cda82b25336e424aedbef11f0 --- /dev/null +++ b/README.md @@ -0,0 +1,16 @@ +--- +title: Gradio Lipsync Wav2lip +emoji: 👄 +colorFrom: indigo +colorTo: blue +sdk: gradio +python_version: 3.8 +sdk_version: 3.40.1 +suggested_hardware: t4-medium +app_file: app.py +pinned: false +license: apache-2.0 +duplicated_from: manavisrani07/gradio-lipsync-wav2lip +--- + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/__pycache__/audio.cpython-310.pyc b/__pycache__/audio.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..28037a84140fdcb32efc8abc65e3a7d6980ddba7 Binary files /dev/null and b/__pycache__/audio.cpython-310.pyc differ diff --git a/__pycache__/audio.cpython-38.pyc b/__pycache__/audio.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ab9bf2116dec6854c52c531eaa73fead08e0bb3a Binary files /dev/null and b/__pycache__/audio.cpython-38.pyc differ diff --git a/__pycache__/hparams.cpython-310.pyc b/__pycache__/hparams.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2a48baf877cde292f85e3b765cc56d43e43b8c31 Binary files /dev/null and b/__pycache__/hparams.cpython-310.pyc differ diff --git a/__pycache__/hparams.cpython-38.pyc b/__pycache__/hparams.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0539d51e88ebd6043b527a02284bf293bedd7e30 Binary files /dev/null and b/__pycache__/hparams.cpython-38.pyc differ diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..8fb1ba65786ca5c993471df127940c966765bba5 --- /dev/null +++ b/app.py @@ -0,0 +1,49 @@ +import gradio as gr +import subprocess +from subprocess import call + +with gr.Blocks() as ui: + with gr.Row(): + video = gr.File(label="Video or Image", info="Filepath of video/image that contains faces to use") + audio = gr.File(label="Audio", info="Filepath of video/audio file to use as raw audio source") + with gr.Column(): + checkpoint = gr.Radio(["wav2lip", "wav2lip_gan"], label="Checkpoint", info="Name of saved checkpoint to load weights from") + no_smooth = gr.Checkbox(label="No Smooth", info="Prevent smoothing face detections over a short temporal window") + resize_factor = gr.Slider(minimum=1, maximum=4, step=1, label="Resize Factor", info="Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p") + with gr.Row(): + with gr.Column(): + pad_top = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Pad Top", info="Padding above") + pad_bottom = gr.Slider(minimum=0, maximum=50, step=1, value=10, label="Pad Bottom (Often increasing this to 20 allows chin to be included)", info="Padding below lips") + pad_left = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Pad Left", info="Padding to the left of lips") + pad_right = gr.Slider(minimum=0, maximum=50, step=1, value=0, label="Pad Right", info="Padding to the right of lips") + generate_btn = gr.Button("Generate") + with gr.Column(): + result = gr.Video() + + def generate(video, audio, checkpoint, no_smooth, resize_factor, pad_top, pad_bottom, pad_left, pad_right): + if video is None or audio is None or checkpoint is None: + return + + smooth = "--nosmooth" if no_smooth else "" + + + cmd = [ + "python", + "inference.py", + "--checkpoint_path", f"checkpoints/{checkpoint}.pth", + "--segmentation_path", "checkpoints/face_segmentation.pth", + "--enhance_face", "gfpgan", + "--face", video.name, + "--audio", audio.name, + "--outfile", "results/output.mp4", + ] + + call(cmd) + return "results/output.mp4" + + generate_btn.click( + generate, + [video, audio, checkpoint, pad_top, pad_bottom, pad_left, pad_right, resize_factor], + result) + +ui.queue().launch(debug=True) \ No newline at end of file diff --git a/audio.py b/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..67c860222453f0a74b23f08eb310e7f1fb3dd722 --- /dev/null +++ b/audio.py @@ -0,0 +1,142 @@ +import librosa +import librosa.filters +import numpy as np +# import tensorflow as tf +from scipy import signal +from scipy.io import wavfile +from hparams import hparams as hp + +def load_wav(path, sr): + return librosa.core.load(path, sr=sr)[0] + +def save_wav(wav, path, sr): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + #proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + +def save_wavenet_wav(wav, path, sr): + librosa.output.write_wav(path, wav, sr=sr) + +def preemphasis(wav, k, preemphasize=True): + if preemphasize: + return signal.lfilter([1, -k], [1], wav) + return wav + +def inv_preemphasis(wav, k, inv_preemphasize=True): + if inv_preemphasize: + return signal.lfilter([1], [1, -k], wav) + return wav + +def get_hop_size(): + hop_size = hp.hop_size + if hop_size is None: + assert hp.frame_shift_ms is not None + hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) + return hop_size + +def linearspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(np.abs(D)) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def melspectrogram(wav): + D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) + S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db + + if hp.signal_normalization: + return _normalize(S) + return S + +def _lws_processor(): + import lws + return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") + +def _stft(y): + if hp.use_lws: + return _lws_processor(hp).stft(y).T + else: + return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) + +########################################################## +#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) +def num_frames(length, fsize, fshift): + """Compute number of time frames of spectrogram + """ + pad = (fsize - fshift) + if length % fshift == 0: + M = (length + pad * 2 - fsize) // fshift + 1 + else: + M = (length + pad * 2 - fsize) // fshift + 2 + return M + + +def pad_lr(x, fsize, fshift): + """Compute left and right padding + """ + M = num_frames(len(x), fsize, fshift) + pad = (fsize - fshift) + T = len(x) + 2 * pad + r = (M - 1) * fshift + fsize - T + return pad, pad + r +########################################################## +#Librosa correct padding +def librosa_pad_lr(x, fsize, fshift): + return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] + +# Conversions +_mel_basis = None + +def _linear_to_mel(spectogram): + global _mel_basis + if _mel_basis is None: + _mel_basis = _build_mel_basis() + return np.dot(_mel_basis, spectogram) + +def _build_mel_basis(): + assert hp.fmax <= hp.sample_rate // 2 + return librosa.filters.mel( + sr=hp.sample_rate, + n_fft=hp.n_fft, + n_mels=hp.num_mels, + fmin=hp.fmin, + fmax=hp.fmax + ) + +def _amp_to_db(x): + min_level = np.exp(hp.min_level_db / 20 * np.log(10)) + return 20 * np.log10(np.maximum(min_level, x)) + +def _db_to_amp(x): + return np.power(10.0, (x) * 0.05) + + +def _normalize(S): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, + -hp.max_abs_value, hp.max_abs_value) + else: + return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) + + assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 + if hp.symmetric_mels: + return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value + else: + return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) + +def _denormalize(D): + if hp.allow_clipping_in_normalization: + if hp.symmetric_mels: + return (((np.clip(D, -hp.max_abs_value, + hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + + hp.min_level_db) + else: + return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) + + if hp.symmetric_mels: + return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) + else: + return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) diff --git a/basicsr/.DS_Store b/basicsr/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..4732bef5917ead8a7b11c7a889439f88057f1d2d Binary files /dev/null and b/basicsr/.DS_Store differ diff --git a/basicsr/__init__.py b/basicsr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5fc6c783308652d3f1cd3aca7507c616a8e421b8 --- /dev/null +++ b/basicsr/__init__.py @@ -0,0 +1,12 @@ +# https://github.com/xinntao/BasicSR +# flake8: noqa +from .archs import * +from .data import * +from .losses import * +from .metrics import * +from .models import * +from .ops import * +from .test import * +from .train import * +from .utils import * +#from .version import __gitsha__, __version__ diff --git a/basicsr/__pycache__/__init__.cpython-38.pyc b/basicsr/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3a54c6760a7fced3478ed679e8f326ddd77d429a Binary files /dev/null and b/basicsr/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/__pycache__/test.cpython-38.pyc b/basicsr/__pycache__/test.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6ab41eb95be70ac2c4ae21c3245f51a80098d2be Binary files /dev/null and b/basicsr/__pycache__/test.cpython-38.pyc differ diff --git a/basicsr/__pycache__/train.cpython-38.pyc b/basicsr/__pycache__/train.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0ae7af18b39014e4a5942c36a8dcdcdba4151546 Binary files /dev/null and b/basicsr/__pycache__/train.cpython-38.pyc differ diff --git a/basicsr/apply_sr.py b/basicsr/apply_sr.py new file mode 100644 index 0000000000000000000000000000000000000000..2bc5da02c5ca700228af406fd6185aa29de656b9 --- /dev/null +++ b/basicsr/apply_sr.py @@ -0,0 +1,25 @@ +import cv2 +import numpy as np +import torch + +from basicsr.archs.rrdbnet_arch import RRDBNet + + +def init_sr_model(model_path): + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32) + model.load_state_dict(torch.load(model_path)['params'], strict=True) + model.eval() + model = model.cuda() + return model + + +def enhance(model, image): + img = image.astype(np.float32) / 255. + img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float() + img = img.unsqueeze(0).cuda() + with torch.no_grad(): + output = model(img) + output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy() + output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) + output = (output * 255.0).round().astype(np.uint8) + return output diff --git a/basicsr/archs/__init__.py b/basicsr/archs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cfb1e4d7bb221c429082bd389d9140e5b1cc07b0 --- /dev/null +++ b/basicsr/archs/__init__.py @@ -0,0 +1,25 @@ +import importlib +from copy import deepcopy +from os import path as osp + +from basicsr.utils import get_root_logger, scandir +from basicsr.utils.registry import ARCH_REGISTRY + +__all__ = ['build_network'] + +# automatically scan and import arch modules for registry +# scan all the files under the 'archs' folder and collect files ending with +# '_arch.py' +arch_folder = osp.dirname(osp.abspath(__file__)) +arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(arch_folder) if v.endswith('_arch.py')] +# import all the arch modules +_arch_modules = [importlib.import_module(f'basicsr.archs.{file_name}') for file_name in arch_filenames] + + +def build_network(opt): + opt = deepcopy(opt) + network_type = opt.pop('type') + net = ARCH_REGISTRY.get(network_type)(**opt) + logger = get_root_logger() + logger.info(f'Network [{net.__class__.__name__}] is created.') + return net diff --git a/basicsr/archs/__pycache__/__init__.cpython-310.pyc b/basicsr/archs/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..94ba5907172046a35eaea51174093c4676bd510e Binary files /dev/null and b/basicsr/archs/__pycache__/__init__.cpython-310.pyc differ diff --git a/basicsr/archs/__pycache__/__init__.cpython-38.pyc b/basicsr/archs/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..305b71d7a2240b321874a1ac6b6ab8e4c45e07d6 Binary files /dev/null and b/basicsr/archs/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/archs/__pycache__/arch_util.cpython-310.pyc b/basicsr/archs/__pycache__/arch_util.cpython-310.pyc new file mode 100644 index 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(list[nn.Module] | nn.Module): Modules to be initialized. + scale (float): Scale initialized weights, especially for residual + blocks. Default: 1. + bias_fill (float): The value to fill bias. Default: 0 + kwargs (dict): Other arguments for initialization function. + """ + if not isinstance(module_list, list): + module_list = [module_list] + for module in module_list: + for m in module.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, nn.Linear): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, _BatchNorm): + init.constant_(m.weight, 1) + if m.bias is not None: + m.bias.data.fill_(bias_fill) + + +def make_layer(basic_block, num_basic_block, **kwarg): + """Make layers by stacking the same blocks. + + Args: + basic_block (nn.module): nn.module class for basic block. + num_basic_block (int): number of blocks. + + Returns: + nn.Sequential: Stacked blocks in nn.Sequential. + """ + layers = [] + for _ in range(num_basic_block): + layers.append(basic_block(**kwarg)) + return nn.Sequential(*layers) + + +class ResidualBlockNoBN(nn.Module): + """Residual block without BN. + + It has a style of: + ---Conv-ReLU-Conv-+- + |________________| + + Args: + num_feat (int): Channel number of intermediate features. + Default: 64. + res_scale (float): Residual scale. Default: 1. + pytorch_init (bool): If set to True, use pytorch default init, + otherwise, use default_init_weights. Default: False. + """ + + def __init__(self, num_feat=64, res_scale=1, pytorch_init=False): + super(ResidualBlockNoBN, self).__init__() + self.res_scale = res_scale + self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) + self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True) + self.relu = nn.ReLU(inplace=True) + + if not pytorch_init: + default_init_weights([self.conv1, self.conv2], 0.1) + + def forward(self, x): + identity = x + out = self.conv2(self.relu(self.conv1(x))) + return identity + out * self.res_scale + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.') + super(Upsample, self).__init__(*m) + + +def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True): + """Warp an image or feature map with optical flow. + + Args: + x (Tensor): Tensor with size (n, c, h, w). + flow (Tensor): Tensor with size (n, h, w, 2), normal value. + interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'. + padding_mode (str): 'zeros' or 'border' or 'reflection'. + Default: 'zeros'. + align_corners (bool): Before pytorch 1.3, the default value is + align_corners=True. After pytorch 1.3, the default value is + align_corners=False. Here, we use the True as default. + + Returns: + Tensor: Warped image or feature map. + """ + assert x.size()[-2:] == flow.size()[1:3] + _, _, h, w = x.size() + # create mesh grid + grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x)) + grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2 + grid.requires_grad = False + + vgrid = grid + flow + # scale grid to [-1,1] + vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0 + vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0 + vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3) + output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners) + + # TODO, what if align_corners=False + return output + + +def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False): + """Resize a flow according to ratio or shape. + + Args: + flow (Tensor): Precomputed flow. shape [N, 2, H, W]. + size_type (str): 'ratio' or 'shape'. + sizes (list[int | float]): the ratio for resizing or the final output + shape. + 1) The order of ratio should be [ratio_h, ratio_w]. For + downsampling, the ratio should be smaller than 1.0 (i.e., ratio + < 1.0). For upsampling, the ratio should be larger than 1.0 (i.e., + ratio > 1.0). + 2) The order of output_size should be [out_h, out_w]. + interp_mode (str): The mode of interpolation for resizing. + Default: 'bilinear'. + align_corners (bool): Whether align corners. Default: False. + + Returns: + Tensor: Resized flow. + """ + _, _, flow_h, flow_w = flow.size() + if size_type == 'ratio': + output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1]) + elif size_type == 'shape': + output_h, output_w = sizes[0], sizes[1] + else: + raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.') + + input_flow = flow.clone() + ratio_h = output_h / flow_h + ratio_w = output_w / flow_w + input_flow[:, 0, :, :] *= ratio_w + input_flow[:, 1, :, :] *= ratio_h + resized_flow = F.interpolate( + input=input_flow, size=(output_h, output_w), mode=interp_mode, align_corners=align_corners) + return resized_flow + + +# TODO: may write a cpp file +def pixel_unshuffle(x, scale): + """ Pixel unshuffle. + + Args: + x (Tensor): Input feature with shape (b, c, hh, hw). + scale (int): Downsample ratio. + + Returns: + Tensor: the pixel unshuffled feature. + """ + b, c, hh, hw = x.size() + out_channel = c * (scale**2) + assert hh % scale == 0 and hw % scale == 0 + h = hh // scale + w = hw // scale + x_view = x.view(b, c, h, scale, w, scale) + return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w) + + +class DCNv2Pack(ModulatedDeformConvPack): + """Modulated deformable conv for deformable alignment. + + Different from the official DCNv2Pack, which generates offsets and masks + from the preceding features, this DCNv2Pack takes another different + features to generate offsets and masks. + + Ref: + Delving Deep into Deformable Alignment in Video Super-Resolution. + """ + + def forward(self, x, feat): + out = self.conv_offset(feat) + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + + offset_absmean = torch.mean(torch.abs(offset)) + if offset_absmean > 50: + logger = get_root_logger() + logger.warning(f'Offset abs mean is {offset_absmean}, larger than 50.') + + if LooseVersion(torchvision.__version__) >= LooseVersion('0.9.0'): + return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, + self.dilation, mask) + else: + return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, + self.dilation, self.groups, self.deformable_groups) + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1. + math.erf(x / math.sqrt(2.))) / 2. + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' + 'The distribution of values may be incorrect.', + stacklevel=2) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + low = norm_cdf((a - mean) / std) + up = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [low, up], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * low - 1, 2 * up - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. + + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py + + The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +# From PyTorch +def _ntuple(n): + + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple diff --git a/basicsr/archs/basicvsr_arch.py b/basicsr/archs/basicvsr_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..ed7b824eae108a9bcca57f1c14dd0d8afafc4f58 --- /dev/null +++ b/basicsr/archs/basicvsr_arch.py @@ -0,0 +1,336 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import ResidualBlockNoBN, flow_warp, make_layer +from .edvr_arch import PCDAlignment, TSAFusion +from .spynet_arch import SpyNet + + +@ARCH_REGISTRY.register() +class BasicVSR(nn.Module): + """A recurrent network for video SR. Now only x4 is supported. + + Args: + num_feat (int): Number of channels. Default: 64. + num_block (int): Number of residual blocks for each branch. Default: 15 + spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. + """ + + def __init__(self, num_feat=64, num_block=15, spynet_path=None): + super().__init__() + self.num_feat = num_feat + + # alignment + self.spynet = SpyNet(spynet_path) + + # propagation + self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) + self.forward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) + + # reconstruction + self.fusion = nn.Conv2d(num_feat * 2, num_feat, 1, 1, 0, bias=True) + self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True) + self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True) + self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) + self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) + + self.pixel_shuffle = nn.PixelShuffle(2) + + # activation functions + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + def get_flow(self, x): + b, n, c, h, w = x.size() + + x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w) + x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w) + + flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w) + flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w) + + return flows_forward, flows_backward + + def forward(self, x): + """Forward function of BasicVSR. + + Args: + x: Input frames with shape (b, n, c, h, w). n is the temporal dimension / number of frames. + """ + flows_forward, flows_backward = self.get_flow(x) + b, n, _, h, w = x.size() + + # backward branch + out_l = [] + feat_prop = x.new_zeros(b, self.num_feat, h, w) + for i in range(n - 1, -1, -1): + x_i = x[:, i, :, :, :] + if i < n - 1: + flow = flows_backward[:, i, :, :, :] + feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) + feat_prop = torch.cat([x_i, feat_prop], dim=1) + feat_prop = self.backward_trunk(feat_prop) + out_l.insert(0, feat_prop) + + # forward branch + feat_prop = torch.zeros_like(feat_prop) + for i in range(0, n): + x_i = x[:, i, :, :, :] + if i > 0: + flow = flows_forward[:, i - 1, :, :, :] + feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) + + feat_prop = torch.cat([x_i, feat_prop], dim=1) + feat_prop = self.forward_trunk(feat_prop) + + # upsample + out = torch.cat([out_l[i], feat_prop], dim=1) + out = self.lrelu(self.fusion(out)) + out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) + out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) + out = self.lrelu(self.conv_hr(out)) + out = self.conv_last(out) + base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False) + out += base + out_l[i] = out + + return torch.stack(out_l, dim=1) + + +class ConvResidualBlocks(nn.Module): + """Conv and residual block used in BasicVSR. + + Args: + num_in_ch (int): Number of input channels. Default: 3. + num_out_ch (int): Number of output channels. Default: 64. + num_block (int): Number of residual blocks. Default: 15. + """ + + def __init__(self, num_in_ch=3, num_out_ch=64, num_block=15): + super().__init__() + self.main = nn.Sequential( + nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True), + make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch)) + + def forward(self, fea): + return self.main(fea) + + +@ARCH_REGISTRY.register() +class IconVSR(nn.Module): + """IconVSR, proposed also in the BasicVSR paper. + + Args: + num_feat (int): Number of channels. Default: 64. + num_block (int): Number of residual blocks for each branch. Default: 15. + keyframe_stride (int): Keyframe stride. Default: 5. + temporal_padding (int): Temporal padding. Default: 2. + spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. + edvr_path (str): Path to the pretrained EDVR model. Default: None. + """ + + def __init__(self, + num_feat=64, + num_block=15, + keyframe_stride=5, + temporal_padding=2, + spynet_path=None, + edvr_path=None): + super().__init__() + + self.num_feat = num_feat + self.temporal_padding = temporal_padding + self.keyframe_stride = keyframe_stride + + # keyframe_branch + self.edvr = EDVRFeatureExtractor(temporal_padding * 2 + 1, num_feat, edvr_path) + # alignment + self.spynet = SpyNet(spynet_path) + + # propagation + self.backward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True) + self.backward_trunk = ConvResidualBlocks(num_feat + 3, num_feat, num_block) + + self.forward_fusion = nn.Conv2d(2 * num_feat, num_feat, 3, 1, 1, bias=True) + self.forward_trunk = ConvResidualBlocks(2 * num_feat + 3, num_feat, num_block) + + # reconstruction + self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1, bias=True) + self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1, bias=True) + self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) + self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) + + self.pixel_shuffle = nn.PixelShuffle(2) + + # activation functions + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + def pad_spatial(self, x): + """Apply padding spatially. + + Since the PCD module in EDVR requires that the resolution is a multiple + of 4, we apply padding to the input LR images if their resolution is + not divisible by 4. + + Args: + x (Tensor): Input LR sequence with shape (n, t, c, h, w). + Returns: + Tensor: Padded LR sequence with shape (n, t, c, h_pad, w_pad). + """ + n, t, c, h, w = x.size() + + pad_h = (4 - h % 4) % 4 + pad_w = (4 - w % 4) % 4 + + # padding + x = x.view(-1, c, h, w) + x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') + + return x.view(n, t, c, h + pad_h, w + pad_w) + + def get_flow(self, x): + b, n, c, h, w = x.size() + + x_1 = x[:, :-1, :, :, :].reshape(-1, c, h, w) + x_2 = x[:, 1:, :, :, :].reshape(-1, c, h, w) + + flows_backward = self.spynet(x_1, x_2).view(b, n - 1, 2, h, w) + flows_forward = self.spynet(x_2, x_1).view(b, n - 1, 2, h, w) + + return flows_forward, flows_backward + + def get_keyframe_feature(self, x, keyframe_idx): + if self.temporal_padding == 2: + x = [x[:, [4, 3]], x, x[:, [-4, -5]]] + elif self.temporal_padding == 3: + x = [x[:, [6, 5, 4]], x, x[:, [-5, -6, -7]]] + x = torch.cat(x, dim=1) + + num_frames = 2 * self.temporal_padding + 1 + feats_keyframe = {} + for i in keyframe_idx: + feats_keyframe[i] = self.edvr(x[:, i:i + num_frames].contiguous()) + return feats_keyframe + + def forward(self, x): + b, n, _, h_input, w_input = x.size() + + x = self.pad_spatial(x) + h, w = x.shape[3:] + + keyframe_idx = list(range(0, n, self.keyframe_stride)) + if keyframe_idx[-1] != n - 1: + keyframe_idx.append(n - 1) # last frame is a keyframe + + # compute flow and keyframe features + flows_forward, flows_backward = self.get_flow(x) + feats_keyframe = self.get_keyframe_feature(x, keyframe_idx) + + # backward branch + out_l = [] + feat_prop = x.new_zeros(b, self.num_feat, h, w) + for i in range(n - 1, -1, -1): + x_i = x[:, i, :, :, :] + if i < n - 1: + flow = flows_backward[:, i, :, :, :] + feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) + if i in keyframe_idx: + feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1) + feat_prop = self.backward_fusion(feat_prop) + feat_prop = torch.cat([x_i, feat_prop], dim=1) + feat_prop = self.backward_trunk(feat_prop) + out_l.insert(0, feat_prop) + + # forward branch + feat_prop = torch.zeros_like(feat_prop) + for i in range(0, n): + x_i = x[:, i, :, :, :] + if i > 0: + flow = flows_forward[:, i - 1, :, :, :] + feat_prop = flow_warp(feat_prop, flow.permute(0, 2, 3, 1)) + if i in keyframe_idx: + feat_prop = torch.cat([feat_prop, feats_keyframe[i]], dim=1) + feat_prop = self.forward_fusion(feat_prop) + + feat_prop = torch.cat([x_i, out_l[i], feat_prop], dim=1) + feat_prop = self.forward_trunk(feat_prop) + + # upsample + out = self.lrelu(self.pixel_shuffle(self.upconv1(feat_prop))) + out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) + out = self.lrelu(self.conv_hr(out)) + out = self.conv_last(out) + base = F.interpolate(x_i, scale_factor=4, mode='bilinear', align_corners=False) + out += base + out_l[i] = out + + return torch.stack(out_l, dim=1)[..., :4 * h_input, :4 * w_input] + + +class EDVRFeatureExtractor(nn.Module): + """EDVR feature extractor used in IconVSR. + + Args: + num_input_frame (int): Number of input frames. + num_feat (int): Number of feature channels + load_path (str): Path to the pretrained weights of EDVR. Default: None. + """ + + def __init__(self, num_input_frame, num_feat, load_path): + + super(EDVRFeatureExtractor, self).__init__() + + self.center_frame_idx = num_input_frame // 2 + + # extract pyramid features + self.conv_first = nn.Conv2d(3, num_feat, 3, 1, 1) + self.feature_extraction = make_layer(ResidualBlockNoBN, 5, num_feat=num_feat) + self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + + # pcd and tsa module + self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=8) + self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_input_frame, center_frame_idx=self.center_frame_idx) + + # activation function + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + if load_path: + self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) + + def forward(self, x): + b, n, c, h, w = x.size() + + # extract features for each frame + # L1 + feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w))) + feat_l1 = self.feature_extraction(feat_l1) + # L2 + feat_l2 = self.lrelu(self.conv_l2_1(feat_l1)) + feat_l2 = self.lrelu(self.conv_l2_2(feat_l2)) + # L3 + feat_l3 = self.lrelu(self.conv_l3_1(feat_l2)) + feat_l3 = self.lrelu(self.conv_l3_2(feat_l3)) + + feat_l1 = feat_l1.view(b, n, -1, h, w) + feat_l2 = feat_l2.view(b, n, -1, h // 2, w // 2) + feat_l3 = feat_l3.view(b, n, -1, h // 4, w // 4) + + # PCD alignment + ref_feat_l = [ # reference feature list + feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(), + feat_l3[:, self.center_frame_idx, :, :, :].clone() + ] + aligned_feat = [] + for i in range(n): + nbr_feat_l = [ # neighboring feature list + feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone() + ] + aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l)) + aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w) + + # TSA fusion + return self.fusion(aligned_feat) diff --git a/basicsr/archs/basicvsrpp_arch.py b/basicsr/archs/basicvsrpp_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..d9699cb0ffa9fc681ce36b1f44e1f81ad8c41e6e --- /dev/null +++ b/basicsr/archs/basicvsrpp_arch.py @@ -0,0 +1,407 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +import warnings + +from basicsr.archs.arch_util import flow_warp +from basicsr.archs.basicvsr_arch import ConvResidualBlocks +from basicsr.archs.spynet_arch import SpyNet +from basicsr.ops.dcn import ModulatedDeformConvPack +from basicsr.utils.registry import ARCH_REGISTRY + + +@ARCH_REGISTRY.register() +class BasicVSRPlusPlus(nn.Module): + """BasicVSR++ network structure. + Support either x4 upsampling or same size output. Since DCN is used in this + model, it can only be used with CUDA enabled. If CUDA is not enabled, + feature alignment will be skipped. Besides, we adopt the official DCN + implementation and the version of torch need to be higher than 1.9. + Paper: + BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation + and Alignment + Args: + mid_channels (int, optional): Channel number of the intermediate + features. Default: 64. + num_blocks (int, optional): The number of residual blocks in each + propagation branch. Default: 7. + max_residue_magnitude (int): The maximum magnitude of the offset + residue (Eq. 6 in paper). Default: 10. + is_low_res_input (bool, optional): Whether the input is low-resolution + or not. If False, the output resolution is equal to the input + resolution. Default: True. + spynet_path (str): Path to the pretrained weights of SPyNet. Default: None. + cpu_cache_length (int, optional): When the length of sequence is larger + than this value, the intermediate features are sent to CPU. This + saves GPU memory, but slows down the inference speed. You can + increase this number if you have a GPU with large memory. + Default: 100. + """ + + def __init__(self, + mid_channels=64, + num_blocks=7, + max_residue_magnitude=10, + is_low_res_input=True, + spynet_path=None, + cpu_cache_length=100): + + super().__init__() + self.mid_channels = mid_channels + self.is_low_res_input = is_low_res_input + self.cpu_cache_length = cpu_cache_length + + # optical flow + self.spynet = SpyNet(spynet_path) + + # feature extraction module + if is_low_res_input: + self.feat_extract = ConvResidualBlocks(3, mid_channels, 5) + else: + self.feat_extract = nn.Sequential( + nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), + nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), + ConvResidualBlocks(mid_channels, mid_channels, 5)) + + # propagation branches + self.deform_align = nn.ModuleDict() + self.backbone = nn.ModuleDict() + modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2'] + for i, module in enumerate(modules): + if torch.cuda.is_available(): + self.deform_align[module] = SecondOrderDeformableAlignment( + 2 * mid_channels, + mid_channels, + 3, + padding=1, + deformable_groups=16, + max_residue_magnitude=max_residue_magnitude) + self.backbone[module] = ConvResidualBlocks((2 + i) * mid_channels, mid_channels, num_blocks) + + # upsampling module + self.reconstruction = ConvResidualBlocks(5 * mid_channels, mid_channels, 5) + + self.upconv1 = nn.Conv2d(mid_channels, mid_channels * 4, 3, 1, 1, bias=True) + self.upconv2 = nn.Conv2d(mid_channels, 64 * 4, 3, 1, 1, bias=True) + + self.pixel_shuffle = nn.PixelShuffle(2) + + self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) + self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) + self.img_upsample = nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False) + + # activation function + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + # check if the sequence is augmented by flipping + self.is_mirror_extended = False + + if len(self.deform_align) > 0: + self.is_with_alignment = True + else: + self.is_with_alignment = False + warnings.warn('Deformable alignment module is not added. ' + 'Probably your CUDA is not configured correctly. DCN can only ' + 'be used with CUDA enabled. Alignment is skipped now.') + + def check_if_mirror_extended(self, lqs): + """Check whether the input is a mirror-extended sequence. + If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the + (t-1-i)-th frame. + Args: + lqs (tensor): Input low quality (LQ) sequence with + shape (n, t, c, h, w). + """ + + if lqs.size(1) % 2 == 0: + lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1) + if torch.norm(lqs_1 - lqs_2.flip(1)) == 0: + self.is_mirror_extended = True + + def compute_flow(self, lqs): + """Compute optical flow using SPyNet for feature alignment. + Note that if the input is an mirror-extended sequence, 'flows_forward' + is not needed, since it is equal to 'flows_backward.flip(1)'. + Args: + lqs (tensor): Input low quality (LQ) sequence with + shape (n, t, c, h, w). + Return: + tuple(Tensor): Optical flow. 'flows_forward' corresponds to the + flows used for forward-time propagation (current to previous). + 'flows_backward' corresponds to the flows used for + backward-time propagation (current to next). + """ + + n, t, c, h, w = lqs.size() + lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w) + lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w) + + flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w) + + if self.is_mirror_extended: # flows_forward = flows_backward.flip(1) + flows_forward = flows_backward.flip(1) + else: + flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w) + + if self.cpu_cache: + flows_backward = flows_backward.cpu() + flows_forward = flows_forward.cpu() + + return flows_forward, flows_backward + + def propagate(self, feats, flows, module_name): + """Propagate the latent features throughout the sequence. + Args: + feats dict(list[tensor]): Features from previous branches. Each + component is a list of tensors with shape (n, c, h, w). + flows (tensor): Optical flows with shape (n, t - 1, 2, h, w). + module_name (str): The name of the propgation branches. Can either + be 'backward_1', 'forward_1', 'backward_2', 'forward_2'. + Return: + dict(list[tensor]): A dictionary containing all the propagated + features. Each key in the dictionary corresponds to a + propagation branch, which is represented by a list of tensors. + """ + + n, t, _, h, w = flows.size() + + frame_idx = range(0, t + 1) + flow_idx = range(-1, t) + mapping_idx = list(range(0, len(feats['spatial']))) + mapping_idx += mapping_idx[::-1] + + if 'backward' in module_name: + frame_idx = frame_idx[::-1] + flow_idx = frame_idx + + feat_prop = flows.new_zeros(n, self.mid_channels, h, w) + for i, idx in enumerate(frame_idx): + feat_current = feats['spatial'][mapping_idx[idx]] + if self.cpu_cache: + feat_current = feat_current.cuda() + feat_prop = feat_prop.cuda() + # second-order deformable alignment + if i > 0 and self.is_with_alignment: + flow_n1 = flows[:, flow_idx[i], :, :, :] + if self.cpu_cache: + flow_n1 = flow_n1.cuda() + + cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1)) + + # initialize second-order features + feat_n2 = torch.zeros_like(feat_prop) + flow_n2 = torch.zeros_like(flow_n1) + cond_n2 = torch.zeros_like(cond_n1) + + if i > 1: # second-order features + feat_n2 = feats[module_name][-2] + if self.cpu_cache: + feat_n2 = feat_n2.cuda() + + flow_n2 = flows[:, flow_idx[i - 1], :, :, :] + if self.cpu_cache: + flow_n2 = flow_n2.cuda() + + flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1)) + cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1)) + + # flow-guided deformable convolution + cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1) + feat_prop = torch.cat([feat_prop, feat_n2], dim=1) + feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2) + + # concatenate and residual blocks + feat = [feat_current] + [feats[k][idx] for k in feats if k not in ['spatial', module_name]] + [feat_prop] + if self.cpu_cache: + feat = [f.cuda() for f in feat] + + feat = torch.cat(feat, dim=1) + feat_prop = feat_prop + self.backbone[module_name](feat) + feats[module_name].append(feat_prop) + + if self.cpu_cache: + feats[module_name][-1] = feats[module_name][-1].cpu() + torch.cuda.empty_cache() + + if 'backward' in module_name: + feats[module_name] = feats[module_name][::-1] + + return feats + + def upsample(self, lqs, feats): + """Compute the output image given the features. + Args: + lqs (tensor): Input low quality (LQ) sequence with + shape (n, t, c, h, w). + feats (dict): The features from the propgation branches. + Returns: + Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). + """ + + outputs = [] + num_outputs = len(feats['spatial']) + + mapping_idx = list(range(0, num_outputs)) + mapping_idx += mapping_idx[::-1] + + for i in range(0, lqs.size(1)): + hr = [feats[k].pop(0) for k in feats if k != 'spatial'] + hr.insert(0, feats['spatial'][mapping_idx[i]]) + hr = torch.cat(hr, dim=1) + if self.cpu_cache: + hr = hr.cuda() + + hr = self.reconstruction(hr) + hr = self.lrelu(self.pixel_shuffle(self.upconv1(hr))) + hr = self.lrelu(self.pixel_shuffle(self.upconv2(hr))) + hr = self.lrelu(self.conv_hr(hr)) + hr = self.conv_last(hr) + if self.is_low_res_input: + hr += self.img_upsample(lqs[:, i, :, :, :]) + else: + hr += lqs[:, i, :, :, :] + + if self.cpu_cache: + hr = hr.cpu() + torch.cuda.empty_cache() + + outputs.append(hr) + + return torch.stack(outputs, dim=1) + + def forward(self, lqs): + """Forward function for BasicVSR++. + Args: + lqs (tensor): Input low quality (LQ) sequence with + shape (n, t, c, h, w). + Returns: + Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). + """ + + n, t, c, h, w = lqs.size() + + # whether to cache the features in CPU + self.cpu_cache = True if t > self.cpu_cache_length else False + + if self.is_low_res_input: + lqs_downsample = lqs.clone() + else: + lqs_downsample = F.interpolate( + lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4) + + # check whether the input is an extended sequence + self.check_if_mirror_extended(lqs) + + feats = {} + # compute spatial features + if self.cpu_cache: + feats['spatial'] = [] + for i in range(0, t): + feat = self.feat_extract(lqs[:, i, :, :, :]).cpu() + feats['spatial'].append(feat) + torch.cuda.empty_cache() + else: + feats_ = self.feat_extract(lqs.view(-1, c, h, w)) + h, w = feats_.shape[2:] + feats_ = feats_.view(n, t, -1, h, w) + feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)] + + # compute optical flow using the low-res inputs + assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, ( + 'The height and width of low-res inputs must be at least 64, ' + f'but got {h} and {w}.') + flows_forward, flows_backward = self.compute_flow(lqs_downsample) + + # feature propgation + for iter_ in [1, 2]: + for direction in ['backward', 'forward']: + module = f'{direction}_{iter_}' + + feats[module] = [] + + if direction == 'backward': + flows = flows_backward + elif flows_forward is not None: + flows = flows_forward + else: + flows = flows_backward.flip(1) + + feats = self.propagate(feats, flows, module) + if self.cpu_cache: + del flows + torch.cuda.empty_cache() + + return self.upsample(lqs, feats) + + +class SecondOrderDeformableAlignment(ModulatedDeformConvPack): + """Second-order deformable alignment module. + Args: + in_channels (int): Same as nn.Conv2d. + out_channels (int): Same as nn.Conv2d. + kernel_size (int or tuple[int]): Same as nn.Conv2d. + stride (int or tuple[int]): Same as nn.Conv2d. + padding (int or tuple[int]): Same as nn.Conv2d. + dilation (int or tuple[int]): Same as nn.Conv2d. + groups (int): Same as nn.Conv2d. + bias (bool or str): If specified as `auto`, it will be decided by the + norm_cfg. Bias will be set as True if norm_cfg is None, otherwise + False. + max_residue_magnitude (int): The maximum magnitude of the offset + residue (Eq. 6 in paper). Default: 10. + """ + + def __init__(self, *args, **kwargs): + self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10) + + super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs) + + self.conv_offset = nn.Sequential( + nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.1, inplace=True), + nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.1, inplace=True), + nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.1, inplace=True), + nn.Conv2d(self.out_channels, 27 * self.deformable_groups, 3, 1, 1), + ) + + self.init_offset() + + def init_offset(self): + + def _constant_init(module, val, bias=0): + if hasattr(module, 'weight') and module.weight is not None: + nn.init.constant_(module.weight, val) + if hasattr(module, 'bias') and module.bias is not None: + nn.init.constant_(module.bias, bias) + + _constant_init(self.conv_offset[-1], val=0, bias=0) + + def forward(self, x, extra_feat, flow_1, flow_2): + extra_feat = torch.cat([extra_feat, flow_1, flow_2], dim=1) + out = self.conv_offset(extra_feat) + o1, o2, mask = torch.chunk(out, 3, dim=1) + + # offset + offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1)) + offset_1, offset_2 = torch.chunk(offset, 2, dim=1) + offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1) + offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1) + offset = torch.cat([offset_1, offset_2], dim=1) + + # mask + mask = torch.sigmoid(mask) + + return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, + self.dilation, mask) + + +# if __name__ == '__main__': +# spynet_path = 'experiments/pretrained_models/flownet/spynet_sintel_final-3d2a1287.pth' +# model = BasicVSRPlusPlus(spynet_path=spynet_path).cuda() +# input = torch.rand(1, 2, 3, 64, 64).cuda() +# output = model(input) +# print('===================') +# print(output.shape) diff --git a/basicsr/archs/codeformer_arch.py b/basicsr/archs/codeformer_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..4d0d8027c8c4ffb26af6f4ba361514e93e320e8d --- /dev/null +++ b/basicsr/archs/codeformer_arch.py @@ -0,0 +1,276 @@ +import math +import numpy as np +import torch +from torch import nn, Tensor +import torch.nn.functional as F +from typing import Optional, List + +from basicsr.archs.vqgan_arch import * +from basicsr.utils import get_root_logger +from basicsr.utils.registry import ARCH_REGISTRY + +def calc_mean_std(feat, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, 'The input feature should be 4D tensor.' + b, c = size[:2] + feat_var = feat.view(b, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(b, c, 1, 1) + feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat): + """Adaptive instance normalization. + + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") + + +class TransformerSALayer(nn.Module): + def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"): + super().__init__() + self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) + # Implementation of Feedforward model - MLP + self.linear1 = nn.Linear(embed_dim, dim_mlp) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_mlp, embed_dim) + + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward(self, tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None): + + # self attention + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask, + key_padding_mask=tgt_key_padding_mask)[0] + tgt = tgt + self.dropout1(tgt2) + + # ffn + tgt2 = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout2(tgt2) + return tgt + +class Fuse_sft_block(nn.Module): + def __init__(self, in_ch, out_ch): + super().__init__() + self.encode_enc = ResBlock(2*in_ch, out_ch) + + self.scale = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) + + self.shift = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1)) + + def forward(self, enc_feat, dec_feat, w=1): + enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) + scale = self.scale(enc_feat) + shift = self.shift(enc_feat) + residual = w * (dec_feat * scale + shift) + out = dec_feat + residual + return out + + +@ARCH_REGISTRY.register() +class CodeFormer(VQAutoEncoder): + def __init__(self, dim_embd=512, n_head=8, n_layers=9, + codebook_size=1024, latent_size=256, + connect_list=['32', '64', '128', '256'], + fix_modules=['quantize','generator']): + super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size) + + if fix_modules is not None: + for module in fix_modules: + for param in getattr(self, module).parameters(): + param.requires_grad = False + + self.connect_list = connect_list + self.n_layers = n_layers + self.dim_embd = dim_embd + self.dim_mlp = dim_embd*2 + + self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) + self.feat_emb = nn.Linear(256, self.dim_embd) + + # transformer + self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0) + for _ in range(self.n_layers)]) + + # logits_predict head + self.idx_pred_layer = nn.Sequential( + nn.LayerNorm(dim_embd), + nn.Linear(dim_embd, codebook_size, bias=False)) + + self.channels = { + '16': 512, + '32': 256, + '64': 256, + '128': 128, + '256': 128, + '512': 64, + } + + # after second residual block for > 16, before attn layer for ==16 + self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18} + # after first residual block for > 16, before attn layer for ==16 + self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21} + + # fuse_convs_dict + self.fuse_convs_dict = nn.ModuleDict() + for f_size in self.connect_list: + in_ch = self.channels[f_size] + self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, x, w=0, detach_16=True, code_only=False, adain=False): + # ################### Encoder ##################### + enc_feat_dict = {} + out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] + for i, block in enumerate(self.encoder.blocks): + x = block(x) + if i in out_list: + enc_feat_dict[str(x.shape[-1])] = x.clone() + + lq_feat = x + # ################# Transformer ################### + # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) + pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1) + # BCHW -> BC(HW) -> (HW)BC + feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1)) + query_emb = feat_emb + # Transformer encoder + for layer in self.ft_layers: + query_emb = layer(query_emb, query_pos=pos_emb) + + # output logits + logits = self.idx_pred_layer(query_emb) # (hw)bn + logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n + + if code_only: # for training stage II + # logits doesn't need softmax before cross_entropy loss + return logits, lq_feat + + # ################# Quantization ################### + # if self.training: + # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) + # # b(hw)c -> bc(hw) -> bchw + # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) + # ------------ + soft_one_hot = F.softmax(logits, dim=2) + _, top_idx = torch.topk(soft_one_hot, 1, dim=2) + quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256]) + # preserve gradients + # quant_feat = lq_feat + (quant_feat - lq_feat).detach() + + if detach_16: + quant_feat = quant_feat.detach() # for training stage III + if adain: + quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) + + # ################## Generator #################### + x = quant_feat + fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] + + for i, block in enumerate(self.generator.blocks): + x = block(x) + if i in fuse_list: # fuse after i-th block + f_size = str(x.shape[-1]) + if w>0: + x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w) + out = x + # logits doesn't need softmax before cross_entropy loss + return out, logits, lq_feat \ No newline at end of file diff --git a/basicsr/archs/dfdnet_arch.py b/basicsr/archs/dfdnet_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..4751434c2f17efbb682d9344951604602d853aaa --- /dev/null +++ b/basicsr/archs/dfdnet_arch.py @@ -0,0 +1,169 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.utils.spectral_norm import spectral_norm + +from basicsr.utils.registry import ARCH_REGISTRY +from .dfdnet_util import AttentionBlock, Blur, MSDilationBlock, UpResBlock, adaptive_instance_normalization +from .vgg_arch import VGGFeatureExtractor + + +class SFTUpBlock(nn.Module): + """Spatial feature transform (SFT) with upsampling block. + + Args: + in_channel (int): Number of input channels. + out_channel (int): Number of output channels. + kernel_size (int): Kernel size in convolutions. Default: 3. + padding (int): Padding in convolutions. Default: 1. + """ + + def __init__(self, in_channel, out_channel, kernel_size=3, padding=1): + super(SFTUpBlock, self).__init__() + self.conv1 = nn.Sequential( + Blur(in_channel), + spectral_norm(nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding)), + nn.LeakyReLU(0.04, True), + # The official codes use two LeakyReLU here, so 0.04 for equivalent + ) + self.convup = nn.Sequential( + nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False), + spectral_norm(nn.Conv2d(out_channel, out_channel, kernel_size, padding=padding)), + nn.LeakyReLU(0.2, True), + ) + + # for SFT scale and shift + self.scale_block = nn.Sequential( + spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), + spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1))) + self.shift_block = nn.Sequential( + spectral_norm(nn.Conv2d(in_channel, out_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), + spectral_norm(nn.Conv2d(out_channel, out_channel, 3, 1, 1)), nn.Sigmoid()) + # The official codes use sigmoid for shift block, do not know why + + def forward(self, x, updated_feat): + out = self.conv1(x) + # SFT + scale = self.scale_block(updated_feat) + shift = self.shift_block(updated_feat) + out = out * scale + shift + # upsample + out = self.convup(out) + return out + + +@ARCH_REGISTRY.register() +class DFDNet(nn.Module): + """DFDNet: Deep Face Dictionary Network. + + It only processes faces with 512x512 size. + + Args: + num_feat (int): Number of feature channels. + dict_path (str): Path to the facial component dictionary. + """ + + def __init__(self, num_feat, dict_path): + super().__init__() + self.parts = ['left_eye', 'right_eye', 'nose', 'mouth'] + # part_sizes: [80, 80, 50, 110] + channel_sizes = [128, 256, 512, 512] + self.feature_sizes = np.array([256, 128, 64, 32]) + self.vgg_layers = ['relu2_2', 'relu3_4', 'relu4_4', 'conv5_4'] + self.flag_dict_device = False + + # dict + self.dict = torch.load(dict_path) + + # vgg face extractor + self.vgg_extractor = VGGFeatureExtractor( + layer_name_list=self.vgg_layers, + vgg_type='vgg19', + use_input_norm=True, + range_norm=True, + requires_grad=False) + + # attention block for fusing dictionary features and input features + self.attn_blocks = nn.ModuleDict() + for idx, feat_size in enumerate(self.feature_sizes): + for name in self.parts: + self.attn_blocks[f'{name}_{feat_size}'] = AttentionBlock(channel_sizes[idx]) + + # multi scale dilation block + self.multi_scale_dilation = MSDilationBlock(num_feat * 8, dilation=[4, 3, 2, 1]) + + # upsampling and reconstruction + self.upsample0 = SFTUpBlock(num_feat * 8, num_feat * 8) + self.upsample1 = SFTUpBlock(num_feat * 8, num_feat * 4) + self.upsample2 = SFTUpBlock(num_feat * 4, num_feat * 2) + self.upsample3 = SFTUpBlock(num_feat * 2, num_feat) + self.upsample4 = nn.Sequential( + spectral_norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1)), nn.LeakyReLU(0.2, True), UpResBlock(num_feat), + UpResBlock(num_feat), nn.Conv2d(num_feat, 3, kernel_size=3, stride=1, padding=1), nn.Tanh()) + + def swap_feat(self, vgg_feat, updated_feat, dict_feat, location, part_name, f_size): + """swap the features from the dictionary.""" + # get the original vgg features + part_feat = vgg_feat[:, :, location[1]:location[3], location[0]:location[2]].clone() + # resize original vgg features + part_resize_feat = F.interpolate(part_feat, dict_feat.size()[2:4], mode='bilinear', align_corners=False) + # use adaptive instance normalization to adjust color and illuminations + dict_feat = adaptive_instance_normalization(dict_feat, part_resize_feat) + # get similarity scores + similarity_score = F.conv2d(part_resize_feat, dict_feat) + similarity_score = F.softmax(similarity_score.view(-1), dim=0) + # select the most similar features in the dict (after norm) + select_idx = torch.argmax(similarity_score) + swap_feat = F.interpolate(dict_feat[select_idx:select_idx + 1], part_feat.size()[2:4]) + # attention + attn = self.attn_blocks[f'{part_name}_' + str(f_size)](swap_feat - part_feat) + attn_feat = attn * swap_feat + # update features + updated_feat[:, :, location[1]:location[3], location[0]:location[2]] = attn_feat + part_feat + return updated_feat + + def put_dict_to_device(self, x): + if self.flag_dict_device is False: + for k, v in self.dict.items(): + for kk, vv in v.items(): + self.dict[k][kk] = vv.to(x) + self.flag_dict_device = True + + def forward(self, x, part_locations): + """ + Now only support testing with batch size = 0. + + Args: + x (Tensor): Input faces with shape (b, c, 512, 512). + part_locations (list[Tensor]): Part locations. + """ + self.put_dict_to_device(x) + # extract vggface features + vgg_features = self.vgg_extractor(x) + # update vggface features using the dictionary for each part + updated_vgg_features = [] + batch = 0 # only supports testing with batch size = 0 + for vgg_layer, f_size in zip(self.vgg_layers, self.feature_sizes): + dict_features = self.dict[f'{f_size}'] + vgg_feat = vgg_features[vgg_layer] + updated_feat = vgg_feat.clone() + + # swap features from dictionary + for part_idx, part_name in enumerate(self.parts): + location = (part_locations[part_idx][batch] // (512 / f_size)).int() + updated_feat = self.swap_feat(vgg_feat, updated_feat, dict_features[part_name], location, part_name, + f_size) + + updated_vgg_features.append(updated_feat) + + vgg_feat_dilation = self.multi_scale_dilation(vgg_features['conv5_4']) + # use updated vgg features to modulate the upsampled features with + # SFT (Spatial Feature Transform) scaling and shifting manner. + upsampled_feat = self.upsample0(vgg_feat_dilation, updated_vgg_features[3]) + upsampled_feat = self.upsample1(upsampled_feat, updated_vgg_features[2]) + upsampled_feat = self.upsample2(upsampled_feat, updated_vgg_features[1]) + upsampled_feat = self.upsample3(upsampled_feat, updated_vgg_features[0]) + out = self.upsample4(upsampled_feat) + + return out diff --git a/basicsr/archs/dfdnet_util.py b/basicsr/archs/dfdnet_util.py new file mode 100644 index 0000000000000000000000000000000000000000..b4dc0ff738c76852e830b32fffbe65bffb5ddf50 --- /dev/null +++ b/basicsr/archs/dfdnet_util.py @@ -0,0 +1,162 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Function +from torch.nn.utils.spectral_norm import spectral_norm + + +class BlurFunctionBackward(Function): + + @staticmethod + def forward(ctx, grad_output, kernel, kernel_flip): + ctx.save_for_backward(kernel, kernel_flip) + grad_input = F.conv2d(grad_output, kernel_flip, padding=1, groups=grad_output.shape[1]) + return grad_input + + @staticmethod + def backward(ctx, gradgrad_output): + kernel, _ = ctx.saved_tensors + grad_input = F.conv2d(gradgrad_output, kernel, padding=1, groups=gradgrad_output.shape[1]) + return grad_input, None, None + + +class BlurFunction(Function): + + @staticmethod + def forward(ctx, x, kernel, kernel_flip): + ctx.save_for_backward(kernel, kernel_flip) + output = F.conv2d(x, kernel, padding=1, groups=x.shape[1]) + return output + + @staticmethod + def backward(ctx, grad_output): + kernel, kernel_flip = ctx.saved_tensors + grad_input = BlurFunctionBackward.apply(grad_output, kernel, kernel_flip) + return grad_input, None, None + + +blur = BlurFunction.apply + + +class Blur(nn.Module): + + def __init__(self, channel): + super().__init__() + kernel = torch.tensor([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=torch.float32) + kernel = kernel.view(1, 1, 3, 3) + kernel = kernel / kernel.sum() + kernel_flip = torch.flip(kernel, [2, 3]) + + self.kernel = kernel.repeat(channel, 1, 1, 1) + self.kernel_flip = kernel_flip.repeat(channel, 1, 1, 1) + + def forward(self, x): + return blur(x, self.kernel.type_as(x), self.kernel_flip.type_as(x)) + + +def calc_mean_std(feat, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, 'The input feature should be 4D tensor.' + n, c = size[:2] + feat_var = feat.view(n, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(n, c, 1, 1) + feat_mean = feat.view(n, c, -1).mean(dim=2).view(n, c, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat): + """Adaptive instance normalization. + + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +def AttentionBlock(in_channel): + return nn.Sequential( + spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1)), nn.LeakyReLU(0.2, True), + spectral_norm(nn.Conv2d(in_channel, in_channel, 3, 1, 1))) + + +def conv_block(in_channels, out_channels, kernel_size=3, stride=1, dilation=1, bias=True): + """Conv block used in MSDilationBlock.""" + + return nn.Sequential( + spectral_norm( + nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + padding=((kernel_size - 1) // 2) * dilation, + bias=bias)), + nn.LeakyReLU(0.2), + spectral_norm( + nn.Conv2d( + out_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + dilation=dilation, + padding=((kernel_size - 1) // 2) * dilation, + bias=bias)), + ) + + +class MSDilationBlock(nn.Module): + """Multi-scale dilation block.""" + + def __init__(self, in_channels, kernel_size=3, dilation=(1, 1, 1, 1), bias=True): + super(MSDilationBlock, self).__init__() + + self.conv_blocks = nn.ModuleList() + for i in range(4): + self.conv_blocks.append(conv_block(in_channels, in_channels, kernel_size, dilation=dilation[i], bias=bias)) + self.conv_fusion = spectral_norm( + nn.Conv2d( + in_channels * 4, + in_channels, + kernel_size=kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + bias=bias)) + + def forward(self, x): + out = [] + for i in range(4): + out.append(self.conv_blocks[i](x)) + out = torch.cat(out, 1) + out = self.conv_fusion(out) + x + return out + + +class UpResBlock(nn.Module): + + def __init__(self, in_channel): + super(UpResBlock, self).__init__() + self.body = nn.Sequential( + nn.Conv2d(in_channel, in_channel, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(in_channel, in_channel, 3, 1, 1), + ) + + def forward(self, x): + out = x + self.body(x) + return out diff --git a/basicsr/archs/discriminator_arch.py b/basicsr/archs/discriminator_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..33f9a8f1b25c2052cd3ba801534861a425752e69 --- /dev/null +++ b/basicsr/archs/discriminator_arch.py @@ -0,0 +1,150 @@ +from torch import nn as nn +from torch.nn import functional as F +from torch.nn.utils import spectral_norm + +from basicsr.utils.registry import ARCH_REGISTRY + + +@ARCH_REGISTRY.register() +class VGGStyleDiscriminator(nn.Module): + """VGG style discriminator with input size 128 x 128 or 256 x 256. + + It is used to train SRGAN, ESRGAN, and VideoGAN. + + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_feat (int): Channel number of base intermediate features.Default: 64. + """ + + def __init__(self, num_in_ch, num_feat, input_size=128): + super(VGGStyleDiscriminator, self).__init__() + self.input_size = input_size + assert self.input_size == 128 or self.input_size == 256, ( + f'input size must be 128 or 256, but received {input_size}') + + self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True) + self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False) + self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True) + + self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False) + self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True) + self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False) + self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True) + + self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False) + self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True) + self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False) + self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True) + + self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False) + self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True) + self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) + self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True) + + self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) + self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True) + self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) + self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True) + + if self.input_size == 256: + self.conv5_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False) + self.bn5_0 = nn.BatchNorm2d(num_feat * 8, affine=True) + self.conv5_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False) + self.bn5_1 = nn.BatchNorm2d(num_feat * 8, affine=True) + + self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100) + self.linear2 = nn.Linear(100, 1) + + # activation function + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + assert x.size(2) == self.input_size, (f'Input size must be identical to input_size, but received {x.size()}.') + + feat = self.lrelu(self.conv0_0(x)) + feat = self.lrelu(self.bn0_1(self.conv0_1(feat))) # output spatial size: /2 + + feat = self.lrelu(self.bn1_0(self.conv1_0(feat))) + feat = self.lrelu(self.bn1_1(self.conv1_1(feat))) # output spatial size: /4 + + feat = self.lrelu(self.bn2_0(self.conv2_0(feat))) + feat = self.lrelu(self.bn2_1(self.conv2_1(feat))) # output spatial size: /8 + + feat = self.lrelu(self.bn3_0(self.conv3_0(feat))) + feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: /16 + + feat = self.lrelu(self.bn4_0(self.conv4_0(feat))) + feat = self.lrelu(self.bn4_1(self.conv4_1(feat))) # output spatial size: /32 + + if self.input_size == 256: + feat = self.lrelu(self.bn5_0(self.conv5_0(feat))) + feat = self.lrelu(self.bn5_1(self.conv5_1(feat))) # output spatial size: / 64 + + # spatial size: (4, 4) + feat = feat.view(feat.size(0), -1) + feat = self.lrelu(self.linear1(feat)) + out = self.linear2(feat) + return out + + +@ARCH_REGISTRY.register(suffix='basicsr') +class UNetDiscriminatorSN(nn.Module): + """Defines a U-Net discriminator with spectral normalization (SN) + + It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. + + Arg: + num_in_ch (int): Channel number of inputs. Default: 3. + num_feat (int): Channel number of base intermediate features. Default: 64. + skip_connection (bool): Whether to use skip connections between U-Net. Default: True. + """ + + def __init__(self, num_in_ch, num_feat=64, skip_connection=True): + super(UNetDiscriminatorSN, self).__init__() + self.skip_connection = skip_connection + norm = spectral_norm + # the first convolution + self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1) + # downsample + self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False)) + self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False)) + self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False)) + # upsample + self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False)) + self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False)) + self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False)) + # extra convolutions + self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) + self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False)) + self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1) + + def forward(self, x): + # downsample + x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True) + x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True) + x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True) + x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True) + + # upsample + x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False) + x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True) + + if self.skip_connection: + x4 = x4 + x2 + x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False) + x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True) + + if self.skip_connection: + x5 = x5 + x1 + x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False) + x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True) + + if self.skip_connection: + x6 = x6 + x0 + + # extra convolutions + out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True) + out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True) + out = self.conv9(out) + + return out diff --git a/basicsr/archs/duf_arch.py b/basicsr/archs/duf_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..0b8a5885df3dfd06a171357375c74eb32b8ccee6 --- /dev/null +++ b/basicsr/archs/duf_arch.py @@ -0,0 +1,277 @@ +import numpy as np +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY + + +class DenseBlocksTemporalReduce(nn.Module): + """A concatenation of 3 dense blocks with reduction in temporal dimension. + + Note that the output temporal dimension is 6 fewer the input temporal dimension, since there are 3 blocks. + + Args: + num_feat (int): Number of channels in the blocks. Default: 64. + num_grow_ch (int): Growing factor of the dense blocks. Default: 32 + adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation. + Set to false if you want to train from scratch. Default: False. + """ + + def __init__(self, num_feat=64, num_grow_ch=32, adapt_official_weights=False): + super(DenseBlocksTemporalReduce, self).__init__() + if adapt_official_weights: + eps = 1e-3 + momentum = 1e-3 + else: # pytorch default values + eps = 1e-05 + momentum = 0.1 + + self.temporal_reduce1 = nn.Sequential( + nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True), + nn.Conv3d(num_feat, num_feat, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True), + nn.BatchNorm3d(num_feat, eps=eps, momentum=momentum), nn.ReLU(inplace=True), + nn.Conv3d(num_feat, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) + + self.temporal_reduce2 = nn.Sequential( + nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), + nn.Conv3d( + num_feat + num_grow_ch, + num_feat + num_grow_ch, (1, 1, 1), + stride=(1, 1, 1), + padding=(0, 0, 0), + bias=True), nn.BatchNorm3d(num_feat + num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), + nn.Conv3d(num_feat + num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) + + self.temporal_reduce3 = nn.Sequential( + nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), + nn.Conv3d( + num_feat + 2 * num_grow_ch, + num_feat + 2 * num_grow_ch, (1, 1, 1), + stride=(1, 1, 1), + padding=(0, 0, 0), + bias=True), nn.BatchNorm3d(num_feat + 2 * num_grow_ch, eps=eps, momentum=momentum), + nn.ReLU(inplace=True), + nn.Conv3d( + num_feat + 2 * num_grow_ch, num_grow_ch, (3, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True)) + + def forward(self, x): + """ + Args: + x (Tensor): Input tensor with shape (b, num_feat, t, h, w). + + Returns: + Tensor: Output with shape (b, num_feat + num_grow_ch * 3, 1, h, w). + """ + x1 = self.temporal_reduce1(x) + x1 = torch.cat((x[:, :, 1:-1, :, :], x1), 1) + + x2 = self.temporal_reduce2(x1) + x2 = torch.cat((x1[:, :, 1:-1, :, :], x2), 1) + + x3 = self.temporal_reduce3(x2) + x3 = torch.cat((x2[:, :, 1:-1, :, :], x3), 1) + + return x3 + + +class DenseBlocks(nn.Module): + """ A concatenation of N dense blocks. + + Args: + num_feat (int): Number of channels in the blocks. Default: 64. + num_grow_ch (int): Growing factor of the dense blocks. Default: 32. + num_block (int): Number of dense blocks. The values are: + DUF-S (16 layers): 3 + DUF-M (18 layers): 9 + DUF-L (52 layers): 21 + adapt_official_weights (bool): Whether to adapt the weights translated from the official implementation. + Set to false if you want to train from scratch. Default: False. + """ + + def __init__(self, num_block, num_feat=64, num_grow_ch=16, adapt_official_weights=False): + super(DenseBlocks, self).__init__() + if adapt_official_weights: + eps = 1e-3 + momentum = 1e-3 + else: # pytorch default values + eps = 1e-05 + momentum = 0.1 + + self.dense_blocks = nn.ModuleList() + for i in range(0, num_block): + self.dense_blocks.append( + nn.Sequential( + nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), nn.ReLU(inplace=True), + nn.Conv3d( + num_feat + i * num_grow_ch, + num_feat + i * num_grow_ch, (1, 1, 1), + stride=(1, 1, 1), + padding=(0, 0, 0), + bias=True), nn.BatchNorm3d(num_feat + i * num_grow_ch, eps=eps, momentum=momentum), + nn.ReLU(inplace=True), + nn.Conv3d( + num_feat + i * num_grow_ch, + num_grow_ch, (3, 3, 3), + stride=(1, 1, 1), + padding=(1, 1, 1), + bias=True))) + + def forward(self, x): + """ + Args: + x (Tensor): Input tensor with shape (b, num_feat, t, h, w). + + Returns: + Tensor: Output with shape (b, num_feat + num_block * num_grow_ch, t, h, w). + """ + for i in range(0, len(self.dense_blocks)): + y = self.dense_blocks[i](x) + x = torch.cat((x, y), 1) + return x + + +class DynamicUpsamplingFilter(nn.Module): + """Dynamic upsampling filter used in DUF. + + Ref: https://github.com/yhjo09/VSR-DUF. + It only supports input with 3 channels. And it applies the same filters to 3 channels. + + Args: + filter_size (tuple): Filter size of generated filters. The shape is (kh, kw). Default: (5, 5). + """ + + def __init__(self, filter_size=(5, 5)): + super(DynamicUpsamplingFilter, self).__init__() + if not isinstance(filter_size, tuple): + raise TypeError(f'The type of filter_size must be tuple, but got type{filter_size}') + if len(filter_size) != 2: + raise ValueError(f'The length of filter size must be 2, but got {len(filter_size)}.') + # generate a local expansion filter, similar to im2col + self.filter_size = filter_size + filter_prod = np.prod(filter_size) + expansion_filter = torch.eye(int(filter_prod)).view(filter_prod, 1, *filter_size) # (kh*kw, 1, kh, kw) + self.expansion_filter = expansion_filter.repeat(3, 1, 1, 1) # repeat for all the 3 channels + + def forward(self, x, filters): + """Forward function for DynamicUpsamplingFilter. + + Args: + x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w). + filters (Tensor): Generated dynamic filters. + The shape is (n, filter_prod, upsampling_square, h, w). + filter_prod: prod of filter kernel size, e.g., 1*5*5=25. + upsampling_square: similar to pixel shuffle, + upsampling_square = upsampling * upsampling + e.g., for x 4 upsampling, upsampling_square= 4*4 = 16 + + Returns: + Tensor: Filtered image with shape (n, 3*upsampling_square, h, w) + """ + n, filter_prod, upsampling_square, h, w = filters.size() + kh, kw = self.filter_size + expanded_input = F.conv2d( + x, self.expansion_filter.to(x), padding=(kh // 2, kw // 2), groups=3) # (n, 3*filter_prod, h, w) + expanded_input = expanded_input.view(n, 3, filter_prod, h, w).permute(0, 3, 4, 1, + 2) # (n, h, w, 3, filter_prod) + filters = filters.permute(0, 3, 4, 1, 2) # (n, h, w, filter_prod, upsampling_square] + out = torch.matmul(expanded_input, filters) # (n, h, w, 3, upsampling_square) + return out.permute(0, 3, 4, 1, 2).view(n, 3 * upsampling_square, h, w) + + +@ARCH_REGISTRY.register() +class DUF(nn.Module): + """Network architecture for DUF + + Paper: Jo et.al. Deep Video Super-Resolution Network Using Dynamic + Upsampling Filters Without Explicit Motion Compensation, CVPR, 2018 + Code reference: + https://github.com/yhjo09/VSR-DUF + For all the models below, 'adapt_official_weights' is only necessary when + loading the weights converted from the official TensorFlow weights. + Please set it to False if you are training the model from scratch. + + There are three models with different model size: DUF16Layers, DUF28Layers, + and DUF52Layers. This class is the base class for these models. + + Args: + scale (int): The upsampling factor. Default: 4. + num_layer (int): The number of layers. Default: 52. + adapt_official_weights_weights (bool): Whether to adapt the weights + translated from the official implementation. Set to false if you + want to train from scratch. Default: False. + """ + + def __init__(self, scale=4, num_layer=52, adapt_official_weights=False): + super(DUF, self).__init__() + self.scale = scale + if adapt_official_weights: + eps = 1e-3 + momentum = 1e-3 + else: # pytorch default values + eps = 1e-05 + momentum = 0.1 + + self.conv3d1 = nn.Conv3d(3, 64, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True) + self.dynamic_filter = DynamicUpsamplingFilter((5, 5)) + + if num_layer == 16: + num_block = 3 + num_grow_ch = 32 + elif num_layer == 28: + num_block = 9 + num_grow_ch = 16 + elif num_layer == 52: + num_block = 21 + num_grow_ch = 16 + else: + raise ValueError(f'Only supported (16, 28, 52) layers, but got {num_layer}.') + + self.dense_block1 = DenseBlocks( + num_block=num_block, num_feat=64, num_grow_ch=num_grow_ch, + adapt_official_weights=adapt_official_weights) # T = 7 + self.dense_block2 = DenseBlocksTemporalReduce( + 64 + num_grow_ch * num_block, num_grow_ch, adapt_official_weights=adapt_official_weights) # T = 1 + channels = 64 + num_grow_ch * num_block + num_grow_ch * 3 + self.bn3d2 = nn.BatchNorm3d(channels, eps=eps, momentum=momentum) + self.conv3d2 = nn.Conv3d(channels, 256, (1, 3, 3), stride=(1, 1, 1), padding=(0, 1, 1), bias=True) + + self.conv3d_r1 = nn.Conv3d(256, 256, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) + self.conv3d_r2 = nn.Conv3d(256, 3 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) + + self.conv3d_f1 = nn.Conv3d(256, 512, (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) + self.conv3d_f2 = nn.Conv3d( + 512, 1 * 5 * 5 * (scale**2), (1, 1, 1), stride=(1, 1, 1), padding=(0, 0, 0), bias=True) + + def forward(self, x): + """ + Args: + x (Tensor): Input with shape (b, 7, c, h, w) + + Returns: + Tensor: Output with shape (b, c, h * scale, w * scale) + """ + num_batches, num_imgs, _, h, w = x.size() + + x = x.permute(0, 2, 1, 3, 4) # (b, c, 7, h, w) for Conv3D + x_center = x[:, :, num_imgs // 2, :, :] + + x = self.conv3d1(x) + x = self.dense_block1(x) + x = self.dense_block2(x) + x = F.relu(self.bn3d2(x), inplace=True) + x = F.relu(self.conv3d2(x), inplace=True) + + # residual image + res = self.conv3d_r2(F.relu(self.conv3d_r1(x), inplace=True)) + + # filter + filter_ = self.conv3d_f2(F.relu(self.conv3d_f1(x), inplace=True)) + filter_ = F.softmax(filter_.view(num_batches, 25, self.scale**2, h, w), dim=1) + + # dynamic filter + out = self.dynamic_filter(x_center, filter_) + out += res.squeeze_(2) + out = F.pixel_shuffle(out, self.scale) + + return out diff --git a/basicsr/archs/ecbsr_arch.py b/basicsr/archs/ecbsr_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..87922a0d0c3e8539bcd4ecaf90b2ee55edd4c84c --- /dev/null +++ b/basicsr/archs/ecbsr_arch.py @@ -0,0 +1,274 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from basicsr.utils.registry import ARCH_REGISTRY + + +class SeqConv3x3(nn.Module): + """The re-parameterizable block used in the ECBSR architecture. + + Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices + Ref git repo: https://github.com/xindongzhang/ECBSR + + Args: + seq_type (str): Sequence type, option: conv1x1-conv3x3 | conv1x1-sobelx | conv1x1-sobely | conv1x1-laplacian. + in_channels (int): Channel number of input. + out_channels (int): Channel number of output. + depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. + """ + + def __init__(self, seq_type, in_channels, out_channels, depth_multiplier=1): + super(SeqConv3x3, self).__init__() + self.seq_type = seq_type + self.in_channels = in_channels + self.out_channels = out_channels + + if self.seq_type == 'conv1x1-conv3x3': + self.mid_planes = int(out_channels * depth_multiplier) + conv0 = torch.nn.Conv2d(self.in_channels, self.mid_planes, kernel_size=1, padding=0) + self.k0 = conv0.weight + self.b0 = conv0.bias + + conv1 = torch.nn.Conv2d(self.mid_planes, self.out_channels, kernel_size=3) + self.k1 = conv1.weight + self.b1 = conv1.bias + + elif self.seq_type == 'conv1x1-sobelx': + conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) + self.k0 = conv0.weight + self.b0 = conv0.bias + + # init scale and bias + scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 + self.scale = nn.Parameter(scale) + bias = torch.randn(self.out_channels) * 1e-3 + bias = torch.reshape(bias, (self.out_channels, )) + self.bias = nn.Parameter(bias) + # init mask + self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) + for i in range(self.out_channels): + self.mask[i, 0, 0, 0] = 1.0 + self.mask[i, 0, 1, 0] = 2.0 + self.mask[i, 0, 2, 0] = 1.0 + self.mask[i, 0, 0, 2] = -1.0 + self.mask[i, 0, 1, 2] = -2.0 + self.mask[i, 0, 2, 2] = -1.0 + self.mask = nn.Parameter(data=self.mask, requires_grad=False) + + elif self.seq_type == 'conv1x1-sobely': + conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) + self.k0 = conv0.weight + self.b0 = conv0.bias + + # init scale and bias + scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 + self.scale = nn.Parameter(torch.FloatTensor(scale)) + bias = torch.randn(self.out_channels) * 1e-3 + bias = torch.reshape(bias, (self.out_channels, )) + self.bias = nn.Parameter(torch.FloatTensor(bias)) + # init mask + self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) + for i in range(self.out_channels): + self.mask[i, 0, 0, 0] = 1.0 + self.mask[i, 0, 0, 1] = 2.0 + self.mask[i, 0, 0, 2] = 1.0 + self.mask[i, 0, 2, 0] = -1.0 + self.mask[i, 0, 2, 1] = -2.0 + self.mask[i, 0, 2, 2] = -1.0 + self.mask = nn.Parameter(data=self.mask, requires_grad=False) + + elif self.seq_type == 'conv1x1-laplacian': + conv0 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1, padding=0) + self.k0 = conv0.weight + self.b0 = conv0.bias + + # init scale and bias + scale = torch.randn(size=(self.out_channels, 1, 1, 1)) * 1e-3 + self.scale = nn.Parameter(torch.FloatTensor(scale)) + bias = torch.randn(self.out_channels) * 1e-3 + bias = torch.reshape(bias, (self.out_channels, )) + self.bias = nn.Parameter(torch.FloatTensor(bias)) + # init mask + self.mask = torch.zeros((self.out_channels, 1, 3, 3), dtype=torch.float32) + for i in range(self.out_channels): + self.mask[i, 0, 0, 1] = 1.0 + self.mask[i, 0, 1, 0] = 1.0 + self.mask[i, 0, 1, 2] = 1.0 + self.mask[i, 0, 2, 1] = 1.0 + self.mask[i, 0, 1, 1] = -4.0 + self.mask = nn.Parameter(data=self.mask, requires_grad=False) + else: + raise ValueError('The type of seqconv is not supported!') + + def forward(self, x): + if self.seq_type == 'conv1x1-conv3x3': + # conv-1x1 + y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1) + # explicitly padding with bias + y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0) + b0_pad = self.b0.view(1, -1, 1, 1) + y0[:, :, 0:1, :] = b0_pad + y0[:, :, -1:, :] = b0_pad + y0[:, :, :, 0:1] = b0_pad + y0[:, :, :, -1:] = b0_pad + # conv-3x3 + y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1) + else: + y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1) + # explicitly padding with bias + y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0) + b0_pad = self.b0.view(1, -1, 1, 1) + y0[:, :, 0:1, :] = b0_pad + y0[:, :, -1:, :] = b0_pad + y0[:, :, :, 0:1] = b0_pad + y0[:, :, :, -1:] = b0_pad + # conv-3x3 + y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_channels) + return y1 + + def rep_params(self): + device = self.k0.get_device() + if device < 0: + device = None + + if self.seq_type == 'conv1x1-conv3x3': + # re-param conv kernel + rep_weight = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3)) + # re-param conv bias + rep_bias = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1) + rep_bias = F.conv2d(input=rep_bias, weight=self.k1).view(-1, ) + self.b1 + else: + tmp = self.scale * self.mask + k1 = torch.zeros((self.out_channels, self.out_channels, 3, 3), device=device) + for i in range(self.out_channels): + k1[i, i, :, :] = tmp[i, 0, :, :] + b1 = self.bias + # re-param conv kernel + rep_weight = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3)) + # re-param conv bias + rep_bias = torch.ones(1, self.out_channels, 3, 3, device=device) * self.b0.view(1, -1, 1, 1) + rep_bias = F.conv2d(input=rep_bias, weight=k1).view(-1, ) + b1 + return rep_weight, rep_bias + + +class ECB(nn.Module): + """The ECB block used in the ECBSR architecture. + + Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices + Ref git repo: https://github.com/xindongzhang/ECBSR + + Args: + in_channels (int): Channel number of input. + out_channels (int): Channel number of output. + depth_multiplier (int): Width multiplier in the expand-and-squeeze conv. Default: 1. + act_type (str): Activation type. Option: prelu | relu | rrelu | softplus | linear. Default: prelu. + with_idt (bool): Whether to use identity connection. Default: False. + """ + + def __init__(self, in_channels, out_channels, depth_multiplier, act_type='prelu', with_idt=False): + super(ECB, self).__init__() + + self.depth_multiplier = depth_multiplier + self.in_channels = in_channels + self.out_channels = out_channels + self.act_type = act_type + + if with_idt and (self.in_channels == self.out_channels): + self.with_idt = True + else: + self.with_idt = False + + self.conv3x3 = torch.nn.Conv2d(self.in_channels, self.out_channels, kernel_size=3, padding=1) + self.conv1x1_3x3 = SeqConv3x3('conv1x1-conv3x3', self.in_channels, self.out_channels, self.depth_multiplier) + self.conv1x1_sbx = SeqConv3x3('conv1x1-sobelx', self.in_channels, self.out_channels) + self.conv1x1_sby = SeqConv3x3('conv1x1-sobely', self.in_channels, self.out_channels) + self.conv1x1_lpl = SeqConv3x3('conv1x1-laplacian', self.in_channels, self.out_channels) + + if self.act_type == 'prelu': + self.act = nn.PReLU(num_parameters=self.out_channels) + elif self.act_type == 'relu': + self.act = nn.ReLU(inplace=True) + elif self.act_type == 'rrelu': + self.act = nn.RReLU(lower=-0.05, upper=0.05) + elif self.act_type == 'softplus': + self.act = nn.Softplus() + elif self.act_type == 'linear': + pass + else: + raise ValueError('The type of activation if not support!') + + def forward(self, x): + if self.training: + y = self.conv3x3(x) + self.conv1x1_3x3(x) + self.conv1x1_sbx(x) + self.conv1x1_sby(x) + self.conv1x1_lpl(x) + if self.with_idt: + y += x + else: + rep_weight, rep_bias = self.rep_params() + y = F.conv2d(input=x, weight=rep_weight, bias=rep_bias, stride=1, padding=1) + if self.act_type != 'linear': + y = self.act(y) + return y + + def rep_params(self): + weight0, bias0 = self.conv3x3.weight, self.conv3x3.bias + weight1, bias1 = self.conv1x1_3x3.rep_params() + weight2, bias2 = self.conv1x1_sbx.rep_params() + weight3, bias3 = self.conv1x1_sby.rep_params() + weight4, bias4 = self.conv1x1_lpl.rep_params() + rep_weight, rep_bias = (weight0 + weight1 + weight2 + weight3 + weight4), ( + bias0 + bias1 + bias2 + bias3 + bias4) + + if self.with_idt: + device = rep_weight.get_device() + if device < 0: + device = None + weight_idt = torch.zeros(self.out_channels, self.out_channels, 3, 3, device=device) + for i in range(self.out_channels): + weight_idt[i, i, 1, 1] = 1.0 + bias_idt = 0.0 + rep_weight, rep_bias = rep_weight + weight_idt, rep_bias + bias_idt + return rep_weight, rep_bias + + +@ARCH_REGISTRY.register() +class ECBSR(nn.Module): + """ECBSR architecture. + + Paper: Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices + Ref git repo: https://github.com/xindongzhang/ECBSR + + Args: + num_in_ch (int): Channel number of inputs. + num_out_ch (int): Channel number of outputs. + num_block (int): Block number in the trunk network. + num_channel (int): Channel number. + with_idt (bool): Whether use identity in convolution layers. + act_type (str): Activation type. + scale (int): Upsampling factor. + """ + + def __init__(self, num_in_ch, num_out_ch, num_block, num_channel, with_idt, act_type, scale): + super(ECBSR, self).__init__() + self.num_in_ch = num_in_ch + self.scale = scale + + backbone = [] + backbone += [ECB(num_in_ch, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)] + for _ in range(num_block): + backbone += [ECB(num_channel, num_channel, depth_multiplier=2.0, act_type=act_type, with_idt=with_idt)] + backbone += [ + ECB(num_channel, num_out_ch * scale * scale, depth_multiplier=2.0, act_type='linear', with_idt=with_idt) + ] + + self.backbone = nn.Sequential(*backbone) + self.upsampler = nn.PixelShuffle(scale) + + def forward(self, x): + if self.num_in_ch > 1: + shortcut = torch.repeat_interleave(x, self.scale * self.scale, dim=1) + else: + shortcut = x # will repeat the input in the channel dimension (repeat scale * scale times) + y = self.backbone(x) + shortcut + y = self.upsampler(y) + return y diff --git a/basicsr/archs/edsr_arch.py b/basicsr/archs/edsr_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b80566f11fbd4782d68eee8fbf7da686f89dc4e7 --- /dev/null +++ b/basicsr/archs/edsr_arch.py @@ -0,0 +1,61 @@ +import torch +from torch import nn as nn + +from basicsr.archs.arch_util import ResidualBlockNoBN, Upsample, make_layer +from basicsr.utils.registry import ARCH_REGISTRY + + +@ARCH_REGISTRY.register() +class EDSR(nn.Module): + """EDSR network structure. + + Paper: Enhanced Deep Residual Networks for Single Image Super-Resolution. + Ref git repo: https://github.com/thstkdgus35/EDSR-PyTorch + + Args: + num_in_ch (int): Channel number of inputs. + num_out_ch (int): Channel number of outputs. + num_feat (int): Channel number of intermediate features. + Default: 64. + num_block (int): Block number in the trunk network. Default: 16. + upscale (int): Upsampling factor. Support 2^n and 3. + Default: 4. + res_scale (float): Used to scale the residual in residual block. + Default: 1. + img_range (float): Image range. Default: 255. + rgb_mean (tuple[float]): Image mean in RGB orders. + Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. + """ + + def __init__(self, + num_in_ch, + num_out_ch, + num_feat=64, + num_block=16, + upscale=4, + res_scale=1, + img_range=255., + rgb_mean=(0.4488, 0.4371, 0.4040)): + super(EDSR, self).__init__() + + self.img_range = img_range + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat, res_scale=res_scale, pytorch_init=True) + self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + def forward(self, x): + self.mean = self.mean.type_as(x) + + x = (x - self.mean) * self.img_range + x = self.conv_first(x) + res = self.conv_after_body(self.body(x)) + res += x + + x = self.conv_last(self.upsample(res)) + x = x / self.img_range + self.mean + + return x diff --git a/basicsr/archs/edvr_arch.py b/basicsr/archs/edvr_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..fc9149363551d772c06b3e47f82024d425c4b3d1 --- /dev/null +++ b/basicsr/archs/edvr_arch.py @@ -0,0 +1,383 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import DCNv2Pack, ResidualBlockNoBN, make_layer + + +class PCDAlignment(nn.Module): + """Alignment module using Pyramid, Cascading and Deformable convolution + (PCD). It is used in EDVR. + + Ref: + EDVR: Video Restoration with Enhanced Deformable Convolutional Networks + + Args: + num_feat (int): Channel number of middle features. Default: 64. + deformable_groups (int): Deformable groups. Defaults: 8. + """ + + def __init__(self, num_feat=64, deformable_groups=8): + super(PCDAlignment, self).__init__() + + # Pyramid has three levels: + # L3: level 3, 1/4 spatial size + # L2: level 2, 1/2 spatial size + # L1: level 1, original spatial size + self.offset_conv1 = nn.ModuleDict() + self.offset_conv2 = nn.ModuleDict() + self.offset_conv3 = nn.ModuleDict() + self.dcn_pack = nn.ModuleDict() + self.feat_conv = nn.ModuleDict() + + # Pyramids + for i in range(3, 0, -1): + level = f'l{i}' + self.offset_conv1[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) + if i == 3: + self.offset_conv2[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + else: + self.offset_conv2[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) + self.offset_conv3[level] = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.dcn_pack[level] = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups) + + if i < 3: + self.feat_conv[level] = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) + + # Cascading dcn + self.cas_offset_conv1 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) + self.cas_offset_conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.cas_dcnpack = DCNv2Pack(num_feat, num_feat, 3, padding=1, deformable_groups=deformable_groups) + + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + def forward(self, nbr_feat_l, ref_feat_l): + """Align neighboring frame features to the reference frame features. + + Args: + nbr_feat_l (list[Tensor]): Neighboring feature list. It + contains three pyramid levels (L1, L2, L3), + each with shape (b, c, h, w). + ref_feat_l (list[Tensor]): Reference feature list. It + contains three pyramid levels (L1, L2, L3), + each with shape (b, c, h, w). + + Returns: + Tensor: Aligned features. + """ + # Pyramids + upsampled_offset, upsampled_feat = None, None + for i in range(3, 0, -1): + level = f'l{i}' + offset = torch.cat([nbr_feat_l[i - 1], ref_feat_l[i - 1]], dim=1) + offset = self.lrelu(self.offset_conv1[level](offset)) + if i == 3: + offset = self.lrelu(self.offset_conv2[level](offset)) + else: + offset = self.lrelu(self.offset_conv2[level](torch.cat([offset, upsampled_offset], dim=1))) + offset = self.lrelu(self.offset_conv3[level](offset)) + + feat = self.dcn_pack[level](nbr_feat_l[i - 1], offset) + if i < 3: + feat = self.feat_conv[level](torch.cat([feat, upsampled_feat], dim=1)) + if i > 1: + feat = self.lrelu(feat) + + if i > 1: # upsample offset and features + # x2: when we upsample the offset, we should also enlarge + # the magnitude. + upsampled_offset = self.upsample(offset) * 2 + upsampled_feat = self.upsample(feat) + + # Cascading + offset = torch.cat([feat, ref_feat_l[0]], dim=1) + offset = self.lrelu(self.cas_offset_conv2(self.lrelu(self.cas_offset_conv1(offset)))) + feat = self.lrelu(self.cas_dcnpack(feat, offset)) + return feat + + +class TSAFusion(nn.Module): + """Temporal Spatial Attention (TSA) fusion module. + + Temporal: Calculate the correlation between center frame and + neighboring frames; + Spatial: It has 3 pyramid levels, the attention is similar to SFT. + (SFT: Recovering realistic texture in image super-resolution by deep + spatial feature transform.) + + Args: + num_feat (int): Channel number of middle features. Default: 64. + num_frame (int): Number of frames. Default: 5. + center_frame_idx (int): The index of center frame. Default: 2. + """ + + def __init__(self, num_feat=64, num_frame=5, center_frame_idx=2): + super(TSAFusion, self).__init__() + self.center_frame_idx = center_frame_idx + # temporal attention (before fusion conv) + self.temporal_attn1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.temporal_attn2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.feat_fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1) + + # spatial attention (after fusion conv) + self.max_pool = nn.MaxPool2d(3, stride=2, padding=1) + self.avg_pool = nn.AvgPool2d(3, stride=2, padding=1) + self.spatial_attn1 = nn.Conv2d(num_frame * num_feat, num_feat, 1) + self.spatial_attn2 = nn.Conv2d(num_feat * 2, num_feat, 1) + self.spatial_attn3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.spatial_attn4 = nn.Conv2d(num_feat, num_feat, 1) + self.spatial_attn5 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.spatial_attn_l1 = nn.Conv2d(num_feat, num_feat, 1) + self.spatial_attn_l2 = nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1) + self.spatial_attn_l3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.spatial_attn_add1 = nn.Conv2d(num_feat, num_feat, 1) + self.spatial_attn_add2 = nn.Conv2d(num_feat, num_feat, 1) + + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) + + def forward(self, aligned_feat): + """ + Args: + aligned_feat (Tensor): Aligned features with shape (b, t, c, h, w). + + Returns: + Tensor: Features after TSA with the shape (b, c, h, w). + """ + b, t, c, h, w = aligned_feat.size() + # temporal attention + embedding_ref = self.temporal_attn1(aligned_feat[:, self.center_frame_idx, :, :, :].clone()) + embedding = self.temporal_attn2(aligned_feat.view(-1, c, h, w)) + embedding = embedding.view(b, t, -1, h, w) # (b, t, c, h, w) + + corr_l = [] # correlation list + for i in range(t): + emb_neighbor = embedding[:, i, :, :, :] + corr = torch.sum(emb_neighbor * embedding_ref, 1) # (b, h, w) + corr_l.append(corr.unsqueeze(1)) # (b, 1, h, w) + corr_prob = torch.sigmoid(torch.cat(corr_l, dim=1)) # (b, t, h, w) + corr_prob = corr_prob.unsqueeze(2).expand(b, t, c, h, w) + corr_prob = corr_prob.contiguous().view(b, -1, h, w) # (b, t*c, h, w) + aligned_feat = aligned_feat.view(b, -1, h, w) * corr_prob + + # fusion + feat = self.lrelu(self.feat_fusion(aligned_feat)) + + # spatial attention + attn = self.lrelu(self.spatial_attn1(aligned_feat)) + attn_max = self.max_pool(attn) + attn_avg = self.avg_pool(attn) + attn = self.lrelu(self.spatial_attn2(torch.cat([attn_max, attn_avg], dim=1))) + # pyramid levels + attn_level = self.lrelu(self.spatial_attn_l1(attn)) + attn_max = self.max_pool(attn_level) + attn_avg = self.avg_pool(attn_level) + attn_level = self.lrelu(self.spatial_attn_l2(torch.cat([attn_max, attn_avg], dim=1))) + attn_level = self.lrelu(self.spatial_attn_l3(attn_level)) + attn_level = self.upsample(attn_level) + + attn = self.lrelu(self.spatial_attn3(attn)) + attn_level + attn = self.lrelu(self.spatial_attn4(attn)) + attn = self.upsample(attn) + attn = self.spatial_attn5(attn) + attn_add = self.spatial_attn_add2(self.lrelu(self.spatial_attn_add1(attn))) + attn = torch.sigmoid(attn) + + # after initialization, * 2 makes (attn * 2) to be close to 1. + feat = feat * attn * 2 + attn_add + return feat + + +class PredeblurModule(nn.Module): + """Pre-dublur module. + + Args: + num_in_ch (int): Channel number of input image. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + hr_in (bool): Whether the input has high resolution. Default: False. + """ + + def __init__(self, num_in_ch=3, num_feat=64, hr_in=False): + super(PredeblurModule, self).__init__() + self.hr_in = hr_in + + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + if self.hr_in: + # downsample x4 by stride conv + self.stride_conv_hr1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + self.stride_conv_hr2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + + # generate feature pyramid + self.stride_conv_l2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + self.stride_conv_l3 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + + self.resblock_l3 = ResidualBlockNoBN(num_feat=num_feat) + self.resblock_l2_1 = ResidualBlockNoBN(num_feat=num_feat) + self.resblock_l2_2 = ResidualBlockNoBN(num_feat=num_feat) + self.resblock_l1 = nn.ModuleList([ResidualBlockNoBN(num_feat=num_feat) for i in range(5)]) + + self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False) + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + def forward(self, x): + feat_l1 = self.lrelu(self.conv_first(x)) + if self.hr_in: + feat_l1 = self.lrelu(self.stride_conv_hr1(feat_l1)) + feat_l1 = self.lrelu(self.stride_conv_hr2(feat_l1)) + + # generate feature pyramid + feat_l2 = self.lrelu(self.stride_conv_l2(feat_l1)) + feat_l3 = self.lrelu(self.stride_conv_l3(feat_l2)) + + feat_l3 = self.upsample(self.resblock_l3(feat_l3)) + feat_l2 = self.resblock_l2_1(feat_l2) + feat_l3 + feat_l2 = self.upsample(self.resblock_l2_2(feat_l2)) + + for i in range(2): + feat_l1 = self.resblock_l1[i](feat_l1) + feat_l1 = feat_l1 + feat_l2 + for i in range(2, 5): + feat_l1 = self.resblock_l1[i](feat_l1) + return feat_l1 + + +@ARCH_REGISTRY.register() +class EDVR(nn.Module): + """EDVR network structure for video super-resolution. + + Now only support X4 upsampling factor. + Paper: + EDVR: Video Restoration with Enhanced Deformable Convolutional Networks + + Args: + num_in_ch (int): Channel number of input image. Default: 3. + num_out_ch (int): Channel number of output image. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + num_frame (int): Number of input frames. Default: 5. + deformable_groups (int): Deformable groups. Defaults: 8. + num_extract_block (int): Number of blocks for feature extraction. + Default: 5. + num_reconstruct_block (int): Number of blocks for reconstruction. + Default: 10. + center_frame_idx (int): The index of center frame. Frame counting from + 0. Default: Middle of input frames. + hr_in (bool): Whether the input has high resolution. Default: False. + with_predeblur (bool): Whether has predeblur module. + Default: False. + with_tsa (bool): Whether has TSA module. Default: True. + """ + + def __init__(self, + num_in_ch=3, + num_out_ch=3, + num_feat=64, + num_frame=5, + deformable_groups=8, + num_extract_block=5, + num_reconstruct_block=10, + center_frame_idx=None, + hr_in=False, + with_predeblur=False, + with_tsa=True): + super(EDVR, self).__init__() + if center_frame_idx is None: + self.center_frame_idx = num_frame // 2 + else: + self.center_frame_idx = center_frame_idx + self.hr_in = hr_in + self.with_predeblur = with_predeblur + self.with_tsa = with_tsa + + # extract features for each frame + if self.with_predeblur: + self.predeblur = PredeblurModule(num_feat=num_feat, hr_in=self.hr_in) + self.conv_1x1 = nn.Conv2d(num_feat, num_feat, 1, 1) + else: + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + + # extract pyramid features + self.feature_extraction = make_layer(ResidualBlockNoBN, num_extract_block, num_feat=num_feat) + self.conv_l2_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + self.conv_l2_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_l3_1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1) + self.conv_l3_2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + + # pcd and tsa module + self.pcd_align = PCDAlignment(num_feat=num_feat, deformable_groups=deformable_groups) + if self.with_tsa: + self.fusion = TSAFusion(num_feat=num_feat, num_frame=num_frame, center_frame_idx=self.center_frame_idx) + else: + self.fusion = nn.Conv2d(num_frame * num_feat, num_feat, 1, 1) + + # reconstruction + self.reconstruction = make_layer(ResidualBlockNoBN, num_reconstruct_block, num_feat=num_feat) + # upsample + self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) + self.upconv2 = nn.Conv2d(num_feat, 64 * 4, 3, 1, 1) + self.pixel_shuffle = nn.PixelShuffle(2) + self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) + self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) + + # activation function + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + def forward(self, x): + b, t, c, h, w = x.size() + if self.hr_in: + assert h % 16 == 0 and w % 16 == 0, ('The height and width must be multiple of 16.') + else: + assert h % 4 == 0 and w % 4 == 0, ('The height and width must be multiple of 4.') + + x_center = x[:, self.center_frame_idx, :, :, :].contiguous() + + # extract features for each frame + # L1 + if self.with_predeblur: + feat_l1 = self.conv_1x1(self.predeblur(x.view(-1, c, h, w))) + if self.hr_in: + h, w = h // 4, w // 4 + else: + feat_l1 = self.lrelu(self.conv_first(x.view(-1, c, h, w))) + + feat_l1 = self.feature_extraction(feat_l1) + # L2 + feat_l2 = self.lrelu(self.conv_l2_1(feat_l1)) + feat_l2 = self.lrelu(self.conv_l2_2(feat_l2)) + # L3 + feat_l3 = self.lrelu(self.conv_l3_1(feat_l2)) + feat_l3 = self.lrelu(self.conv_l3_2(feat_l3)) + + feat_l1 = feat_l1.view(b, t, -1, h, w) + feat_l2 = feat_l2.view(b, t, -1, h // 2, w // 2) + feat_l3 = feat_l3.view(b, t, -1, h // 4, w // 4) + + # PCD alignment + ref_feat_l = [ # reference feature list + feat_l1[:, self.center_frame_idx, :, :, :].clone(), feat_l2[:, self.center_frame_idx, :, :, :].clone(), + feat_l3[:, self.center_frame_idx, :, :, :].clone() + ] + aligned_feat = [] + for i in range(t): + nbr_feat_l = [ # neighboring feature list + feat_l1[:, i, :, :, :].clone(), feat_l2[:, i, :, :, :].clone(), feat_l3[:, i, :, :, :].clone() + ] + aligned_feat.append(self.pcd_align(nbr_feat_l, ref_feat_l)) + aligned_feat = torch.stack(aligned_feat, dim=1) # (b, t, c, h, w) + + if not self.with_tsa: + aligned_feat = aligned_feat.view(b, -1, h, w) + feat = self.fusion(aligned_feat) + + out = self.reconstruction(feat) + out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) + out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) + out = self.lrelu(self.conv_hr(out)) + out = self.conv_last(out) + if self.hr_in: + base = x_center + else: + base = F.interpolate(x_center, scale_factor=4, mode='bilinear', align_corners=False) + out += base + return out diff --git a/basicsr/archs/hifacegan_arch.py b/basicsr/archs/hifacegan_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..de26e4c2c8f2848dd855e409da19b81fef2538bb --- /dev/null +++ b/basicsr/archs/hifacegan_arch.py @@ -0,0 +1,259 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .hifacegan_util import BaseNetwork, LIPEncoder, SPADEResnetBlock, get_nonspade_norm_layer + + +class SPADEGenerator(BaseNetwork): + """Generator with SPADEResBlock""" + + def __init__(self, + num_in_ch=3, + num_feat=64, + use_vae=False, + z_dim=256, + crop_size=512, + norm_g='spectralspadesyncbatch3x3', + is_train=True, + init_train_phase=3): # progressive training disabled + super().__init__() + self.nf = num_feat + self.input_nc = num_in_ch + self.is_train = is_train + self.train_phase = init_train_phase + + self.scale_ratio = 5 # hardcoded now + self.sw = crop_size // (2**self.scale_ratio) + self.sh = self.sw # 20210519: By default use square image, aspect_ratio = 1.0 + + if use_vae: + # In case of VAE, we will sample from random z vector + self.fc = nn.Linear(z_dim, 16 * self.nf * self.sw * self.sh) + else: + # Otherwise, we make the network deterministic by starting with + # downsampled segmentation map instead of random z + self.fc = nn.Conv2d(num_in_ch, 16 * self.nf, 3, padding=1) + + self.head_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) + + self.g_middle_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) + self.g_middle_1 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g) + + self.ups = nn.ModuleList([ + SPADEResnetBlock(16 * self.nf, 8 * self.nf, norm_g), + SPADEResnetBlock(8 * self.nf, 4 * self.nf, norm_g), + SPADEResnetBlock(4 * self.nf, 2 * self.nf, norm_g), + SPADEResnetBlock(2 * self.nf, 1 * self.nf, norm_g) + ]) + + self.to_rgbs = nn.ModuleList([ + nn.Conv2d(8 * self.nf, 3, 3, padding=1), + nn.Conv2d(4 * self.nf, 3, 3, padding=1), + nn.Conv2d(2 * self.nf, 3, 3, padding=1), + nn.Conv2d(1 * self.nf, 3, 3, padding=1) + ]) + + self.up = nn.Upsample(scale_factor=2) + + def encode(self, input_tensor): + """ + Encode input_tensor into feature maps, can be overridden in derived classes + Default: nearest downsampling of 2**5 = 32 times + """ + h, w = input_tensor.size()[-2:] + sh, sw = h // 2**self.scale_ratio, w // 2**self.scale_ratio + x = F.interpolate(input_tensor, size=(sh, sw)) + return self.fc(x) + + def forward(self, x): + # In oroginal SPADE, seg means a segmentation map, but here we use x instead. + seg = x + + x = self.encode(x) + x = self.head_0(x, seg) + + x = self.up(x) + x = self.g_middle_0(x, seg) + x = self.g_middle_1(x, seg) + + if self.is_train: + phase = self.train_phase + 1 + else: + phase = len(self.to_rgbs) + + for i in range(phase): + x = self.up(x) + x = self.ups[i](x, seg) + + x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1)) + x = torch.tanh(x) + + return x + + def mixed_guidance_forward(self, input_x, seg=None, n=0, mode='progressive'): + """ + A helper class for subspace visualization. Input and seg are different images. + For the first n levels (including encoder) we use input, for the rest we use seg. + + If mode = 'progressive', the output's like: AAABBB + If mode = 'one_plug', the output's like: AAABAA + If mode = 'one_ablate', the output's like: BBBABB + """ + + if seg is None: + return self.forward(input_x) + + if self.is_train: + phase = self.train_phase + 1 + else: + phase = len(self.to_rgbs) + + if mode == 'progressive': + n = max(min(n, 4 + phase), 0) + guide_list = [input_x] * n + [seg] * (4 + phase - n) + elif mode == 'one_plug': + n = max(min(n, 4 + phase - 1), 0) + guide_list = [seg] * (4 + phase) + guide_list[n] = input_x + elif mode == 'one_ablate': + if n > 3 + phase: + return self.forward(input_x) + guide_list = [input_x] * (4 + phase) + guide_list[n] = seg + + x = self.encode(guide_list[0]) + x = self.head_0(x, guide_list[1]) + + x = self.up(x) + x = self.g_middle_0(x, guide_list[2]) + x = self.g_middle_1(x, guide_list[3]) + + for i in range(phase): + x = self.up(x) + x = self.ups[i](x, guide_list[4 + i]) + + x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1)) + x = torch.tanh(x) + + return x + + +@ARCH_REGISTRY.register() +class HiFaceGAN(SPADEGenerator): + """ + HiFaceGAN: SPADEGenerator with a learnable feature encoder + Current encoder design: LIPEncoder + """ + + def __init__(self, + num_in_ch=3, + num_feat=64, + use_vae=False, + z_dim=256, + crop_size=512, + norm_g='spectralspadesyncbatch3x3', + is_train=True, + init_train_phase=3): + super().__init__(num_in_ch, num_feat, use_vae, z_dim, crop_size, norm_g, is_train, init_train_phase) + self.lip_encoder = LIPEncoder(num_in_ch, num_feat, self.sw, self.sh, self.scale_ratio) + + def encode(self, input_tensor): + return self.lip_encoder(input_tensor) + + +@ARCH_REGISTRY.register() +class HiFaceGANDiscriminator(BaseNetwork): + """ + Inspired by pix2pixHD multiscale discriminator. + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_out_ch (int): Channel number of outputs. Default: 3. + conditional_d (bool): Whether use conditional discriminator. + Default: True. + num_d (int): Number of Multiscale discriminators. Default: 3. + n_layers_d (int): Number of downsample layers in each D. Default: 4. + num_feat (int): Channel number of base intermediate features. + Default: 64. + norm_d (str): String to determine normalization layers in D. + Choices: [spectral][instance/batch/syncbatch] + Default: 'spectralinstance'. + keep_features (bool): Keep intermediate features for matching loss, etc. + Default: True. + """ + + def __init__(self, + num_in_ch=3, + num_out_ch=3, + conditional_d=True, + num_d=2, + n_layers_d=4, + num_feat=64, + norm_d='spectralinstance', + keep_features=True): + super().__init__() + self.num_d = num_d + + input_nc = num_in_ch + if conditional_d: + input_nc += num_out_ch + + for i in range(num_d): + subnet_d = NLayerDiscriminator(input_nc, n_layers_d, num_feat, norm_d, keep_features) + self.add_module(f'discriminator_{i}', subnet_d) + + def downsample(self, x): + return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False) + + # Returns list of lists of discriminator outputs. + # The final result is of size opt.num_d x opt.n_layers_D + def forward(self, x): + result = [] + for _, _net_d in self.named_children(): + out = _net_d(x) + result.append(out) + x = self.downsample(x) + + return result + + +class NLayerDiscriminator(BaseNetwork): + """Defines the PatchGAN discriminator with the specified arguments.""" + + def __init__(self, input_nc, n_layers_d, num_feat, norm_d, keep_features): + super().__init__() + kw = 4 + padw = int(np.ceil((kw - 1.0) / 2)) + nf = num_feat + self.keep_features = keep_features + + norm_layer = get_nonspade_norm_layer(norm_d) + sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)]] + + for n in range(1, n_layers_d): + nf_prev = nf + nf = min(nf * 2, 512) + stride = 1 if n == n_layers_d - 1 else 2 + sequence += [[ + norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=stride, padding=padw)), + nn.LeakyReLU(0.2, False) + ]] + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + # We divide the layers into groups to extract intermediate layer outputs + for n in range(len(sequence)): + self.add_module('model' + str(n), nn.Sequential(*sequence[n])) + + def forward(self, x): + results = [x] + for submodel in self.children(): + intermediate_output = submodel(results[-1]) + results.append(intermediate_output) + + if self.keep_features: + return results[1:] + else: + return results[-1] diff --git a/basicsr/archs/hifacegan_util.py b/basicsr/archs/hifacegan_util.py new file mode 100644 index 0000000000000000000000000000000000000000..35cbef3f532fcc6aab0fa57ab316a546d3a17bd5 --- /dev/null +++ b/basicsr/archs/hifacegan_util.py @@ -0,0 +1,255 @@ +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init +# Warning: spectral norm could be buggy +# under eval mode and multi-GPU inference +# A workaround is sticking to single-GPU inference and train mode +from torch.nn.utils import spectral_norm + + +class SPADE(nn.Module): + + def __init__(self, config_text, norm_nc, label_nc): + super().__init__() + + assert config_text.startswith('spade') + parsed = re.search('spade(\\D+)(\\d)x\\d', config_text) + param_free_norm_type = str(parsed.group(1)) + ks = int(parsed.group(2)) + + if param_free_norm_type == 'instance': + self.param_free_norm = nn.InstanceNorm2d(norm_nc) + elif param_free_norm_type == 'syncbatch': + print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') + self.param_free_norm = nn.InstanceNorm2d(norm_nc) + elif param_free_norm_type == 'batch': + self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False) + else: + raise ValueError(f'{param_free_norm_type} is not a recognized param-free norm type in SPADE') + + # The dimension of the intermediate embedding space. Yes, hardcoded. + nhidden = 128 if norm_nc > 128 else norm_nc + + pw = ks // 2 + self.mlp_shared = nn.Sequential(nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), nn.ReLU()) + self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) + self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) + + def forward(self, x, segmap): + + # Part 1. generate parameter-free normalized activations + normalized = self.param_free_norm(x) + + # Part 2. produce scaling and bias conditioned on semantic map + segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') + actv = self.mlp_shared(segmap) + gamma = self.mlp_gamma(actv) + beta = self.mlp_beta(actv) + + # apply scale and bias + out = normalized * gamma + beta + + return out + + +class SPADEResnetBlock(nn.Module): + """ + ResNet block that uses SPADE. It differs from the ResNet block of pix2pixHD in that + it takes in the segmentation map as input, learns the skip connection if necessary, + and applies normalization first and then convolution. + This architecture seemed like a standard architecture for unconditional or + class-conditional GAN architecture using residual block. + The code was inspired from https://github.com/LMescheder/GAN_stability. + """ + + def __init__(self, fin, fout, norm_g='spectralspadesyncbatch3x3', semantic_nc=3): + super().__init__() + # Attributes + self.learned_shortcut = (fin != fout) + fmiddle = min(fin, fout) + + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + + # apply spectral norm if specified + if 'spectral' in norm_g: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + + # define normalization layers + spade_config_str = norm_g.replace('spectral', '') + self.norm_0 = SPADE(spade_config_str, fin, semantic_nc) + self.norm_1 = SPADE(spade_config_str, fmiddle, semantic_nc) + if self.learned_shortcut: + self.norm_s = SPADE(spade_config_str, fin, semantic_nc) + + # note the resnet block with SPADE also takes in |seg|, + # the semantic segmentation map as input + def forward(self, x, seg): + x_s = self.shortcut(x, seg) + dx = self.conv_0(self.act(self.norm_0(x, seg))) + dx = self.conv_1(self.act(self.norm_1(dx, seg))) + out = x_s + dx + return out + + def shortcut(self, x, seg): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, seg)) + else: + x_s = x + return x_s + + def act(self, x): + return F.leaky_relu(x, 2e-1) + + +class BaseNetwork(nn.Module): + """ A basis for hifacegan archs with custom initialization """ + + def init_weights(self, init_type='normal', gain=0.02): + + def init_func(m): + classname = m.__class__.__name__ + if classname.find('BatchNorm2d') != -1: + if hasattr(m, 'weight') and m.weight is not None: + init.normal_(m.weight.data, 1.0, gain) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'xavier_uniform': + init.xavier_uniform_(m.weight.data, gain=1.0) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + elif init_type == 'none': # uses pytorch's default init method + m.reset_parameters() + else: + raise NotImplementedError(f'initialization method [{init_type}] is not implemented') + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + + self.apply(init_func) + + # propagate to children + for m in self.children(): + if hasattr(m, 'init_weights'): + m.init_weights(init_type, gain) + + def forward(self, x): + pass + + +def lip2d(x, logit, kernel=3, stride=2, padding=1): + weight = logit.exp() + return F.avg_pool2d(x * weight, kernel, stride, padding) / F.avg_pool2d(weight, kernel, stride, padding) + + +class SoftGate(nn.Module): + COEFF = 12.0 + + def forward(self, x): + return torch.sigmoid(x).mul(self.COEFF) + + +class SimplifiedLIP(nn.Module): + + def __init__(self, channels): + super(SimplifiedLIP, self).__init__() + self.logit = nn.Sequential( + nn.Conv2d(channels, channels, 3, padding=1, bias=False), nn.InstanceNorm2d(channels, affine=True), + SoftGate()) + + def init_layer(self): + self.logit[0].weight.data.fill_(0.0) + + def forward(self, x): + frac = lip2d(x, self.logit(x)) + return frac + + +class LIPEncoder(BaseNetwork): + """Local Importance-based Pooling (Ziteng Gao et.al.,ICCV 2019)""" + + def __init__(self, input_nc, ngf, sw, sh, n_2xdown, norm_layer=nn.InstanceNorm2d): + super().__init__() + self.sw = sw + self.sh = sh + self.max_ratio = 16 + # 20200310: Several Convolution (stride 1) + LIP blocks, 4 fold + kw = 3 + pw = (kw - 1) // 2 + + model = [ + nn.Conv2d(input_nc, ngf, kw, stride=1, padding=pw, bias=False), + norm_layer(ngf), + nn.ReLU(), + ] + cur_ratio = 1 + for i in range(n_2xdown): + next_ratio = min(cur_ratio * 2, self.max_ratio) + model += [ + SimplifiedLIP(ngf * cur_ratio), + nn.Conv2d(ngf * cur_ratio, ngf * next_ratio, kw, stride=1, padding=pw), + norm_layer(ngf * next_ratio), + ] + cur_ratio = next_ratio + if i < n_2xdown - 1: + model += [nn.ReLU(inplace=True)] + + self.model = nn.Sequential(*model) + + def forward(self, x): + return self.model(x) + + +def get_nonspade_norm_layer(norm_type='instance'): + # helper function to get # output channels of the previous layer + def get_out_channel(layer): + if hasattr(layer, 'out_channels'): + return getattr(layer, 'out_channels') + return layer.weight.size(0) + + # this function will be returned + def add_norm_layer(layer): + nonlocal norm_type + if norm_type.startswith('spectral'): + layer = spectral_norm(layer) + subnorm_type = norm_type[len('spectral'):] + + if subnorm_type == 'none' or len(subnorm_type) == 0: + return layer + + # remove bias in the previous layer, which is meaningless + # since it has no effect after normalization + if getattr(layer, 'bias', None) is not None: + delattr(layer, 'bias') + layer.register_parameter('bias', None) + + if subnorm_type == 'batch': + norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True) + elif subnorm_type == 'sync_batch': + print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') + # norm_layer = SynchronizedBatchNorm2d( + # get_out_channel(layer), affine=True) + norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) + elif subnorm_type == 'instance': + norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) + else: + raise ValueError(f'normalization layer {subnorm_type} is not recognized') + + return nn.Sequential(layer, norm_layer) + + print('This is a legacy from nvlabs/SPADE, and will be removed in future versions.') + return add_norm_layer diff --git a/basicsr/archs/inception.py b/basicsr/archs/inception.py new file mode 100644 index 0000000000000000000000000000000000000000..de1abef67270dc1aba770943b53577029141f527 --- /dev/null +++ b/basicsr/archs/inception.py @@ -0,0 +1,307 @@ +# Modified from https://github.com/mseitzer/pytorch-fid/blob/master/pytorch_fid/inception.py # noqa: E501 +# For FID metric + +import os +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.model_zoo import load_url +from torchvision import models + +# Inception weights ported to Pytorch from +# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz +FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 +LOCAL_FID_WEIGHTS = 'experiments/pretrained_models/pt_inception-2015-12-05-6726825d.pth' # noqa: E501 + + +class InceptionV3(nn.Module): + """Pretrained InceptionV3 network returning feature maps""" + + # Index of default block of inception to return, + # corresponds to output of final average pooling + DEFAULT_BLOCK_INDEX = 3 + + # Maps feature dimensionality to their output blocks indices + BLOCK_INDEX_BY_DIM = { + 64: 0, # First max pooling features + 192: 1, # Second max pooling features + 768: 2, # Pre-aux classifier features + 2048: 3 # Final average pooling features + } + + def __init__(self, + output_blocks=(DEFAULT_BLOCK_INDEX), + resize_input=True, + normalize_input=True, + requires_grad=False, + use_fid_inception=True): + """Build pretrained InceptionV3. + + Args: + output_blocks (list[int]): Indices of blocks to return features of. + Possible values are: + - 0: corresponds to output of first max pooling + - 1: corresponds to output of second max pooling + - 2: corresponds to output which is fed to aux classifier + - 3: corresponds to output of final average pooling + resize_input (bool): If true, bilinearly resizes input to width and + height 299 before feeding input to model. As the network + without fully connected layers is fully convolutional, it + should be able to handle inputs of arbitrary size, so resizing + might not be strictly needed. Default: True. + normalize_input (bool): If true, scales the input from range (0, 1) + to the range the pretrained Inception network expects, + namely (-1, 1). Default: True. + requires_grad (bool): If true, parameters of the model require + gradients. Possibly useful for finetuning the network. + Default: False. + use_fid_inception (bool): If true, uses the pretrained Inception + model used in Tensorflow's FID implementation. + If false, uses the pretrained Inception model available in + torchvision. The FID Inception model has different weights + and a slightly different structure from torchvision's + Inception model. If you want to compute FID scores, you are + strongly advised to set this parameter to true to get + comparable results. Default: True. + """ + super(InceptionV3, self).__init__() + + self.resize_input = resize_input + self.normalize_input = normalize_input + self.output_blocks = sorted(output_blocks) + self.last_needed_block = max(output_blocks) + + assert self.last_needed_block <= 3, ('Last possible output block index is 3') + + self.blocks = nn.ModuleList() + + if use_fid_inception: + inception = fid_inception_v3() + else: + try: + inception = models.inception_v3(pretrained=True, init_weights=False) + except TypeError: + # pytorch < 1.5 does not have init_weights for inception_v3 + inception = models.inception_v3(pretrained=True) + + # Block 0: input to maxpool1 + block0 = [ + inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, + nn.MaxPool2d(kernel_size=3, stride=2) + ] + self.blocks.append(nn.Sequential(*block0)) + + # Block 1: maxpool1 to maxpool2 + if self.last_needed_block >= 1: + block1 = [inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2)] + self.blocks.append(nn.Sequential(*block1)) + + # Block 2: maxpool2 to aux classifier + if self.last_needed_block >= 2: + block2 = [ + inception.Mixed_5b, + inception.Mixed_5c, + inception.Mixed_5d, + inception.Mixed_6a, + inception.Mixed_6b, + inception.Mixed_6c, + inception.Mixed_6d, + inception.Mixed_6e, + ] + self.blocks.append(nn.Sequential(*block2)) + + # Block 3: aux classifier to final avgpool + if self.last_needed_block >= 3: + block3 = [ + inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, + nn.AdaptiveAvgPool2d(output_size=(1, 1)) + ] + self.blocks.append(nn.Sequential(*block3)) + + for param in self.parameters(): + param.requires_grad = requires_grad + + def forward(self, x): + """Get Inception feature maps. + + Args: + x (Tensor): Input tensor of shape (b, 3, h, w). + Values are expected to be in range (-1, 1). You can also input + (0, 1) with setting normalize_input = True. + + Returns: + list[Tensor]: Corresponding to the selected output block, sorted + ascending by index. + """ + output = [] + + if self.resize_input: + x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) + + if self.normalize_input: + x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) + + for idx, block in enumerate(self.blocks): + x = block(x) + if idx in self.output_blocks: + output.append(x) + + if idx == self.last_needed_block: + break + + return output + + +def fid_inception_v3(): + """Build pretrained Inception model for FID computation. + + The Inception model for FID computation uses a different set of weights + and has a slightly different structure than torchvision's Inception. + + This method first constructs torchvision's Inception and then patches the + necessary parts that are different in the FID Inception model. + """ + try: + inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False, init_weights=False) + except TypeError: + # pytorch < 1.5 does not have init_weights for inception_v3 + inception = models.inception_v3(num_classes=1008, aux_logits=False, pretrained=False) + + inception.Mixed_5b = FIDInceptionA(192, pool_features=32) + inception.Mixed_5c = FIDInceptionA(256, pool_features=64) + inception.Mixed_5d = FIDInceptionA(288, pool_features=64) + inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128) + inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160) + inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192) + inception.Mixed_7b = FIDInceptionE_1(1280) + inception.Mixed_7c = FIDInceptionE_2(2048) + + if os.path.exists(LOCAL_FID_WEIGHTS): + state_dict = torch.load(LOCAL_FID_WEIGHTS, map_location=lambda storage, loc: storage) + else: + state_dict = load_url(FID_WEIGHTS_URL, progress=True) + + inception.load_state_dict(state_dict) + return inception + + +class FIDInceptionA(models.inception.InceptionA): + """InceptionA block patched for FID computation""" + + def __init__(self, in_channels, pool_features): + super(FIDInceptionA, self).__init__(in_channels, pool_features) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionC(models.inception.InceptionC): + """InceptionC block patched for FID computation""" + + def __init__(self, in_channels, channels_7x7): + super(FIDInceptionC, self).__init__(in_channels, channels_7x7) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_1(models.inception.InceptionE): + """First InceptionE block patched for FID computation""" + + def __init__(self, in_channels): + super(FIDInceptionE_1, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: Tensorflow's average pool does not use the padded zero's in + # its average calculation + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1, count_include_pad=False) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class FIDInceptionE_2(models.inception.InceptionE): + """Second InceptionE block patched for FID computation""" + + def __init__(self, in_channels): + super(FIDInceptionE_2, self).__init__(in_channels) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + # Patch: The FID Inception model uses max pooling instead of average + # pooling. This is likely an error in this specific Inception + # implementation, as other Inception models use average pooling here + # (which matches the description in the paper). + branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) diff --git a/basicsr/archs/rcan_arch.py b/basicsr/archs/rcan_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..f15e9215e3ce402a1b71ab82061421658896b0e8 --- /dev/null +++ b/basicsr/archs/rcan_arch.py @@ -0,0 +1,135 @@ +import torch +from torch import nn as nn + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import Upsample, make_layer + + +class ChannelAttention(nn.Module): + """Channel attention used in RCAN. + + Args: + num_feat (int): Channel number of intermediate features. + squeeze_factor (int): Channel squeeze factor. Default: 16. + """ + + def __init__(self, num_feat, squeeze_factor=16): + super(ChannelAttention, self).__init__() + self.attention = nn.Sequential( + nn.AdaptiveAvgPool2d(1), nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), + nn.ReLU(inplace=True), nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), nn.Sigmoid()) + + def forward(self, x): + y = self.attention(x) + return x * y + + +class RCAB(nn.Module): + """Residual Channel Attention Block (RCAB) used in RCAN. + + Args: + num_feat (int): Channel number of intermediate features. + squeeze_factor (int): Channel squeeze factor. Default: 16. + res_scale (float): Scale the residual. Default: 1. + """ + + def __init__(self, num_feat, squeeze_factor=16, res_scale=1): + super(RCAB, self).__init__() + self.res_scale = res_scale + + self.rcab = nn.Sequential( + nn.Conv2d(num_feat, num_feat, 3, 1, 1), nn.ReLU(True), nn.Conv2d(num_feat, num_feat, 3, 1, 1), + ChannelAttention(num_feat, squeeze_factor)) + + def forward(self, x): + res = self.rcab(x) * self.res_scale + return res + x + + +class ResidualGroup(nn.Module): + """Residual Group of RCAB. + + Args: + num_feat (int): Channel number of intermediate features. + num_block (int): Block number in the body network. + squeeze_factor (int): Channel squeeze factor. Default: 16. + res_scale (float): Scale the residual. Default: 1. + """ + + def __init__(self, num_feat, num_block, squeeze_factor=16, res_scale=1): + super(ResidualGroup, self).__init__() + + self.residual_group = make_layer( + RCAB, num_block, num_feat=num_feat, squeeze_factor=squeeze_factor, res_scale=res_scale) + self.conv = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + + def forward(self, x): + res = self.conv(self.residual_group(x)) + return res + x + + +@ARCH_REGISTRY.register() +class RCAN(nn.Module): + """Residual Channel Attention Networks. + + Paper: Image Super-Resolution Using Very Deep Residual Channel Attention + Networks + Ref git repo: https://github.com/yulunzhang/RCAN. + + Args: + num_in_ch (int): Channel number of inputs. + num_out_ch (int): Channel number of outputs. + num_feat (int): Channel number of intermediate features. + Default: 64. + num_group (int): Number of ResidualGroup. Default: 10. + num_block (int): Number of RCAB in ResidualGroup. Default: 16. + squeeze_factor (int): Channel squeeze factor. Default: 16. + upscale (int): Upsampling factor. Support 2^n and 3. + Default: 4. + res_scale (float): Used to scale the residual in residual block. + Default: 1. + img_range (float): Image range. Default: 255. + rgb_mean (tuple[float]): Image mean in RGB orders. + Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. + """ + + def __init__(self, + num_in_ch, + num_out_ch, + num_feat=64, + num_group=10, + num_block=16, + squeeze_factor=16, + upscale=4, + res_scale=1, + img_range=255., + rgb_mean=(0.4488, 0.4371, 0.4040)): + super(RCAN, self).__init__() + + self.img_range = img_range + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = make_layer( + ResidualGroup, + num_group, + num_feat=num_feat, + num_block=num_block, + squeeze_factor=squeeze_factor, + res_scale=res_scale) + self.conv_after_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + def forward(self, x): + self.mean = self.mean.type_as(x) + + x = (x - self.mean) * self.img_range + x = self.conv_first(x) + res = self.conv_after_body(self.body(x)) + res += x + + x = self.conv_last(self.upsample(res)) + x = x / self.img_range + self.mean + + return x diff --git a/basicsr/archs/ridnet_arch.py b/basicsr/archs/ridnet_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..3432ee412fca0c68593d35bc0254d0437e372f83 --- /dev/null +++ b/basicsr/archs/ridnet_arch.py @@ -0,0 +1,184 @@ +import torch +import torch.nn as nn + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import ResidualBlockNoBN, make_layer + + +class MeanShift(nn.Conv2d): + """ Data normalization with mean and std. + + Args: + rgb_range (int): Maximum value of RGB. + rgb_mean (list[float]): Mean for RGB channels. + rgb_std (list[float]): Std for RGB channels. + sign (int): For subtraction, sign is -1, for addition, sign is 1. + Default: -1. + requires_grad (bool): Whether to update the self.weight and self.bias. + Default: True. + """ + + def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1, requires_grad=True): + super(MeanShift, self).__init__(3, 3, kernel_size=1) + std = torch.Tensor(rgb_std) + self.weight.data = torch.eye(3).view(3, 3, 1, 1) + self.weight.data.div_(std.view(3, 1, 1, 1)) + self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) + self.bias.data.div_(std) + self.requires_grad = requires_grad + + +class EResidualBlockNoBN(nn.Module): + """Enhanced Residual block without BN. + + There are three convolution layers in residual branch. + + It has a style of: + ---Conv-ReLU-Conv-ReLU-Conv-+-ReLU- + |__________________________| + """ + + def __init__(self, in_channels, out_channels): + super(EResidualBlockNoBN, self).__init__() + + self.body = nn.Sequential( + nn.Conv2d(in_channels, out_channels, 3, 1, 1), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, 1, 1, 0), + ) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + out = self.body(x) + out = self.relu(out + x) + return out + + +class MergeRun(nn.Module): + """ Merge-and-run unit. + + This unit contains two branches with different dilated convolutions, + followed by a convolution to process the concatenated features. + + Paper: Real Image Denoising with Feature Attention + Ref git repo: https://github.com/saeed-anwar/RIDNet + """ + + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1): + super(MergeRun, self).__init__() + + self.dilation1 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size, stride, 2, 2), nn.ReLU(inplace=True)) + self.dilation2 = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size, stride, 3, 3), nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size, stride, 4, 4), nn.ReLU(inplace=True)) + + self.aggregation = nn.Sequential( + nn.Conv2d(out_channels * 2, out_channels, kernel_size, stride, padding), nn.ReLU(inplace=True)) + + def forward(self, x): + dilation1 = self.dilation1(x) + dilation2 = self.dilation2(x) + out = torch.cat([dilation1, dilation2], dim=1) + out = self.aggregation(out) + out = out + x + return out + + +class ChannelAttention(nn.Module): + """Channel attention. + + Args: + num_feat (int): Channel number of intermediate features. + squeeze_factor (int): Channel squeeze factor. Default: + """ + + def __init__(self, mid_channels, squeeze_factor=16): + super(ChannelAttention, self).__init__() + self.attention = nn.Sequential( + nn.AdaptiveAvgPool2d(1), nn.Conv2d(mid_channels, mid_channels // squeeze_factor, 1, padding=0), + nn.ReLU(inplace=True), nn.Conv2d(mid_channels // squeeze_factor, mid_channels, 1, padding=0), nn.Sigmoid()) + + def forward(self, x): + y = self.attention(x) + return x * y + + +class EAM(nn.Module): + """Enhancement attention modules (EAM) in RIDNet. + + This module contains a merge-and-run unit, a residual block, + an enhanced residual block and a feature attention unit. + + Attributes: + merge: The merge-and-run unit. + block1: The residual block. + block2: The enhanced residual block. + ca: The feature/channel attention unit. + """ + + def __init__(self, in_channels, mid_channels, out_channels): + super(EAM, self).__init__() + + self.merge = MergeRun(in_channels, mid_channels) + self.block1 = ResidualBlockNoBN(mid_channels) + self.block2 = EResidualBlockNoBN(mid_channels, out_channels) + self.ca = ChannelAttention(out_channels) + # The residual block in the paper contains a relu after addition. + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + out = self.merge(x) + out = self.relu(self.block1(out)) + out = self.block2(out) + out = self.ca(out) + return out + + +@ARCH_REGISTRY.register() +class RIDNet(nn.Module): + """RIDNet: Real Image Denoising with Feature Attention. + + Ref git repo: https://github.com/saeed-anwar/RIDNet + + Args: + in_channels (int): Channel number of inputs. + mid_channels (int): Channel number of EAM modules. + Default: 64. + out_channels (int): Channel number of outputs. + num_block (int): Number of EAM. Default: 4. + img_range (float): Image range. Default: 255. + rgb_mean (tuple[float]): Image mean in RGB orders. + Default: (0.4488, 0.4371, 0.4040), calculated from DIV2K dataset. + """ + + def __init__(self, + in_channels, + mid_channels, + out_channels, + num_block=4, + img_range=255., + rgb_mean=(0.4488, 0.4371, 0.4040), + rgb_std=(1.0, 1.0, 1.0)): + super(RIDNet, self).__init__() + + self.sub_mean = MeanShift(img_range, rgb_mean, rgb_std) + self.add_mean = MeanShift(img_range, rgb_mean, rgb_std, 1) + + self.head = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) + self.body = make_layer( + EAM, num_block, in_channels=mid_channels, mid_channels=mid_channels, out_channels=mid_channels) + self.tail = nn.Conv2d(mid_channels, out_channels, 3, 1, 1) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + res = self.sub_mean(x) + res = self.tail(self.body(self.relu(self.head(res)))) + res = self.add_mean(res) + + out = x + res + return out diff --git a/basicsr/archs/rrdbnet_arch.py b/basicsr/archs/rrdbnet_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..63d07080c2ec1305090c59b7bfbbda2b003b18e4 --- /dev/null +++ b/basicsr/archs/rrdbnet_arch.py @@ -0,0 +1,119 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import default_init_weights, make_layer, pixel_unshuffle + + +class ResidualDenseBlock(nn.Module): + """Residual Dense Block. + + Used in RRDB block in ESRGAN. + + Args: + num_feat (int): Channel number of intermediate features. + num_grow_ch (int): Channels for each growth. + """ + + def __init__(self, num_feat=64, num_grow_ch=32): + super(ResidualDenseBlock, self).__init__() + self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) + self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) + self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) + + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + # initialization + default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) + + def forward(self, x): + x1 = self.lrelu(self.conv1(x)) + x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) + x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) + x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + # Empirically, we use 0.2 to scale the residual for better performance + return x5 * 0.2 + x + + +class RRDB(nn.Module): + """Residual in Residual Dense Block. + + Used in RRDB-Net in ESRGAN. + + Args: + num_feat (int): Channel number of intermediate features. + num_grow_ch (int): Channels for each growth. + """ + + def __init__(self, num_feat, num_grow_ch=32): + super(RRDB, self).__init__() + self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) + self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) + + def forward(self, x): + out = self.rdb1(x) + out = self.rdb2(out) + out = self.rdb3(out) + # Empirically, we use 0.2 to scale the residual for better performance + return out * 0.2 + x + + +@ARCH_REGISTRY.register() +class RRDBNet(nn.Module): + """Networks consisting of Residual in Residual Dense Block, which is used + in ESRGAN. + + ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. + + We extend ESRGAN for scale x2 and scale x1. + Note: This is one option for scale 1, scale 2 in RRDBNet. + We first employ the pixel-unshuffle (an inverse operation of pixelshuffle to reduce the spatial size + and enlarge the channel size before feeding inputs into the main ESRGAN architecture. + + Args: + num_in_ch (int): Channel number of inputs. + num_out_ch (int): Channel number of outputs. + num_feat (int): Channel number of intermediate features. + Default: 64 + num_block (int): Block number in the trunk network. Defaults: 23 + num_grow_ch (int): Channels for each growth. Default: 32. + """ + + def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32): + super(RRDBNet, self).__init__() + self.scale = scale + if scale == 2: + num_in_ch = num_in_ch * 4 + elif scale == 1: + num_in_ch = num_in_ch * 16 + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch) + self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + # upsample + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x): + if self.scale == 2: + feat = pixel_unshuffle(x, scale=2) + elif self.scale == 1: + feat = pixel_unshuffle(x, scale=4) + else: + feat = x + feat = self.conv_first(feat) + body_feat = self.conv_body(self.body(feat)) + feat = feat + body_feat + # upsample + feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest'))) + feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest'))) + out = self.conv_last(self.lrelu(self.conv_hr(feat))) + return out diff --git a/basicsr/archs/spynet_arch.py b/basicsr/archs/spynet_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..4c7af133daef0496b79a57517e1942d06f2d0061 --- /dev/null +++ b/basicsr/archs/spynet_arch.py @@ -0,0 +1,96 @@ +import math +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import flow_warp + + +class BasicModule(nn.Module): + """Basic Module for SpyNet. + """ + + def __init__(self): + super(BasicModule, self).__init__() + + self.basic_module = nn.Sequential( + nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), + nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), + nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), + nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False), + nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) + + def forward(self, tensor_input): + return self.basic_module(tensor_input) + + +@ARCH_REGISTRY.register() +class SpyNet(nn.Module): + """SpyNet architecture. + + Args: + load_path (str): path for pretrained SpyNet. Default: None. + """ + + def __init__(self, load_path=None): + super(SpyNet, self).__init__() + self.basic_module = nn.ModuleList([BasicModule() for _ in range(6)]) + if load_path: + self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) + + self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) + self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) + + def preprocess(self, tensor_input): + tensor_output = (tensor_input - self.mean) / self.std + return tensor_output + + def process(self, ref, supp): + flow = [] + + ref = [self.preprocess(ref)] + supp = [self.preprocess(supp)] + + for level in range(5): + ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False)) + supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False)) + + flow = ref[0].new_zeros( + [ref[0].size(0), 2, + int(math.floor(ref[0].size(2) / 2.0)), + int(math.floor(ref[0].size(3) / 2.0))]) + + for level in range(len(ref)): + upsampled_flow = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0 + + if upsampled_flow.size(2) != ref[level].size(2): + upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 0, 0, 1], mode='replicate') + if upsampled_flow.size(3) != ref[level].size(3): + upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 1, 0, 0], mode='replicate') + + flow = self.basic_module[level](torch.cat([ + ref[level], + flow_warp( + supp[level], upsampled_flow.permute(0, 2, 3, 1), interp_mode='bilinear', padding_mode='border'), + upsampled_flow + ], 1)) + upsampled_flow + + return flow + + def forward(self, ref, supp): + assert ref.size() == supp.size() + + h, w = ref.size(2), ref.size(3) + w_floor = math.floor(math.ceil(w / 32.0) * 32.0) + h_floor = math.floor(math.ceil(h / 32.0) * 32.0) + + ref = F.interpolate(input=ref, size=(h_floor, w_floor), mode='bilinear', align_corners=False) + supp = F.interpolate(input=supp, size=(h_floor, w_floor), mode='bilinear', align_corners=False) + + flow = F.interpolate(input=self.process(ref, supp), size=(h, w), mode='bilinear', align_corners=False) + + flow[:, 0, :, :] *= float(w) / float(w_floor) + flow[:, 1, :, :] *= float(h) / float(h_floor) + + return flow diff --git a/basicsr/archs/srresnet_arch.py b/basicsr/archs/srresnet_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..7f571557cd7d9ba8791bd6462fccf648c57186d2 --- /dev/null +++ b/basicsr/archs/srresnet_arch.py @@ -0,0 +1,65 @@ +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import ResidualBlockNoBN, default_init_weights, make_layer + + +@ARCH_REGISTRY.register() +class MSRResNet(nn.Module): + """Modified SRResNet. + + A compacted version modified from SRResNet in + "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" + It uses residual blocks without BN, similar to EDSR. + Currently, it supports x2, x3 and x4 upsampling scale factor. + + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_out_ch (int): Channel number of outputs. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + num_block (int): Block number in the body network. Default: 16. + upscale (int): Upsampling factor. Support x2, x3 and x4. Default: 4. + """ + + def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=16, upscale=4): + super(MSRResNet, self).__init__() + self.upscale = upscale + + self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.body = make_layer(ResidualBlockNoBN, num_block, num_feat=num_feat) + + # upsampling + if self.upscale in [2, 3]: + self.upconv1 = nn.Conv2d(num_feat, num_feat * self.upscale * self.upscale, 3, 1, 1) + self.pixel_shuffle = nn.PixelShuffle(self.upscale) + elif self.upscale == 4: + self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) + self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1) + self.pixel_shuffle = nn.PixelShuffle(2) + + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + # activation function + self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) + + # initialization + default_init_weights([self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1) + if self.upscale == 4: + default_init_weights(self.upconv2, 0.1) + + def forward(self, x): + feat = self.lrelu(self.conv_first(x)) + out = self.body(feat) + + if self.upscale == 4: + out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) + out = self.lrelu(self.pixel_shuffle(self.upconv2(out))) + elif self.upscale in [2, 3]: + out = self.lrelu(self.pixel_shuffle(self.upconv1(out))) + + out = self.conv_last(self.lrelu(self.conv_hr(out))) + base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False) + out += base + return out diff --git a/basicsr/archs/srvgg_arch.py b/basicsr/archs/srvgg_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..d8fe5ceb40ed9edd35d81ee17aff86f2e3d9adb4 --- /dev/null +++ b/basicsr/archs/srvgg_arch.py @@ -0,0 +1,70 @@ +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY + + +@ARCH_REGISTRY.register(suffix='basicsr') +class SRVGGNetCompact(nn.Module): + """A compact VGG-style network structure for super-resolution. + + It is a compact network structure, which performs upsampling in the last layer and no convolution is + conducted on the HR feature space. + + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_out_ch (int): Channel number of outputs. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + num_conv (int): Number of convolution layers in the body network. Default: 16. + upscale (int): Upsampling factor. Default: 4. + act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. + """ + + def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'): + super(SRVGGNetCompact, self).__init__() + self.num_in_ch = num_in_ch + self.num_out_ch = num_out_ch + self.num_feat = num_feat + self.num_conv = num_conv + self.upscale = upscale + self.act_type = act_type + + self.body = nn.ModuleList() + # the first conv + self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)) + # the first activation + if act_type == 'relu': + activation = nn.ReLU(inplace=True) + elif act_type == 'prelu': + activation = nn.PReLU(num_parameters=num_feat) + elif act_type == 'leakyrelu': + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) + + # the body structure + for _ in range(num_conv): + self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1)) + # activation + if act_type == 'relu': + activation = nn.ReLU(inplace=True) + elif act_type == 'prelu': + activation = nn.PReLU(num_parameters=num_feat) + elif act_type == 'leakyrelu': + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) + + # the last conv + self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1)) + # upsample + self.upsampler = nn.PixelShuffle(upscale) + + def forward(self, x): + out = x + for i in range(0, len(self.body)): + out = self.body[i](out) + + out = self.upsampler(out) + # add the nearest upsampled image, so that the network learns the residual + base = F.interpolate(x, scale_factor=self.upscale, mode='nearest') + out += base + return out diff --git a/basicsr/archs/stylegan2_arch.py b/basicsr/archs/stylegan2_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..9ab37f5a33a2ef21641de35109c16b511a6df163 --- /dev/null +++ b/basicsr/archs/stylegan2_arch.py @@ -0,0 +1,799 @@ +import math +import random +import torch +from torch import nn +from torch.nn import functional as F + +from basicsr.ops.fused_act import FusedLeakyReLU, fused_leaky_relu +from basicsr.ops.upfirdn2d import upfirdn2d +from basicsr.utils.registry import ARCH_REGISTRY + + +class NormStyleCode(nn.Module): + + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +def make_resample_kernel(k): + """Make resampling kernel for UpFirDn. + + Args: + k (list[int]): A list indicating the 1D resample kernel magnitude. + + Returns: + Tensor: 2D resampled kernel. + """ + k = torch.tensor(k, dtype=torch.float32) + if k.ndim == 1: + k = k[None, :] * k[:, None] # to 2D kernel, outer product + # normalize + k /= k.sum() + return k + + +class UpFirDnUpsample(nn.Module): + """Upsample, FIR filter, and downsample (upsampole version). + + References: + 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 + 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Upsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnUpsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) * (factor**2) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(factor={self.factor})') + + +class UpFirDnDownsample(nn.Module): + """Upsample, FIR filter, and downsample (downsampole version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Downsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnDownsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(factor={self.factor})') + + +class UpFirDnSmooth(nn.Module): + """Upsample, FIR filter, and downsample (smooth version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + upsample_factor (int): Upsampling scale factor. Default: 1. + downsample_factor (int): Downsampling scale factor. Default: 1. + kernel_size (int): Kernel size: Default: 1. + """ + + def __init__(self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1): + super(UpFirDnSmooth, self).__init__() + self.upsample_factor = upsample_factor + self.downsample_factor = downsample_factor + self.kernel = make_resample_kernel(resample_kernel) + if upsample_factor > 1: + self.kernel = self.kernel * (upsample_factor**2) + + if upsample_factor > 1: + pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) + self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) + elif downsample_factor > 1: + pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) + self.pad = ((pad + 1) // 2, pad // 2) + else: + raise NotImplementedError + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(upsample_factor={self.upsample_factor}' + f', downsample_factor={self.downsample_factor})') + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__(self, in_channels, out_channels, bias=True, bias_init_val=0, lr_mul=1, activation=None): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ['fused_lrelu', None]: + raise ValueError(f'Wrong activation value in EqualLinear: {activation}' + "Supported ones are: ['fused_lrelu', None].") + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter('bias', None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == 'fused_lrelu': + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' + f'out_channels={self.out_channels}, bias={self.bias is not None})') + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + eps=1e-8): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + if self.sample_mode == 'upsample': + self.smooth = UpFirDnSmooth( + resample_kernel, upsample_factor=2, downsample_factor=1, kernel_size=kernel_size) + elif self.sample_mode == 'downsample': + self.smooth = UpFirDnSmooth( + resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size) + elif self.sample_mode is None: + pass + else: + raise ValueError(f'Wrong sample mode {self.sample_mode}, ' + "supported ones are ['upsample', 'downsample', None].") + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, in_channels, bias=True, bias_init_val=1, lr_mul=1, activation=None) + + self.weight = nn.Parameter(torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) + + if self.sample_mode == 'upsample': + x = x.view(1, b * c, h, w) + weight = weight.view(b, self.out_channels, c, self.kernel_size, self.kernel_size) + weight = weight.transpose(1, 2).reshape(b * c, self.out_channels, self.kernel_size, self.kernel_size) + out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + out = self.smooth(out) + elif self.sample_mode == 'downsample': + x = self.smooth(x) + x = x.view(1, b * c, *x.shape[2:4]) + out = F.conv2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + else: + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' + f'out_channels={self.out_channels}, ' + f'kernel_size={self.kernel_size}, ' + f'demodulate={self.demodulate}, sample_mode={self.sample_mode})') + + +class StyleConv(nn.Module): + """Style conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1)): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + resample_kernel=resample_kernel) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__(self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)): + super(ToRGB, self).__init__() + if upsample: + self.upsample = UpFirDnUpsample(resample_kernel, factor=2) + else: + self.upsample = None + self.modulated_conv = ModulatedConv2d( + in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = self.upsample(skip) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +@ARCH_REGISTRY.register() +class StyleGAN2Generator(nn.Module): + """StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. A cross production will be applied to extent 1D resample + kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__(self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1): + super(StyleGAN2Generator, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, num_style_feat, bias=True, bias_init_val=0, lr_mul=lr_mlp, + activation='fused_lrelu')) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + '4': int(512 * narrow), + '8': int(512 * narrow), + '16': int(512 * narrow), + '32': int(512 * narrow), + '64': int(256 * channel_multiplier * narrow), + '128': int(128 * channel_multiplier * narrow), + '256': int(64 * channel_multiplier * narrow), + '512': int(32 * channel_multiplier * narrow), + '1024': int(16 * channel_multiplier * narrow) + } + self.channels = channels + + self.constant_input = ConstantInput(channels['4'], size=4) + self.style_conv1 = StyleConv( + channels['4'], + channels['4'], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel) + self.to_rgb1 = ToRGB(channels['4'], num_style_feat, upsample=False, resample_kernel=resample_kernel) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels['4'] + # noise + for layer_idx in range(self.num_layers): + resolution = 2**((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f'noise{layer_idx}', torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f'{2**i}'] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode='upsample', + resample_kernel=resample_kernel, + )) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel)) + self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True, resample_kernel=resample_kernel)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward(self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False): + """Forward function for StyleGAN2Generator. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [getattr(self.noises, f'noise{i}') for i in range(self.num_layers)] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], noise[1::2], + noise[2::2], self.to_rgbs): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True, bias_init_val=0): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels, kernel_size, kernel_size)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter('bias', None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return (f'{self.__class__.__name__}(in_channels={self.in_channels}, ' + f'out_channels={self.out_channels}, ' + f'kernel_size={self.kernel_size},' + f' stride={self.stride}, padding={self.padding}, ' + f'bias={self.bias is not None})') + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True): + layers = [] + # downsample + if downsample: + layers.append( + UpFirDnSmooth(resample_kernel, upsample_factor=1, downsample_factor=2, kernel_size=kernel_size)) + stride = 2 + self.padding = 0 + else: + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, out_channels, kernel_size, stride=stride, padding=self.padding, bias=bias + and not activate)) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + """ + + def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, out_channels, 3, downsample=True, resample_kernel=resample_kernel, bias=True, activate=True) + self.skip = ConvLayer( + in_channels, out_channels, 1, downsample=True, resample_kernel=resample_kernel, bias=False, activate=False) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out + + +@ARCH_REGISTRY.register() +class StyleGAN2Discriminator(nn.Module): + """StyleGAN2 Discriminator. + + Args: + out_size (int): The spatial size of outputs. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. A cross production will be applied to extent 1D resample + kernel to 2D resample kernel. Default: (1, 3, 3, 1). + stddev_group (int): For group stddev statistics. Default: 4. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__(self, out_size, channel_multiplier=2, resample_kernel=(1, 3, 3, 1), stddev_group=4, narrow=1): + super(StyleGAN2Discriminator, self).__init__() + + channels = { + '4': int(512 * narrow), + '8': int(512 * narrow), + '16': int(512 * narrow), + '32': int(512 * narrow), + '64': int(256 * channel_multiplier * narrow), + '128': int(128 * channel_multiplier * narrow), + '256': int(64 * channel_multiplier * narrow), + '512': int(32 * channel_multiplier * narrow), + '1024': int(16 * channel_multiplier * narrow) + } + + log_size = int(math.log(out_size, 2)) + + conv_body = [ConvLayer(3, channels[f'{out_size}'], 1, bias=True, activate=True)] + + in_channels = channels[f'{out_size}'] + for i in range(log_size, 2, -1): + out_channels = channels[f'{2**(i - 1)}'] + conv_body.append(ResBlock(in_channels, out_channels, resample_kernel)) + in_channels = out_channels + self.conv_body = nn.Sequential(*conv_body) + + self.final_conv = ConvLayer(in_channels + 1, channels['4'], 3, bias=True, activate=True) + self.final_linear = nn.Sequential( + EqualLinear( + channels['4'] * 4 * 4, channels['4'], bias=True, bias_init_val=0, lr_mul=1, activation='fused_lrelu'), + EqualLinear(channels['4'], 1, bias=True, bias_init_val=0, lr_mul=1, activation=None), + ) + self.stddev_group = stddev_group + self.stddev_feat = 1 + + def forward(self, x): + out = self.conv_body(x) + + b, c, h, w = out.shape + # concatenate a group stddev statistics to out + group = min(b, self.stddev_group) # Minibatch must be divisible by (or smaller than) group_size + stddev = out.view(group, -1, self.stddev_feat, c // self.stddev_feat, h, w) + stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) + stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) + stddev = stddev.repeat(group, 1, h, w) + out = torch.cat([out, stddev], 1) + + out = self.final_conv(out) + out = out.view(b, -1) + out = self.final_linear(out) + + return out diff --git a/basicsr/archs/swinir_arch.py b/basicsr/archs/swinir_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..3917fa2c7408e1f5b55b9930c643a9af920a4d81 --- /dev/null +++ b/basicsr/archs/swinir_arch.py @@ -0,0 +1,956 @@ +# Modified from https://github.com/JingyunLiang/SwinIR +# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 +# Originally Written by Ze Liu, Modified by Jingyun Liang. + +import math +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import to_2tuple, trunc_normal_ + + +def drop_path(x, drop_prob: float = 0., training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py + """ + if drop_prob == 0. or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0], ) + (1, ) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + +class Mlp(nn.Module): + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (b, h, w, c) + window_size (int): window size + + Returns: + windows: (num_windows*b, window_size, window_size, c) + """ + b, h, w, c = x.shape + x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) + return windows + + +def window_reverse(windows, window_size, h, w): + """ + Args: + windows: (num_windows*b, window_size, window_size, c) + window_size (int): Window size + h (int): Height of image + w (int): Width of image + + Returns: + x: (b, h, w, c) + """ + b = int(windows.shape[0] / (h * w / window_size / window_size)) + x = windows.view(b, h // window_size, w // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) + return x + + +class WindowAttention(nn.Module): + r""" Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): + + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer('relative_position_index', relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*b, n, c) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + b_, n, c = x.shape + qkv = self.qkv(x).reshape(b_, n, 3, self.num_heads, c // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nw = mask.shape[0] + attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(1).unsqueeze(0) + attn = attn.view(-1, self.num_heads, n, n) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(b_, n, c) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}' + + def flops(self, n): + # calculate flops for 1 window with token length of n + flops = 0 + # qkv = self.qkv(x) + flops += n * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * n * (self.dim // self.num_heads) * n + # x = (attn @ v) + flops += self.num_heads * n * n * (self.dim // self.num_heads) + # x = self.proj(x) + flops += n * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r""" Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + act_layer=nn.GELU, + norm_layer=nn.LayerNorm): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert 0 <= self.shift_size < self.window_size, 'shift_size must in 0-window_size' + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop) + + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer('attn_mask', attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + h, w = x_size + img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 + h_slices = (slice(0, -self.window_size), slice(-self.window_size, + -self.shift_size), slice(-self.shift_size, None)) + w_slices = (slice(0, -self.window_size), slice(-self.window_size, + -self.shift_size), slice(-self.shift_size, None)) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition(img_mask, self.window_size) # nw, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) + + return attn_mask + + def forward(self, x, x_size): + h, w = x_size + b, _, c = x.shape + # assert seq_len == h * w, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) + else: + shifted_x = x + + # partition windows + x_windows = window_partition(shifted_x, self.window_size) # nw*b, window_size, window_size, c + x_windows = x_windows.view(-1, self.window_size * self.window_size, c) # nw*b, window_size*window_size, c + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn(x_windows, mask=self.attn_mask) # nw*b, window_size*window_size, c + else: + attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device)) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) + shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) + else: + x = shifted_x + x = x.view(b, h * w, c) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return (f'dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, ' + f'window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}') + + def flops(self): + flops = 0 + h, w = self.input_resolution + # norm1 + flops += self.dim * h * w + # W-MSA/SW-MSA + nw = h * w / self.window_size / self.window_size + flops += nw * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * h * w * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * h * w + return flops + + +class PatchMerging(nn.Module): + r""" Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: b, h*w, c + """ + h, w = self.input_resolution + b, seq_len, c = x.shape + assert seq_len == h * w, 'input feature has wrong size' + assert h % 2 == 0 and w % 2 == 0, f'x size ({h}*{w}) are not even.' + + x = x.view(b, h, w, c) + + x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c + x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c + x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c + x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c + x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c + x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f'input_resolution={self.input_resolution}, dim={self.dim}' + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.dim + flops += (h // 2) * (w // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """ A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__(self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False): + + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList([ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer) for i in range(depth) + ]) + + # patch merging layer + if downsample is not None: + self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f'dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}' + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__(self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop=0., + attn_drop=0., + drop_path=0., + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection='1conv'): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint) + + if resi_connection == '1conv': + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1)) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim, norm_layer=None) + + def forward(self, x, x_size): + return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + h, w = self.input_resolution + flops += h * w * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchEmbed(nn.Module): + r""" Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # b Ph*Pw c + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + flops = 0 + h, w = self.img_size + if self.norm is not None: + flops += h * w * self.embed_dim + return flops + + +class PatchUnEmbed(nn.Module): + r""" Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + x = x.transpose(1, 2).view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) # b Ph*Pw c + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.') + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.num_feat * 3 * 9 + return flops + + +@ARCH_REGISTRY.register() +class SwinIR(nn.Module): + r""" SwinIR + A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, + img_size=64, + patch_size=1, + in_chans=3, + embed_dim=96, + depths=(6, 6, 6, 6), + num_heads=(6, 6, 6, 6), + window_size=7, + mlp_ratio=4., + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.1, + norm_layer=nn.LayerNorm, + ape=False, + patch_norm=True, + use_checkpoint=False, + upscale=2, + img_range=1., + upsampler='', + resi_connection='1conv', + **kwargs): + super(SwinIR, self).__init__() + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # ------------------------- 1, shallow feature extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, deep feature extraction ------------------------- # + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + trunc_normal_(self.absolute_pos_embed, std=.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == '1conv': + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == '3conv': + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1)) + + # ------------------------- 3, high quality image reconstruction ------------------------- # + if self.upsampler == 'pixelshuffle': + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch, + (patches_resolution[0], patches_resolution[1])) + elif self.upsampler == 'nearest+conv': + # for real-world SR (less artifacts) + assert self.upscale == 4, 'only support x4 now.' + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # b seq_len c + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == 'pixelshuffle': + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == 'pixelshuffledirect': + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == 'nearest+conv': + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + + return x + + def flops(self): + flops = 0 + h, w = self.patches_resolution + flops += h * w * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for layer in self.layers: + flops += layer.flops() + flops += h * w * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() + return flops + + +if __name__ == '__main__': + upscale = 4 + window_size = 8 + height = (1024 // upscale // window_size + 1) * window_size + width = (720 // upscale // window_size + 1) * window_size + model = SwinIR( + upscale=2, + img_size=(height, width), + window_size=window_size, + img_range=1., + depths=[6, 6, 6, 6], + embed_dim=60, + num_heads=[6, 6, 6, 6], + mlp_ratio=2, + upsampler='pixelshuffledirect') + print(model) + print(height, width, model.flops() / 1e9) + + x = torch.randn((1, 3, height, width)) + x = model(x) + print(x.shape) diff --git a/basicsr/archs/tof_arch.py b/basicsr/archs/tof_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..09cf7069157604b8b925219d8c8be2a14c9df26f --- /dev/null +++ b/basicsr/archs/tof_arch.py @@ -0,0 +1,172 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import ARCH_REGISTRY +from .arch_util import flow_warp + + +class BasicModule(nn.Module): + """Basic module of SPyNet. + + Note that unlike the architecture in spynet_arch.py, the basic module + here contains batch normalization. + """ + + def __init__(self): + super(BasicModule, self).__init__() + self.basic_module = nn.Sequential( + nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), + nn.BatchNorm2d(32), nn.ReLU(inplace=True), + nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3, bias=False), + nn.BatchNorm2d(64), nn.ReLU(inplace=True), + nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), + nn.BatchNorm2d(32), nn.ReLU(inplace=True), + nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3, bias=False), + nn.BatchNorm2d(16), nn.ReLU(inplace=True), + nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) + + def forward(self, tensor_input): + """ + Args: + tensor_input (Tensor): Input tensor with shape (b, 8, h, w). + 8 channels contain: + [reference image (3), neighbor image (3), initial flow (2)]. + + Returns: + Tensor: Estimated flow with shape (b, 2, h, w) + """ + return self.basic_module(tensor_input) + + +class SPyNetTOF(nn.Module): + """SPyNet architecture for TOF. + + Note that this implementation is specifically for TOFlow. Please use + spynet_arch.py for general use. They differ in the following aspects: + 1. The basic modules here contain BatchNorm. + 2. Normalization and denormalization are not done here, as + they are done in TOFlow. + Paper: + Optical Flow Estimation using a Spatial Pyramid Network + Code reference: + https://github.com/Coldog2333/pytoflow + + Args: + load_path (str): Path for pretrained SPyNet. Default: None. + """ + + def __init__(self, load_path=None): + super(SPyNetTOF, self).__init__() + + self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)]) + if load_path: + self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) + + def forward(self, ref, supp): + """ + Args: + ref (Tensor): Reference image with shape of (b, 3, h, w). + supp: The supporting image to be warped: (b, 3, h, w). + + Returns: + Tensor: Estimated optical flow: (b, 2, h, w). + """ + num_batches, _, h, w = ref.size() + ref = [ref] + supp = [supp] + + # generate downsampled frames + for _ in range(3): + ref.insert(0, F.avg_pool2d(input=ref[0], kernel_size=2, stride=2, count_include_pad=False)) + supp.insert(0, F.avg_pool2d(input=supp[0], kernel_size=2, stride=2, count_include_pad=False)) + + # flow computation + flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16) + for i in range(4): + flow_up = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0 + flow = flow_up + self.basic_module[i]( + torch.cat([ref[i], flow_warp(supp[i], flow_up.permute(0, 2, 3, 1)), flow_up], 1)) + return flow + + +@ARCH_REGISTRY.register() +class TOFlow(nn.Module): + """PyTorch implementation of TOFlow. + + In TOFlow, the LR frames are pre-upsampled and have the same size with + the GT frames. + Paper: + Xue et al., Video Enhancement with Task-Oriented Flow, IJCV 2018 + Code reference: + 1. https://github.com/anchen1011/toflow + 2. https://github.com/Coldog2333/pytoflow + + Args: + adapt_official_weights (bool): Whether to adapt the weights translated + from the official implementation. Set to false if you want to + train from scratch. Default: False + """ + + def __init__(self, adapt_official_weights=False): + super(TOFlow, self).__init__() + self.adapt_official_weights = adapt_official_weights + self.ref_idx = 0 if adapt_official_weights else 3 + + self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) + self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) + + # flow estimation module + self.spynet = SPyNetTOF() + + # reconstruction module + self.conv_1 = nn.Conv2d(3 * 7, 64, 9, 1, 4) + self.conv_2 = nn.Conv2d(64, 64, 9, 1, 4) + self.conv_3 = nn.Conv2d(64, 64, 1) + self.conv_4 = nn.Conv2d(64, 3, 1) + + # activation function + self.relu = nn.ReLU(inplace=True) + + def normalize(self, img): + return (img - self.mean) / self.std + + def denormalize(self, img): + return img * self.std + self.mean + + def forward(self, lrs): + """ + Args: + lrs: Input lr frames: (b, 7, 3, h, w). + + Returns: + Tensor: SR frame: (b, 3, h, w). + """ + # In the official implementation, the 0-th frame is the reference frame + if self.adapt_official_weights: + lrs = lrs[:, [3, 0, 1, 2, 4, 5, 6], :, :, :] + + num_batches, num_lrs, _, h, w = lrs.size() + + lrs = self.normalize(lrs.view(-1, 3, h, w)) + lrs = lrs.view(num_batches, num_lrs, 3, h, w) + + lr_ref = lrs[:, self.ref_idx, :, :, :] + lr_aligned = [] + for i in range(7): # 7 frames + if i == self.ref_idx: + lr_aligned.append(lr_ref) + else: + lr_supp = lrs[:, i, :, :, :] + flow = self.spynet(lr_ref, lr_supp) + lr_aligned.append(flow_warp(lr_supp, flow.permute(0, 2, 3, 1))) + + # reconstruction + hr = torch.stack(lr_aligned, dim=1) + hr = hr.view(num_batches, -1, h, w) + hr = self.relu(self.conv_1(hr)) + hr = self.relu(self.conv_2(hr)) + hr = self.relu(self.conv_3(hr)) + hr = self.conv_4(hr) + lr_ref + + return self.denormalize(hr) diff --git a/basicsr/archs/vgg_arch.py b/basicsr/archs/vgg_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..05200334e477e59feefd1e4a0b5e94204e4eb2fa --- /dev/null +++ b/basicsr/archs/vgg_arch.py @@ -0,0 +1,161 @@ +import os +import torch +from collections import OrderedDict +from torch import nn as nn +from torchvision.models import vgg as vgg + +from basicsr.utils.registry import ARCH_REGISTRY + +VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth' +NAMES = { + 'vgg11': [ + 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', + 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', + 'pool5' + ], + 'vgg13': [ + 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', + 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', + 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' + ], + 'vgg16': [ + 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', + 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', + 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', + 'pool5' + ], + 'vgg19': [ + 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', + 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', + 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', + 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5' + ] +} + + +def insert_bn(names): + """Insert bn layer after each conv. + + Args: + names (list): The list of layer names. + + Returns: + list: The list of layer names with bn layers. + """ + names_bn = [] + for name in names: + names_bn.append(name) + if 'conv' in name: + position = name.replace('conv', '') + names_bn.append('bn' + position) + return names_bn + + +@ARCH_REGISTRY.register() +class VGGFeatureExtractor(nn.Module): + """VGG network for feature extraction. + + In this implementation, we allow users to choose whether use normalization + in the input feature and the type of vgg network. Note that the pretrained + path must fit the vgg type. + + Args: + layer_name_list (list[str]): Forward function returns the corresponding + features according to the layer_name_list. + Example: {'relu1_1', 'relu2_1', 'relu3_1'}. + vgg_type (str): Set the type of vgg network. Default: 'vgg19'. + use_input_norm (bool): If True, normalize the input image. Importantly, + the input feature must in the range [0, 1]. Default: True. + range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. + Default: False. + requires_grad (bool): If true, the parameters of VGG network will be + optimized. Default: False. + remove_pooling (bool): If true, the max pooling operations in VGG net + will be removed. Default: False. + pooling_stride (int): The stride of max pooling operation. Default: 2. + """ + + def __init__(self, + layer_name_list, + vgg_type='vgg19', + use_input_norm=True, + range_norm=False, + requires_grad=False, + remove_pooling=False, + pooling_stride=2): + super(VGGFeatureExtractor, self).__init__() + + self.layer_name_list = layer_name_list + self.use_input_norm = use_input_norm + self.range_norm = range_norm + + self.names = NAMES[vgg_type.replace('_bn', '')] + if 'bn' in vgg_type: + self.names = insert_bn(self.names) + + # only borrow layers that will be used to avoid unused params + max_idx = 0 + for v in layer_name_list: + idx = self.names.index(v) + if idx > max_idx: + max_idx = idx + + if os.path.exists(VGG_PRETRAIN_PATH): + vgg_net = getattr(vgg, vgg_type)(pretrained=False) + state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage) + vgg_net.load_state_dict(state_dict) + else: + vgg_net = getattr(vgg, vgg_type)(pretrained=True) + + features = vgg_net.features[:max_idx + 1] + + modified_net = OrderedDict() + for k, v in zip(self.names, features): + if 'pool' in k: + # if remove_pooling is true, pooling operation will be removed + if remove_pooling: + continue + else: + # in some cases, we may want to change the default stride + modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride) + else: + modified_net[k] = v + + self.vgg_net = nn.Sequential(modified_net) + + if not requires_grad: + self.vgg_net.eval() + for param in self.parameters(): + param.requires_grad = False + else: + self.vgg_net.train() + for param in self.parameters(): + param.requires_grad = True + + if self.use_input_norm: + # the mean is for image with range [0, 1] + self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) + # the std is for image with range [0, 1] + self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) + + def forward(self, x): + """Forward function. + + Args: + x (Tensor): Input tensor with shape (n, c, h, w). + + Returns: + Tensor: Forward results. + """ + if self.range_norm: + x = (x + 1) / 2 + if self.use_input_norm: + x = (x - self.mean) / self.std + + output = {} + for key, layer in self.vgg_net._modules.items(): + x = layer(x) + if key in self.layer_name_list: + output[key] = x.clone() + + return output diff --git a/basicsr/archs/vqgan_arch.py b/basicsr/archs/vqgan_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..f6dfcf4c9983b431f0a978701e5ddd9598faf381 --- /dev/null +++ b/basicsr/archs/vqgan_arch.py @@ -0,0 +1,435 @@ +''' +VQGAN code, adapted from the original created by the Unleashing Transformers authors: +https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py + +''' +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import copy +from basicsr.utils import get_root_logger +from basicsr.utils.registry import ARCH_REGISTRY + +def normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +@torch.jit.script +def swish(x): + return x*torch.sigmoid(x) + + +# Define VQVAE classes +class VectorQuantizer(nn.Module): + def __init__(self, codebook_size, emb_dim, beta): + super(VectorQuantizer, self).__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) + self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.emb_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \ + 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + + mean_distance = torch.mean(d) + # find closest encodings + # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False) + # [0-1], higher score, higher confidence + min_encoding_scores = torch.exp(-min_encoding_scores/10) + + min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # compute loss for embedding + loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2) + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, { + "perplexity": perplexity, + "min_encodings": min_encodings, + "min_encoding_indices": min_encoding_indices, + "min_encoding_scores": min_encoding_scores, + "mean_distance": mean_distance + } + + def get_codebook_feat(self, indices, shape): + # input indices: batch*token_num -> (batch*token_num)*1 + # shape: batch, height, width, channel + indices = indices.view(-1,1) + min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) + min_encodings.scatter_(1, indices, 1) + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: # reshape back to match original input shape + z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantizer(nn.Module): + def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0): + super().__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + self.proj = nn.Conv2d(num_hiddens, codebook_size, 1) # projects last encoder layer to quantized logits + self.embed = nn.Embedding(codebook_size, emb_dim) + + def forward(self, z): + hard = self.straight_through if self.training else True + + logits = self.proj(z) + + soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) + + z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() + min_encoding_indices = soft_one_hot.argmax(dim=1) + + return z_q, diff, { + "min_encoding_indices": min_encoding_indices + } + + +class Downsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x): + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + + return x + + +class ResBlock(nn.Module): + def __init__(self, in_channels, out_channels=None): + super(ResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + self.norm1 = normalize(in_channels) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.norm2 = normalize(out_channels) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x_in): + x = x_in + x = self.norm1(x) + x = swish(x) + x = self.conv1(x) + x = self.norm2(x) + x = swish(x) + x = self.conv2(x) + if self.in_channels != self.out_channels: + x_in = self.conv_out(x_in) + + return x + x_in + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h*w) + q = q.permute(0, 2, 1) + k = k.reshape(b, c, h*w) + w_ = torch.bmm(q, k) + w_ = w_ * (int(c)**(-0.5)) + w_ = F.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h*w) + w_ = w_.permute(0, 2, 1) + h_ = torch.bmm(v, w_) + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x+h_ + + +class Encoder(nn.Module): + def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions): + super().__init__() + self.nf = nf + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.attn_resolutions = attn_resolutions + + curr_res = self.resolution + in_ch_mult = (1,)+tuple(ch_mult) + + blocks = [] + # initial convultion + blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) + + # residual and downsampling blocks, with attention on smaller res (16x16) + for i in range(self.num_resolutions): + block_in_ch = nf * in_ch_mult[i] + block_out_ch = nf * ch_mult[i] + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + if curr_res in attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != self.num_resolutions - 1: + blocks.append(Downsample(block_in_ch)) + curr_res = curr_res // 2 + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + # normalise and convert to latent size + blocks.append(normalize(block_in_ch)) + blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1)) + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class Generator(nn.Module): + def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions): + super().__init__() + self.nf = nf + self.ch_mult = ch_mult + self.num_resolutions = len(self.ch_mult) + self.num_res_blocks = res_blocks + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.in_channels = emb_dim + self.out_channels = 3 + block_in_ch = self.nf * self.ch_mult[-1] + curr_res = self.resolution // 2 ** (self.num_resolutions-1) + + blocks = [] + # initial conv + blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)) + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + for i in reversed(range(self.num_resolutions)): + block_out_ch = self.nf * self.ch_mult[i] + + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + + if curr_res in self.attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != 0: + blocks.append(Upsample(block_in_ch)) + curr_res = curr_res * 2 + + blocks.append(normalize(block_in_ch)) + blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1)) + + self.blocks = nn.ModuleList(blocks) + + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +@ARCH_REGISTRY.register() +class VQAutoEncoder(nn.Module): + def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=[16], codebook_size=1024, emb_dim=256, + beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None): + super().__init__() + logger = get_root_logger() + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks + self.codebook_size = codebook_size + self.embed_dim = emb_dim + self.ch_mult = ch_mult + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.quantizer_type = quantizer + self.encoder = Encoder( + self.in_channels, + self.nf, + self.embed_dim, + self.ch_mult, + self.n_blocks, + self.resolution, + self.attn_resolutions + ) + if self.quantizer_type == "nearest": + self.beta = beta #0.25 + self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta) + elif self.quantizer_type == "gumbel": + self.gumbel_num_hiddens = emb_dim + self.straight_through = gumbel_straight_through + self.kl_weight = gumbel_kl_weight + self.quantize = GumbelQuantizer( + self.codebook_size, + self.embed_dim, + self.gumbel_num_hiddens, + self.straight_through, + self.kl_weight + ) + self.generator = Generator( + self.nf, + self.embed_dim, + self.ch_mult, + self.n_blocks, + self.resolution, + self.attn_resolutions + ) + + if model_path is not None: + chkpt = torch.load(model_path, map_location='cpu') + if 'params_ema' in chkpt: + self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema']) + logger.info(f'vqgan is loaded from: {model_path} [params_ema]') + elif 'params' in chkpt: + self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) + logger.info(f'vqgan is loaded from: {model_path} [params]') + else: + raise ValueError(f'Wrong params!') + + + def forward(self, x): + x = self.encoder(x) + quant, codebook_loss, quant_stats = self.quantize(x) + x = self.generator(quant) + return x, codebook_loss, quant_stats + + + +# patch based discriminator +@ARCH_REGISTRY.register() +class VQGANDiscriminator(nn.Module): + def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None): + super().__init__() + + layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] + ndf_mult = 1 + ndf_mult_prev = 1 + for n in range(1, n_layers): # gradually increase the number of filters + ndf_mult_prev = ndf_mult + ndf_mult = min(2 ** n, 8) + layers += [ + nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False), + nn.BatchNorm2d(ndf * ndf_mult), + nn.LeakyReLU(0.2, True) + ] + + ndf_mult_prev = ndf_mult + ndf_mult = min(2 ** n_layers, 8) + + layers += [ + nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False), + nn.BatchNorm2d(ndf * ndf_mult), + nn.LeakyReLU(0.2, True) + ] + + layers += [ + nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)] # output 1 channel prediction map + self.main = nn.Sequential(*layers) + + if model_path is not None: + chkpt = torch.load(model_path, map_location='cpu') + if 'params_d' in chkpt: + self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d']) + elif 'params' in chkpt: + self.load_state_dict(torch.load(model_path, map_location='cpu')['params']) + else: + raise ValueError(f'Wrong params!') + + def forward(self, x): + return self.main(x) \ No newline at end of file diff --git a/basicsr/data/__init__.py b/basicsr/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..510df16771d153f61fbf2126baac24f69d3de7e4 --- /dev/null +++ b/basicsr/data/__init__.py @@ -0,0 +1,101 @@ +import importlib +import numpy as np +import random +import torch +import torch.utils.data +from copy import deepcopy +from functools import partial +from os import path as osp + +from basicsr.data.prefetch_dataloader import PrefetchDataLoader +from basicsr.utils import get_root_logger, scandir +from basicsr.utils.dist_util import get_dist_info +from basicsr.utils.registry import DATASET_REGISTRY + +__all__ = ['build_dataset', 'build_dataloader'] + +# automatically scan and import dataset modules for registry +# scan all the files under the data folder with '_dataset' in file names +data_folder = osp.dirname(osp.abspath(__file__)) +dataset_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(data_folder) if v.endswith('_dataset.py')] +# import all the dataset modules +_dataset_modules = [importlib.import_module(f'basicsr.data.{file_name}') for file_name in dataset_filenames] + + +def build_dataset(dataset_opt): + """Build dataset from options. + + Args: + dataset_opt (dict): Configuration for dataset. It must contain: + name (str): Dataset name. + type (str): Dataset type. + """ + dataset_opt = deepcopy(dataset_opt) + dataset = DATASET_REGISTRY.get(dataset_opt['type'])(dataset_opt) + logger = get_root_logger() + logger.info(f'Dataset [{dataset.__class__.__name__}] - {dataset_opt["name"]} is built.') + return dataset + + +def build_dataloader(dataset, dataset_opt, num_gpu=1, dist=False, sampler=None, seed=None): + """Build dataloader. + + Args: + dataset (torch.utils.data.Dataset): Dataset. + dataset_opt (dict): Dataset options. It contains the following keys: + phase (str): 'train' or 'val'. + num_worker_per_gpu (int): Number of workers for each GPU. + batch_size_per_gpu (int): Training batch size for each GPU. + num_gpu (int): Number of GPUs. Used only in the train phase. + Default: 1. + dist (bool): Whether in distributed training. Used only in the train + phase. Default: False. + sampler (torch.utils.data.sampler): Data sampler. Default: None. + seed (int | None): Seed. Default: None + """ + phase = dataset_opt['phase'] + rank, _ = get_dist_info() + if phase == 'train': + if dist: # distributed training + batch_size = dataset_opt['batch_size_per_gpu'] + num_workers = dataset_opt['num_worker_per_gpu'] + else: # non-distributed training + multiplier = 1 if num_gpu == 0 else num_gpu + batch_size = dataset_opt['batch_size_per_gpu'] * multiplier + num_workers = dataset_opt['num_worker_per_gpu'] * multiplier + dataloader_args = dict( + dataset=dataset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + sampler=sampler, + drop_last=True) + if sampler is None: + dataloader_args['shuffle'] = True + dataloader_args['worker_init_fn'] = partial( + worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None + elif phase in ['val', 'test']: # validation + dataloader_args = dict(dataset=dataset, batch_size=1, shuffle=False, num_workers=0) + else: + raise ValueError(f"Wrong dataset phase: {phase}. Supported ones are 'train', 'val' and 'test'.") + + dataloader_args['pin_memory'] = dataset_opt.get('pin_memory', False) + dataloader_args['persistent_workers'] = dataset_opt.get('persistent_workers', False) + + prefetch_mode = dataset_opt.get('prefetch_mode') + if prefetch_mode == 'cpu': # CPUPrefetcher + num_prefetch_queue = dataset_opt.get('num_prefetch_queue', 1) + logger = get_root_logger() + logger.info(f'Use {prefetch_mode} prefetch dataloader: num_prefetch_queue = {num_prefetch_queue}') + return PrefetchDataLoader(num_prefetch_queue=num_prefetch_queue, **dataloader_args) + else: + # prefetch_mode=None: Normal dataloader + # prefetch_mode='cuda': dataloader for CUDAPrefetcher + return torch.utils.data.DataLoader(**dataloader_args) + + +def worker_init_fn(worker_id, num_workers, rank, seed): + # Set the worker seed to num_workers * rank + worker_id + seed + worker_seed = num_workers * rank + worker_id + seed + np.random.seed(worker_seed) + random.seed(worker_seed) diff --git 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torch.utils.data.distributed.DistributedSampler + Support enlarging the dataset for iteration-based training, for saving + time when restart the dataloader after each epoch + + Args: + dataset (torch.utils.data.Dataset): Dataset used for sampling. + num_replicas (int | None): Number of processes participating in + the training. It is usually the world_size. + rank (int | None): Rank of the current process within num_replicas. + ratio (int): Enlarging ratio. Default: 1. + """ + + def __init__(self, dataset, num_replicas, rank, ratio=1): + self.dataset = dataset + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.num_samples = math.ceil(len(self.dataset) * ratio / self.num_replicas) + self.total_size = self.num_samples * self.num_replicas + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch) + indices = torch.randperm(self.total_size, generator=g).tolist() + + dataset_size = len(self.dataset) + indices = [v % dataset_size for v in indices] + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/basicsr/data/data_util.py b/basicsr/data/data_util.py new file mode 100644 index 0000000000000000000000000000000000000000..328c3cb4b56160da12c12acdd7f0c5f31d11b24f --- /dev/null +++ b/basicsr/data/data_util.py @@ -0,0 +1,313 @@ +import cv2 +import numpy as np +import torch +from os import path as osp +from torch.nn import functional as F + +from basicsr.data.transforms import mod_crop +from basicsr.utils import img2tensor, scandir + + +def read_img_seq(path, require_mod_crop=False, scale=1, return_imgname=False): + """Read a sequence of images from a given folder path. + + Args: + path (list[str] | str): List of image paths or image folder path. + require_mod_crop (bool): Require mod crop for each image. + Default: False. + scale (int): Scale factor for mod_crop. Default: 1. + return_imgname(bool): Whether return image names. Default False. + + Returns: + Tensor: size (t, c, h, w), RGB, [0, 1]. + list[str]: Returned image name list. + """ + if isinstance(path, list): + img_paths = path + else: + img_paths = sorted(list(scandir(path, full_path=True))) + imgs = [cv2.imread(v).astype(np.float32) / 255. for v in img_paths] + + if require_mod_crop: + imgs = [mod_crop(img, scale) for img in imgs] + imgs = img2tensor(imgs, bgr2rgb=True, float32=True) + imgs = torch.stack(imgs, dim=0) + + if return_imgname: + imgnames = [osp.splitext(osp.basename(path))[0] for path in img_paths] + return imgs, imgnames + else: + return imgs + + +def generate_frame_indices(crt_idx, max_frame_num, num_frames, padding='reflection'): + """Generate an index list for reading `num_frames` frames from a sequence + of images. + + Args: + crt_idx (int): Current center index. + max_frame_num (int): Max number of the sequence of images (from 1). + num_frames (int): Reading num_frames frames. + padding (str): Padding mode, one of + 'replicate' | 'reflection' | 'reflection_circle' | 'circle' + Examples: current_idx = 0, num_frames = 5 + The generated frame indices under different padding mode: + replicate: [0, 0, 0, 1, 2] + reflection: [2, 1, 0, 1, 2] + reflection_circle: [4, 3, 0, 1, 2] + circle: [3, 4, 0, 1, 2] + + Returns: + list[int]: A list of indices. + """ + assert num_frames % 2 == 1, 'num_frames should be an odd number.' + assert padding in ('replicate', 'reflection', 'reflection_circle', 'circle'), f'Wrong padding mode: {padding}.' + + max_frame_num = max_frame_num - 1 # start from 0 + num_pad = num_frames // 2 + + indices = [] + for i in range(crt_idx - num_pad, crt_idx + num_pad + 1): + if i < 0: + if padding == 'replicate': + pad_idx = 0 + elif padding == 'reflection': + pad_idx = -i + elif padding == 'reflection_circle': + pad_idx = crt_idx + num_pad - i + else: + pad_idx = num_frames + i + elif i > max_frame_num: + if padding == 'replicate': + pad_idx = max_frame_num + elif padding == 'reflection': + pad_idx = max_frame_num * 2 - i + elif padding == 'reflection_circle': + pad_idx = (crt_idx - num_pad) - (i - max_frame_num) + else: + pad_idx = i - num_frames + else: + pad_idx = i + indices.append(pad_idx) + return indices + + +def paired_paths_from_lmdb(folders, keys): + """Generate paired paths from lmdb files. + + Contents of lmdb. Taking the `lq.lmdb` for example, the file structure is: + + lq.lmdb + ├── data.mdb + ├── lock.mdb + ├── meta_info.txt + + The data.mdb and lock.mdb are standard lmdb files and you can refer to + https://lmdb.readthedocs.io/en/release/ for more details. + + The meta_info.txt is a specified txt file to record the meta information + of our datasets. It will be automatically created when preparing + datasets by our provided dataset tools. + Each line in the txt file records + 1)image name (with extension), + 2)image shape, + 3)compression level, separated by a white space. + Example: `baboon.png (120,125,3) 1` + + We use the image name without extension as the lmdb key. + Note that we use the same key for the corresponding lq and gt images. + + Args: + folders (list[str]): A list of folder path. The order of list should + be [input_folder, gt_folder]. + keys (list[str]): A list of keys identifying folders. The order should + be in consistent with folders, e.g., ['lq', 'gt']. + Note that this key is different from lmdb keys. + + Returns: + list[str]: Returned path list. + """ + assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' + f'But got {len(folders)}') + assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' + input_folder, gt_folder = folders + input_key, gt_key = keys + + if not (input_folder.endswith('.lmdb') and gt_folder.endswith('.lmdb')): + raise ValueError(f'{input_key} folder and {gt_key} folder should both in lmdb ' + f'formats. But received {input_key}: {input_folder}; ' + f'{gt_key}: {gt_folder}') + # ensure that the two meta_info files are the same + with open(osp.join(input_folder, 'meta_info.txt')) as fin: + input_lmdb_keys = [line.split('.')[0] for line in fin] + with open(osp.join(gt_folder, 'meta_info.txt')) as fin: + gt_lmdb_keys = [line.split('.')[0] for line in fin] + if set(input_lmdb_keys) != set(gt_lmdb_keys): + raise ValueError(f'Keys in {input_key}_folder and {gt_key}_folder are different.') + else: + paths = [] + for lmdb_key in sorted(input_lmdb_keys): + paths.append(dict([(f'{input_key}_path', lmdb_key), (f'{gt_key}_path', lmdb_key)])) + return paths + + +def paired_paths_from_meta_info_file(folders, keys, meta_info_file, filename_tmpl): + """Generate paired paths from an meta information file. + + Each line in the meta information file contains the image names and + image shape (usually for gt), separated by a white space. + + Example of an meta information file: + ``` + 0001_s001.png (480,480,3) + 0001_s002.png (480,480,3) + ``` + + Args: + folders (list[str]): A list of folder path. The order of list should + be [input_folder, gt_folder]. + keys (list[str]): A list of keys identifying folders. The order should + be in consistent with folders, e.g., ['lq', 'gt']. + meta_info_file (str): Path to the meta information file. + filename_tmpl (str): Template for each filename. Note that the + template excludes the file extension. Usually the filename_tmpl is + for files in the input folder. + + Returns: + list[str]: Returned path list. + """ + assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' + f'But got {len(folders)}') + assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' + input_folder, gt_folder = folders + input_key, gt_key = keys + + with open(meta_info_file, 'r') as fin: + gt_names = [line.strip().split(' ')[0] for line in fin] + + paths = [] + for gt_name in gt_names: + basename, ext = osp.splitext(osp.basename(gt_name)) + input_name = f'{filename_tmpl.format(basename)}{ext}' + input_path = osp.join(input_folder, input_name) + gt_path = osp.join(gt_folder, gt_name) + paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)])) + return paths + + +def paired_paths_from_folder(folders, keys, filename_tmpl): + """Generate paired paths from folders. + + Args: + folders (list[str]): A list of folder path. The order of list should + be [input_folder, gt_folder]. + keys (list[str]): A list of keys identifying folders. The order should + be in consistent with folders, e.g., ['lq', 'gt']. + filename_tmpl (str): Template for each filename. Note that the + template excludes the file extension. Usually the filename_tmpl is + for files in the input folder. + + Returns: + list[str]: Returned path list. + """ + assert len(folders) == 2, ('The len of folders should be 2 with [input_folder, gt_folder]. ' + f'But got {len(folders)}') + assert len(keys) == 2, f'The len of keys should be 2 with [input_key, gt_key]. But got {len(keys)}' + input_folder, gt_folder = folders + input_key, gt_key = keys + + input_paths = list(scandir(input_folder)) + gt_paths = list(scandir(gt_folder)) + assert len(input_paths) == len(gt_paths), (f'{input_key} and {gt_key} datasets have different number of images: ' + f'{len(input_paths)}, {len(gt_paths)}.') + paths = [] + for gt_path in gt_paths: + basename, ext = osp.splitext(osp.basename(gt_path)) + input_name = f'{filename_tmpl.format(basename)}{ext}' + input_path = osp.join(input_folder, input_name) + assert input_name in input_paths, f'{input_name} is not in {input_key}_paths.' + gt_path = osp.join(gt_folder, gt_path) + paths.append(dict([(f'{input_key}_path', input_path), (f'{gt_key}_path', gt_path)])) + return paths + + +def paths_from_folder(folder): + """Generate paths from folder. + + Args: + folder (str): Folder path. + + Returns: + list[str]: Returned path list. + """ + + paths = list(scandir(folder)) + paths = [osp.join(folder, path) for path in paths] + return paths + + +def paths_from_lmdb(folder): + """Generate paths from lmdb. + + Args: + folder (str): Folder path. + + Returns: + list[str]: Returned path list. + """ + if not folder.endswith('.lmdb'): + raise ValueError(f'Folder {folder}folder should in lmdb format.') + with open(osp.join(folder, 'meta_info.txt')) as fin: + paths = [line.split('.')[0] for line in fin] + return paths + + +def generate_gaussian_kernel(kernel_size=13, sigma=1.6): + """Generate Gaussian kernel used in `duf_downsample`. + + Args: + kernel_size (int): Kernel size. Default: 13. + sigma (float): Sigma of the Gaussian kernel. Default: 1.6. + + Returns: + np.array: The Gaussian kernel. + """ + from scipy.ndimage import filters as filters + kernel = np.zeros((kernel_size, kernel_size)) + # set element at the middle to one, a dirac delta + kernel[kernel_size // 2, kernel_size // 2] = 1 + # gaussian-smooth the dirac, resulting in a gaussian filter + return filters.gaussian_filter(kernel, sigma) + + +def duf_downsample(x, kernel_size=13, scale=4): + """Downsamping with Gaussian kernel used in the DUF official code. + + Args: + x (Tensor): Frames to be downsampled, with shape (b, t, c, h, w). + kernel_size (int): Kernel size. Default: 13. + scale (int): Downsampling factor. Supported scale: (2, 3, 4). + Default: 4. + + Returns: + Tensor: DUF downsampled frames. + """ + assert scale in (2, 3, 4), f'Only support scale (2, 3, 4), but got {scale}.' + + squeeze_flag = False + if x.ndim == 4: + squeeze_flag = True + x = x.unsqueeze(0) + b, t, c, h, w = x.size() + x = x.view(-1, 1, h, w) + pad_w, pad_h = kernel_size // 2 + scale * 2, kernel_size // 2 + scale * 2 + x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), 'reflect') + + gaussian_filter = generate_gaussian_kernel(kernel_size, 0.4 * scale) + gaussian_filter = torch.from_numpy(gaussian_filter).type_as(x).unsqueeze(0).unsqueeze(0) + x = F.conv2d(x, gaussian_filter, stride=scale) + x = x[:, :, 2:-2, 2:-2] + x = x.view(b, t, c, x.size(2), x.size(3)) + if squeeze_flag: + x = x.squeeze(0) + return x diff --git a/basicsr/data/degradations.py b/basicsr/data/degradations.py new file mode 100644 index 0000000000000000000000000000000000000000..578967483e20c969931dc6082c9b007ea9f1c714 --- /dev/null +++ b/basicsr/data/degradations.py @@ -0,0 +1,765 @@ +import cv2 +import math +import numpy as np +import random +import torch +from scipy import special +from scipy.stats import multivariate_normal +from torchvision.transforms.functional_tensor import rgb_to_grayscale + +# -------------------------------------------------------------------- # +# --------------------------- blur kernels --------------------------- # +# -------------------------------------------------------------------- # + + +# --------------------------- util functions --------------------------- # +def sigma_matrix2(sig_x, sig_y, theta): + """Calculate the rotated sigma matrix (two dimensional matrix). + + Args: + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + + Returns: + ndarray: Rotated sigma matrix. + """ + d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]]) + u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) + return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T)) + + +def mesh_grid(kernel_size): + """Generate the mesh grid, centering at zero. + + Args: + kernel_size (int): + + Returns: + xy (ndarray): with the shape (kernel_size, kernel_size, 2) + xx (ndarray): with the shape (kernel_size, kernel_size) + yy (ndarray): with the shape (kernel_size, kernel_size) + """ + ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.) + xx, yy = np.meshgrid(ax, ax) + xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size, + 1))).reshape(kernel_size, kernel_size, 2) + return xy, xx, yy + + +def pdf2(sigma_matrix, grid): + """Calculate PDF of the bivariate Gaussian distribution. + + Args: + sigma_matrix (ndarray): with the shape (2, 2) + grid (ndarray): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. + + Returns: + kernel (ndarrray): un-normalized kernel. + """ + inverse_sigma = np.linalg.inv(sigma_matrix) + kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2)) + return kernel + + +def cdf2(d_matrix, grid): + """Calculate the CDF of the standard bivariate Gaussian distribution. + Used in skewed Gaussian distribution. + + Args: + d_matrix (ndarrasy): skew matrix. + grid (ndarray): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. + + Returns: + cdf (ndarray): skewed cdf. + """ + rv = multivariate_normal([0, 0], [[1, 0], [0, 1]]) + grid = np.dot(grid, d_matrix) + cdf = rv.cdf(grid) + return cdf + + +def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True): + """Generate a bivariate isotropic or anisotropic Gaussian kernel. + + In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. + + Args: + kernel_size (int): + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + grid (ndarray, optional): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. Default: None + isotropic (bool): + + Returns: + kernel (ndarray): normalized kernel. + """ + if grid is None: + grid, _, _ = mesh_grid(kernel_size) + if isotropic: + sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) + else: + sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) + kernel = pdf2(sigma_matrix, grid) + kernel = kernel / np.sum(kernel) + return kernel + + +def bivariate_generalized_Gaussian(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): + """Generate a bivariate generalized Gaussian kernel. + Described in `Parameter Estimation For Multivariate Generalized + Gaussian Distributions`_ + by Pascal et. al (2013). + + In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. + + Args: + kernel_size (int): + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + beta (float): shape parameter, beta = 1 is the normal distribution. + grid (ndarray, optional): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. Default: None + + Returns: + kernel (ndarray): normalized kernel. + + .. _Parameter Estimation For Multivariate Generalized Gaussian + Distributions: https://arxiv.org/abs/1302.6498 + """ + if grid is None: + grid, _, _ = mesh_grid(kernel_size) + if isotropic: + sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) + else: + sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) + inverse_sigma = np.linalg.inv(sigma_matrix) + kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta)) + kernel = kernel / np.sum(kernel) + return kernel + + +def bivariate_plateau(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True): + """Generate a plateau-like anisotropic kernel. + 1 / (1+x^(beta)) + + Ref: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution + + In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored. + + Args: + kernel_size (int): + sig_x (float): + sig_y (float): + theta (float): Radian measurement. + beta (float): shape parameter, beta = 1 is the normal distribution. + grid (ndarray, optional): generated by :func:`mesh_grid`, + with the shape (K, K, 2), K is the kernel size. Default: None + + Returns: + kernel (ndarray): normalized kernel. + """ + if grid is None: + grid, _, _ = mesh_grid(kernel_size) + if isotropic: + sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]]) + else: + sigma_matrix = sigma_matrix2(sig_x, sig_y, theta) + inverse_sigma = np.linalg.inv(sigma_matrix) + kernel = np.reciprocal(np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta) + 1) + kernel = kernel / np.sum(kernel) + return kernel + + +def random_bivariate_Gaussian(kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + noise_range=None, + isotropic=True): + """Randomly generate bivariate isotropic or anisotropic Gaussian kernels. + + In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. + + Args: + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi, math.pi] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' + sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) + if isotropic is False: + assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' + assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' + sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) + rotation = np.random.uniform(rotation_range[0], rotation_range[1]) + else: + sigma_y = sigma_x + rotation = 0 + + kernel = bivariate_Gaussian(kernel_size, sigma_x, sigma_y, rotation, isotropic=isotropic) + + # add multiplicative noise + if noise_range is not None: + assert noise_range[0] < noise_range[1], 'Wrong noise range.' + noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) + kernel = kernel * noise + kernel = kernel / np.sum(kernel) + return kernel + + +def random_bivariate_generalized_Gaussian(kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + beta_range, + noise_range=None, + isotropic=True): + """Randomly generate bivariate generalized Gaussian kernels. + + In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. + + Args: + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi, math.pi] + beta_range (tuple): [0.5, 8] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' + sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) + if isotropic is False: + assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' + assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' + sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) + rotation = np.random.uniform(rotation_range[0], rotation_range[1]) + else: + sigma_y = sigma_x + rotation = 0 + + # assume beta_range[0] < 1 < beta_range[1] + if np.random.uniform() < 0.5: + beta = np.random.uniform(beta_range[0], 1) + else: + beta = np.random.uniform(1, beta_range[1]) + + kernel = bivariate_generalized_Gaussian(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) + + # add multiplicative noise + if noise_range is not None: + assert noise_range[0] < noise_range[1], 'Wrong noise range.' + noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) + kernel = kernel * noise + kernel = kernel / np.sum(kernel) + return kernel + + +def random_bivariate_plateau(kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + beta_range, + noise_range=None, + isotropic=True): + """Randomly generate bivariate plateau kernels. + + In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored. + + Args: + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi/2, math.pi/2] + beta_range (tuple): [1, 4] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.' + sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1]) + if isotropic is False: + assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.' + assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.' + sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1]) + rotation = np.random.uniform(rotation_range[0], rotation_range[1]) + else: + sigma_y = sigma_x + rotation = 0 + + # TODO: this may be not proper + if np.random.uniform() < 0.5: + beta = np.random.uniform(beta_range[0], 1) + else: + beta = np.random.uniform(1, beta_range[1]) + + kernel = bivariate_plateau(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic) + # add multiplicative noise + if noise_range is not None: + assert noise_range[0] < noise_range[1], 'Wrong noise range.' + noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape) + kernel = kernel * noise + kernel = kernel / np.sum(kernel) + + return kernel + + +def random_mixed_kernels(kernel_list, + kernel_prob, + kernel_size=21, + sigma_x_range=(0.6, 5), + sigma_y_range=(0.6, 5), + rotation_range=(-math.pi, math.pi), + betag_range=(0.5, 8), + betap_range=(0.5, 8), + noise_range=None): + """Randomly generate mixed kernels. + + Args: + kernel_list (tuple): a list name of kernel types, + support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso', + 'plateau_aniso'] + kernel_prob (tuple): corresponding kernel probability for each + kernel type + kernel_size (int): + sigma_x_range (tuple): [0.6, 5] + sigma_y_range (tuple): [0.6, 5] + rotation range (tuple): [-math.pi, math.pi] + beta_range (tuple): [0.5, 8] + noise_range(tuple, optional): multiplicative kernel noise, + [0.75, 1.25]. Default: None + + Returns: + kernel (ndarray): + """ + kernel_type = random.choices(kernel_list, kernel_prob)[0] + if kernel_type == 'iso': + kernel = random_bivariate_Gaussian( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True) + elif kernel_type == 'aniso': + kernel = random_bivariate_Gaussian( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False) + elif kernel_type == 'generalized_iso': + kernel = random_bivariate_generalized_Gaussian( + kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + betag_range, + noise_range=noise_range, + isotropic=True) + elif kernel_type == 'generalized_aniso': + kernel = random_bivariate_generalized_Gaussian( + kernel_size, + sigma_x_range, + sigma_y_range, + rotation_range, + betag_range, + noise_range=noise_range, + isotropic=False) + elif kernel_type == 'plateau_iso': + kernel = random_bivariate_plateau( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True) + elif kernel_type == 'plateau_aniso': + kernel = random_bivariate_plateau( + kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False) + return kernel + + +np.seterr(divide='ignore', invalid='ignore') + + +def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0): + """2D sinc filter, ref: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-filter + + Args: + cutoff (float): cutoff frequency in radians (pi is max) + kernel_size (int): horizontal and vertical size, must be odd. + pad_to (int): pad kernel size to desired size, must be odd or zero. + """ + assert kernel_size % 2 == 1, 'Kernel size must be an odd number.' + kernel = np.fromfunction( + lambda x, y: cutoff * special.j1(cutoff * np.sqrt( + (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt( + (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size]) + kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi) + kernel = kernel / np.sum(kernel) + if pad_to > kernel_size: + pad_size = (pad_to - kernel_size) // 2 + kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) + return kernel + + +# ------------------------------------------------------------- # +# --------------------------- noise --------------------------- # +# ------------------------------------------------------------- # + +# ----------------------- Gaussian Noise ----------------------- # + + +def generate_gaussian_noise(img, sigma=10, gray_noise=False): + """Generate Gaussian noise. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + sigma (float): Noise scale (measured in range 255). Default: 10. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + if gray_noise: + noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255. + noise = np.expand_dims(noise, axis=2).repeat(3, axis=2) + else: + noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255. + return noise + + +def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False): + """Add Gaussian noise. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + sigma (float): Noise scale (measured in range 255). Default: 10. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + noise = generate_gaussian_noise(img, sigma, gray_noise) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def generate_gaussian_noise_pt(img, sigma=10, gray_noise=0): + """Add Gaussian noise (PyTorch version). + + Args: + img (Tensor): Shape (b, c, h, w), range[0, 1], float32. + scale (float | Tensor): Noise scale. Default: 1.0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + b, _, h, w = img.size() + if not isinstance(sigma, (float, int)): + sigma = sigma.view(img.size(0), 1, 1, 1) + if isinstance(gray_noise, (float, int)): + cal_gray_noise = gray_noise > 0 + else: + gray_noise = gray_noise.view(b, 1, 1, 1) + cal_gray_noise = torch.sum(gray_noise) > 0 + + if cal_gray_noise: + noise_gray = torch.randn(*img.size()[2:4], dtype=img.dtype, device=img.device) * sigma / 255. + noise_gray = noise_gray.view(b, 1, h, w) + + # always calculate color noise + noise = torch.randn(*img.size(), dtype=img.dtype, device=img.device) * sigma / 255. + + if cal_gray_noise: + noise = noise * (1 - gray_noise) + noise_gray * gray_noise + return noise + + +def add_gaussian_noise_pt(img, sigma=10, gray_noise=0, clip=True, rounds=False): + """Add Gaussian noise (PyTorch version). + + Args: + img (Tensor): Shape (b, c, h, w), range[0, 1], float32. + scale (float | Tensor): Noise scale. Default: 1.0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + noise = generate_gaussian_noise_pt(img, sigma, gray_noise) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ----------------------- Random Gaussian Noise ----------------------- # +def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0): + sigma = np.random.uniform(sigma_range[0], sigma_range[1]) + if np.random.uniform() < gray_prob: + gray_noise = True + else: + gray_noise = False + return generate_gaussian_noise(img, sigma, gray_noise) + + +def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_gaussian_noise(img, sigma_range, gray_prob) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def random_generate_gaussian_noise_pt(img, sigma_range=(0, 10), gray_prob=0): + sigma = torch.rand( + img.size(0), dtype=img.dtype, device=img.device) * (sigma_range[1] - sigma_range[0]) + sigma_range[0] + gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) + gray_noise = (gray_noise < gray_prob).float() + return generate_gaussian_noise_pt(img, sigma, gray_noise) + + +def random_add_gaussian_noise_pt(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_gaussian_noise_pt(img, sigma_range, gray_prob) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ----------------------- Poisson (Shot) Noise ----------------------- # + + +def generate_poisson_noise(img, scale=1.0, gray_noise=False): + """Generate poisson noise. + + Ref: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219 + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + scale (float): Noise scale. Default: 1.0. + gray_noise (bool): Whether generate gray noise. Default: False. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + if gray_noise: + img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # round and clip image for counting vals correctly + img = np.clip((img * 255.0).round(), 0, 255) / 255. + vals = len(np.unique(img)) + vals = 2**np.ceil(np.log2(vals)) + out = np.float32(np.random.poisson(img * vals) / float(vals)) + noise = out - img + if gray_noise: + noise = np.repeat(noise[:, :, np.newaxis], 3, axis=2) + return noise * scale + + +def add_poisson_noise(img, scale=1.0, clip=True, rounds=False, gray_noise=False): + """Add poisson noise. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + scale (float): Noise scale. Default: 1.0. + gray_noise (bool): Whether generate gray noise. Default: False. + + Returns: + (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1], + float32. + """ + noise = generate_poisson_noise(img, scale, gray_noise) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def generate_poisson_noise_pt(img, scale=1.0, gray_noise=0): + """Generate a batch of poisson noise (PyTorch version) + + Args: + img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. + scale (float | Tensor): Noise scale. Number or Tensor with shape (b). + Default: 1.0. + gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). + 0 for False, 1 for True. Default: 0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + b, _, h, w = img.size() + if isinstance(gray_noise, (float, int)): + cal_gray_noise = gray_noise > 0 + else: + gray_noise = gray_noise.view(b, 1, 1, 1) + cal_gray_noise = torch.sum(gray_noise) > 0 + if cal_gray_noise: + img_gray = rgb_to_grayscale(img, num_output_channels=1) + # round and clip image for counting vals correctly + img_gray = torch.clamp((img_gray * 255.0).round(), 0, 255) / 255. + # use for-loop to get the unique values for each sample + vals_list = [len(torch.unique(img_gray[i, :, :, :])) for i in range(b)] + vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] + vals = img_gray.new_tensor(vals_list).view(b, 1, 1, 1) + out = torch.poisson(img_gray * vals) / vals + noise_gray = out - img_gray + noise_gray = noise_gray.expand(b, 3, h, w) + + # always calculate color noise + # round and clip image for counting vals correctly + img = torch.clamp((img * 255.0).round(), 0, 255) / 255. + # use for-loop to get the unique values for each sample + vals_list = [len(torch.unique(img[i, :, :, :])) for i in range(b)] + vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list] + vals = img.new_tensor(vals_list).view(b, 1, 1, 1) + out = torch.poisson(img * vals) / vals + noise = out - img + if cal_gray_noise: + noise = noise * (1 - gray_noise) + noise_gray * gray_noise + if not isinstance(scale, (float, int)): + scale = scale.view(b, 1, 1, 1) + return noise * scale + + +def add_poisson_noise_pt(img, scale=1.0, clip=True, rounds=False, gray_noise=0): + """Add poisson noise to a batch of images (PyTorch version). + + Args: + img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32. + scale (float | Tensor): Noise scale. Number or Tensor with shape (b). + Default: 1.0. + gray_noise (float | Tensor): 0-1 number or Tensor with shape (b). + 0 for False, 1 for True. Default: 0. + + Returns: + (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1], + float32. + """ + noise = generate_poisson_noise_pt(img, scale, gray_noise) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ----------------------- Random Poisson (Shot) Noise ----------------------- # + + +def random_generate_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0): + scale = np.random.uniform(scale_range[0], scale_range[1]) + if np.random.uniform() < gray_prob: + gray_noise = True + else: + gray_noise = False + return generate_poisson_noise(img, scale, gray_noise) + + +def random_add_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_poisson_noise(img, scale_range, gray_prob) + out = img + noise + if clip and rounds: + out = np.clip((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = np.clip(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +def random_generate_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0): + scale = torch.rand( + img.size(0), dtype=img.dtype, device=img.device) * (scale_range[1] - scale_range[0]) + scale_range[0] + gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device) + gray_noise = (gray_noise < gray_prob).float() + return generate_poisson_noise_pt(img, scale, gray_noise) + + +def random_add_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False): + noise = random_generate_poisson_noise_pt(img, scale_range, gray_prob) + out = img + noise + if clip and rounds: + out = torch.clamp((out * 255.0).round(), 0, 255) / 255. + elif clip: + out = torch.clamp(out, 0, 1) + elif rounds: + out = (out * 255.0).round() / 255. + return out + + +# ------------------------------------------------------------------------ # +# --------------------------- JPEG compression --------------------------- # +# ------------------------------------------------------------------------ # + + +def add_jpg_compression(img, quality=90): + """Add JPG compression artifacts. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + quality (float): JPG compression quality. 0 for lowest quality, 100 for + best quality. Default: 90. + + Returns: + (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], + float32. + """ + img = np.clip(img, 0, 1) + encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality] + _, encimg = cv2.imencode('.jpg', img * 255., encode_param) + img = np.float32(cv2.imdecode(encimg, 1)) / 255. + return img + + +def random_add_jpg_compression(img, quality_range=(90, 100)): + """Randomly add JPG compression artifacts. + + Args: + img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. + quality_range (tuple[float] | list[float]): JPG compression quality + range. 0 for lowest quality, 100 for best quality. + Default: (90, 100). + + Returns: + (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1], + float32. + """ + quality = np.random.uniform(quality_range[0], quality_range[1]) + return add_jpg_compression(img, quality) diff --git a/basicsr/data/ffhq_dataset.py b/basicsr/data/ffhq_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..23992eb877f6b7b46cf5f40ed3667fc10916269b --- /dev/null +++ b/basicsr/data/ffhq_dataset.py @@ -0,0 +1,80 @@ +import random +import time +from os import path as osp +from torch.utils import data as data +from torchvision.transforms.functional import normalize + +from basicsr.data.transforms import augment +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +class FFHQDataset(data.Dataset): + """FFHQ dataset for StyleGAN. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + io_backend (dict): IO backend type and other kwarg. + mean (list | tuple): Image mean. + std (list | tuple): Image std. + use_hflip (bool): Whether to horizontally flip. + + """ + + def __init__(self, opt): + super(FFHQDataset, self).__init__() + self.opt = opt + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + + self.gt_folder = opt['dataroot_gt'] + self.mean = opt['mean'] + self.std = opt['std'] + + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = self.gt_folder + if not self.gt_folder.endswith('.lmdb'): + raise ValueError("'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") + with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: + self.paths = [line.split('.')[0] for line in fin] + else: + # FFHQ has 70000 images in total + self.paths = [osp.join(self.gt_folder, f'{v:08d}.png') for v in range(70000)] + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # load gt image + gt_path = self.paths[index] + # avoid errors caused by high latency in reading files + retry = 3 + while retry > 0: + try: + img_bytes = self.file_client.get(gt_path) + except Exception as e: + logger = get_root_logger() + logger.warning(f'File client error: {e}, remaining retry times: {retry - 1}') + # change another file to read + index = random.randint(0, self.__len__()) + gt_path = self.paths[index] + time.sleep(1) # sleep 1s for occasional server congestion + else: + break + finally: + retry -= 1 + img_gt = imfrombytes(img_bytes, float32=True) + + # random horizontal flip + img_gt = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False) + # BGR to RGB, HWC to CHW, numpy to tensor + img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) + # normalize + normalize(img_gt, self.mean, self.std, inplace=True) + return {'gt': img_gt, 'gt_path': gt_path} + + def __len__(self): + return len(self.paths) diff --git a/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt b/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt new file mode 100644 index 0000000000000000000000000000000000000000..081969018371c1891961a9f7bf5f00f80c9dee9f --- /dev/null +++ b/basicsr/data/meta_info/meta_info_DIV2K800sub_GT.txt @@ -0,0 +1,32592 @@ +0001_s001.png (480,480,3) +0001_s002.png (480,480,3) +0001_s003.png (480,480,3) +0001_s004.png (480,480,3) +0001_s005.png (480,480,3) +0001_s006.png (480,480,3) +0001_s007.png (480,480,3) +0001_s008.png (480,480,3) +0001_s009.png (480,480,3) +0001_s010.png (480,480,3) +0001_s011.png (480,480,3) +0001_s012.png (480,480,3) +0001_s013.png (480,480,3) +0001_s014.png (480,480,3) +0001_s015.png (480,480,3) +0001_s016.png (480,480,3) +0001_s017.png (480,480,3) +0001_s018.png (480,480,3) +0001_s019.png (480,480,3) +0001_s020.png (480,480,3) +0001_s021.png (480,480,3) +0001_s022.png 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a/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt new file mode 100644 index 0000000000000000000000000000000000000000..b407561e6baca502c386a66b4e52b06729f88284 --- /dev/null +++ b/basicsr/data/meta_info/meta_info_REDSofficial4_test_GT.txt @@ -0,0 +1,4 @@ +240 100 (720,1280,3) +241 100 (720,1280,3) +246 100 (720,1280,3) +257 100 (720,1280,3) diff --git a/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt b/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt new file mode 100644 index 0000000000000000000000000000000000000000..11121311d80a5c1a0c373fbbf40d322288751a7c --- /dev/null +++ b/basicsr/data/meta_info/meta_info_REDSval_official_test_GT.txt @@ -0,0 +1,30 @@ +240 100 (720,1280,3) +241 100 (720,1280,3) +242 100 (720,1280,3) +243 100 (720,1280,3) +244 100 (720,1280,3) +245 100 (720,1280,3) +246 100 (720,1280,3) +247 100 (720,1280,3) +248 100 (720,1280,3) +249 100 (720,1280,3) +250 100 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data as data +from torchvision.transforms.functional import normalize + +from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb, paired_paths_from_meta_info_file +from basicsr.data.transforms import augment, paired_random_crop +from basicsr.utils import FileClient, bgr2ycbcr, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +class PairedImageDataset(data.Dataset): + """Paired image dataset for image restoration. + + Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. + + There are three modes: + 1. 'lmdb': Use lmdb files. + If opt['io_backend'] == lmdb. + 2. 'meta_info_file': Use meta information file to generate paths. + If opt['io_backend'] != lmdb and opt['meta_info_file'] is not None. + 3. 'folder': Scan folders to generate paths. + The rest. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + meta_info_file (str): Path for meta information file. + io_backend (dict): IO backend type and other kwarg. + filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. + Default: '{}'. + gt_size (int): Cropped patched size for gt patches. + use_hflip (bool): Use horizontal flips. + use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). + + scale (bool): Scale, which will be added automatically. + phase (str): 'train' or 'val'. + """ + + def __init__(self, opt): + super(PairedImageDataset, self).__init__() + self.opt = opt + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + self.mean = opt['mean'] if 'mean' in opt else None + self.std = opt['std'] if 'std' in opt else None + + self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] + if 'filename_tmpl' in opt: + self.filename_tmpl = opt['filename_tmpl'] + else: + self.filename_tmpl = '{}' + + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] + self.io_backend_opt['client_keys'] = ['lq', 'gt'] + self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) + elif 'meta_info_file' in self.opt and self.opt['meta_info_file'] is not None: + self.paths = paired_paths_from_meta_info_file([self.lq_folder, self.gt_folder], ['lq', 'gt'], + self.opt['meta_info_file'], self.filename_tmpl) + else: + self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + scale = self.opt['scale'] + + # Load gt and lq images. Dimension order: HWC; channel order: BGR; + # image range: [0, 1], float32. + gt_path = self.paths[index]['gt_path'] + img_bytes = self.file_client.get(gt_path, 'gt') + img_gt = imfrombytes(img_bytes, float32=True) + lq_path = self.paths[index]['lq_path'] + img_bytes = self.file_client.get(lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + + # augmentation for training + if self.opt['phase'] == 'train': + gt_size = self.opt['gt_size'] + # random crop + img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) + # flip, rotation + img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) + + # color space transform + if 'color' in self.opt and self.opt['color'] == 'y': + img_gt = bgr2ycbcr(img_gt, y_only=True)[..., None] + img_lq = bgr2ycbcr(img_lq, y_only=True)[..., None] + + # crop the unmatched GT images during validation or testing, especially for SR benchmark datasets + # TODO: It is better to update the datasets, rather than force to crop + if self.opt['phase'] != 'train': + img_gt = img_gt[0:img_lq.shape[0] * scale, 0:img_lq.shape[1] * scale, :] + + # BGR to RGB, HWC to CHW, numpy to tensor + img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) + # normalize + if self.mean is not None or self.std is not None: + normalize(img_lq, self.mean, self.std, inplace=True) + normalize(img_gt, self.mean, self.std, inplace=True) + + return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} + + def __len__(self): + return len(self.paths) diff --git a/basicsr/data/prefetch_dataloader.py b/basicsr/data/prefetch_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..5088425050d4cc98114a9b93eb50ea60273f35a0 --- /dev/null +++ b/basicsr/data/prefetch_dataloader.py @@ -0,0 +1,125 @@ +import queue as Queue +import threading +import torch +from torch.utils.data import DataLoader + + +class PrefetchGenerator(threading.Thread): + """A general prefetch generator. + + Ref: + https://stackoverflow.com/questions/7323664/python-generator-pre-fetch + + Args: + generator: Python generator. + num_prefetch_queue (int): Number of prefetch queue. + """ + + def __init__(self, generator, num_prefetch_queue): + threading.Thread.__init__(self) + self.queue = Queue.Queue(num_prefetch_queue) + self.generator = generator + self.daemon = True + self.start() + + def run(self): + for item in self.generator: + self.queue.put(item) + self.queue.put(None) + + def __next__(self): + next_item = self.queue.get() + if next_item is None: + raise StopIteration + return next_item + + def __iter__(self): + return self + + +class PrefetchDataLoader(DataLoader): + """Prefetch version of dataloader. + + Ref: + https://github.com/IgorSusmelj/pytorch-styleguide/issues/5# + + TODO: + Need to test on single gpu and ddp (multi-gpu). There is a known issue in + ddp. + + Args: + num_prefetch_queue (int): Number of prefetch queue. + kwargs (dict): Other arguments for dataloader. + """ + + def __init__(self, num_prefetch_queue, **kwargs): + self.num_prefetch_queue = num_prefetch_queue + super(PrefetchDataLoader, self).__init__(**kwargs) + + def __iter__(self): + return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue) + + +class CPUPrefetcher(): + """CPU prefetcher. + + Args: + loader: Dataloader. + """ + + def __init__(self, loader): + self.ori_loader = loader + self.loader = iter(loader) + + def next(self): + try: + return next(self.loader) + except StopIteration: + return None + + def reset(self): + self.loader = iter(self.ori_loader) + + +class CUDAPrefetcher(): + """CUDA prefetcher. + + Ref: + https://github.com/NVIDIA/apex/issues/304# + + It may consums more GPU memory. + + Args: + loader: Dataloader. + opt (dict): Options. + """ + + def __init__(self, loader, opt): + self.ori_loader = loader + self.loader = iter(loader) + self.opt = opt + self.stream = torch.cuda.Stream() + self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') + self.preload() + + def preload(self): + try: + self.batch = next(self.loader) # self.batch is a dict + except StopIteration: + self.batch = None + return None + # put tensors to gpu + with torch.cuda.stream(self.stream): + for k, v in self.batch.items(): + if torch.is_tensor(v): + self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True) + + def next(self): + torch.cuda.current_stream().wait_stream(self.stream) + batch = self.batch + self.preload() + return batch + + def reset(self): + self.loader = iter(self.ori_loader) + self.preload() diff --git a/basicsr/data/realesrgan_dataset.py b/basicsr/data/realesrgan_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..1616e9b9171d63c47ee13ef82f6dea9f158bed73 --- /dev/null +++ b/basicsr/data/realesrgan_dataset.py @@ -0,0 +1,193 @@ +import cv2 +import math +import numpy as np +import os +import os.path as osp +import random +import time +import torch +from torch.utils import data as data + +from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels +from basicsr.data.transforms import augment +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register(suffix='basicsr') +class RealESRGANDataset(data.Dataset): + """Dataset used for Real-ESRGAN model: + Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. + + It loads gt (Ground-Truth) images, and augments them. + It also generates blur kernels and sinc kernels for generating low-quality images. + Note that the low-quality images are processed in tensors on GPUS for faster processing. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + meta_info (str): Path for meta information file. + io_backend (dict): IO backend type and other kwarg. + use_hflip (bool): Use horizontal flips. + use_rot (bool): Use rotation (use vertical flip and transposing h and w for implementation). + Please see more options in the codes. + """ + + def __init__(self, opt): + super(RealESRGANDataset, self).__init__() + self.opt = opt + self.file_client = None + self.io_backend_opt = opt['io_backend'] + self.gt_folder = opt['dataroot_gt'] + + # file client (lmdb io backend) + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = [self.gt_folder] + self.io_backend_opt['client_keys'] = ['gt'] + if not self.gt_folder.endswith('.lmdb'): + raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") + with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: + self.paths = [line.split('.')[0] for line in fin] + else: + # disk backend with meta_info + # Each line in the meta_info describes the relative path to an image + with open(self.opt['meta_info']) as fin: + paths = [line.strip().split(' ')[0] for line in fin] + self.paths = [os.path.join(self.gt_folder, v) for v in paths] + + # blur settings for the first degradation + self.blur_kernel_size = opt['blur_kernel_size'] + self.kernel_list = opt['kernel_list'] + self.kernel_prob = opt['kernel_prob'] # a list for each kernel probability + self.blur_sigma = opt['blur_sigma'] + self.betag_range = opt['betag_range'] # betag used in generalized Gaussian blur kernels + self.betap_range = opt['betap_range'] # betap used in plateau blur kernels + self.sinc_prob = opt['sinc_prob'] # the probability for sinc filters + + # blur settings for the second degradation + self.blur_kernel_size2 = opt['blur_kernel_size2'] + self.kernel_list2 = opt['kernel_list2'] + self.kernel_prob2 = opt['kernel_prob2'] + self.blur_sigma2 = opt['blur_sigma2'] + self.betag_range2 = opt['betag_range2'] + self.betap_range2 = opt['betap_range2'] + self.sinc_prob2 = opt['sinc_prob2'] + + # a final sinc filter + self.final_sinc_prob = opt['final_sinc_prob'] + + self.kernel_range = [2 * v + 1 for v in range(3, 11)] # kernel size ranges from 7 to 21 + # TODO: kernel range is now hard-coded, should be in the configure file + self.pulse_tensor = torch.zeros(21, 21).float() # convolving with pulse tensor brings no blurry effect + self.pulse_tensor[10, 10] = 1 + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # -------------------------------- Load gt images -------------------------------- # + # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. + gt_path = self.paths[index] + # avoid errors caused by high latency in reading files + retry = 3 + while retry > 0: + try: + img_bytes = self.file_client.get(gt_path, 'gt') + except (IOError, OSError) as e: + logger = get_root_logger() + logger.warn(f'File client error: {e}, remaining retry times: {retry - 1}') + # change another file to read + index = random.randint(0, self.__len__()) + gt_path = self.paths[index] + time.sleep(1) # sleep 1s for occasional server congestion + else: + break + finally: + retry -= 1 + img_gt = imfrombytes(img_bytes, float32=True) + + # -------------------- Do augmentation for training: flip, rotation -------------------- # + img_gt = augment(img_gt, self.opt['use_hflip'], self.opt['use_rot']) + + # crop or pad to 400 + # TODO: 400 is hard-coded. You may change it accordingly + h, w = img_gt.shape[0:2] + crop_pad_size = 400 + # pad + if h < crop_pad_size or w < crop_pad_size: + pad_h = max(0, crop_pad_size - h) + pad_w = max(0, crop_pad_size - w) + img_gt = cv2.copyMakeBorder(img_gt, 0, pad_h, 0, pad_w, cv2.BORDER_REFLECT_101) + # crop + if img_gt.shape[0] > crop_pad_size or img_gt.shape[1] > crop_pad_size: + h, w = img_gt.shape[0:2] + # randomly choose top and left coordinates + top = random.randint(0, h - crop_pad_size) + left = random.randint(0, w - crop_pad_size) + img_gt = img_gt[top:top + crop_pad_size, left:left + crop_pad_size, ...] + + # ------------------------ Generate kernels (used in the first degradation) ------------------------ # + kernel_size = random.choice(self.kernel_range) + if np.random.uniform() < self.opt['sinc_prob']: + # this sinc filter setting is for kernels ranging from [7, 21] + if kernel_size < 13: + omega_c = np.random.uniform(np.pi / 3, np.pi) + else: + omega_c = np.random.uniform(np.pi / 5, np.pi) + kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) + else: + kernel = random_mixed_kernels( + self.kernel_list, + self.kernel_prob, + kernel_size, + self.blur_sigma, + self.blur_sigma, [-math.pi, math.pi], + self.betag_range, + self.betap_range, + noise_range=None) + # pad kernel + pad_size = (21 - kernel_size) // 2 + kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) + + # ------------------------ Generate kernels (used in the second degradation) ------------------------ # + kernel_size = random.choice(self.kernel_range) + if np.random.uniform() < self.opt['sinc_prob2']: + if kernel_size < 13: + omega_c = np.random.uniform(np.pi / 3, np.pi) + else: + omega_c = np.random.uniform(np.pi / 5, np.pi) + kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) + else: + kernel2 = random_mixed_kernels( + self.kernel_list2, + self.kernel_prob2, + kernel_size, + self.blur_sigma2, + self.blur_sigma2, [-math.pi, math.pi], + self.betag_range2, + self.betap_range2, + noise_range=None) + + # pad kernel + pad_size = (21 - kernel_size) // 2 + kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) + + # ------------------------------------- the final sinc kernel ------------------------------------- # + if np.random.uniform() < self.opt['final_sinc_prob']: + kernel_size = random.choice(self.kernel_range) + omega_c = np.random.uniform(np.pi / 3, np.pi) + sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=21) + sinc_kernel = torch.FloatTensor(sinc_kernel) + else: + sinc_kernel = self.pulse_tensor + + # BGR to RGB, HWC to CHW, numpy to tensor + img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0] + kernel = torch.FloatTensor(kernel) + kernel2 = torch.FloatTensor(kernel2) + + return_d = {'gt': img_gt, 'kernel1': kernel, 'kernel2': kernel2, 'sinc_kernel': sinc_kernel, 'gt_path': gt_path} + return return_d + + def __len__(self): + return len(self.paths) diff --git a/basicsr/data/realesrgan_paired_dataset.py b/basicsr/data/realesrgan_paired_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..e014f8311f202f62caec40a7690e3b66aefe2265 --- /dev/null +++ b/basicsr/data/realesrgan_paired_dataset.py @@ -0,0 +1,109 @@ +import os +from torch.utils import data as data +from torchvision.transforms.functional import normalize + +from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb +from basicsr.data.transforms import augment, paired_random_crop +from basicsr.utils import FileClient, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register(suffix='basicsr') +class RealESRGANPairedDataset(data.Dataset): + """Paired image dataset for image restoration. + + Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs. + + There are three modes: + 1. 'lmdb': Use lmdb files. + If opt['io_backend'] == lmdb. + 2. 'meta_info': Use meta information file to generate paths. + If opt['io_backend'] != lmdb and opt['meta_info'] is not None. + 3. 'folder': Scan folders to generate paths. + The rest. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + meta_info (str): Path for meta information file. + io_backend (dict): IO backend type and other kwarg. + filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. + Default: '{}'. + gt_size (int): Cropped patched size for gt patches. + use_hflip (bool): Use horizontal flips. + use_rot (bool): Use rotation (use vertical flip and transposing h + and w for implementation). + + scale (bool): Scale, which will be added automatically. + phase (str): 'train' or 'val'. + """ + + def __init__(self, opt): + super(RealESRGANPairedDataset, self).__init__() + self.opt = opt + self.file_client = None + self.io_backend_opt = opt['io_backend'] + # mean and std for normalizing the input images + self.mean = opt['mean'] if 'mean' in opt else None + self.std = opt['std'] if 'std' in opt else None + + self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq'] + self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}' + + # file client (lmdb io backend) + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder] + self.io_backend_opt['client_keys'] = ['lq', 'gt'] + self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt']) + elif 'meta_info' in self.opt and self.opt['meta_info'] is not None: + # disk backend with meta_info + # Each line in the meta_info describes the relative path to an image + with open(self.opt['meta_info']) as fin: + paths = [line.strip() for line in fin] + self.paths = [] + for path in paths: + gt_path, lq_path = path.split(', ') + gt_path = os.path.join(self.gt_folder, gt_path) + lq_path = os.path.join(self.lq_folder, lq_path) + self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)])) + else: + # disk backend + # it will scan the whole folder to get meta info + # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file + self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl) + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + scale = self.opt['scale'] + + # Load gt and lq images. Dimension order: HWC; channel order: BGR; + # image range: [0, 1], float32. + gt_path = self.paths[index]['gt_path'] + img_bytes = self.file_client.get(gt_path, 'gt') + img_gt = imfrombytes(img_bytes, float32=True) + lq_path = self.paths[index]['lq_path'] + img_bytes = self.file_client.get(lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + + # augmentation for training + if self.opt['phase'] == 'train': + gt_size = self.opt['gt_size'] + # random crop + img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path) + # flip, rotation + img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot']) + + # BGR to RGB, HWC to CHW, numpy to tensor + img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) + # normalize + if self.mean is not None or self.std is not None: + normalize(img_lq, self.mean, self.std, inplace=True) + normalize(img_gt, self.mean, self.std, inplace=True) + + return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path} + + def __len__(self): + return len(self.paths) diff --git a/basicsr/data/reds_dataset.py b/basicsr/data/reds_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0d4a91724689626b7310bb08debda70e7e0186c0 --- /dev/null +++ b/basicsr/data/reds_dataset.py @@ -0,0 +1,360 @@ +import numpy as np +import random +import torch +from pathlib import Path +from torch.utils import data as data + +from basicsr.data.transforms import augment, paired_random_crop +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.flow_util import dequantize_flow +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +class REDSDataset(data.Dataset): + """REDS dataset for training. + + The keys are generated from a meta info txt file. + basicsr/data/meta_info/meta_info_REDS_GT.txt + + Each line contains: + 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by + a white space. + Examples: + 000 100 (720,1280,3) + 001 100 (720,1280,3) + ... + + Key examples: "000/00000000" + GT (gt): Ground-Truth; + LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. + + Args: + opt (dict): Config for train dataset. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + dataroot_flow (str, optional): Data root path for flow. + meta_info_file (str): Path for meta information file. + val_partition (str): Validation partition types. 'REDS4' or + 'official'. + io_backend (dict): IO backend type and other kwarg. + + num_frame (int): Window size for input frames. + gt_size (int): Cropped patched size for gt patches. + interval_list (list): Interval list for temporal augmentation. + random_reverse (bool): Random reverse input frames. + use_hflip (bool): Use horizontal flips. + use_rot (bool): Use rotation (use vertical flip and transposing h + and w for implementation). + + scale (bool): Scale, which will be added automatically. + """ + + def __init__(self, opt): + super(REDSDataset, self).__init__() + self.opt = opt + self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) + self.flow_root = Path(opt['dataroot_flow']) if opt['dataroot_flow'] is not None else None + assert opt['num_frame'] % 2 == 1, (f'num_frame should be odd number, but got {opt["num_frame"]}') + self.num_frame = opt['num_frame'] + self.num_half_frames = opt['num_frame'] // 2 + + self.keys = [] + with open(opt['meta_info_file'], 'r') as fin: + for line in fin: + folder, frame_num, _ = line.split(' ') + self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) + + # remove the video clips used in validation + if opt['val_partition'] == 'REDS4': + val_partition = ['000', '011', '015', '020'] + elif opt['val_partition'] == 'official': + val_partition = [f'{v:03d}' for v in range(240, 270)] + else: + raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' + f"Supported ones are ['official', 'REDS4'].") + self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] + + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + self.is_lmdb = False + if self.io_backend_opt['type'] == 'lmdb': + self.is_lmdb = True + if self.flow_root is not None: + self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] + self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] + else: + self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] + self.io_backend_opt['client_keys'] = ['lq', 'gt'] + + # temporal augmentation configs + self.interval_list = opt['interval_list'] + self.random_reverse = opt['random_reverse'] + interval_str = ','.join(str(x) for x in opt['interval_list']) + logger = get_root_logger() + logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' + f'random reverse is {self.random_reverse}.') + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + scale = self.opt['scale'] + gt_size = self.opt['gt_size'] + key = self.keys[index] + clip_name, frame_name = key.split('/') # key example: 000/00000000 + center_frame_idx = int(frame_name) + + # determine the neighboring frames + interval = random.choice(self.interval_list) + + # ensure not exceeding the borders + start_frame_idx = center_frame_idx - self.num_half_frames * interval + end_frame_idx = center_frame_idx + self.num_half_frames * interval + # each clip has 100 frames starting from 0 to 99 + while (start_frame_idx < 0) or (end_frame_idx > 99): + center_frame_idx = random.randint(0, 99) + start_frame_idx = (center_frame_idx - self.num_half_frames * interval) + end_frame_idx = center_frame_idx + self.num_half_frames * interval + frame_name = f'{center_frame_idx:08d}' + neighbor_list = list(range(start_frame_idx, end_frame_idx + 1, interval)) + # random reverse + if self.random_reverse and random.random() < 0.5: + neighbor_list.reverse() + + assert len(neighbor_list) == self.num_frame, (f'Wrong length of neighbor list: {len(neighbor_list)}') + + # get the GT frame (as the center frame) + if self.is_lmdb: + img_gt_path = f'{clip_name}/{frame_name}' + else: + img_gt_path = self.gt_root / clip_name / f'{frame_name}.png' + img_bytes = self.file_client.get(img_gt_path, 'gt') + img_gt = imfrombytes(img_bytes, float32=True) + + # get the neighboring LQ frames + img_lqs = [] + for neighbor in neighbor_list: + if self.is_lmdb: + img_lq_path = f'{clip_name}/{neighbor:08d}' + else: + img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' + img_bytes = self.file_client.get(img_lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + img_lqs.append(img_lq) + + # get flows + if self.flow_root is not None: + img_flows = [] + # read previous flows + for i in range(self.num_half_frames, 0, -1): + if self.is_lmdb: + flow_path = f'{clip_name}/{frame_name}_p{i}' + else: + flow_path = (self.flow_root / clip_name / f'{frame_name}_p{i}.png') + img_bytes = self.file_client.get(flow_path, 'flow') + cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] + dx, dy = np.split(cat_flow, 2, axis=0) + flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. + img_flows.append(flow) + # read next flows + for i in range(1, self.num_half_frames + 1): + if self.is_lmdb: + flow_path = f'{clip_name}/{frame_name}_n{i}' + else: + flow_path = (self.flow_root / clip_name / f'{frame_name}_n{i}.png') + img_bytes = self.file_client.get(flow_path, 'flow') + cat_flow = imfrombytes(img_bytes, flag='grayscale', float32=False) # uint8, [0, 255] + dx, dy = np.split(cat_flow, 2, axis=0) + flow = dequantize_flow(dx, dy, max_val=20, denorm=False) # we use max_val 20 here. + img_flows.append(flow) + + # for random crop, here, img_flows and img_lqs have the same + # spatial size + img_lqs.extend(img_flows) + + # randomly crop + img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) + if self.flow_root is not None: + img_lqs, img_flows = img_lqs[:self.num_frame], img_lqs[self.num_frame:] + + # augmentation - flip, rotate + img_lqs.append(img_gt) + if self.flow_root is not None: + img_results, img_flows = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot'], img_flows) + else: + img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) + + img_results = img2tensor(img_results) + img_lqs = torch.stack(img_results[0:-1], dim=0) + img_gt = img_results[-1] + + if self.flow_root is not None: + img_flows = img2tensor(img_flows) + # add the zero center flow + img_flows.insert(self.num_half_frames, torch.zeros_like(img_flows[0])) + img_flows = torch.stack(img_flows, dim=0) + + # img_lqs: (t, c, h, w) + # img_flows: (t, 2, h, w) + # img_gt: (c, h, w) + # key: str + if self.flow_root is not None: + return {'lq': img_lqs, 'flow': img_flows, 'gt': img_gt, 'key': key} + else: + return {'lq': img_lqs, 'gt': img_gt, 'key': key} + + def __len__(self): + return len(self.keys) + + +@DATASET_REGISTRY.register() +class REDSRecurrentDataset(data.Dataset): + """REDS dataset for training recurrent networks. + + The keys are generated from a meta info txt file. + basicsr/data/meta_info/meta_info_REDS_GT.txt + + Each line contains: + 1. subfolder (clip) name; 2. frame number; 3. image shape, separated by + a white space. + Examples: + 000 100 (720,1280,3) + 001 100 (720,1280,3) + ... + + Key examples: "000/00000000" + GT (gt): Ground-Truth; + LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. + + Args: + opt (dict): Config for train dataset. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + dataroot_flow (str, optional): Data root path for flow. + meta_info_file (str): Path for meta information file. + val_partition (str): Validation partition types. 'REDS4' or + 'official'. + io_backend (dict): IO backend type and other kwarg. + + num_frame (int): Window size for input frames. + gt_size (int): Cropped patched size for gt patches. + interval_list (list): Interval list for temporal augmentation. + random_reverse (bool): Random reverse input frames. + use_hflip (bool): Use horizontal flips. + use_rot (bool): Use rotation (use vertical flip and transposing h + and w for implementation). + + scale (bool): Scale, which will be added automatically. + """ + + def __init__(self, opt): + super(REDSRecurrentDataset, self).__init__() + self.opt = opt + self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) + self.num_frame = opt['num_frame'] + + self.keys = [] + with open(opt['meta_info_file'], 'r') as fin: + for line in fin: + folder, frame_num, _ = line.split(' ') + self.keys.extend([f'{folder}/{i:08d}' for i in range(int(frame_num))]) + + # remove the video clips used in validation + if opt['val_partition'] == 'REDS4': + val_partition = ['000', '011', '015', '020'] + elif opt['val_partition'] == 'official': + val_partition = [f'{v:03d}' for v in range(240, 270)] + else: + raise ValueError(f'Wrong validation partition {opt["val_partition"]}.' + f"Supported ones are ['official', 'REDS4'].") + if opt['test_mode']: + self.keys = [v for v in self.keys if v.split('/')[0] in val_partition] + else: + self.keys = [v for v in self.keys if v.split('/')[0] not in val_partition] + + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + self.is_lmdb = False + if self.io_backend_opt['type'] == 'lmdb': + self.is_lmdb = True + if hasattr(self, 'flow_root') and self.flow_root is not None: + self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root, self.flow_root] + self.io_backend_opt['client_keys'] = ['lq', 'gt', 'flow'] + else: + self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] + self.io_backend_opt['client_keys'] = ['lq', 'gt'] + + # temporal augmentation configs + self.interval_list = opt.get('interval_list', [1]) + self.random_reverse = opt.get('random_reverse', False) + interval_str = ','.join(str(x) for x in self.interval_list) + logger = get_root_logger() + logger.info(f'Temporal augmentation interval list: [{interval_str}]; ' + f'random reverse is {self.random_reverse}.') + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + scale = self.opt['scale'] + gt_size = self.opt['gt_size'] + key = self.keys[index] + clip_name, frame_name = key.split('/') # key example: 000/00000000 + + # determine the neighboring frames + interval = random.choice(self.interval_list) + + # ensure not exceeding the borders + start_frame_idx = int(frame_name) + if start_frame_idx > 100 - self.num_frame * interval: + start_frame_idx = random.randint(0, 100 - self.num_frame * interval) + end_frame_idx = start_frame_idx + self.num_frame * interval + + neighbor_list = list(range(start_frame_idx, end_frame_idx, interval)) + + # random reverse + if self.random_reverse and random.random() < 0.5: + neighbor_list.reverse() + + # get the neighboring LQ and GT frames + img_lqs = [] + img_gts = [] + for neighbor in neighbor_list: + if self.is_lmdb: + img_lq_path = f'{clip_name}/{neighbor:08d}' + img_gt_path = f'{clip_name}/{neighbor:08d}' + else: + img_lq_path = self.lq_root / clip_name / f'{neighbor:08d}.png' + img_gt_path = self.gt_root / clip_name / f'{neighbor:08d}.png' + + # get LQ + img_bytes = self.file_client.get(img_lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + img_lqs.append(img_lq) + + # get GT + img_bytes = self.file_client.get(img_gt_path, 'gt') + img_gt = imfrombytes(img_bytes, float32=True) + img_gts.append(img_gt) + + # randomly crop + img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) + + # augmentation - flip, rotate + img_lqs.extend(img_gts) + img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) + + img_results = img2tensor(img_results) + img_gts = torch.stack(img_results[len(img_lqs) // 2:], dim=0) + img_lqs = torch.stack(img_results[:len(img_lqs) // 2], dim=0) + + # img_lqs: (t, c, h, w) + # img_gts: (t, c, h, w) + # key: str + return {'lq': img_lqs, 'gt': img_gts, 'key': key} + + def __len__(self): + return len(self.keys) diff --git a/basicsr/data/single_image_dataset.py b/basicsr/data/single_image_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..acbc7d921777f455e1b0c434f88807c15d81d924 --- /dev/null +++ b/basicsr/data/single_image_dataset.py @@ -0,0 +1,68 @@ +from os import path as osp +from torch.utils import data as data +from torchvision.transforms.functional import normalize + +from basicsr.data.data_util import paths_from_lmdb +from basicsr.utils import FileClient, imfrombytes, img2tensor, rgb2ycbcr, scandir +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +class SingleImageDataset(data.Dataset): + """Read only lq images in the test phase. + + Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc). + + There are two modes: + 1. 'meta_info_file': Use meta information file to generate paths. + 2. 'folder': Scan folders to generate paths. + + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_lq (str): Data root path for lq. + meta_info_file (str): Path for meta information file. + io_backend (dict): IO backend type and other kwarg. + """ + + def __init__(self, opt): + super(SingleImageDataset, self).__init__() + self.opt = opt + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + self.mean = opt['mean'] if 'mean' in opt else None + self.std = opt['std'] if 'std' in opt else None + self.lq_folder = opt['dataroot_lq'] + + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = [self.lq_folder] + self.io_backend_opt['client_keys'] = ['lq'] + self.paths = paths_from_lmdb(self.lq_folder) + elif 'meta_info_file' in self.opt: + with open(self.opt['meta_info_file'], 'r') as fin: + self.paths = [osp.join(self.lq_folder, line.rstrip().split(' ')[0]) for line in fin] + else: + self.paths = sorted(list(scandir(self.lq_folder, full_path=True))) + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # load lq image + lq_path = self.paths[index] + img_bytes = self.file_client.get(lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + + # color space transform + if 'color' in self.opt and self.opt['color'] == 'y': + img_lq = rgb2ycbcr(img_lq, y_only=True)[..., None] + + # BGR to RGB, HWC to CHW, numpy to tensor + img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) + # normalize + if self.mean is not None or self.std is not None: + normalize(img_lq, self.mean, self.std, inplace=True) + return {'lq': img_lq, 'lq_path': lq_path} + + def __len__(self): + return len(self.paths) diff --git a/basicsr/data/transforms.py b/basicsr/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..d9bbb5fb7daef5edfb425fafb4d67d471b3001e6 --- /dev/null +++ b/basicsr/data/transforms.py @@ -0,0 +1,179 @@ +import cv2 +import random +import torch + + +def mod_crop(img, scale): + """Mod crop images, used during testing. + + Args: + img (ndarray): Input image. + scale (int): Scale factor. + + Returns: + ndarray: Result image. + """ + img = img.copy() + if img.ndim in (2, 3): + h, w = img.shape[0], img.shape[1] + h_remainder, w_remainder = h % scale, w % scale + img = img[:h - h_remainder, :w - w_remainder, ...] + else: + raise ValueError(f'Wrong img ndim: {img.ndim}.') + return img + + +def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None): + """Paired random crop. Support Numpy array and Tensor inputs. + + It crops lists of lq and gt images with corresponding locations. + + Args: + img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images + should have the same shape. If the input is an ndarray, it will + be transformed to a list containing itself. + img_lqs (list[ndarray] | ndarray): LQ images. Note that all images + should have the same shape. If the input is an ndarray, it will + be transformed to a list containing itself. + gt_patch_size (int): GT patch size. + scale (int): Scale factor. + gt_path (str): Path to ground-truth. Default: None. + + Returns: + list[ndarray] | ndarray: GT images and LQ images. If returned results + only have one element, just return ndarray. + """ + + if not isinstance(img_gts, list): + img_gts = [img_gts] + if not isinstance(img_lqs, list): + img_lqs = [img_lqs] + + # determine input type: Numpy array or Tensor + input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy' + + if input_type == 'Tensor': + h_lq, w_lq = img_lqs[0].size()[-2:] + h_gt, w_gt = img_gts[0].size()[-2:] + else: + h_lq, w_lq = img_lqs[0].shape[0:2] + h_gt, w_gt = img_gts[0].shape[0:2] + lq_patch_size = gt_patch_size // scale + + if h_gt != h_lq * scale or w_gt != w_lq * scale: + raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ', + f'multiplication of LQ ({h_lq}, {w_lq}).') + if h_lq < lq_patch_size or w_lq < lq_patch_size: + raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size ' + f'({lq_patch_size}, {lq_patch_size}). ' + f'Please remove {gt_path}.') + + # randomly choose top and left coordinates for lq patch + top = random.randint(0, h_lq - lq_patch_size) + left = random.randint(0, w_lq - lq_patch_size) + + # crop lq patch + if input_type == 'Tensor': + img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs] + else: + img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs] + + # crop corresponding gt patch + top_gt, left_gt = int(top * scale), int(left * scale) + if input_type == 'Tensor': + img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts] + else: + img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts] + if len(img_gts) == 1: + img_gts = img_gts[0] + if len(img_lqs) == 1: + img_lqs = img_lqs[0] + return img_gts, img_lqs + + +def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False): + """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees). + + We use vertical flip and transpose for rotation implementation. + All the images in the list use the same augmentation. + + Args: + imgs (list[ndarray] | ndarray): Images to be augmented. If the input + is an ndarray, it will be transformed to a list. + hflip (bool): Horizontal flip. Default: True. + rotation (bool): Ratotation. Default: True. + flows (list[ndarray]: Flows to be augmented. If the input is an + ndarray, it will be transformed to a list. + Dimension is (h, w, 2). Default: None. + return_status (bool): Return the status of flip and rotation. + Default: False. + + Returns: + list[ndarray] | ndarray: Augmented images and flows. If returned + results only have one element, just return ndarray. + + """ + hflip = hflip and random.random() < 0.5 + vflip = rotation and random.random() < 0.5 + rot90 = rotation and random.random() < 0.5 + + def _augment(img): + if hflip: # horizontal + cv2.flip(img, 1, img) + if vflip: # vertical + cv2.flip(img, 0, img) + if rot90: + img = img.transpose(1, 0, 2) + return img + + def _augment_flow(flow): + if hflip: # horizontal + cv2.flip(flow, 1, flow) + flow[:, :, 0] *= -1 + if vflip: # vertical + cv2.flip(flow, 0, flow) + flow[:, :, 1] *= -1 + if rot90: + flow = flow.transpose(1, 0, 2) + flow = flow[:, :, [1, 0]] + return flow + + if not isinstance(imgs, list): + imgs = [imgs] + imgs = [_augment(img) for img in imgs] + if len(imgs) == 1: + imgs = imgs[0] + + if flows is not None: + if not isinstance(flows, list): + flows = [flows] + flows = [_augment_flow(flow) for flow in flows] + if len(flows) == 1: + flows = flows[0] + return imgs, flows + else: + if return_status: + return imgs, (hflip, vflip, rot90) + else: + return imgs + + +def img_rotate(img, angle, center=None, scale=1.0): + """Rotate image. + + Args: + img (ndarray): Image to be rotated. + angle (float): Rotation angle in degrees. Positive values mean + counter-clockwise rotation. + center (tuple[int]): Rotation center. If the center is None, + initialize it as the center of the image. Default: None. + scale (float): Isotropic scale factor. Default: 1.0. + """ + (h, w) = img.shape[:2] + + if center is None: + center = (w // 2, h // 2) + + matrix = cv2.getRotationMatrix2D(center, angle, scale) + rotated_img = cv2.warpAffine(img, matrix, (w, h)) + return rotated_img diff --git a/basicsr/data/video_test_dataset.py b/basicsr/data/video_test_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..05de2615bcdea8a92b686ef0289bae8f72b0564d --- /dev/null +++ b/basicsr/data/video_test_dataset.py @@ -0,0 +1,287 @@ +import glob +import torch +from os import path as osp +from torch.utils import data as data + +from basicsr.data.data_util import duf_downsample, generate_frame_indices, read_img_seq +from basicsr.utils import get_root_logger, scandir +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +class VideoTestDataset(data.Dataset): + """Video test dataset. + + Supported datasets: Vid4, REDS4, REDSofficial. + More generally, it supports testing dataset with following structures: + + dataroot + ├── subfolder1 + ├── frame000 + ├── frame001 + ├── ... + ├── subfolder1 + ├── frame000 + ├── frame001 + ├── ... + ├── ... + + For testing datasets, there is no need to prepare LMDB files. + + Args: + opt (dict): Config for train dataset. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + io_backend (dict): IO backend type and other kwarg. + cache_data (bool): Whether to cache testing datasets. + name (str): Dataset name. + meta_info_file (str): The path to the file storing the list of test + folders. If not provided, all the folders in the dataroot will + be used. + num_frame (int): Window size for input frames. + padding (str): Padding mode. + """ + + def __init__(self, opt): + super(VideoTestDataset, self).__init__() + self.opt = opt + self.cache_data = opt['cache_data'] + self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] + self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' + + logger = get_root_logger() + logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') + self.imgs_lq, self.imgs_gt = {}, {} + if 'meta_info_file' in opt: + with open(opt['meta_info_file'], 'r') as fin: + subfolders = [line.split(' ')[0] for line in fin] + subfolders_lq = [osp.join(self.lq_root, key) for key in subfolders] + subfolders_gt = [osp.join(self.gt_root, key) for key in subfolders] + else: + subfolders_lq = sorted(glob.glob(osp.join(self.lq_root, '*'))) + subfolders_gt = sorted(glob.glob(osp.join(self.gt_root, '*'))) + + if opt['name'].lower() in ['vid4', 'reds4', 'redsofficial']: + for subfolder_lq, subfolder_gt in zip(subfolders_lq, subfolders_gt): + # get frame list for lq and gt + subfolder_name = osp.basename(subfolder_lq) + img_paths_lq = sorted(list(scandir(subfolder_lq, full_path=True))) + img_paths_gt = sorted(list(scandir(subfolder_gt, full_path=True))) + + max_idx = len(img_paths_lq) + assert max_idx == len(img_paths_gt), (f'Different number of images in lq ({max_idx})' + f' and gt folders ({len(img_paths_gt)})') + + self.data_info['lq_path'].extend(img_paths_lq) + self.data_info['gt_path'].extend(img_paths_gt) + self.data_info['folder'].extend([subfolder_name] * max_idx) + for i in range(max_idx): + self.data_info['idx'].append(f'{i}/{max_idx}') + border_l = [0] * max_idx + for i in range(self.opt['num_frame'] // 2): + border_l[i] = 1 + border_l[max_idx - i - 1] = 1 + self.data_info['border'].extend(border_l) + + # cache data or save the frame list + if self.cache_data: + logger.info(f'Cache {subfolder_name} for VideoTestDataset...') + self.imgs_lq[subfolder_name] = read_img_seq(img_paths_lq) + self.imgs_gt[subfolder_name] = read_img_seq(img_paths_gt) + else: + self.imgs_lq[subfolder_name] = img_paths_lq + self.imgs_gt[subfolder_name] = img_paths_gt + else: + raise ValueError(f'Non-supported video test dataset: {type(opt["name"])}') + + def __getitem__(self, index): + folder = self.data_info['folder'][index] + idx, max_idx = self.data_info['idx'][index].split('/') + idx, max_idx = int(idx), int(max_idx) + border = self.data_info['border'][index] + lq_path = self.data_info['lq_path'][index] + + select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) + + if self.cache_data: + imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) + img_gt = self.imgs_gt[folder][idx] + else: + img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] + imgs_lq = read_img_seq(img_paths_lq) + img_gt = read_img_seq([self.imgs_gt[folder][idx]]) + img_gt.squeeze_(0) + + return { + 'lq': imgs_lq, # (t, c, h, w) + 'gt': img_gt, # (c, h, w) + 'folder': folder, # folder name + 'idx': self.data_info['idx'][index], # e.g., 0/99 + 'border': border, # 1 for border, 0 for non-border + 'lq_path': lq_path # center frame + } + + def __len__(self): + return len(self.data_info['gt_path']) + + +@DATASET_REGISTRY.register() +class VideoTestVimeo90KDataset(data.Dataset): + """Video test dataset for Vimeo90k-Test dataset. + + It only keeps the center frame for testing. + For testing datasets, there is no need to prepare LMDB files. + + Args: + opt (dict): Config for train dataset. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + io_backend (dict): IO backend type and other kwarg. + cache_data (bool): Whether to cache testing datasets. + name (str): Dataset name. + meta_info_file (str): The path to the file storing the list of test + folders. If not provided, all the folders in the dataroot will + be used. + num_frame (int): Window size for input frames. + padding (str): Padding mode. + """ + + def __init__(self, opt): + super(VideoTestVimeo90KDataset, self).__init__() + self.opt = opt + self.cache_data = opt['cache_data'] + if self.cache_data: + raise NotImplementedError('cache_data in Vimeo90K-Test dataset is not implemented.') + self.gt_root, self.lq_root = opt['dataroot_gt'], opt['dataroot_lq'] + self.data_info = {'lq_path': [], 'gt_path': [], 'folder': [], 'idx': [], 'border': []} + neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] + + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + assert self.io_backend_opt['type'] != 'lmdb', 'No need to use lmdb during validation/test.' + + logger = get_root_logger() + logger.info(f'Generate data info for VideoTestDataset - {opt["name"]}') + with open(opt['meta_info_file'], 'r') as fin: + subfolders = [line.split(' ')[0] for line in fin] + for idx, subfolder in enumerate(subfolders): + gt_path = osp.join(self.gt_root, subfolder, 'im4.png') + self.data_info['gt_path'].append(gt_path) + lq_paths = [osp.join(self.lq_root, subfolder, f'im{i}.png') for i in neighbor_list] + self.data_info['lq_path'].append(lq_paths) + self.data_info['folder'].append('vimeo90k') + self.data_info['idx'].append(f'{idx}/{len(subfolders)}') + self.data_info['border'].append(0) + + def __getitem__(self, index): + lq_path = self.data_info['lq_path'][index] + gt_path = self.data_info['gt_path'][index] + imgs_lq = read_img_seq(lq_path) + img_gt = read_img_seq([gt_path]) + img_gt.squeeze_(0) + + return { + 'lq': imgs_lq, # (t, c, h, w) + 'gt': img_gt, # (c, h, w) + 'folder': self.data_info['folder'][index], # folder name + 'idx': self.data_info['idx'][index], # e.g., 0/843 + 'border': self.data_info['border'][index], # 0 for non-border + 'lq_path': lq_path[self.opt['num_frame'] // 2] # center frame + } + + def __len__(self): + return len(self.data_info['gt_path']) + + +@DATASET_REGISTRY.register() +class VideoTestDUFDataset(VideoTestDataset): + """ Video test dataset for DUF dataset. + + Args: + opt (dict): Config for train dataset. + Most of keys are the same as VideoTestDataset. + It has the following extra keys: + + use_duf_downsampling (bool): Whether to use duf downsampling to + generate low-resolution frames. + scale (bool): Scale, which will be added automatically. + """ + + def __getitem__(self, index): + folder = self.data_info['folder'][index] + idx, max_idx = self.data_info['idx'][index].split('/') + idx, max_idx = int(idx), int(max_idx) + border = self.data_info['border'][index] + lq_path = self.data_info['lq_path'][index] + + select_idx = generate_frame_indices(idx, max_idx, self.opt['num_frame'], padding=self.opt['padding']) + + if self.cache_data: + if self.opt['use_duf_downsampling']: + # read imgs_gt to generate low-resolution frames + imgs_lq = self.imgs_gt[folder].index_select(0, torch.LongTensor(select_idx)) + imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) + else: + imgs_lq = self.imgs_lq[folder].index_select(0, torch.LongTensor(select_idx)) + img_gt = self.imgs_gt[folder][idx] + else: + if self.opt['use_duf_downsampling']: + img_paths_lq = [self.imgs_gt[folder][i] for i in select_idx] + # read imgs_gt to generate low-resolution frames + imgs_lq = read_img_seq(img_paths_lq, require_mod_crop=True, scale=self.opt['scale']) + imgs_lq = duf_downsample(imgs_lq, kernel_size=13, scale=self.opt['scale']) + else: + img_paths_lq = [self.imgs_lq[folder][i] for i in select_idx] + imgs_lq = read_img_seq(img_paths_lq) + img_gt = read_img_seq([self.imgs_gt[folder][idx]], require_mod_crop=True, scale=self.opt['scale']) + img_gt.squeeze_(0) + + return { + 'lq': imgs_lq, # (t, c, h, w) + 'gt': img_gt, # (c, h, w) + 'folder': folder, # folder name + 'idx': self.data_info['idx'][index], # e.g., 0/99 + 'border': border, # 1 for border, 0 for non-border + 'lq_path': lq_path # center frame + } + + +@DATASET_REGISTRY.register() +class VideoRecurrentTestDataset(VideoTestDataset): + """Video test dataset for recurrent architectures, which takes LR video + frames as input and output corresponding HR video frames. + + Args: + Same as VideoTestDataset. + Unused opt: + padding (str): Padding mode. + + """ + + def __init__(self, opt): + super(VideoRecurrentTestDataset, self).__init__(opt) + # Find unique folder strings + self.folders = sorted(list(set(self.data_info['folder']))) + + def __getitem__(self, index): + folder = self.folders[index] + + if self.cache_data: + imgs_lq = self.imgs_lq[folder] + imgs_gt = self.imgs_gt[folder] + else: + raise NotImplementedError('Without cache_data is not implemented.') + + return { + 'lq': imgs_lq, + 'gt': imgs_gt, + 'folder': folder, + } + + def __len__(self): + return len(self.folders) diff --git a/basicsr/data/vimeo90k_dataset.py b/basicsr/data/vimeo90k_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..4f1b84110ec5a010f484bf7af66b1b321ee2f021 --- /dev/null +++ b/basicsr/data/vimeo90k_dataset.py @@ -0,0 +1,192 @@ +import random +import torch +from pathlib import Path +from torch.utils import data as data + +from basicsr.data.transforms import augment, paired_random_crop +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY + + +@DATASET_REGISTRY.register() +class Vimeo90KDataset(data.Dataset): + """Vimeo90K dataset for training. + + The keys are generated from a meta info txt file. + basicsr/data/meta_info/meta_info_Vimeo90K_train_GT.txt + + Each line contains: + 1. clip name; 2. frame number; 3. image shape, separated by a white space. + Examples: + 00001/0001 7 (256,448,3) + 00001/0002 7 (256,448,3) + + Key examples: "00001/0001" + GT (gt): Ground-Truth; + LQ (lq): Low-Quality, e.g., low-resolution/blurry/noisy/compressed frames. + + The neighboring frame list for different num_frame: + num_frame | frame list + 1 | 4 + 3 | 3,4,5 + 5 | 2,3,4,5,6 + 7 | 1,2,3,4,5,6,7 + + Args: + opt (dict): Config for train dataset. It contains the following keys: + dataroot_gt (str): Data root path for gt. + dataroot_lq (str): Data root path for lq. + meta_info_file (str): Path for meta information file. + io_backend (dict): IO backend type and other kwarg. + + num_frame (int): Window size for input frames. + gt_size (int): Cropped patched size for gt patches. + random_reverse (bool): Random reverse input frames. + use_hflip (bool): Use horizontal flips. + use_rot (bool): Use rotation (use vertical flip and transposing h + and w for implementation). + + scale (bool): Scale, which will be added automatically. + """ + + def __init__(self, opt): + super(Vimeo90KDataset, self).__init__() + self.opt = opt + self.gt_root, self.lq_root = Path(opt['dataroot_gt']), Path(opt['dataroot_lq']) + + with open(opt['meta_info_file'], 'r') as fin: + self.keys = [line.split(' ')[0] for line in fin] + + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + self.is_lmdb = False + if self.io_backend_opt['type'] == 'lmdb': + self.is_lmdb = True + self.io_backend_opt['db_paths'] = [self.lq_root, self.gt_root] + self.io_backend_opt['client_keys'] = ['lq', 'gt'] + + # indices of input images + self.neighbor_list = [i + (9 - opt['num_frame']) // 2 for i in range(opt['num_frame'])] + + # temporal augmentation configs + self.random_reverse = opt['random_reverse'] + logger = get_root_logger() + logger.info(f'Random reverse is {self.random_reverse}.') + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # random reverse + if self.random_reverse and random.random() < 0.5: + self.neighbor_list.reverse() + + scale = self.opt['scale'] + gt_size = self.opt['gt_size'] + key = self.keys[index] + clip, seq = key.split('/') # key example: 00001/0001 + + # get the GT frame (im4.png) + if self.is_lmdb: + img_gt_path = f'{key}/im4' + else: + img_gt_path = self.gt_root / clip / seq / 'im4.png' + img_bytes = self.file_client.get(img_gt_path, 'gt') + img_gt = imfrombytes(img_bytes, float32=True) + + # get the neighboring LQ frames + img_lqs = [] + for neighbor in self.neighbor_list: + if self.is_lmdb: + img_lq_path = f'{clip}/{seq}/im{neighbor}' + else: + img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' + img_bytes = self.file_client.get(img_lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + img_lqs.append(img_lq) + + # randomly crop + img_gt, img_lqs = paired_random_crop(img_gt, img_lqs, gt_size, scale, img_gt_path) + + # augmentation - flip, rotate + img_lqs.append(img_gt) + img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) + + img_results = img2tensor(img_results) + img_lqs = torch.stack(img_results[0:-1], dim=0) + img_gt = img_results[-1] + + # img_lqs: (t, c, h, w) + # img_gt: (c, h, w) + # key: str + return {'lq': img_lqs, 'gt': img_gt, 'key': key} + + def __len__(self): + return len(self.keys) + + +@DATASET_REGISTRY.register() +class Vimeo90KRecurrentDataset(Vimeo90KDataset): + + def __init__(self, opt): + super(Vimeo90KRecurrentDataset, self).__init__(opt) + + self.flip_sequence = opt['flip_sequence'] + self.neighbor_list = [1, 2, 3, 4, 5, 6, 7] + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # random reverse + if self.random_reverse and random.random() < 0.5: + self.neighbor_list.reverse() + + scale = self.opt['scale'] + gt_size = self.opt['gt_size'] + key = self.keys[index] + clip, seq = key.split('/') # key example: 00001/0001 + + # get the neighboring LQ and GT frames + img_lqs = [] + img_gts = [] + for neighbor in self.neighbor_list: + if self.is_lmdb: + img_lq_path = f'{clip}/{seq}/im{neighbor}' + img_gt_path = f'{clip}/{seq}/im{neighbor}' + else: + img_lq_path = self.lq_root / clip / seq / f'im{neighbor}.png' + img_gt_path = self.gt_root / clip / seq / f'im{neighbor}.png' + # LQ + img_bytes = self.file_client.get(img_lq_path, 'lq') + img_lq = imfrombytes(img_bytes, float32=True) + # GT + img_bytes = self.file_client.get(img_gt_path, 'gt') + img_gt = imfrombytes(img_bytes, float32=True) + + img_lqs.append(img_lq) + img_gts.append(img_gt) + + # randomly crop + img_gts, img_lqs = paired_random_crop(img_gts, img_lqs, gt_size, scale, img_gt_path) + + # augmentation - flip, rotate + img_lqs.extend(img_gts) + img_results = augment(img_lqs, self.opt['use_hflip'], self.opt['use_rot']) + + img_results = img2tensor(img_results) + img_lqs = torch.stack(img_results[:7], dim=0) + img_gts = torch.stack(img_results[7:], dim=0) + + if self.flip_sequence: # flip the sequence: 7 frames to 14 frames + img_lqs = torch.cat([img_lqs, img_lqs.flip(0)], dim=0) + img_gts = torch.cat([img_gts, img_gts.flip(0)], dim=0) + + # img_lqs: (t, c, h, w) + # img_gt: (c, h, w) + # key: str + return {'lq': img_lqs, 'gt': img_gts, 'key': key} + + def __len__(self): + return len(self.keys) diff --git a/basicsr/losses/__init__.py b/basicsr/losses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..70a172aeed5b388ae102466eb1f02d40ba30e9b4 --- /dev/null +++ b/basicsr/losses/__init__.py @@ -0,0 +1,31 @@ +import importlib +from copy import deepcopy +from os import path as osp + +from basicsr.utils import get_root_logger, scandir +from basicsr.utils.registry import LOSS_REGISTRY +from .gan_loss import g_path_regularize, gradient_penalty_loss, r1_penalty + +__all__ = ['build_loss', 'gradient_penalty_loss', 'r1_penalty', 'g_path_regularize'] + +# automatically scan and import loss modules for registry +# scan all the files under the 'losses' folder and collect files ending with '_loss.py' +loss_folder = osp.dirname(osp.abspath(__file__)) +loss_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(loss_folder) if v.endswith('_loss.py')] +# import all the loss modules +_model_modules = [importlib.import_module(f'basicsr.losses.{file_name}') for file_name in loss_filenames] + + +def build_loss(opt): + """Build loss from options. + + Args: + opt (dict): Configuration. It must contain: + type (str): Model type. + """ + opt = deepcopy(opt) + loss_type = opt.pop('type') + loss = LOSS_REGISTRY.get(loss_type)(**opt) + logger = get_root_logger() + logger.info(f'Loss [{loss.__class__.__name__}] is created.') + return loss diff --git a/basicsr/losses/__pycache__/__init__.cpython-310.pyc b/basicsr/losses/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ed425207cbd54395dd4b8d2ac5c9b17bcb4f022d Binary files /dev/null and b/basicsr/losses/__pycache__/__init__.cpython-310.pyc differ diff --git a/basicsr/losses/__pycache__/__init__.cpython-38.pyc b/basicsr/losses/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..19c0047752269cacbb9640b6ac447c62778cfb55 Binary files /dev/null and b/basicsr/losses/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/losses/__pycache__/basic_loss.cpython-310.pyc b/basicsr/losses/__pycache__/basic_loss.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fdf7beb6c8375eb4433292018481a41af57b7f7d Binary files /dev/null and b/basicsr/losses/__pycache__/basic_loss.cpython-310.pyc differ diff --git a/basicsr/losses/__pycache__/basic_loss.cpython-38.pyc b/basicsr/losses/__pycache__/basic_loss.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2a256adf9747163c2ec58759cfb0fb4dd9d47ee3 Binary files /dev/null and b/basicsr/losses/__pycache__/basic_loss.cpython-38.pyc differ diff --git a/basicsr/losses/__pycache__/gan_loss.cpython-310.pyc b/basicsr/losses/__pycache__/gan_loss.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b795c84a4285c836f0517588b1ad8e8a2b7f3ff3 Binary files /dev/null and b/basicsr/losses/__pycache__/gan_loss.cpython-310.pyc differ diff --git a/basicsr/losses/__pycache__/gan_loss.cpython-38.pyc b/basicsr/losses/__pycache__/gan_loss.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c287c68eab4c60e9832d734059c859dd549c0050 Binary files /dev/null and b/basicsr/losses/__pycache__/gan_loss.cpython-38.pyc differ diff --git a/basicsr/losses/__pycache__/loss_util.cpython-310.pyc b/basicsr/losses/__pycache__/loss_util.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b47e41d38bcbe94b20fa633c62db6d2352600127 Binary files /dev/null and b/basicsr/losses/__pycache__/loss_util.cpython-310.pyc differ diff --git a/basicsr/losses/__pycache__/loss_util.cpython-38.pyc b/basicsr/losses/__pycache__/loss_util.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7ec8a7465aa8510f13ace6da18c9bf27153acb45 Binary files /dev/null and b/basicsr/losses/__pycache__/loss_util.cpython-38.pyc differ diff --git a/basicsr/losses/__pycache__/losses.cpython-310.pyc b/basicsr/losses/__pycache__/losses.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..be8dd800d94c86c0245a72704da4ddbe0bc64a80 Binary files /dev/null and b/basicsr/losses/__pycache__/losses.cpython-310.pyc differ diff --git a/basicsr/losses/basic_loss.py b/basicsr/losses/basic_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..d2e965526a9b0e2686575bf93f0173cc2664d9bb --- /dev/null +++ b/basicsr/losses/basic_loss.py @@ -0,0 +1,253 @@ +import torch +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.archs.vgg_arch import VGGFeatureExtractor +from basicsr.utils.registry import LOSS_REGISTRY +from .loss_util import weighted_loss + +_reduction_modes = ['none', 'mean', 'sum'] + + +@weighted_loss +def l1_loss(pred, target): + return F.l1_loss(pred, target, reduction='none') + + +@weighted_loss +def mse_loss(pred, target): + return F.mse_loss(pred, target, reduction='none') + + +@weighted_loss +def charbonnier_loss(pred, target, eps=1e-12): + return torch.sqrt((pred - target)**2 + eps) + + +@LOSS_REGISTRY.register() +class L1Loss(nn.Module): + """L1 (mean absolute error, MAE) loss. + + Args: + loss_weight (float): Loss weight for L1 loss. Default: 1.0. + reduction (str): Specifies the reduction to apply to the output. + Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. + """ + + def __init__(self, loss_weight=1.0, reduction='mean'): + super(L1Loss, self).__init__() + if reduction not in ['none', 'mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') + + self.loss_weight = loss_weight + self.reduction = reduction + + def forward(self, pred, target, weight=None, **kwargs): + """ + Args: + pred (Tensor): of shape (N, C, H, W). Predicted tensor. + target (Tensor): of shape (N, C, H, W). Ground truth tensor. + weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. + """ + return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction) + + +@LOSS_REGISTRY.register() +class MSELoss(nn.Module): + """MSE (L2) loss. + + Args: + loss_weight (float): Loss weight for MSE loss. Default: 1.0. + reduction (str): Specifies the reduction to apply to the output. + Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. + """ + + def __init__(self, loss_weight=1.0, reduction='mean'): + super(MSELoss, self).__init__() + if reduction not in ['none', 'mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') + + self.loss_weight = loss_weight + self.reduction = reduction + + def forward(self, pred, target, weight=None, **kwargs): + """ + Args: + pred (Tensor): of shape (N, C, H, W). Predicted tensor. + target (Tensor): of shape (N, C, H, W). Ground truth tensor. + weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. + """ + return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction) + + +@LOSS_REGISTRY.register() +class CharbonnierLoss(nn.Module): + """Charbonnier loss (one variant of Robust L1Loss, a differentiable + variant of L1Loss). + + Described in "Deep Laplacian Pyramid Networks for Fast and Accurate + Super-Resolution". + + Args: + loss_weight (float): Loss weight for L1 loss. Default: 1.0. + reduction (str): Specifies the reduction to apply to the output. + Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. + eps (float): A value used to control the curvature near zero. Default: 1e-12. + """ + + def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12): + super(CharbonnierLoss, self).__init__() + if reduction not in ['none', 'mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: {_reduction_modes}') + + self.loss_weight = loss_weight + self.reduction = reduction + self.eps = eps + + def forward(self, pred, target, weight=None, **kwargs): + """ + Args: + pred (Tensor): of shape (N, C, H, W). Predicted tensor. + target (Tensor): of shape (N, C, H, W). Ground truth tensor. + weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. + """ + return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction) + + +@LOSS_REGISTRY.register() +class WeightedTVLoss(L1Loss): + """Weighted TV loss. + + Args: + loss_weight (float): Loss weight. Default: 1.0. + """ + + def __init__(self, loss_weight=1.0, reduction='mean'): + if reduction not in ['mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. Supported ones are: mean | sum') + super(WeightedTVLoss, self).__init__(loss_weight=loss_weight, reduction=reduction) + + def forward(self, pred, weight=None): + if weight is None: + y_weight = None + x_weight = None + else: + y_weight = weight[:, :, :-1, :] + x_weight = weight[:, :, :, :-1] + + y_diff = super().forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=y_weight) + x_diff = super().forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=x_weight) + + loss = x_diff + y_diff + + return loss + + +@LOSS_REGISTRY.register() +class PerceptualLoss(nn.Module): + """Perceptual loss with commonly used style loss. + + Args: + layer_weights (dict): The weight for each layer of vgg feature. + Here is an example: {'conv5_4': 1.}, which means the conv5_4 + feature layer (before relu5_4) will be extracted with weight + 1.0 in calculating losses. + vgg_type (str): The type of vgg network used as feature extractor. + Default: 'vgg19'. + use_input_norm (bool): If True, normalize the input image in vgg. + Default: True. + range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. + Default: False. + perceptual_weight (float): If `perceptual_weight > 0`, the perceptual + loss will be calculated and the loss will multiplied by the + weight. Default: 1.0. + style_weight (float): If `style_weight > 0`, the style loss will be + calculated and the loss will multiplied by the weight. + Default: 0. + criterion (str): Criterion used for perceptual loss. Default: 'l1'. + """ + + def __init__(self, + layer_weights, + vgg_type='vgg19', + use_input_norm=True, + range_norm=False, + perceptual_weight=1.0, + style_weight=0., + criterion='l1'): + super(PerceptualLoss, self).__init__() + self.perceptual_weight = perceptual_weight + self.style_weight = style_weight + self.layer_weights = layer_weights + self.vgg = VGGFeatureExtractor( + layer_name_list=list(layer_weights.keys()), + vgg_type=vgg_type, + use_input_norm=use_input_norm, + range_norm=range_norm) + + self.criterion_type = criterion + if self.criterion_type == 'l1': + self.criterion = torch.nn.L1Loss() + elif self.criterion_type == 'l2': + self.criterion = torch.nn.L2loss() + elif self.criterion_type == 'fro': + self.criterion = None + else: + raise NotImplementedError(f'{criterion} criterion has not been supported.') + + def forward(self, x, gt): + """Forward function. + + Args: + x (Tensor): Input tensor with shape (n, c, h, w). + gt (Tensor): Ground-truth tensor with shape (n, c, h, w). + + Returns: + Tensor: Forward results. + """ + # extract vgg features + x_features = self.vgg(x) + gt_features = self.vgg(gt.detach()) + + # calculate perceptual loss + if self.perceptual_weight > 0: + percep_loss = 0 + for k in x_features.keys(): + if self.criterion_type == 'fro': + percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] + else: + percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] + percep_loss *= self.perceptual_weight + else: + percep_loss = None + + # calculate style loss + if self.style_weight > 0: + style_loss = 0 + for k in x_features.keys(): + if self.criterion_type == 'fro': + style_loss += torch.norm( + self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] + else: + style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( + gt_features[k])) * self.layer_weights[k] + style_loss *= self.style_weight + else: + style_loss = None + + return percep_loss, style_loss + + def _gram_mat(self, x): + """Calculate Gram matrix. + + Args: + x (torch.Tensor): Tensor with shape of (n, c, h, w). + + Returns: + torch.Tensor: Gram matrix. + """ + n, c, h, w = x.size() + features = x.view(n, c, w * h) + features_t = features.transpose(1, 2) + gram = features.bmm(features_t) / (c * h * w) + return gram diff --git a/basicsr/losses/gan_loss.py b/basicsr/losses/gan_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..447eb6bb2803372b6a1417ce0559c00c2f70ef4e --- /dev/null +++ b/basicsr/losses/gan_loss.py @@ -0,0 +1,208 @@ +import math +import torch +from torch import autograd as autograd +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.utils.registry import LOSS_REGISTRY + + +@LOSS_REGISTRY.register() +class GANLoss(nn.Module): + """Define GAN loss. + + Args: + gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'. + real_label_val (float): The value for real label. Default: 1.0. + fake_label_val (float): The value for fake label. Default: 0.0. + loss_weight (float): Loss weight. Default: 1.0. + Note that loss_weight is only for generators; and it is always 1.0 + for discriminators. + """ + + def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): + super(GANLoss, self).__init__() + self.gan_type = gan_type + self.loss_weight = loss_weight + self.real_label_val = real_label_val + self.fake_label_val = fake_label_val + + if self.gan_type == 'vanilla': + self.loss = nn.BCEWithLogitsLoss() + elif self.gan_type == 'lsgan': + self.loss = nn.MSELoss() + elif self.gan_type == 'wgan': + self.loss = self._wgan_loss + elif self.gan_type == 'wgan_softplus': + self.loss = self._wgan_softplus_loss + elif self.gan_type == 'hinge': + self.loss = nn.ReLU() + else: + raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.') + + def _wgan_loss(self, input, target): + """wgan loss. + + Args: + input (Tensor): Input tensor. + target (bool): Target label. + + Returns: + Tensor: wgan loss. + """ + return -input.mean() if target else input.mean() + + def _wgan_softplus_loss(self, input, target): + """wgan loss with soft plus. softplus is a smooth approximation to the + ReLU function. + + In StyleGAN2, it is called: + Logistic loss for discriminator; + Non-saturating loss for generator. + + Args: + input (Tensor): Input tensor. + target (bool): Target label. + + Returns: + Tensor: wgan loss. + """ + return F.softplus(-input).mean() if target else F.softplus(input).mean() + + def get_target_label(self, input, target_is_real): + """Get target label. + + Args: + input (Tensor): Input tensor. + target_is_real (bool): Whether the target is real or fake. + + Returns: + (bool | Tensor): Target tensor. Return bool for wgan, otherwise, + return Tensor. + """ + + if self.gan_type in ['wgan', 'wgan_softplus']: + return target_is_real + target_val = (self.real_label_val if target_is_real else self.fake_label_val) + return input.new_ones(input.size()) * target_val + + def forward(self, input, target_is_real, is_disc=False): + """ + Args: + input (Tensor): The input for the loss module, i.e., the network + prediction. + target_is_real (bool): Whether the targe is real or fake. + is_disc (bool): Whether the loss for discriminators or not. + Default: False. + + Returns: + Tensor: GAN loss value. + """ + target_label = self.get_target_label(input, target_is_real) + if self.gan_type == 'hinge': + if is_disc: # for discriminators in hinge-gan + input = -input if target_is_real else input + loss = self.loss(1 + input).mean() + else: # for generators in hinge-gan + loss = -input.mean() + else: # other gan types + loss = self.loss(input, target_label) + + # loss_weight is always 1.0 for discriminators + return loss if is_disc else loss * self.loss_weight + + +@LOSS_REGISTRY.register() +class MultiScaleGANLoss(GANLoss): + """ + MultiScaleGANLoss accepts a list of predictions + """ + + def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): + super(MultiScaleGANLoss, self).__init__(gan_type, real_label_val, fake_label_val, loss_weight) + + def forward(self, input, target_is_real, is_disc=False): + """ + The input is a list of tensors, or a list of (a list of tensors) + """ + if isinstance(input, list): + loss = 0 + for pred_i in input: + if isinstance(pred_i, list): + # Only compute GAN loss for the last layer + # in case of multiscale feature matching + pred_i = pred_i[-1] + # Safe operation: 0-dim tensor calling self.mean() does nothing + loss_tensor = super().forward(pred_i, target_is_real, is_disc).mean() + loss += loss_tensor + return loss / len(input) + else: + return super().forward(input, target_is_real, is_disc) + + +def r1_penalty(real_pred, real_img): + """R1 regularization for discriminator. The core idea is to + penalize the gradient on real data alone: when the + generator distribution produces the true data distribution + and the discriminator is equal to 0 on the data manifold, the + gradient penalty ensures that the discriminator cannot create + a non-zero gradient orthogonal to the data manifold without + suffering a loss in the GAN game. + + Ref: + Eq. 9 in Which training methods for GANs do actually converge. + """ + grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0] + grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean() + return grad_penalty + + +def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): + noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3]) + grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0] + path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) + + path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) + + path_penalty = (path_lengths - path_mean).pow(2).mean() + + return path_penalty, path_lengths.detach().mean(), path_mean.detach() + + +def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None): + """Calculate gradient penalty for wgan-gp. + + Args: + discriminator (nn.Module): Network for the discriminator. + real_data (Tensor): Real input data. + fake_data (Tensor): Fake input data. + weight (Tensor): Weight tensor. Default: None. + + Returns: + Tensor: A tensor for gradient penalty. + """ + + batch_size = real_data.size(0) + alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1)) + + # interpolate between real_data and fake_data + interpolates = alpha * real_data + (1. - alpha) * fake_data + interpolates = autograd.Variable(interpolates, requires_grad=True) + + disc_interpolates = discriminator(interpolates) + gradients = autograd.grad( + outputs=disc_interpolates, + inputs=interpolates, + grad_outputs=torch.ones_like(disc_interpolates), + create_graph=True, + retain_graph=True, + only_inputs=True)[0] + + if weight is not None: + gradients = gradients * weight + + gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean() + if weight is not None: + gradients_penalty /= torch.mean(weight) + + return gradients_penalty diff --git a/basicsr/losses/loss_util.py b/basicsr/losses/loss_util.py new file mode 100644 index 0000000000000000000000000000000000000000..fd293ff9e6a22814e5aeff6ae11fb54d2e4bafff --- /dev/null +++ b/basicsr/losses/loss_util.py @@ -0,0 +1,145 @@ +import functools +import torch +from torch.nn import functional as F + + +def reduce_loss(loss, reduction): + """Reduce loss as specified. + + Args: + loss (Tensor): Elementwise loss tensor. + reduction (str): Options are 'none', 'mean' and 'sum'. + + Returns: + Tensor: Reduced loss tensor. + """ + reduction_enum = F._Reduction.get_enum(reduction) + # none: 0, elementwise_mean:1, sum: 2 + if reduction_enum == 0: + return loss + elif reduction_enum == 1: + return loss.mean() + else: + return loss.sum() + + +def weight_reduce_loss(loss, weight=None, reduction='mean'): + """Apply element-wise weight and reduce loss. + + Args: + loss (Tensor): Element-wise loss. + weight (Tensor): Element-wise weights. Default: None. + reduction (str): Same as built-in losses of PyTorch. Options are + 'none', 'mean' and 'sum'. Default: 'mean'. + + Returns: + Tensor: Loss values. + """ + # if weight is specified, apply element-wise weight + if weight is not None: + assert weight.dim() == loss.dim() + assert weight.size(1) == 1 or weight.size(1) == loss.size(1) + loss = loss * weight + + # if weight is not specified or reduction is sum, just reduce the loss + if weight is None or reduction == 'sum': + loss = reduce_loss(loss, reduction) + # if reduction is mean, then compute mean over weight region + elif reduction == 'mean': + if weight.size(1) > 1: + weight = weight.sum() + else: + weight = weight.sum() * loss.size(1) + loss = loss.sum() / weight + + return loss + + +def weighted_loss(loss_func): + """Create a weighted version of a given loss function. + + To use this decorator, the loss function must have the signature like + `loss_func(pred, target, **kwargs)`. The function only needs to compute + element-wise loss without any reduction. This decorator will add weight + and reduction arguments to the function. The decorated function will have + the signature like `loss_func(pred, target, weight=None, reduction='mean', + **kwargs)`. + + :Example: + + >>> import torch + >>> @weighted_loss + >>> def l1_loss(pred, target): + >>> return (pred - target).abs() + + >>> pred = torch.Tensor([0, 2, 3]) + >>> target = torch.Tensor([1, 1, 1]) + >>> weight = torch.Tensor([1, 0, 1]) + + >>> l1_loss(pred, target) + tensor(1.3333) + >>> l1_loss(pred, target, weight) + tensor(1.5000) + >>> l1_loss(pred, target, reduction='none') + tensor([1., 1., 2.]) + >>> l1_loss(pred, target, weight, reduction='sum') + tensor(3.) + """ + + @functools.wraps(loss_func) + def wrapper(pred, target, weight=None, reduction='mean', **kwargs): + # get element-wise loss + loss = loss_func(pred, target, **kwargs) + loss = weight_reduce_loss(loss, weight, reduction) + return loss + + return wrapper + + +def get_local_weights(residual, ksize): + """Get local weights for generating the artifact map of LDL. + + It is only called by the `get_refined_artifact_map` function. + + Args: + residual (Tensor): Residual between predicted and ground truth images. + ksize (Int): size of the local window. + + Returns: + Tensor: weight for each pixel to be discriminated as an artifact pixel + """ + + pad = (ksize - 1) // 2 + residual_pad = F.pad(residual, pad=[pad, pad, pad, pad], mode='reflect') + + unfolded_residual = residual_pad.unfold(2, ksize, 1).unfold(3, ksize, 1) + pixel_level_weight = torch.var(unfolded_residual, dim=(-1, -2), unbiased=True, keepdim=True).squeeze(-1).squeeze(-1) + + return pixel_level_weight + + +def get_refined_artifact_map(img_gt, img_output, img_ema, ksize): + """Calculate the artifact map of LDL + (Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution. In CVPR 2022) + + Args: + img_gt (Tensor): ground truth images. + img_output (Tensor): output images given by the optimizing model. + img_ema (Tensor): output images given by the ema model. + ksize (Int): size of the local window. + + Returns: + overall_weight: weight for each pixel to be discriminated as an artifact pixel + (calculated based on both local and global observations). + """ + + residual_ema = torch.sum(torch.abs(img_gt - img_ema), 1, keepdim=True) + residual_sr = torch.sum(torch.abs(img_gt - img_output), 1, keepdim=True) + + patch_level_weight = torch.var(residual_sr.clone(), dim=(-1, -2, -3), keepdim=True)**(1 / 5) + pixel_level_weight = get_local_weights(residual_sr.clone(), ksize) + overall_weight = patch_level_weight * pixel_level_weight + + overall_weight[residual_sr < residual_ema] = 0 + + return overall_weight diff --git a/basicsr/losses/losses.py b/basicsr/losses/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..dbd513dde6c65389672378c07e1e470e6464603d --- /dev/null +++ b/basicsr/losses/losses.py @@ -0,0 +1,423 @@ +import math +import torch +from torch import autograd as autograd +from torch import nn as nn +from torch.nn import functional as F + +from basicsr.archs.vgg_arch import VGGFeatureExtractor +from basicsr.utils.registry import LOSS_REGISTRY +from .loss_util import weighted_loss + +_reduction_modes = ['none', 'mean', 'sum'] + + +@weighted_loss +def l1_loss(pred, target): + return F.l1_loss(pred, target, reduction='none') + + +@weighted_loss +def mse_loss(pred, target): + return F.mse_loss(pred, target, reduction='none') + + +@weighted_loss +def charbonnier_loss(pred, target, eps=1e-12): + return torch.sqrt((pred - target)**2 + eps) + + +@LOSS_REGISTRY.register() +class L1Loss(nn.Module): + """L1 (mean absolute error, MAE) loss. + + Args: + loss_weight (float): Loss weight for L1 loss. Default: 1.0. + reduction (str): Specifies the reduction to apply to the output. + Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. + """ + + def __init__(self, loss_weight=1.0, reduction='mean'): + super(L1Loss, self).__init__() + if reduction not in ['none', 'mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}') + + self.loss_weight = loss_weight + self.reduction = reduction + + def forward(self, pred, target, weight=None, **kwargs): + """ + Args: + pred (Tensor): of shape (N, C, H, W). Predicted tensor. + target (Tensor): of shape (N, C, H, W). Ground truth tensor. + weight (Tensor, optional): of shape (N, C, H, W). Element-wise + weights. Default: None. + """ + return self.loss_weight * l1_loss(pred, target, weight, reduction=self.reduction) + + +@LOSS_REGISTRY.register() +class MSELoss(nn.Module): + """MSE (L2) loss. + + Args: + loss_weight (float): Loss weight for MSE loss. Default: 1.0. + reduction (str): Specifies the reduction to apply to the output. + Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. + """ + + def __init__(self, loss_weight=1.0, reduction='mean'): + super(MSELoss, self).__init__() + if reduction not in ['none', 'mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}') + + self.loss_weight = loss_weight + self.reduction = reduction + + def forward(self, pred, target, weight=None, **kwargs): + """ + Args: + pred (Tensor): of shape (N, C, H, W). Predicted tensor. + target (Tensor): of shape (N, C, H, W). Ground truth tensor. + weight (Tensor, optional): of shape (N, C, H, W). Element-wise + weights. Default: None. + """ + return self.loss_weight * mse_loss(pred, target, weight, reduction=self.reduction) + + +@LOSS_REGISTRY.register() +class CharbonnierLoss(nn.Module): + """Charbonnier loss (one variant of Robust L1Loss, a differentiable + variant of L1Loss). + + Described in "Deep Laplacian Pyramid Networks for Fast and Accurate + Super-Resolution". + + Args: + loss_weight (float): Loss weight for L1 loss. Default: 1.0. + reduction (str): Specifies the reduction to apply to the output. + Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. + eps (float): A value used to control the curvature near zero. + Default: 1e-12. + """ + + def __init__(self, loss_weight=1.0, reduction='mean', eps=1e-12): + super(CharbonnierLoss, self).__init__() + if reduction not in ['none', 'mean', 'sum']: + raise ValueError(f'Unsupported reduction mode: {reduction}. ' f'Supported ones are: {_reduction_modes}') + + self.loss_weight = loss_weight + self.reduction = reduction + self.eps = eps + + def forward(self, pred, target, weight=None, **kwargs): + """ + Args: + pred (Tensor): of shape (N, C, H, W). Predicted tensor. + target (Tensor): of shape (N, C, H, W). Ground truth tensor. + weight (Tensor, optional): of shape (N, C, H, W). Element-wise + weights. Default: None. + """ + return self.loss_weight * charbonnier_loss(pred, target, weight, eps=self.eps, reduction=self.reduction) + + +@LOSS_REGISTRY.register() +class WeightedTVLoss(L1Loss): + """Weighted TV loss. + + Args: + loss_weight (float): Loss weight. Default: 1.0. + """ + + def __init__(self, loss_weight=1.0): + super(WeightedTVLoss, self).__init__(loss_weight=loss_weight) + + def forward(self, pred, weight=None): + y_diff = super(WeightedTVLoss, self).forward(pred[:, :, :-1, :], pred[:, :, 1:, :], weight=weight[:, :, :-1, :]) + x_diff = super(WeightedTVLoss, self).forward(pred[:, :, :, :-1], pred[:, :, :, 1:], weight=weight[:, :, :, :-1]) + + loss = x_diff + y_diff + + return loss + + +@LOSS_REGISTRY.register() +class PerceptualLoss(nn.Module): + """Perceptual loss with commonly used style loss. + + Args: + layer_weights (dict): The weight for each layer of vgg feature. + Here is an example: {'conv5_4': 1.}, which means the conv5_4 + feature layer (before relu5_4) will be extracted with weight + 1.0 in calculting losses. + vgg_type (str): The type of vgg network used as feature extractor. + Default: 'vgg19'. + use_input_norm (bool): If True, normalize the input image in vgg. + Default: True. + range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. + Default: False. + perceptual_weight (float): If `perceptual_weight > 0`, the perceptual + loss will be calculated and the loss will multiplied by the + weight. Default: 1.0. + style_weight (float): If `style_weight > 0`, the style loss will be + calculated and the loss will multiplied by the weight. + Default: 0. + criterion (str): Criterion used for perceptual loss. Default: 'l1'. + """ + + def __init__(self, + layer_weights, + vgg_type='vgg19', + use_input_norm=True, + range_norm=False, + perceptual_weight=1.0, + style_weight=0., + criterion='l1'): + super(PerceptualLoss, self).__init__() + self.perceptual_weight = perceptual_weight + self.style_weight = style_weight + self.layer_weights = layer_weights + self.vgg = VGGFeatureExtractor( + layer_name_list=list(layer_weights.keys()), + vgg_type=vgg_type, + use_input_norm=use_input_norm, + range_norm=range_norm) + + self.criterion_type = criterion + if self.criterion_type == 'l1': + self.criterion = torch.nn.L1Loss() + elif self.criterion_type == 'l2': + self.criterion = torch.nn.L2loss() + elif self.criterion_type == 'fro': + self.criterion = None + else: + raise NotImplementedError(f'{criterion} criterion has not been supported.') + + def forward(self, x, gt): + """Forward function. + + Args: + x (Tensor): Input tensor with shape (n, c, h, w). + gt (Tensor): Ground-truth tensor with shape (n, c, h, w). + + Returns: + Tensor: Forward results. + """ + # extract vgg features + x_features = self.vgg(x) + gt_features = self.vgg(gt.detach()) + + # calculate perceptual loss + if self.perceptual_weight > 0: + percep_loss = 0 + for k in x_features.keys(): + if self.criterion_type == 'fro': + percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] + else: + percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] + percep_loss *= self.perceptual_weight + else: + percep_loss = None + + # calculate style loss + if self.style_weight > 0: + style_loss = 0 + for k in x_features.keys(): + if self.criterion_type == 'fro': + style_loss += torch.norm( + self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] + else: + style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( + gt_features[k])) * self.layer_weights[k] + style_loss *= self.style_weight + else: + style_loss = None + + return percep_loss, style_loss + + def _gram_mat(self, x): + """Calculate Gram matrix. + + Args: + x (torch.Tensor): Tensor with shape of (n, c, h, w). + + Returns: + torch.Tensor: Gram matrix. + """ + n, c, h, w = x.size() + features = x.view(n, c, w * h) + features_t = features.transpose(1, 2) + gram = features.bmm(features_t) / (c * h * w) + return gram + + +@LOSS_REGISTRY.register() +class GANLoss(nn.Module): + """Define GAN loss. + + Args: + gan_type (str): Support 'vanilla', 'lsgan', 'wgan', 'hinge'. + real_label_val (float): The value for real label. Default: 1.0. + fake_label_val (float): The value for fake label. Default: 0.0. + loss_weight (float): Loss weight. Default: 1.0. + Note that loss_weight is only for generators; and it is always 1.0 + for discriminators. + """ + + def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0, loss_weight=1.0): + super(GANLoss, self).__init__() + self.gan_type = gan_type + self.loss_weight = loss_weight + self.real_label_val = real_label_val + self.fake_label_val = fake_label_val + + if self.gan_type == 'vanilla': + self.loss = nn.BCEWithLogitsLoss() + elif self.gan_type == 'lsgan': + self.loss = nn.MSELoss() + elif self.gan_type == 'wgan': + self.loss = self._wgan_loss + elif self.gan_type == 'wgan_softplus': + self.loss = self._wgan_softplus_loss + elif self.gan_type == 'hinge': + self.loss = nn.ReLU() + else: + raise NotImplementedError(f'GAN type {self.gan_type} is not implemented.') + + def _wgan_loss(self, input, target): + """wgan loss. + + Args: + input (Tensor): Input tensor. + target (bool): Target label. + + Returns: + Tensor: wgan loss. + """ + return -input.mean() if target else input.mean() + + def _wgan_softplus_loss(self, input, target): + """wgan loss with soft plus. softplus is a smooth approximation to the + ReLU function. + + In StyleGAN2, it is called: + Logistic loss for discriminator; + Non-saturating loss for generator. + + Args: + input (Tensor): Input tensor. + target (bool): Target label. + + Returns: + Tensor: wgan loss. + """ + return F.softplus(-input).mean() if target else F.softplus(input).mean() + + def get_target_label(self, input, target_is_real): + """Get target label. + + Args: + input (Tensor): Input tensor. + target_is_real (bool): Whether the target is real or fake. + + Returns: + (bool | Tensor): Target tensor. Return bool for wgan, otherwise, + return Tensor. + """ + + if self.gan_type in ['wgan', 'wgan_softplus']: + return target_is_real + target_val = (self.real_label_val if target_is_real else self.fake_label_val) + return input.new_ones(input.size()) * target_val + + def forward(self, input, target_is_real, is_disc=False): + """ + Args: + input (Tensor): The input for the loss module, i.e., the network + prediction. + target_is_real (bool): Whether the targe is real or fake. + is_disc (bool): Whether the loss for discriminators or not. + Default: False. + + Returns: + Tensor: GAN loss value. + """ + target_label = self.get_target_label(input, target_is_real) + if self.gan_type == 'hinge': + if is_disc: # for discriminators in hinge-gan + input = -input if target_is_real else input + loss = self.loss(1 + input).mean() + else: # for generators in hinge-gan + loss = -input.mean() + else: # other gan types + loss = self.loss(input, target_label) + + # loss_weight is always 1.0 for discriminators + return loss if is_disc else loss * self.loss_weight + + +def r1_penalty(real_pred, real_img): + """R1 regularization for discriminator. The core idea is to + penalize the gradient on real data alone: when the + generator distribution produces the true data distribution + and the discriminator is equal to 0 on the data manifold, the + gradient penalty ensures that the discriminator cannot create + a non-zero gradient orthogonal to the data manifold without + suffering a loss in the GAN game. + + Ref: + Eq. 9 in Which training methods for GANs do actually converge. + """ + grad_real = autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True)[0] + grad_penalty = grad_real.pow(2).view(grad_real.shape[0], -1).sum(1).mean() + return grad_penalty + + +def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): + noise = torch.randn_like(fake_img) / math.sqrt(fake_img.shape[2] * fake_img.shape[3]) + grad = autograd.grad(outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True)[0] + path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) + + path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) + + path_penalty = (path_lengths - path_mean).pow(2).mean() + + return path_penalty, path_lengths.detach().mean(), path_mean.detach() + + +def gradient_penalty_loss(discriminator, real_data, fake_data, weight=None): + """Calculate gradient penalty for wgan-gp. + + Args: + discriminator (nn.Module): Network for the discriminator. + real_data (Tensor): Real input data. + fake_data (Tensor): Fake input data. + weight (Tensor): Weight tensor. Default: None. + + Returns: + Tensor: A tensor for gradient penalty. + """ + + batch_size = real_data.size(0) + alpha = real_data.new_tensor(torch.rand(batch_size, 1, 1, 1)) + + # interpolate between real_data and fake_data + interpolates = alpha * real_data + (1. - alpha) * fake_data + interpolates = autograd.Variable(interpolates, requires_grad=True) + + disc_interpolates = discriminator(interpolates) + gradients = autograd.grad( + outputs=disc_interpolates, + inputs=interpolates, + grad_outputs=torch.ones_like(disc_interpolates), + create_graph=True, + retain_graph=True, + only_inputs=True)[0] + + if weight is not None: + gradients = gradients * weight + + gradients_penalty = ((gradients.norm(2, dim=1) - 1)**2).mean() + if weight is not None: + gradients_penalty /= torch.mean(weight) + + return gradients_penalty diff --git a/basicsr/metrics/README.md b/basicsr/metrics/README.md new file mode 100644 index 0000000000000000000000000000000000000000..98d00308ab79e92a2393f9759190de8122a8e79d --- /dev/null +++ b/basicsr/metrics/README.md @@ -0,0 +1,48 @@ +# Metrics + +[English](README.md) **|** [简体中文](README_CN.md) + +- [约定](#约定) +- [PSNR 和 SSIM](#psnr-和-ssim) + +## 约定 + +因为不同的输入类型会导致结果的不同，因此我们对输入做如下约定: + +- Numpy 类型 (一般是 cv2 的结果) + - UINT8: BGR, [0, 255], (h, w, c) + - float: BGR, [0, 1], (h, w, c). 一般作为中间结果 +- Tensor 类型 + - float: RGB, [0, 1], (n, c, h, w) + +其他约定: + +- 以 `_pt` 结尾的是 PyTorch 结果 +- PyTorch version 支持 batch 计算 +- 颜色转换在 float32 上做；metric计算在 float64 上做 + +## PSNR 和 SSIM + +PSNR 和 SSIM 的结果趋势是一致的，即一般 PSNR 高，则 SSIM 也高。 +在实现上, PSNR 的各种实现都很一致。SSIM 有各种各样的实现，我们这里和 MATLAB 最原始版本保持 (参考 [NTIRE17比赛](https://competitions.codalab.org/competitions/16306#participate) 的 [evaluation代码](https://competitions.codalab.org/my/datasets/download/ebe960d8-0ec8-4846-a1a2-7c4a586a7378)) + +下面列了各个实现的结果比对. +总结：PyTorch 实现和 MATLAB 实现基本一致，在 GPU 运行上会有稍许差异 + +- PSNR 比对 + +|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | +|:---| :---: | :---: | :---: | :---: | :---: | +|baboon| RGB | 20.419710 | 20.419710 | 20.419710 |20.419710 | +|baboon| Y | - |22.441898 | 22.441899 | 22.444916| +|comic | RGB | 20.239912 | 20.239912 | 20.239912 | 20.239912 | +|comic | Y | - | 21.720398 | 21.720398 | 21.721663| + +- SSIM 比对 + +|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | +|:---| :---: | :---: | :---: | :---: | :---: | +|baboon| RGB | 0.391853 | 0.391853 | 0.391853|0.391853 | +|baboon| Y | - |0.453097| 0.453097 | 0.453171| +|comic | RGB | 0.567738 | 0.567738 | 0.567738 | 0.567738| +|comic | Y | - | 0.585511 | 0.585511 | 0.585522 | diff --git a/basicsr/metrics/README_CN.md b/basicsr/metrics/README_CN.md new file mode 100644 index 0000000000000000000000000000000000000000..98d00308ab79e92a2393f9759190de8122a8e79d --- /dev/null +++ b/basicsr/metrics/README_CN.md @@ -0,0 +1,48 @@ +# Metrics + +[English](README.md) **|** [简体中文](README_CN.md) + +- [约定](#约定) +- [PSNR 和 SSIM](#psnr-和-ssim) + +## 约定 + +因为不同的输入类型会导致结果的不同，因此我们对输入做如下约定: + +- Numpy 类型 (一般是 cv2 的结果) + - UINT8: BGR, [0, 255], (h, w, c) + - float: BGR, [0, 1], (h, w, c). 一般作为中间结果 +- Tensor 类型 + - float: RGB, [0, 1], (n, c, h, w) + +其他约定: + +- 以 `_pt` 结尾的是 PyTorch 结果 +- PyTorch version 支持 batch 计算 +- 颜色转换在 float32 上做；metric计算在 float64 上做 + +## PSNR 和 SSIM + +PSNR 和 SSIM 的结果趋势是一致的，即一般 PSNR 高，则 SSIM 也高。 +在实现上, PSNR 的各种实现都很一致。SSIM 有各种各样的实现，我们这里和 MATLAB 最原始版本保持 (参考 [NTIRE17比赛](https://competitions.codalab.org/competitions/16306#participate) 的 [evaluation代码](https://competitions.codalab.org/my/datasets/download/ebe960d8-0ec8-4846-a1a2-7c4a586a7378)) + +下面列了各个实现的结果比对. +总结：PyTorch 实现和 MATLAB 实现基本一致，在 GPU 运行上会有稍许差异 + +- PSNR 比对 + +|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | +|:---| :---: | :---: | :---: | :---: | :---: | +|baboon| RGB | 20.419710 | 20.419710 | 20.419710 |20.419710 | +|baboon| Y | - |22.441898 | 22.441899 | 22.444916| +|comic | RGB | 20.239912 | 20.239912 | 20.239912 | 20.239912 | +|comic | Y | - | 21.720398 | 21.720398 | 21.721663| + +- SSIM 比对 + +|Image | Color Space | MATLAB | Numpy | PyTorch CPU | PyTorch GPU | +|:---| :---: | :---: | :---: | :---: | :---: | +|baboon| RGB | 0.391853 | 0.391853 | 0.391853|0.391853 | +|baboon| Y | - |0.453097| 0.453097 | 0.453171| +|comic | RGB | 0.567738 | 0.567738 | 0.567738 | 0.567738| +|comic | Y | - | 0.585511 | 0.585511 | 0.585522 | diff --git a/basicsr/metrics/__init__.py b/basicsr/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4fb044a93878c7d0dbfafa602b7f80536a6d3a0d --- /dev/null +++ b/basicsr/metrics/__init__.py @@ -0,0 +1,20 @@ +from copy import deepcopy + +from basicsr.utils.registry import METRIC_REGISTRY +from .niqe import calculate_niqe +from .psnr_ssim import calculate_psnr, calculate_ssim + +__all__ = ['calculate_psnr', 'calculate_ssim', 'calculate_niqe'] + + +def calculate_metric(data, opt): + """Calculate metric from data and options. + + Args: + opt (dict): Configuration. It must contain: + type (str): Model type. + """ + opt = deepcopy(opt) + metric_type = opt.pop('type') + metric = METRIC_REGISTRY.get(metric_type)(**data, **opt) + return metric diff --git a/basicsr/metrics/__pycache__/__init__.cpython-38.pyc b/basicsr/metrics/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..06a9194ad5d8bb8730f34dc6d59fcaba10c64693 Binary files /dev/null and b/basicsr/metrics/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/metrics/__pycache__/metric_util.cpython-38.pyc b/basicsr/metrics/__pycache__/metric_util.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0a80096d4f20686af74ee967b6a310cc39273059 Binary files /dev/null and b/basicsr/metrics/__pycache__/metric_util.cpython-38.pyc differ diff --git a/basicsr/metrics/__pycache__/niqe.cpython-38.pyc b/basicsr/metrics/__pycache__/niqe.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a3728234cf7707fca5982090dbbc654e5ccf499a Binary files /dev/null and b/basicsr/metrics/__pycache__/niqe.cpython-38.pyc differ diff --git a/basicsr/metrics/__pycache__/psnr_ssim.cpython-38.pyc b/basicsr/metrics/__pycache__/psnr_ssim.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a17b67605a8c8384a30790f8ebd9584fb5a6f41f Binary files /dev/null and b/basicsr/metrics/__pycache__/psnr_ssim.cpython-38.pyc differ diff --git a/basicsr/metrics/fid.py b/basicsr/metrics/fid.py new file mode 100644 index 0000000000000000000000000000000000000000..2b926eb38418d62ec69fe2feb10d2a9231cbc181 --- /dev/null +++ b/basicsr/metrics/fid.py @@ -0,0 +1,93 @@ +import numpy as np +import torch +import torch.nn as nn +from scipy import linalg +from tqdm import tqdm + +from basicsr.archs.inception import InceptionV3 + + +def load_patched_inception_v3(device='cuda', resize_input=True, normalize_input=False): + # we may not resize the input, but in [rosinality/stylegan2-pytorch] it + # does resize the input. + inception = InceptionV3([3], resize_input=resize_input, normalize_input=normalize_input) + inception = nn.DataParallel(inception).eval().to(device) + return inception + + +@torch.no_grad() +def extract_inception_features(data_generator, inception, len_generator=None, device='cuda'): + """Extract inception features. + + Args: + data_generator (generator): A data generator. + inception (nn.Module): Inception model. + len_generator (int): Length of the data_generator to show the + progressbar. Default: None. + device (str): Device. Default: cuda. + + Returns: + Tensor: Extracted features. + """ + if len_generator is not None: + pbar = tqdm(total=len_generator, unit='batch', desc='Extract') + else: + pbar = None + features = [] + + for data in data_generator: + if pbar: + pbar.update(1) + data = data.to(device) + feature = inception(data)[0].view(data.shape[0], -1) + features.append(feature.to('cpu')) + if pbar: + pbar.close() + features = torch.cat(features, 0) + return features + + +def calculate_fid(mu1, sigma1, mu2, sigma2, eps=1e-6): + """Numpy implementation of the Frechet Distance. + + The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) + and X_2 ~ N(mu_2, C_2) is + d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). + Stable version by Dougal J. Sutherland. + + Args: + mu1 (np.array): The sample mean over activations. + sigma1 (np.array): The covariance matrix over activations for + generated samples. + mu2 (np.array): The sample mean over activations, precalculated on an + representative data set. + sigma2 (np.array): The covariance matrix over activations, + precalculated on an representative data set. + + Returns: + float: The Frechet Distance. + """ + assert mu1.shape == mu2.shape, 'Two mean vectors have different lengths' + assert sigma1.shape == sigma2.shape, ('Two covariances have different dimensions') + + cov_sqrt, _ = linalg.sqrtm(sigma1 @ sigma2, disp=False) + + # Product might be almost singular + if not np.isfinite(cov_sqrt).all(): + print('Product of cov matrices is singular. Adding {eps} to diagonal of cov estimates') + offset = np.eye(sigma1.shape[0]) * eps + cov_sqrt = linalg.sqrtm((sigma1 + offset) @ (sigma2 + offset)) + + # Numerical error might give slight imaginary component + if np.iscomplexobj(cov_sqrt): + if not np.allclose(np.diagonal(cov_sqrt).imag, 0, atol=1e-3): + m = np.max(np.abs(cov_sqrt.imag)) + raise ValueError(f'Imaginary component {m}') + cov_sqrt = cov_sqrt.real + + mean_diff = mu1 - mu2 + mean_norm = mean_diff @ mean_diff + trace = np.trace(sigma1) + np.trace(sigma2) - 2 * np.trace(cov_sqrt) + fid = mean_norm + trace + + return fid diff --git a/basicsr/metrics/metric_util.py b/basicsr/metrics/metric_util.py new file mode 100644 index 0000000000000000000000000000000000000000..2a27c70a043beeeb59cfaf533079492293065448 --- /dev/null +++ b/basicsr/metrics/metric_util.py @@ -0,0 +1,45 @@ +import numpy as np + +from basicsr.utils import bgr2ycbcr + + +def reorder_image(img, input_order='HWC'): + """Reorder images to 'HWC' order. + + If the input_order is (h, w), return (h, w, 1); + If the input_order is (c, h, w), return (h, w, c); + If the input_order is (h, w, c), return as it is. + + Args: + img (ndarray): Input image. + input_order (str): Whether the input order is 'HWC' or 'CHW'. + If the input image shape is (h, w), input_order will not have + effects. Default: 'HWC'. + + Returns: + ndarray: reordered image. + """ + + if input_order not in ['HWC', 'CHW']: + raise ValueError(f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'") + if len(img.shape) == 2: + img = img[..., None] + if input_order == 'CHW': + img = img.transpose(1, 2, 0) + return img + + +def to_y_channel(img): + """Change to Y channel of YCbCr. + + Args: + img (ndarray): Images with range [0, 255]. + + Returns: + (ndarray): Images with range [0, 255] (float type) without round. + """ + img = img.astype(np.float32) / 255. + if img.ndim == 3 and img.shape[2] == 3: + img = bgr2ycbcr(img, y_only=True) + img = img[..., None] + return img * 255. diff --git a/basicsr/metrics/niqe.py b/basicsr/metrics/niqe.py new file mode 100644 index 0000000000000000000000000000000000000000..e261da25f457151a86f1062da2e45dd7909ee0dd --- /dev/null +++ b/basicsr/metrics/niqe.py @@ -0,0 +1,197 @@ +import cv2 +import math +import numpy as np +import os +from scipy.ndimage.filters import convolve +from scipy.special import gamma + +from basicsr.metrics.metric_util import reorder_image, to_y_channel +from basicsr.utils.matlab_functions import imresize +from basicsr.utils.registry import METRIC_REGISTRY + + +def estimate_aggd_param(block): + """Estimate AGGD (Asymmetric Generalized Gaussian Distribution) parameters. + + Args: + block (ndarray): 2D Image block. + + Returns: + tuple: alpha (float), beta_l (float) and beta_r (float) for the AGGD + distribution (Estimating the parames in Equation 7 in the paper). + """ + block = block.flatten() + gam = np.arange(0.2, 10.001, 0.001) # len = 9801 + gam_reciprocal = np.reciprocal(gam) + r_gam = np.square(gamma(gam_reciprocal * 2)) / (gamma(gam_reciprocal) * gamma(gam_reciprocal * 3)) + + left_std = np.sqrt(np.mean(block[block < 0]**2)) + right_std = np.sqrt(np.mean(block[block > 0]**2)) + gammahat = left_std / right_std + rhat = (np.mean(np.abs(block)))**2 / np.mean(block**2) + rhatnorm = (rhat * (gammahat**3 + 1) * (gammahat + 1)) / ((gammahat**2 + 1)**2) + array_position = np.argmin((r_gam - rhatnorm)**2) + + alpha = gam[array_position] + beta_l = left_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha)) + beta_r = right_std * np.sqrt(gamma(1 / alpha) / gamma(3 / alpha)) + return (alpha, beta_l, beta_r) + + +def compute_feature(block): + """Compute features. + + Args: + block (ndarray): 2D Image block. + + Returns: + list: Features with length of 18. + """ + feat = [] + alpha, beta_l, beta_r = estimate_aggd_param(block) + feat.extend([alpha, (beta_l + beta_r) / 2]) + + # distortions disturb the fairly regular structure of natural images. + # This deviation can be captured by analyzing the sample distribution of + # the products of pairs of adjacent coefficients computed along + # horizontal, vertical and diagonal orientations. + shifts = [[0, 1], [1, 0], [1, 1], [1, -1]] + for i in range(len(shifts)): + shifted_block = np.roll(block, shifts[i], axis=(0, 1)) + alpha, beta_l, beta_r = estimate_aggd_param(block * shifted_block) + # Eq. 8 + mean = (beta_r - beta_l) * (gamma(2 / alpha) / gamma(1 / alpha)) + feat.extend([alpha, mean, beta_l, beta_r]) + return feat + + +def niqe(img, mu_pris_param, cov_pris_param, gaussian_window, block_size_h=96, block_size_w=96): + """Calculate NIQE (Natural Image Quality Evaluator) metric. + + Ref: Making a "Completely Blind" Image Quality Analyzer. + This implementation could produce almost the same results as the official + MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip + + Note that we do not include block overlap height and width, since they are + always 0 in the official implementation. + + For good performance, it is advisable by the official implementation to + divide the distorted image in to the same size patched as used for the + construction of multivariate Gaussian model. + + Args: + img (ndarray): Input image whose quality needs to be computed. The + image must be a gray or Y (of YCbCr) image with shape (h, w). + Range [0, 255] with float type. + mu_pris_param (ndarray): Mean of a pre-defined multivariate Gaussian + model calculated on the pristine dataset. + cov_pris_param (ndarray): Covariance of a pre-defined multivariate + Gaussian model calculated on the pristine dataset. + gaussian_window (ndarray): A 7x7 Gaussian window used for smoothing the + image. + block_size_h (int): Height of the blocks in to which image is divided. + Default: 96 (the official recommended value). + block_size_w (int): Width of the blocks in to which image is divided. + Default: 96 (the official recommended value). + """ + assert img.ndim == 2, ('Input image must be a gray or Y (of YCbCr) image with shape (h, w).') + # crop image + h, w = img.shape + num_block_h = math.floor(h / block_size_h) + num_block_w = math.floor(w / block_size_w) + img = img[0:num_block_h * block_size_h, 0:num_block_w * block_size_w] + + distparam = [] # dist param is actually the multiscale features + for scale in (1, 2): # perform on two scales (1, 2) + mu = convolve(img, gaussian_window, mode='nearest') + sigma = np.sqrt(np.abs(convolve(np.square(img), gaussian_window, mode='nearest') - np.square(mu))) + # normalize, as in Eq. 1 in the paper + img_nomalized = (img - mu) / (sigma + 1) + + feat = [] + for idx_w in range(num_block_w): + for idx_h in range(num_block_h): + # process ecah block + block = img_nomalized[idx_h * block_size_h // scale:(idx_h + 1) * block_size_h // scale, + idx_w * block_size_w // scale:(idx_w + 1) * block_size_w // scale] + feat.append(compute_feature(block)) + + distparam.append(np.array(feat)) + + if scale == 1: + img = imresize(img / 255., scale=0.5, antialiasing=True) + img = img * 255. + + distparam = np.concatenate(distparam, axis=1) + + # fit a MVG (multivariate Gaussian) model to distorted patch features + mu_distparam = np.nanmean(distparam, axis=0) + # use nancov. ref: https://ww2.mathworks.cn/help/stats/nancov.html + distparam_no_nan = distparam[~np.isnan(distparam).any(axis=1)] + cov_distparam = np.cov(distparam_no_nan, rowvar=False) + + # compute niqe quality, Eq. 10 in the paper + invcov_param = np.linalg.pinv((cov_pris_param + cov_distparam) / 2) + quality = np.matmul( + np.matmul((mu_pris_param - mu_distparam), invcov_param), np.transpose((mu_pris_param - mu_distparam))) + + quality = np.sqrt(quality) + quality = float(np.squeeze(quality)) + return quality + + +@METRIC_REGISTRY.register() +def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs): + """Calculate NIQE (Natural Image Quality Evaluator) metric. + + Ref: Making a "Completely Blind" Image Quality Analyzer. + This implementation could produce almost the same results as the official + MATLAB codes: http://live.ece.utexas.edu/research/quality/niqe_release.zip + + > MATLAB R2021a result for tests/data/baboon.png: 5.72957338 (5.7296) + > Our re-implementation result for tests/data/baboon.png: 5.7295763 (5.7296) + + We use the official params estimated from the pristine dataset. + We use the recommended block size (96, 96) without overlaps. + + Args: + img (ndarray): Input image whose quality needs to be computed. + The input image must be in range [0, 255] with float/int type. + The input_order of image can be 'HW' or 'HWC' or 'CHW'. (BGR order) + If the input order is 'HWC' or 'CHW', it will be converted to gray + or Y (of YCbCr) image according to the ``convert_to`` argument. + crop_border (int): Cropped pixels in each edge of an image. These + pixels are not involved in the metric calculation. + input_order (str): Whether the input order is 'HW', 'HWC' or 'CHW'. + Default: 'HWC'. + convert_to (str): Whether converted to 'y' (of MATLAB YCbCr) or 'gray'. + Default: 'y'. + + Returns: + float: NIQE result. + """ + ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) + # we use the official params estimated from the pristine dataset. + niqe_pris_params = np.load(os.path.join(ROOT_DIR, 'niqe_pris_params.npz')) + mu_pris_param = niqe_pris_params['mu_pris_param'] + cov_pris_param = niqe_pris_params['cov_pris_param'] + gaussian_window = niqe_pris_params['gaussian_window'] + + img = img.astype(np.float32) + if input_order != 'HW': + img = reorder_image(img, input_order=input_order) + if convert_to == 'y': + img = to_y_channel(img) + elif convert_to == 'gray': + img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255. + img = np.squeeze(img) + + if crop_border != 0: + img = img[crop_border:-crop_border, crop_border:-crop_border] + + # round is necessary for being consistent with MATLAB's result + img = img.round() + + niqe_result = niqe(img, mu_pris_param, cov_pris_param, gaussian_window) + + return niqe_result diff --git a/basicsr/metrics/niqe_pris_params.npz b/basicsr/metrics/niqe_pris_params.npz new file mode 100644 index 0000000000000000000000000000000000000000..42f06a9a18e6ed8bbf7933bec1477b189ef798de --- /dev/null +++ b/basicsr/metrics/niqe_pris_params.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a7c182a68c9e7f1b2e2e5ec723279d6f65d912b6fcaf37eb2bf03d7367c4296 +size 11850 diff --git a/basicsr/metrics/psnr_ssim.py b/basicsr/metrics/psnr_ssim.py new file mode 100644 index 0000000000000000000000000000000000000000..539d5faa5b3ca412021cb512984e6a42860762f2 --- /dev/null +++ b/basicsr/metrics/psnr_ssim.py @@ -0,0 +1,233 @@ +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + +from basicsr.metrics.metric_util import reorder_image, to_y_channel +from basicsr.utils.color_util import rgb2ycbcr_pt +from basicsr.utils.registry import METRIC_REGISTRY + + +@METRIC_REGISTRY.register() +def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): + """Calculate PSNR (Peak Signal-to-Noise Ratio). + + Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio + + Args: + img (ndarray): Images with range [0, 255]. + img2 (ndarray): Images with range [0, 255]. + crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. + input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. + test_y_channel (bool): Test on Y channel of YCbCr. Default: False. + + Returns: + float: PSNR result. + """ + + assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') + if input_order not in ['HWC', 'CHW']: + raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') + img = reorder_image(img, input_order=input_order) + img2 = reorder_image(img2, input_order=input_order) + + if crop_border != 0: + img = img[crop_border:-crop_border, crop_border:-crop_border, ...] + img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] + + if test_y_channel: + img = to_y_channel(img) + img2 = to_y_channel(img2) + + img = img.astype(np.float64) + img2 = img2.astype(np.float64) + + mse = np.mean((img - img2)**2) + if mse == 0: + return float('inf') + return 10. * np.log10(255. * 255. / mse) + + +@METRIC_REGISTRY.register() +def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs): + """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). + + Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio + + Args: + img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). + img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). + crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. + test_y_channel (bool): Test on Y channel of YCbCr. Default: False. + + Returns: + float: PSNR result. + """ + + assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') + + if crop_border != 0: + img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] + img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] + + if test_y_channel: + img = rgb2ycbcr_pt(img, y_only=True) + img2 = rgb2ycbcr_pt(img2, y_only=True) + + img = img.to(torch.float64) + img2 = img2.to(torch.float64) + + mse = torch.mean((img - img2)**2, dim=[1, 2, 3]) + return 10. * torch.log10(1. / (mse + 1e-8)) + + +@METRIC_REGISTRY.register() +def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): + """Calculate SSIM (structural similarity). + + Ref: + Image quality assessment: From error visibility to structural similarity + + The results are the same as that of the official released MATLAB code in + https://ece.uwaterloo.ca/~z70wang/research/ssim/. + + For three-channel images, SSIM is calculated for each channel and then + averaged. + + Args: + img (ndarray): Images with range [0, 255]. + img2 (ndarray): Images with range [0, 255]. + crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. + input_order (str): Whether the input order is 'HWC' or 'CHW'. + Default: 'HWC'. + test_y_channel (bool): Test on Y channel of YCbCr. Default: False. + + Returns: + float: SSIM result. + """ + + assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') + if input_order not in ['HWC', 'CHW']: + raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') + img = reorder_image(img, input_order=input_order) + img2 = reorder_image(img2, input_order=input_order) + + if crop_border != 0: + img = img[crop_border:-crop_border, crop_border:-crop_border, ...] + img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] + + if test_y_channel: + img = to_y_channel(img) + img2 = to_y_channel(img2) + + img = img.astype(np.float64) + img2 = img2.astype(np.float64) + + ssims = [] + for i in range(img.shape[2]): + ssims.append(_ssim(img[..., i], img2[..., i])) + return np.array(ssims).mean() + + +@METRIC_REGISTRY.register() +def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs): + """Calculate SSIM (structural similarity) (PyTorch version). + + Ref: + Image quality assessment: From error visibility to structural similarity + + The results are the same as that of the official released MATLAB code in + https://ece.uwaterloo.ca/~z70wang/research/ssim/. + + For three-channel images, SSIM is calculated for each channel and then + averaged. + + Args: + img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). + img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). + crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. + test_y_channel (bool): Test on Y channel of YCbCr. Default: False. + + Returns: + float: SSIM result. + """ + + assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') + + if crop_border != 0: + img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] + img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] + + if test_y_channel: + img = rgb2ycbcr_pt(img, y_only=True) + img2 = rgb2ycbcr_pt(img2, y_only=True) + + img = img.to(torch.float64) + img2 = img2.to(torch.float64) + + ssim = _ssim_pth(img * 255., img2 * 255.) + return ssim + + +def _ssim(img, img2): + """Calculate SSIM (structural similarity) for one channel images. + + It is called by func:`calculate_ssim`. + + Args: + img (ndarray): Images with range [0, 255] with order 'HWC'. + img2 (ndarray): Images with range [0, 255] with order 'HWC'. + + Returns: + float: SSIM result. + """ + + c1 = (0.01 * 255)**2 + c2 = (0.03 * 255)**2 + kernel = cv2.getGaussianKernel(11, 1.5) + window = np.outer(kernel, kernel.transpose()) + + mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5] # valid mode for window size 11 + mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] + mu1_sq = mu1**2 + mu2_sq = mu2**2 + mu1_mu2 = mu1 * mu2 + sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq + sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq + sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 + + ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) + return ssim_map.mean() + + +def _ssim_pth(img, img2): + """Calculate SSIM (structural similarity) (PyTorch version). + + It is called by func:`calculate_ssim_pt`. + + Args: + img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). + img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). + + Returns: + float: SSIM result. + """ + c1 = (0.01 * 255)**2 + c2 = (0.03 * 255)**2 + + kernel = cv2.getGaussianKernel(11, 1.5) + window = np.outer(kernel, kernel.transpose()) + window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device) + + mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1]) # valid mode + mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1]) # valid mode + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq + sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq + sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2 + + cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2) + ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map + return ssim_map.mean([1, 2, 3]) diff --git a/basicsr/metrics/test_metrics/test_psnr_ssim.py b/basicsr/metrics/test_metrics/test_psnr_ssim.py new file mode 100644 index 0000000000000000000000000000000000000000..18b05a73a0e38e89b2321ddc9415123a92f5c5a4 --- /dev/null +++ b/basicsr/metrics/test_metrics/test_psnr_ssim.py @@ -0,0 +1,52 @@ +import cv2 +import torch + +from basicsr.metrics import calculate_psnr, calculate_ssim +from basicsr.metrics.psnr_ssim import calculate_psnr_pt, calculate_ssim_pt +from basicsr.utils import img2tensor + + +def test(img_path, img_path2, crop_border, test_y_channel=False): + img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) + img2 = cv2.imread(img_path2, cv2.IMREAD_UNCHANGED) + + # --------------------- Numpy --------------------- + psnr = calculate_psnr(img, img2, crop_border=crop_border, input_order='HWC', test_y_channel=test_y_channel) + ssim = calculate_ssim(img, img2, crop_border=crop_border, input_order='HWC', test_y_channel=test_y_channel) + print(f'\tNumpy\tPSNR: {psnr:.6f} dB, \tSSIM: {ssim:.6f}') + + # --------------------- PyTorch (CPU) --------------------- + img = img2tensor(img / 255., bgr2rgb=True, float32=True).unsqueeze_(0) + img2 = img2tensor(img2 / 255., bgr2rgb=True, float32=True).unsqueeze_(0) + + psnr_pth = calculate_psnr_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) + ssim_pth = calculate_ssim_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) + print(f'\tTensor (CPU) \tPSNR: {psnr_pth[0]:.6f} dB, \tSSIM: {ssim_pth[0]:.6f}') + + # --------------------- PyTorch (GPU) --------------------- + img = img.cuda() + img2 = img2.cuda() + psnr_pth = calculate_psnr_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) + ssim_pth = calculate_ssim_pt(img, img2, crop_border=crop_border, test_y_channel=test_y_channel) + print(f'\tTensor (GPU) \tPSNR: {psnr_pth[0]:.6f} dB, \tSSIM: {ssim_pth[0]:.6f}') + + psnr_pth = calculate_psnr_pt( + torch.repeat_interleave(img, 2, dim=0), + torch.repeat_interleave(img2, 2, dim=0), + crop_border=crop_border, + test_y_channel=test_y_channel) + ssim_pth = calculate_ssim_pt( + torch.repeat_interleave(img, 2, dim=0), + torch.repeat_interleave(img2, 2, dim=0), + crop_border=crop_border, + test_y_channel=test_y_channel) + print(f'\tTensor (GPU batch) \tPSNR: {psnr_pth[0]:.6f}, {psnr_pth[1]:.6f} dB,' + f'\tSSIM: {ssim_pth[0]:.6f}, {ssim_pth[1]:.6f}') + + +if __name__ == '__main__': + test('tests/data/bic/baboon.png', 'tests/data/gt/baboon.png', crop_border=4, test_y_channel=False) + test('tests/data/bic/baboon.png', 'tests/data/gt/baboon.png', crop_border=4, test_y_channel=True) + + test('tests/data/bic/comic.png', 'tests/data/gt/comic.png', crop_border=4, test_y_channel=False) + test('tests/data/bic/comic.png', 'tests/data/gt/comic.png', crop_border=4, test_y_channel=True) diff --git a/basicsr/models/__init__.py b/basicsr/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..85796deae014c20a9aa600133468d04900c4fb89 --- /dev/null +++ b/basicsr/models/__init__.py @@ -0,0 +1,29 @@ +import importlib +from copy import deepcopy +from os import path as osp + +from basicsr.utils import get_root_logger, scandir +from basicsr.utils.registry import MODEL_REGISTRY + +__all__ = ['build_model'] + +# automatically scan and import model modules for registry +# scan all the files under the 'models' folder and collect files ending with '_model.py' +model_folder = osp.dirname(osp.abspath(__file__)) +model_filenames = [osp.splitext(osp.basename(v))[0] for v in scandir(model_folder) if v.endswith('_model.py')] +# import all the model modules +_model_modules = [importlib.import_module(f'basicsr.models.{file_name}') for file_name in model_filenames] + + +def build_model(opt): + """Build model from options. + + Args: + opt (dict): Configuration. It must contain: + model_type (str): Model type. + """ + opt = deepcopy(opt) + model = MODEL_REGISTRY.get(opt['model_type'])(opt) + logger = get_root_logger() + logger.info(f'Model [{model.__class__.__name__}] is created.') + return model diff --git a/basicsr/models/__pycache__/__init__.cpython-38.pyc b/basicsr/models/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..17597b40df87c7687c21e3278efc8839a7c1773a Binary files /dev/null and b/basicsr/models/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/models/__pycache__/base_model.cpython-38.pyc b/basicsr/models/__pycache__/base_model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c23ed32ea79541357e64253b6b9a5f2ef71361de Binary files /dev/null and b/basicsr/models/__pycache__/base_model.cpython-38.pyc differ diff --git a/basicsr/models/__pycache__/edvr_model.cpython-38.pyc b/basicsr/models/__pycache__/edvr_model.cpython-38.pyc new 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basicsr.models import lr_scheduler as lr_scheduler +from basicsr.utils import get_root_logger +from basicsr.utils.dist_util import master_only + + +class BaseModel(): + """Base model.""" + + def __init__(self, opt): + self.opt = opt + self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') + self.is_train = opt['is_train'] + self.schedulers = [] + self.optimizers = [] + + def feed_data(self, data): + pass + + def optimize_parameters(self): + pass + + def get_current_visuals(self): + pass + + def save(self, epoch, current_iter): + """Save networks and training state.""" + pass + + def validation(self, dataloader, current_iter, tb_logger, save_img=False): + """Validation function. + + Args: + dataloader (torch.utils.data.DataLoader): Validation dataloader. + current_iter (int): Current iteration. + tb_logger (tensorboard logger): Tensorboard logger. + save_img (bool): Whether to save images. Default: False. + """ + if self.opt['dist']: + self.dist_validation(dataloader, current_iter, tb_logger, save_img) + else: + self.nondist_validation(dataloader, current_iter, tb_logger, save_img) + + def _initialize_best_metric_results(self, dataset_name): + """Initialize the best metric results dict for recording the best metric value and iteration.""" + if hasattr(self, 'best_metric_results') and dataset_name in self.best_metric_results: + return + elif not hasattr(self, 'best_metric_results'): + self.best_metric_results = dict() + + # add a dataset record + record = dict() + for metric, content in self.opt['val']['metrics'].items(): + better = content.get('better', 'higher') + init_val = float('-inf') if better == 'higher' else float('inf') + record[metric] = dict(better=better, val=init_val, iter=-1) + self.best_metric_results[dataset_name] = record + + def _update_best_metric_result(self, dataset_name, metric, val, current_iter): + if self.best_metric_results[dataset_name][metric]['better'] == 'higher': + if val >= self.best_metric_results[dataset_name][metric]['val']: + self.best_metric_results[dataset_name][metric]['val'] = val + self.best_metric_results[dataset_name][metric]['iter'] = current_iter + else: + if val <= self.best_metric_results[dataset_name][metric]['val']: + self.best_metric_results[dataset_name][metric]['val'] = val + self.best_metric_results[dataset_name][metric]['iter'] = current_iter + + def model_ema(self, decay=0.999): + net_g = self.get_bare_model(self.net_g) + + net_g_params = dict(net_g.named_parameters()) + net_g_ema_params = dict(self.net_g_ema.named_parameters()) + + for k in net_g_ema_params.keys(): + net_g_ema_params[k].data.mul_(decay).add_(net_g_params[k].data, alpha=1 - decay) + + def get_current_log(self): + return self.log_dict + + def model_to_device(self, net): + """Model to device. It also warps models with DistributedDataParallel + or DataParallel. + + Args: + net (nn.Module) + """ + net = net.to(self.device) + if self.opt['dist']: + find_unused_parameters = self.opt.get('find_unused_parameters', False) + net = DistributedDataParallel( + net, device_ids=[torch.cuda.current_device()], find_unused_parameters=find_unused_parameters) + elif self.opt['num_gpu'] > 1: + net = DataParallel(net) + return net + + def get_optimizer(self, optim_type, params, lr, **kwargs): + if optim_type == 'Adam': + optimizer = torch.optim.Adam(params, lr, **kwargs) + else: + raise NotImplementedError(f'optimizer {optim_type} is not supperted yet.') + return optimizer + + def setup_schedulers(self): + """Set up schedulers.""" + train_opt = self.opt['train'] + scheduler_type = train_opt['scheduler'].pop('type') + if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: + for optimizer in self.optimizers: + self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler'])) + elif scheduler_type == 'CosineAnnealingRestartLR': + for optimizer in self.optimizers: + self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler'])) + else: + raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.') + + def get_bare_model(self, net): + """Get bare model, especially under wrapping with + DistributedDataParallel or DataParallel. + """ + if isinstance(net, (DataParallel, DistributedDataParallel)): + net = net.module + return net + + @master_only + def print_network(self, net): + """Print the str and parameter number of a network. + + Args: + net (nn.Module) + """ + if isinstance(net, (DataParallel, DistributedDataParallel)): + net_cls_str = f'{net.__class__.__name__} - {net.module.__class__.__name__}' + else: + net_cls_str = f'{net.__class__.__name__}' + + net = self.get_bare_model(net) + net_str = str(net) + net_params = sum(map(lambda x: x.numel(), net.parameters())) + + logger = get_root_logger() + logger.info(f'Network: {net_cls_str}, with parameters: {net_params:,d}') + logger.info(net_str) + + def _set_lr(self, lr_groups_l): + """Set learning rate for warm-up. + + Args: + lr_groups_l (list): List for lr_groups, each for an optimizer. + """ + for optimizer, lr_groups in zip(self.optimizers, lr_groups_l): + for param_group, lr in zip(optimizer.param_groups, lr_groups): + param_group['lr'] = lr + + def _get_init_lr(self): + """Get the initial lr, which is set by the scheduler. + """ + init_lr_groups_l = [] + for optimizer in self.optimizers: + init_lr_groups_l.append([v['initial_lr'] for v in optimizer.param_groups]) + return init_lr_groups_l + + def update_learning_rate(self, current_iter, warmup_iter=-1): + """Update learning rate. + + Args: + current_iter (int): Current iteration. + warmup_iter (int)： Warm-up iter numbers. -1 for no warm-up. + Default： -1. + """ + if current_iter > 1: + for scheduler in self.schedulers: + scheduler.step() + # set up warm-up learning rate + if current_iter < warmup_iter: + # get initial lr for each group + init_lr_g_l = self._get_init_lr() + # modify warming-up learning rates + # currently only support linearly warm up + warm_up_lr_l = [] + for init_lr_g in init_lr_g_l: + warm_up_lr_l.append([v / warmup_iter * current_iter for v in init_lr_g]) + # set learning rate + self._set_lr(warm_up_lr_l) + + def get_current_learning_rate(self): + return [param_group['lr'] for param_group in self.optimizers[0].param_groups] + + @master_only + def save_network(self, net, net_label, current_iter, param_key='params'): + """Save networks. + + Args: + net (nn.Module | list[nn.Module]): Network(s) to be saved. + net_label (str): Network label. + current_iter (int): Current iter number. + param_key (str | list[str]): The parameter key(s) to save network. + Default: 'params'. + """ + if current_iter == -1: + current_iter = 'latest' + save_filename = f'{net_label}_{current_iter}.pth' + save_path = os.path.join(self.opt['path']['models'], save_filename) + + net = net if isinstance(net, list) else [net] + param_key = param_key if isinstance(param_key, list) else [param_key] + assert len(net) == len(param_key), 'The lengths of net and param_key should be the same.' + + save_dict = {} + for net_, param_key_ in zip(net, param_key): + net_ = self.get_bare_model(net_) + state_dict = net_.state_dict() + for key, param in state_dict.items(): + if key.startswith('module.'): # remove unnecessary 'module.' + key = key[7:] + state_dict[key] = param.cpu() + save_dict[param_key_] = state_dict + + # avoid occasional writing errors + retry = 3 + while retry > 0: + try: + torch.save(save_dict, save_path) + except Exception as e: + logger = get_root_logger() + logger.warning(f'Save model error: {e}, remaining retry times: {retry - 1}') + time.sleep(1) + else: + break + finally: + retry -= 1 + if retry == 0: + logger.warning(f'Still cannot save {save_path}. Just ignore it.') + # raise IOError(f'Cannot save {save_path}.') + + def _print_different_keys_loading(self, crt_net, load_net, strict=True): + """Print keys with different name or different size when loading models. + + 1. Print keys with different names. + 2. If strict=False, print the same key but with different tensor size. + It also ignore these keys with different sizes (not load). + + Args: + crt_net (torch model): Current network. + load_net (dict): Loaded network. + strict (bool): Whether strictly loaded. Default: True. + """ + crt_net = self.get_bare_model(crt_net) + crt_net = crt_net.state_dict() + crt_net_keys = set(crt_net.keys()) + load_net_keys = set(load_net.keys()) + + logger = get_root_logger() + if crt_net_keys != load_net_keys: + logger.warning('Current net - loaded net:') + for v in sorted(list(crt_net_keys - load_net_keys)): + logger.warning(f' {v}') + logger.warning('Loaded net - current net:') + for v in sorted(list(load_net_keys - crt_net_keys)): + logger.warning(f' {v}') + + # check the size for the same keys + if not strict: + common_keys = crt_net_keys & load_net_keys + for k in common_keys: + if crt_net[k].size() != load_net[k].size(): + logger.warning(f'Size different, ignore [{k}]: crt_net: ' + f'{crt_net[k].shape}; load_net: {load_net[k].shape}') + load_net[k + '.ignore'] = load_net.pop(k) + + def load_network(self, net, load_path, strict=True, param_key='params'): + """Load network. + + Args: + load_path (str): The path of networks to be loaded. + net (nn.Module): Network. + strict (bool): Whether strictly loaded. + param_key (str): The parameter key of loaded network. If set to + None, use the root 'path'. + Default: 'params'. + """ + logger = get_root_logger() + net = self.get_bare_model(net) + load_net = torch.load(load_path, map_location=lambda storage, loc: storage) + if param_key is not None: + if param_key not in load_net and 'params' in load_net: + param_key = 'params' + logger.info('Loading: params_ema does not exist, use params.') + load_net = load_net[param_key] + logger.info(f'Loading {net.__class__.__name__} model from {load_path}, with param key: [{param_key}].') + # remove unnecessary 'module.' + for k, v in deepcopy(load_net).items(): + if k.startswith('module.'): + load_net[k[7:]] = v + load_net.pop(k) + self._print_different_keys_loading(net, load_net, strict) + net.load_state_dict(load_net, strict=strict) + + @master_only + def save_training_state(self, epoch, current_iter): + """Save training states during training, which will be used for + resuming. + + Args: + epoch (int): Current epoch. + current_iter (int): Current iteration. + """ + if current_iter != -1: + state = {'epoch': epoch, 'iter': current_iter, 'optimizers': [], 'schedulers': []} + for o in self.optimizers: + state['optimizers'].append(o.state_dict()) + for s in self.schedulers: + state['schedulers'].append(s.state_dict()) + save_filename = f'{current_iter}.state' + save_path = os.path.join(self.opt['path']['training_states'], save_filename) + + # avoid occasional writing errors + retry = 3 + while retry > 0: + try: + torch.save(state, save_path) + except Exception as e: + logger = get_root_logger() + logger.warning(f'Save training state error: {e}, remaining retry times: {retry - 1}') + time.sleep(1) + else: + break + finally: + retry -= 1 + if retry == 0: + logger.warning(f'Still cannot save {save_path}. Just ignore it.') + # raise IOError(f'Cannot save {save_path}.') + + def resume_training(self, resume_state): + """Reload the optimizers and schedulers for resumed training. + + Args: + resume_state (dict): Resume state. + """ + resume_optimizers = resume_state['optimizers'] + resume_schedulers = resume_state['schedulers'] + assert len(resume_optimizers) == len(self.optimizers), 'Wrong lengths of optimizers' + assert len(resume_schedulers) == len(self.schedulers), 'Wrong lengths of schedulers' + for i, o in enumerate(resume_optimizers): + self.optimizers[i].load_state_dict(o) + for i, s in enumerate(resume_schedulers): + self.schedulers[i].load_state_dict(s) + + def reduce_loss_dict(self, loss_dict): + """reduce loss dict. + + In distributed training, it averages the losses among different GPUs . + + Args: + loss_dict (OrderedDict): Loss dict. + """ + with torch.no_grad(): + if self.opt['dist']: + keys = [] + losses = [] + for name, value in loss_dict.items(): + keys.append(name) + losses.append(value) + losses = torch.stack(losses, 0) + torch.distributed.reduce(losses, dst=0) + if self.opt['rank'] == 0: + losses /= self.opt['world_size'] + loss_dict = {key: loss for key, loss in zip(keys, losses)} + + log_dict = OrderedDict() + for name, value in loss_dict.items(): + log_dict[name] = value.mean().item() + + return log_dict diff --git a/basicsr/models/edvr_model.py b/basicsr/models/edvr_model.py new file mode 100644 index 0000000000000000000000000000000000000000..9bdbf7b94fe3f06c76fbf2a4941621f64e0003e7 --- /dev/null +++ b/basicsr/models/edvr_model.py @@ -0,0 +1,62 @@ +from basicsr.utils import get_root_logger +from basicsr.utils.registry import MODEL_REGISTRY +from .video_base_model import VideoBaseModel + + +@MODEL_REGISTRY.register() +class EDVRModel(VideoBaseModel): + """EDVR Model. + + Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. # noqa: E501 + """ + + def __init__(self, opt): + super(EDVRModel, self).__init__(opt) + if self.is_train: + self.train_tsa_iter = opt['train'].get('tsa_iter') + + def setup_optimizers(self): + train_opt = self.opt['train'] + dcn_lr_mul = train_opt.get('dcn_lr_mul', 1) + logger = get_root_logger() + logger.info(f'Multiple the learning rate for dcn with {dcn_lr_mul}.') + if dcn_lr_mul == 1: + optim_params = self.net_g.parameters() + else: # separate dcn params and normal params for different lr + normal_params = [] + dcn_params = [] + for name, param in self.net_g.named_parameters(): + if 'dcn' in name: + dcn_params.append(param) + else: + normal_params.append(param) + optim_params = [ + { # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }, + { + 'params': dcn_params, + 'lr': train_opt['optim_g']['lr'] * dcn_lr_mul + }, + ] + + optim_type = train_opt['optim_g'].pop('type') + self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) + self.optimizers.append(self.optimizer_g) + + def optimize_parameters(self, current_iter): + if self.train_tsa_iter: + if current_iter == 1: + logger = get_root_logger() + logger.info(f'Only train TSA module for {self.train_tsa_iter} iters.') + for name, param in self.net_g.named_parameters(): + if 'fusion' not in name: + param.requires_grad = False + elif current_iter == self.train_tsa_iter: + logger = get_root_logger() + logger.warning('Train all the parameters.') + for param in self.net_g.parameters(): + param.requires_grad = True + + super(EDVRModel, self).optimize_parameters(current_iter) diff --git a/basicsr/models/esrgan_model.py b/basicsr/models/esrgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3d746d0e29418d9e8f35fa9c1e3a315d694075be --- /dev/null +++ b/basicsr/models/esrgan_model.py @@ -0,0 +1,83 @@ +import torch +from collections import OrderedDict + +from basicsr.utils.registry import MODEL_REGISTRY +from .srgan_model import SRGANModel + + +@MODEL_REGISTRY.register() +class ESRGANModel(SRGANModel): + """ESRGAN model for single image super-resolution.""" + + def optimize_parameters(self, current_iter): + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + + self.optimizer_g.zero_grad() + self.output = self.net_g(self.lq) + + l_g_total = 0 + loss_dict = OrderedDict() + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, self.gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + # gan loss (relativistic gan) + real_d_pred = self.net_d(self.gt).detach() + fake_g_pred = self.net_d(self.output) + l_g_real = self.cri_gan(real_d_pred - torch.mean(fake_g_pred), False, is_disc=False) + l_g_fake = self.cri_gan(fake_g_pred - torch.mean(real_d_pred), True, is_disc=False) + l_g_gan = (l_g_real + l_g_fake) / 2 + + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + l_g_total.backward() + self.optimizer_g.step() + + # optimize net_d + for p in self.net_d.parameters(): + p.requires_grad = True + + self.optimizer_d.zero_grad() + # gan loss (relativistic gan) + + # In order to avoid the error in distributed training: + # "Error detected in CudnnBatchNormBackward: RuntimeError: one of + # the variables needed for gradient computation has been modified by + # an inplace operation", + # we separate the backwards for real and fake, and also detach the + # tensor for calculating mean. + + # real + fake_d_pred = self.net_d(self.output).detach() + real_d_pred = self.net_d(self.gt) + l_d_real = self.cri_gan(real_d_pred - torch.mean(fake_d_pred), True, is_disc=True) * 0.5 + l_d_real.backward() + # fake + fake_d_pred = self.net_d(self.output.detach()) + l_d_fake = self.cri_gan(fake_d_pred - torch.mean(real_d_pred.detach()), False, is_disc=True) * 0.5 + l_d_fake.backward() + self.optimizer_d.step() + + loss_dict['l_d_real'] = l_d_real + loss_dict['l_d_fake'] = l_d_fake + loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) + loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) + + self.log_dict = self.reduce_loss_dict(loss_dict) + + if self.ema_decay > 0: + self.model_ema(decay=self.ema_decay) diff --git a/basicsr/models/hifacegan_model.py b/basicsr/models/hifacegan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..435a2b179d6b7c670fe96a83ce45b461300b2c89 --- /dev/null +++ b/basicsr/models/hifacegan_model.py @@ -0,0 +1,288 @@ +import torch +from collections import OrderedDict +from os import path as osp +from tqdm import tqdm + +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.metrics import calculate_metric +from basicsr.utils import imwrite, tensor2img +from basicsr.utils.registry import MODEL_REGISTRY +from .sr_model import SRModel + + +@MODEL_REGISTRY.register() +class HiFaceGANModel(SRModel): + """HiFaceGAN model for generic-purpose face restoration. + No prior modeling required, works for any degradations. + Currently doesn't support EMA for inference. + """ + + def init_training_settings(self): + + train_opt = self.opt['train'] + self.ema_decay = train_opt.get('ema_decay', 0) + if self.ema_decay > 0: + raise (NotImplementedError('HiFaceGAN does not support EMA now. Pass')) + + self.net_g.train() + + self.net_d = build_network(self.opt['network_d']) + self.net_d = self.model_to_device(self.net_d) + self.print_network(self.net_d) + + # define losses + # HiFaceGAN does not use pixel loss by default + if train_opt.get('pixel_opt'): + self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) + else: + self.cri_pix = None + + if train_opt.get('perceptual_opt'): + self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) + else: + self.cri_perceptual = None + + if train_opt.get('feature_matching_opt'): + self.cri_feat = build_loss(train_opt['feature_matching_opt']).to(self.device) + else: + self.cri_feat = None + + if self.cri_pix is None and self.cri_perceptual is None: + raise ValueError('Both pixel and perceptual losses are None.') + + if train_opt.get('gan_opt'): + self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) + + self.net_d_iters = train_opt.get('net_d_iters', 1) + self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + # optimizer g + optim_type = train_opt['optim_g'].pop('type') + self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) + self.optimizers.append(self.optimizer_g) + # optimizer d + optim_type = train_opt['optim_d'].pop('type') + self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) + self.optimizers.append(self.optimizer_d) + + def discriminate(self, input_lq, output, ground_truth): + """ + This is a conditional (on the input) discriminator + In Batch Normalization, the fake and real images are + recommended to be in the same batch to avoid disparate + statistics in fake and real images. + So both fake and real images are fed to D all at once. + """ + h, w = output.shape[-2:] + if output.shape[-2:] != input_lq.shape[-2:]: + lq = torch.nn.functional.interpolate(input_lq, (h, w)) + real = torch.nn.functional.interpolate(ground_truth, (h, w)) + fake_concat = torch.cat([lq, output], dim=1) + real_concat = torch.cat([lq, real], dim=1) + else: + fake_concat = torch.cat([input_lq, output], dim=1) + real_concat = torch.cat([input_lq, ground_truth], dim=1) + + fake_and_real = torch.cat([fake_concat, real_concat], dim=0) + discriminator_out = self.net_d(fake_and_real) + pred_fake, pred_real = self._divide_pred(discriminator_out) + return pred_fake, pred_real + + @staticmethod + def _divide_pred(pred): + """ + Take the prediction of fake and real images from the combined batch. + The prediction contains the intermediate outputs of multiscale GAN, + so it's usually a list + """ + if type(pred) == list: + fake = [] + real = [] + for p in pred: + fake.append([tensor[:tensor.size(0) // 2] for tensor in p]) + real.append([tensor[tensor.size(0) // 2:] for tensor in p]) + else: + fake = pred[:pred.size(0) // 2] + real = pred[pred.size(0) // 2:] + + return fake, real + + def optimize_parameters(self, current_iter): + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + + self.optimizer_g.zero_grad() + self.output = self.net_g(self.lq) + + l_g_total = 0 + loss_dict = OrderedDict() + + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, self.gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + + # Requires real prediction for feature matching loss + pred_fake, pred_real = self.discriminate(self.lq, self.output, self.gt) + l_g_gan = self.cri_gan(pred_fake, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + # feature matching loss + if self.cri_feat: + l_g_feat = self.cri_feat(pred_fake, pred_real) + l_g_total += l_g_feat + loss_dict['l_g_feat'] = l_g_feat + + l_g_total.backward() + self.optimizer_g.step() + + # optimize net_d + for p in self.net_d.parameters(): + p.requires_grad = True + + self.optimizer_d.zero_grad() + # TODO: Benchmark test between HiFaceGAN and SRGAN implementation: + # SRGAN use the same fake output for discriminator update + # while HiFaceGAN regenerate a new output using updated net_g + # This should not make too much difference though. Stick to SRGAN now. + # ------------------------------------------------------------------- + # ---------- Below are original HiFaceGAN code snippet -------------- + # ------------------------------------------------------------------- + # with torch.no_grad(): + # fake_image = self.net_g(self.lq) + # fake_image = fake_image.detach() + # fake_image.requires_grad_() + # pred_fake, pred_real = self.discriminate(self.lq, fake_image, self.gt) + + # real + pred_fake, pred_real = self.discriminate(self.lq, self.output.detach(), self.gt) + l_d_real = self.cri_gan(pred_real, True, is_disc=True) + loss_dict['l_d_real'] = l_d_real + # fake + l_d_fake = self.cri_gan(pred_fake, False, is_disc=True) + loss_dict['l_d_fake'] = l_d_fake + + l_d_total = (l_d_real + l_d_fake) / 2 + l_d_total.backward() + self.optimizer_d.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + if self.ema_decay > 0: + print('HiFaceGAN does not support EMA now. pass') + + def validation(self, dataloader, current_iter, tb_logger, save_img=False): + """ + Warning: HiFaceGAN requires train() mode even for validation + For more info, see https://github.com/Lotayou/Face-Renovation/issues/31 + + Args: + dataloader (torch.utils.data.DataLoader): Validation dataloader. + current_iter (int): Current iteration. + tb_logger (tensorboard logger): Tensorboard logger. + save_img (bool): Whether to save images. Default: False. + """ + + if self.opt['network_g']['type'] in ('HiFaceGAN', 'SPADEGenerator'): + self.net_g.train() + + if self.opt['dist']: + self.dist_validation(dataloader, current_iter, tb_logger, save_img) + else: + print('In HiFaceGANModel: The new metrics package is under development.' + + 'Using super method now (Only PSNR & SSIM are supported)') + super().nondist_validation(dataloader, current_iter, tb_logger, save_img) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + """ + TODO: Validation using updated metric system + The metrics are now evaluated after all images have been tested + This allows batch processing, and also allows evaluation of + distributional metrics, such as: + + @ Frechet Inception Distance: FID + @ Maximum Mean Discrepancy: MMD + + Warning: + Need careful batch management for different inference settings. + + """ + dataset_name = dataloader.dataset.opt['name'] + with_metrics = self.opt['val'].get('metrics') is not None + if with_metrics: + self.metric_results = dict() # {metric: 0 for metric in self.opt['val']['metrics'].keys()} + sr_tensors = [] + gt_tensors = [] + + pbar = tqdm(total=len(dataloader), unit='image') + for val_data in dataloader: + img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] + self.feed_data(val_data) + self.test() + + visuals = self.get_current_visuals() # detached cpu tensor, non-squeeze + sr_tensors.append(visuals['result']) + if 'gt' in visuals: + gt_tensors.append(visuals['gt']) + del self.gt + + # tentative for out of GPU memory + del self.lq + del self.output + torch.cuda.empty_cache() + + if save_img: + if self.opt['is_train']: + save_img_path = osp.join(self.opt['path']['visualization'], img_name, + f'{img_name}_{current_iter}.png') + else: + if self.opt['val']['suffix']: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["val"]["suffix"]}.png') + else: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["name"]}.png') + + imwrite(tensor2img(visuals['result']), save_img_path) + + pbar.update(1) + pbar.set_description(f'Test {img_name}') + pbar.close() + + if with_metrics: + sr_pack = torch.cat(sr_tensors, dim=0) + gt_pack = torch.cat(gt_tensors, dim=0) + # calculate metrics + for name, opt_ in self.opt['val']['metrics'].items(): + # The new metric caller automatically returns mean value + # FIXME: ERROR: calculate_metric only supports two arguments. Now the codes cannot be successfully run + self.metric_results[name] = calculate_metric(dict(sr_pack=sr_pack, gt_pack=gt_pack), opt_) + self._log_validation_metric_values(current_iter, dataset_name, tb_logger) + + def save(self, epoch, current_iter): + if hasattr(self, 'net_g_ema'): + print('HiFaceGAN does not support EMA now. Fallback to normal mode.') + + self.save_network(self.net_g, 'net_g', current_iter) + self.save_network(self.net_d, 'net_d', current_iter) + self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/lr_scheduler.py b/basicsr/models/lr_scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..11e1c6c7a74f5233accda52370f92681d3d3cecf --- /dev/null +++ b/basicsr/models/lr_scheduler.py @@ -0,0 +1,96 @@ +import math +from collections import Counter +from torch.optim.lr_scheduler import _LRScheduler + + +class MultiStepRestartLR(_LRScheduler): + """ MultiStep with restarts learning rate scheme. + + Args: + optimizer (torch.nn.optimizer): Torch optimizer. + milestones (list): Iterations that will decrease learning rate. + gamma (float): Decrease ratio. Default: 0.1. + restarts (list): Restart iterations. Default: [0]. + restart_weights (list): Restart weights at each restart iteration. + Default: [1]. + last_epoch (int): Used in _LRScheduler. Default: -1. + """ + + def __init__(self, optimizer, milestones, gamma=0.1, restarts=(0, ), restart_weights=(1, ), last_epoch=-1): + self.milestones = Counter(milestones) + self.gamma = gamma + self.restarts = restarts + self.restart_weights = restart_weights + assert len(self.restarts) == len(self.restart_weights), 'restarts and their weights do not match.' + super(MultiStepRestartLR, self).__init__(optimizer, last_epoch) + + def get_lr(self): + if self.last_epoch in self.restarts: + weight = self.restart_weights[self.restarts.index(self.last_epoch)] + return [group['initial_lr'] * weight for group in self.optimizer.param_groups] + if self.last_epoch not in self.milestones: + return [group['lr'] for group in self.optimizer.param_groups] + return [group['lr'] * self.gamma**self.milestones[self.last_epoch] for group in self.optimizer.param_groups] + + +def get_position_from_periods(iteration, cumulative_period): + """Get the position from a period list. + + It will return the index of the right-closest number in the period list. + For example, the cumulative_period = [100, 200, 300, 400], + if iteration == 50, return 0; + if iteration == 210, return 2; + if iteration == 300, return 2. + + Args: + iteration (int): Current iteration. + cumulative_period (list[int]): Cumulative period list. + + Returns: + int: The position of the right-closest number in the period list. + """ + for i, period in enumerate(cumulative_period): + if iteration <= period: + return i + + +class CosineAnnealingRestartLR(_LRScheduler): + """ Cosine annealing with restarts learning rate scheme. + + An example of config: + periods = [10, 10, 10, 10] + restart_weights = [1, 0.5, 0.5, 0.5] + eta_min=1e-7 + + It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the + scheduler will restart with the weights in restart_weights. + + Args: + optimizer (torch.nn.optimizer): Torch optimizer. + periods (list): Period for each cosine anneling cycle. + restart_weights (list): Restart weights at each restart iteration. + Default: [1]. + eta_min (float): The minimum lr. Default: 0. + last_epoch (int): Used in _LRScheduler. Default: -1. + """ + + def __init__(self, optimizer, periods, restart_weights=(1, ), eta_min=0, last_epoch=-1): + self.periods = periods + self.restart_weights = restart_weights + self.eta_min = eta_min + assert (len(self.periods) == len( + self.restart_weights)), 'periods and restart_weights should have the same length.' + self.cumulative_period = [sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))] + super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch) + + def get_lr(self): + idx = get_position_from_periods(self.last_epoch, self.cumulative_period) + current_weight = self.restart_weights[idx] + nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1] + current_period = self.periods[idx] + + return [ + self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) * + (1 + math.cos(math.pi * ((self.last_epoch - nearest_restart) / current_period))) + for base_lr in self.base_lrs + ] diff --git a/basicsr/models/realesrgan_model.py b/basicsr/models/realesrgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..c74b28fb1dc6a7f5c5ad3f7d8bb96c19c52ee92b --- /dev/null +++ b/basicsr/models/realesrgan_model.py @@ -0,0 +1,267 @@ +import numpy as np +import random +import torch +from collections import OrderedDict +from torch.nn import functional as F + +from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt +from basicsr.data.transforms import paired_random_crop +from basicsr.losses.loss_util import get_refined_artifact_map +from basicsr.models.srgan_model import SRGANModel +from basicsr.utils import DiffJPEG, USMSharp +from basicsr.utils.img_process_util import filter2D +from basicsr.utils.registry import MODEL_REGISTRY + + +@MODEL_REGISTRY.register(suffix='basicsr') +class RealESRGANModel(SRGANModel): + """RealESRGAN Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. + + It mainly performs: + 1. randomly synthesize LQ images in GPU tensors + 2. optimize the networks with GAN training. + """ + + def __init__(self, opt): + super(RealESRGANModel, self).__init__(opt) + self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts + self.usm_sharpener = USMSharp().cuda() # do usm sharpening + self.queue_size = opt.get('queue_size', 180) + + @torch.no_grad() + def _dequeue_and_enqueue(self): + """It is the training pair pool for increasing the diversity in a batch. + + Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a + batch could not have different resize scaling factors. Therefore, we employ this training pair pool + to increase the degradation diversity in a batch. + """ + # initialize + b, c, h, w = self.lq.size() + if not hasattr(self, 'queue_lr'): + assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' + self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() + _, c, h, w = self.gt.size() + self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() + self.queue_ptr = 0 + if self.queue_ptr == self.queue_size: # the pool is full + # do dequeue and enqueue + # shuffle + idx = torch.randperm(self.queue_size) + self.queue_lr = self.queue_lr[idx] + self.queue_gt = self.queue_gt[idx] + # get first b samples + lq_dequeue = self.queue_lr[0:b, :, :, :].clone() + gt_dequeue = self.queue_gt[0:b, :, :, :].clone() + # update the queue + self.queue_lr[0:b, :, :, :] = self.lq.clone() + self.queue_gt[0:b, :, :, :] = self.gt.clone() + + self.lq = lq_dequeue + self.gt = gt_dequeue + else: + # only do enqueue + self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() + self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() + self.queue_ptr = self.queue_ptr + b + + @torch.no_grad() + def feed_data(self, data): + """Accept data from dataloader, and then add two-order degradations to obtain LQ images. + """ + if self.is_train and self.opt.get('high_order_degradation', True): + # training data synthesis + self.gt = data['gt'].to(self.device) + self.gt_usm = self.usm_sharpener(self.gt) + + self.kernel1 = data['kernel1'].to(self.device) + self.kernel2 = data['kernel2'].to(self.device) + self.sinc_kernel = data['sinc_kernel'].to(self.device) + + ori_h, ori_w = self.gt.size()[2:4] + + # ----------------------- The first degradation process ----------------------- # + # blur + out = filter2D(self.gt_usm, self.kernel1) + # random resize + updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] + if updown_type == 'up': + scale = np.random.uniform(1, self.opt['resize_range'][1]) + elif updown_type == 'down': + scale = np.random.uniform(self.opt['resize_range'][0], 1) + else: + scale = 1 + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate(out, scale_factor=scale, mode=mode) + # add noise + gray_noise_prob = self.opt['gray_noise_prob'] + if np.random.uniform() < self.opt['gaussian_noise_prob']: + out = random_add_gaussian_noise_pt( + out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) + else: + out = random_add_poisson_noise_pt( + out, + scale_range=self.opt['poisson_scale_range'], + gray_prob=gray_noise_prob, + clip=True, + rounds=False) + # JPEG compression + jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) + out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts + out = self.jpeger(out, quality=jpeg_p) + + # ----------------------- The second degradation process ----------------------- # + # blur + if np.random.uniform() < self.opt['second_blur_prob']: + out = filter2D(out, self.kernel2) + # random resize + updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] + if updown_type == 'up': + scale = np.random.uniform(1, self.opt['resize_range2'][1]) + elif updown_type == 'down': + scale = np.random.uniform(self.opt['resize_range2'][0], 1) + else: + scale = 1 + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate( + out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) + # add noise + gray_noise_prob = self.opt['gray_noise_prob2'] + if np.random.uniform() < self.opt['gaussian_noise_prob2']: + out = random_add_gaussian_noise_pt( + out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) + else: + out = random_add_poisson_noise_pt( + out, + scale_range=self.opt['poisson_scale_range2'], + gray_prob=gray_noise_prob, + clip=True, + rounds=False) + + # JPEG compression + the final sinc filter + # We also need to resize images to desired sizes. We group [resize back + sinc filter] together + # as one operation. + # We consider two orders: + # 1. [resize back + sinc filter] + JPEG compression + # 2. JPEG compression + [resize back + sinc filter] + # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. + if np.random.uniform() < 0.5: + # resize back + the final sinc filter + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) + out = filter2D(out, self.sinc_kernel) + # JPEG compression + jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) + out = torch.clamp(out, 0, 1) + out = self.jpeger(out, quality=jpeg_p) + else: + # JPEG compression + jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) + out = torch.clamp(out, 0, 1) + out = self.jpeger(out, quality=jpeg_p) + # resize back + the final sinc filter + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) + out = filter2D(out, self.sinc_kernel) + + # clamp and round + self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. + + # random crop + gt_size = self.opt['gt_size'] + (self.gt, self.gt_usm), self.lq = paired_random_crop([self.gt, self.gt_usm], self.lq, gt_size, + self.opt['scale']) + + # training pair pool + self._dequeue_and_enqueue() + # sharpen self.gt again, as we have changed the self.gt with self._dequeue_and_enqueue + self.gt_usm = self.usm_sharpener(self.gt) + self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract + else: + # for paired training or validation + self.lq = data['lq'].to(self.device) + if 'gt' in data: + self.gt = data['gt'].to(self.device) + self.gt_usm = self.usm_sharpener(self.gt) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + # do not use the synthetic process during validation + self.is_train = False + super(RealESRGANModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) + self.is_train = True + + def optimize_parameters(self, current_iter): + # usm sharpening + l1_gt = self.gt_usm + percep_gt = self.gt_usm + gan_gt = self.gt_usm + if self.opt['l1_gt_usm'] is False: + l1_gt = self.gt + if self.opt['percep_gt_usm'] is False: + percep_gt = self.gt + if self.opt['gan_gt_usm'] is False: + gan_gt = self.gt + + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + + self.optimizer_g.zero_grad() + self.output = self.net_g(self.lq) + if self.cri_ldl: + self.output_ema = self.net_g_ema(self.lq) + + l_g_total = 0 + loss_dict = OrderedDict() + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, l1_gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + if self.cri_ldl: + pixel_weight = get_refined_artifact_map(self.gt, self.output, self.output_ema, 7) + l_g_ldl = self.cri_ldl(torch.mul(pixel_weight, self.output), torch.mul(pixel_weight, self.gt)) + l_g_total += l_g_ldl + loss_dict['l_g_ldl'] = l_g_ldl + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output, percep_gt) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + # gan loss + fake_g_pred = self.net_d(self.output) + l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + l_g_total.backward() + self.optimizer_g.step() + + # optimize net_d + for p in self.net_d.parameters(): + p.requires_grad = True + + self.optimizer_d.zero_grad() + # real + real_d_pred = self.net_d(gan_gt) + l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) + loss_dict['l_d_real'] = l_d_real + loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) + l_d_real.backward() + # fake + fake_d_pred = self.net_d(self.output.detach().clone()) # clone for pt1.9 + l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) + loss_dict['l_d_fake'] = l_d_fake + loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) + l_d_fake.backward() + self.optimizer_d.step() + + if self.ema_decay > 0: + self.model_ema(decay=self.ema_decay) + + self.log_dict = self.reduce_loss_dict(loss_dict) diff --git a/basicsr/models/realesrnet_model.py b/basicsr/models/realesrnet_model.py new file mode 100644 index 0000000000000000000000000000000000000000..f5790918b969682a0db0e2ed9236b7046d627b90 --- /dev/null +++ b/basicsr/models/realesrnet_model.py @@ -0,0 +1,189 @@ +import numpy as np +import random +import torch +from torch.nn import functional as F + +from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt +from basicsr.data.transforms import paired_random_crop +from basicsr.models.sr_model import SRModel +from basicsr.utils import DiffJPEG, USMSharp +from basicsr.utils.img_process_util import filter2D +from basicsr.utils.registry import MODEL_REGISTRY + + +@MODEL_REGISTRY.register(suffix='basicsr') +class RealESRNetModel(SRModel): + """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. + + It is trained without GAN losses. + It mainly performs: + 1. randomly synthesize LQ images in GPU tensors + 2. optimize the networks with GAN training. + """ + + def __init__(self, opt): + super(RealESRNetModel, self).__init__(opt) + self.jpeger = DiffJPEG(differentiable=False).cuda() # simulate JPEG compression artifacts + self.usm_sharpener = USMSharp().cuda() # do usm sharpening + self.queue_size = opt.get('queue_size', 180) + + @torch.no_grad() + def _dequeue_and_enqueue(self): + """It is the training pair pool for increasing the diversity in a batch. + + Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a + batch could not have different resize scaling factors. Therefore, we employ this training pair pool + to increase the degradation diversity in a batch. + """ + # initialize + b, c, h, w = self.lq.size() + if not hasattr(self, 'queue_lr'): + assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}' + self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda() + _, c, h, w = self.gt.size() + self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda() + self.queue_ptr = 0 + if self.queue_ptr == self.queue_size: # the pool is full + # do dequeue and enqueue + # shuffle + idx = torch.randperm(self.queue_size) + self.queue_lr = self.queue_lr[idx] + self.queue_gt = self.queue_gt[idx] + # get first b samples + lq_dequeue = self.queue_lr[0:b, :, :, :].clone() + gt_dequeue = self.queue_gt[0:b, :, :, :].clone() + # update the queue + self.queue_lr[0:b, :, :, :] = self.lq.clone() + self.queue_gt[0:b, :, :, :] = self.gt.clone() + + self.lq = lq_dequeue + self.gt = gt_dequeue + else: + # only do enqueue + self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone() + self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone() + self.queue_ptr = self.queue_ptr + b + + @torch.no_grad() + def feed_data(self, data): + """Accept data from dataloader, and then add two-order degradations to obtain LQ images. + """ + if self.is_train and self.opt.get('high_order_degradation', True): + # training data synthesis + self.gt = data['gt'].to(self.device) + # USM sharpen the GT images + if self.opt['gt_usm'] is True: + self.gt = self.usm_sharpener(self.gt) + + self.kernel1 = data['kernel1'].to(self.device) + self.kernel2 = data['kernel2'].to(self.device) + self.sinc_kernel = data['sinc_kernel'].to(self.device) + + ori_h, ori_w = self.gt.size()[2:4] + + # ----------------------- The first degradation process ----------------------- # + # blur + out = filter2D(self.gt, self.kernel1) + # random resize + updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0] + if updown_type == 'up': + scale = np.random.uniform(1, self.opt['resize_range'][1]) + elif updown_type == 'down': + scale = np.random.uniform(self.opt['resize_range'][0], 1) + else: + scale = 1 + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate(out, scale_factor=scale, mode=mode) + # add noise + gray_noise_prob = self.opt['gray_noise_prob'] + if np.random.uniform() < self.opt['gaussian_noise_prob']: + out = random_add_gaussian_noise_pt( + out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob) + else: + out = random_add_poisson_noise_pt( + out, + scale_range=self.opt['poisson_scale_range'], + gray_prob=gray_noise_prob, + clip=True, + rounds=False) + # JPEG compression + jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range']) + out = torch.clamp(out, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts + out = self.jpeger(out, quality=jpeg_p) + + # ----------------------- The second degradation process ----------------------- # + # blur + if np.random.uniform() < self.opt['second_blur_prob']: + out = filter2D(out, self.kernel2) + # random resize + updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0] + if updown_type == 'up': + scale = np.random.uniform(1, self.opt['resize_range2'][1]) + elif updown_type == 'down': + scale = np.random.uniform(self.opt['resize_range2'][0], 1) + else: + scale = 1 + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate( + out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode) + # add noise + gray_noise_prob = self.opt['gray_noise_prob2'] + if np.random.uniform() < self.opt['gaussian_noise_prob2']: + out = random_add_gaussian_noise_pt( + out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob) + else: + out = random_add_poisson_noise_pt( + out, + scale_range=self.opt['poisson_scale_range2'], + gray_prob=gray_noise_prob, + clip=True, + rounds=False) + + # JPEG compression + the final sinc filter + # We also need to resize images to desired sizes. We group [resize back + sinc filter] together + # as one operation. + # We consider two orders: + # 1. [resize back + sinc filter] + JPEG compression + # 2. JPEG compression + [resize back + sinc filter] + # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. + if np.random.uniform() < 0.5: + # resize back + the final sinc filter + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) + out = filter2D(out, self.sinc_kernel) + # JPEG compression + jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) + out = torch.clamp(out, 0, 1) + out = self.jpeger(out, quality=jpeg_p) + else: + # JPEG compression + jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2']) + out = torch.clamp(out, 0, 1) + out = self.jpeger(out, quality=jpeg_p) + # resize back + the final sinc filter + mode = random.choice(['area', 'bilinear', 'bicubic']) + out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode) + out = filter2D(out, self.sinc_kernel) + + # clamp and round + self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255. + + # random crop + gt_size = self.opt['gt_size'] + self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale']) + + # training pair pool + self._dequeue_and_enqueue() + self.lq = self.lq.contiguous() # for the warning: grad and param do not obey the gradient layout contract + else: + # for paired training or validation + self.lq = data['lq'].to(self.device) + if 'gt' in data: + self.gt = data['gt'].to(self.device) + self.gt_usm = self.usm_sharpener(self.gt) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + # do not use the synthetic process during validation + self.is_train = False + super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img) + self.is_train = True diff --git a/basicsr/models/sr_model.py b/basicsr/models/sr_model.py new file mode 100644 index 0000000000000000000000000000000000000000..54c80bd6d4fa6c17245e155a598903eb127dd8f4 --- /dev/null +++ b/basicsr/models/sr_model.py @@ -0,0 +1,231 @@ +import torch +from collections import OrderedDict +from os import path as osp +from tqdm import tqdm + +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.metrics import calculate_metric +from basicsr.utils import get_root_logger, imwrite, tensor2img +from basicsr.utils.registry import MODEL_REGISTRY +from .base_model import BaseModel + + +@MODEL_REGISTRY.register() +class SRModel(BaseModel): + """Base SR model for single image super-resolution.""" + + def __init__(self, opt): + super(SRModel, self).__init__(opt) + + # define network + self.net_g = build_network(opt['network_g']) + self.net_g = self.model_to_device(self.net_g) + self.print_network(self.net_g) + + # load pretrained models + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_g', 'params') + self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) + + if self.is_train: + self.init_training_settings() + + def init_training_settings(self): + self.net_g.train() + train_opt = self.opt['train'] + + self.ema_decay = train_opt.get('ema_decay', 0) + if self.ema_decay > 0: + logger = get_root_logger() + logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') + # define network net_g with Exponential Moving Average (EMA) + # net_g_ema is used only for testing on one GPU and saving + # There is no need to wrap with DistributedDataParallel + self.net_g_ema = build_network(self.opt['network_g']).to(self.device) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') + else: + self.model_ema(0) # copy net_g weight + self.net_g_ema.eval() + + # define losses + if train_opt.get('pixel_opt'): + self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) + else: + self.cri_pix = None + + if train_opt.get('perceptual_opt'): + self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) + else: + self.cri_perceptual = None + + if self.cri_pix is None and self.cri_perceptual is None: + raise ValueError('Both pixel and perceptual losses are None.') + + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + optim_params = [] + for k, v in self.net_g.named_parameters(): + if v.requires_grad: + optim_params.append(v) + else: + logger = get_root_logger() + logger.warning(f'Params {k} will not be optimized.') + + optim_type = train_opt['optim_g'].pop('type') + self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) + self.optimizers.append(self.optimizer_g) + + def feed_data(self, data): + self.lq = data['lq'].to(self.device) + if 'gt' in data: + self.gt = data['gt'].to(self.device) + + def optimize_parameters(self, current_iter): + self.optimizer_g.zero_grad() + self.output = self.net_g(self.lq) + + l_total = 0 + loss_dict = OrderedDict() + # pixel loss + if self.cri_pix: + l_pix = self.cri_pix(self.output, self.gt) + l_total += l_pix + loss_dict['l_pix'] = l_pix + # perceptual loss + if self.cri_perceptual: + l_percep, l_style = self.cri_perceptual(self.output, self.gt) + if l_percep is not None: + l_total += l_percep + loss_dict['l_percep'] = l_percep + if l_style is not None: + l_total += l_style + loss_dict['l_style'] = l_style + + l_total.backward() + self.optimizer_g.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + if self.ema_decay > 0: + self.model_ema(decay=self.ema_decay) + + def test(self): + if hasattr(self, 'net_g_ema'): + self.net_g_ema.eval() + with torch.no_grad(): + self.output = self.net_g_ema(self.lq) + else: + self.net_g.eval() + with torch.no_grad(): + self.output = self.net_g(self.lq) + self.net_g.train() + + def dist_validation(self, dataloader, current_iter, tb_logger, save_img): + if self.opt['rank'] == 0: + self.nondist_validation(dataloader, current_iter, tb_logger, save_img) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + dataset_name = dataloader.dataset.opt['name'] + with_metrics = self.opt['val'].get('metrics') is not None + use_pbar = self.opt['val'].get('pbar', False) + + if with_metrics: + if not hasattr(self, 'metric_results'): # only execute in the first run + self.metric_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} + # initialize the best metric results for each dataset_name (supporting multiple validation datasets) + self._initialize_best_metric_results(dataset_name) + # zero self.metric_results + if with_metrics: + self.metric_results = {metric: 0 for metric in self.metric_results} + + metric_data = dict() + if use_pbar: + pbar = tqdm(total=len(dataloader), unit='image') + + for idx, val_data in enumerate(dataloader): + img_name = osp.splitext(osp.basename(val_data['lq_path'][0]))[0] + self.feed_data(val_data) + self.test() + + visuals = self.get_current_visuals() + sr_img = tensor2img([visuals['result']]) + metric_data['img'] = sr_img + if 'gt' in visuals: + gt_img = tensor2img([visuals['gt']]) + metric_data['img2'] = gt_img + del self.gt + + # tentative for out of GPU memory + del self.lq + del self.output + torch.cuda.empty_cache() + + if save_img: + if self.opt['is_train']: + save_img_path = osp.join(self.opt['path']['visualization'], img_name, + f'{img_name}_{current_iter}.png') + else: + if self.opt['val']['suffix']: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["val"]["suffix"]}.png') + else: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, + f'{img_name}_{self.opt["name"]}.png') + imwrite(sr_img, save_img_path) + + if with_metrics: + # calculate metrics + for name, opt_ in self.opt['val']['metrics'].items(): + self.metric_results[name] += calculate_metric(metric_data, opt_) + if use_pbar: + pbar.update(1) + pbar.set_description(f'Test {img_name}') + if use_pbar: + pbar.close() + + if with_metrics: + for metric in self.metric_results.keys(): + self.metric_results[metric] /= (idx + 1) + # update the best metric result + self._update_best_metric_result(dataset_name, metric, self.metric_results[metric], current_iter) + + self._log_validation_metric_values(current_iter, dataset_name, tb_logger) + + def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): + log_str = f'Validation {dataset_name}\n' + for metric, value in self.metric_results.items(): + log_str += f'\t # {metric}: {value:.4f}' + if hasattr(self, 'best_metric_results'): + log_str += (f'\tBest: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' + f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') + log_str += '\n' + + logger = get_root_logger() + logger.info(log_str) + if tb_logger: + for metric, value in self.metric_results.items(): + tb_logger.add_scalar(f'metrics/{dataset_name}/{metric}', value, current_iter) + + def get_current_visuals(self): + out_dict = OrderedDict() + out_dict['lq'] = self.lq.detach().cpu() + out_dict['result'] = self.output.detach().cpu() + if hasattr(self, 'gt'): + out_dict['gt'] = self.gt.detach().cpu() + return out_dict + + def save(self, epoch, current_iter): + if hasattr(self, 'net_g_ema'): + self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) + else: + self.save_network(self.net_g, 'net_g', current_iter) + self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/srgan_model.py b/basicsr/models/srgan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..45387ca7908e3f38f59a605adb8242ad12fcf1a1 --- /dev/null +++ b/basicsr/models/srgan_model.py @@ -0,0 +1,149 @@ +import torch +from collections import OrderedDict + +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.utils import get_root_logger +from basicsr.utils.registry import MODEL_REGISTRY +from .sr_model import SRModel + + +@MODEL_REGISTRY.register() +class SRGANModel(SRModel): + """SRGAN model for single image super-resolution.""" + + def init_training_settings(self): + train_opt = self.opt['train'] + + self.ema_decay = train_opt.get('ema_decay', 0) + if self.ema_decay > 0: + logger = get_root_logger() + logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') + # define network net_g with Exponential Moving Average (EMA) + # net_g_ema is used only for testing on one GPU and saving + # There is no need to wrap with DistributedDataParallel + self.net_g_ema = build_network(self.opt['network_g']).to(self.device) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') + else: + self.model_ema(0) # copy net_g weight + self.net_g_ema.eval() + + # define network net_d + self.net_d = build_network(self.opt['network_d']) + self.net_d = self.model_to_device(self.net_d) + self.print_network(self.net_d) + + # load pretrained models + load_path = self.opt['path'].get('pretrain_network_d', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_d', 'params') + self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) + + self.net_g.train() + self.net_d.train() + + # define losses + if train_opt.get('pixel_opt'): + self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) + else: + self.cri_pix = None + + if train_opt.get('ldl_opt'): + self.cri_ldl = build_loss(train_opt['ldl_opt']).to(self.device) + else: + self.cri_ldl = None + + if train_opt.get('perceptual_opt'): + self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) + else: + self.cri_perceptual = None + + if train_opt.get('gan_opt'): + self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) + + self.net_d_iters = train_opt.get('net_d_iters', 1) + self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) + + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + # optimizer g + optim_type = train_opt['optim_g'].pop('type') + self.optimizer_g = self.get_optimizer(optim_type, self.net_g.parameters(), **train_opt['optim_g']) + self.optimizers.append(self.optimizer_g) + # optimizer d + optim_type = train_opt['optim_d'].pop('type') + self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) + self.optimizers.append(self.optimizer_d) + + def optimize_parameters(self, current_iter): + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + + self.optimizer_g.zero_grad() + self.output = self.net_g(self.lq) + + l_g_total = 0 + loss_dict = OrderedDict() + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, self.gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output, self.gt) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + # gan loss + fake_g_pred = self.net_d(self.output) + l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + l_g_total.backward() + self.optimizer_g.step() + + # optimize net_d + for p in self.net_d.parameters(): + p.requires_grad = True + + self.optimizer_d.zero_grad() + # real + real_d_pred = self.net_d(self.gt) + l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) + loss_dict['l_d_real'] = l_d_real + loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) + l_d_real.backward() + # fake + fake_d_pred = self.net_d(self.output.detach()) + l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) + loss_dict['l_d_fake'] = l_d_fake + loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) + l_d_fake.backward() + self.optimizer_d.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + if self.ema_decay > 0: + self.model_ema(decay=self.ema_decay) + + def save(self, epoch, current_iter): + if hasattr(self, 'net_g_ema'): + self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) + else: + self.save_network(self.net_g, 'net_g', current_iter) + self.save_network(self.net_d, 'net_d', current_iter) + self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/stylegan2_model.py b/basicsr/models/stylegan2_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d7da708122160f2be51a98a6a635349f34ee042e --- /dev/null +++ b/basicsr/models/stylegan2_model.py @@ -0,0 +1,283 @@ +import cv2 +import math +import numpy as np +import random +import torch +from collections import OrderedDict +from os import path as osp + +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.losses.gan_loss import g_path_regularize, r1_penalty +from basicsr.utils import imwrite, tensor2img +from basicsr.utils.registry import MODEL_REGISTRY +from .base_model import BaseModel + + +@MODEL_REGISTRY.register() +class StyleGAN2Model(BaseModel): + """StyleGAN2 model.""" + + def __init__(self, opt): + super(StyleGAN2Model, self).__init__(opt) + + # define network net_g + self.net_g = build_network(opt['network_g']) + self.net_g = self.model_to_device(self.net_g) + self.print_network(self.net_g) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_g', 'params') + self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key) + + # latent dimension: self.num_style_feat + self.num_style_feat = opt['network_g']['num_style_feat'] + num_val_samples = self.opt['val'].get('num_val_samples', 16) + self.fixed_sample = torch.randn(num_val_samples, self.num_style_feat, device=self.device) + + if self.is_train: + self.init_training_settings() + + def init_training_settings(self): + train_opt = self.opt['train'] + + # define network net_d + self.net_d = build_network(self.opt['network_d']) + self.net_d = self.model_to_device(self.net_d) + self.print_network(self.net_d) + + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_d', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_d', 'params') + self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) + + # define network net_g with Exponential Moving Average (EMA) + # net_g_ema only used for testing on one GPU and saving, do not need to + # wrap with DistributedDataParallel + self.net_g_ema = build_network(self.opt['network_g']).to(self.device) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') + else: + self.model_ema(0) # copy net_g weight + + self.net_g.train() + self.net_d.train() + self.net_g_ema.eval() + + # define losses + # gan loss (wgan) + self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) + # regularization weights + self.r1_reg_weight = train_opt['r1_reg_weight'] # for discriminator + self.path_reg_weight = train_opt['path_reg_weight'] # for generator + + self.net_g_reg_every = train_opt['net_g_reg_every'] + self.net_d_reg_every = train_opt['net_d_reg_every'] + self.mixing_prob = train_opt['mixing_prob'] + + self.mean_path_length = 0 + + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + # optimizer g + net_g_reg_ratio = self.net_g_reg_every / (self.net_g_reg_every + 1) + if self.opt['network_g']['type'] == 'StyleGAN2GeneratorC': + normal_params = [] + style_mlp_params = [] + modulation_conv_params = [] + for name, param in self.net_g.named_parameters(): + if 'modulation' in name: + normal_params.append(param) + elif 'style_mlp' in name: + style_mlp_params.append(param) + elif 'modulated_conv' in name: + modulation_conv_params.append(param) + else: + normal_params.append(param) + optim_params_g = [ + { # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }, + { + 'params': style_mlp_params, + 'lr': train_opt['optim_g']['lr'] * 0.01 + }, + { + 'params': modulation_conv_params, + 'lr': train_opt['optim_g']['lr'] / 3 + } + ] + else: + normal_params = [] + for name, param in self.net_g.named_parameters(): + normal_params.append(param) + optim_params_g = [{ # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }] + + optim_type = train_opt['optim_g'].pop('type') + lr = train_opt['optim_g']['lr'] * net_g_reg_ratio + betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio) + self.optimizer_g = self.get_optimizer(optim_type, optim_params_g, lr, betas=betas) + self.optimizers.append(self.optimizer_g) + + # optimizer d + net_d_reg_ratio = self.net_d_reg_every / (self.net_d_reg_every + 1) + if self.opt['network_d']['type'] == 'StyleGAN2DiscriminatorC': + normal_params = [] + linear_params = [] + for name, param in self.net_d.named_parameters(): + if 'final_linear' in name: + linear_params.append(param) + else: + normal_params.append(param) + optim_params_d = [ + { # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_d']['lr'] + }, + { + 'params': linear_params, + 'lr': train_opt['optim_d']['lr'] * (1 / math.sqrt(512)) + } + ] + else: + normal_params = [] + for name, param in self.net_d.named_parameters(): + normal_params.append(param) + optim_params_d = [{ # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_d']['lr'] + }] + + optim_type = train_opt['optim_d'].pop('type') + lr = train_opt['optim_d']['lr'] * net_d_reg_ratio + betas = (0**net_d_reg_ratio, 0.99**net_d_reg_ratio) + self.optimizer_d = self.get_optimizer(optim_type, optim_params_d, lr, betas=betas) + self.optimizers.append(self.optimizer_d) + + def feed_data(self, data): + self.real_img = data['gt'].to(self.device) + + def make_noise(self, batch, num_noise): + if num_noise == 1: + noises = torch.randn(batch, self.num_style_feat, device=self.device) + else: + noises = torch.randn(num_noise, batch, self.num_style_feat, device=self.device).unbind(0) + return noises + + def mixing_noise(self, batch, prob): + if random.random() < prob: + return self.make_noise(batch, 2) + else: + return [self.make_noise(batch, 1)] + + def optimize_parameters(self, current_iter): + loss_dict = OrderedDict() + + # optimize net_d + for p in self.net_d.parameters(): + p.requires_grad = True + self.optimizer_d.zero_grad() + + batch = self.real_img.size(0) + noise = self.mixing_noise(batch, self.mixing_prob) + fake_img, _ = self.net_g(noise) + fake_pred = self.net_d(fake_img.detach()) + + real_pred = self.net_d(self.real_img) + # wgan loss with softplus (logistic loss) for discriminator + l_d = self.cri_gan(real_pred, True, is_disc=True) + self.cri_gan(fake_pred, False, is_disc=True) + loss_dict['l_d'] = l_d + # In wgan, real_score should be positive and fake_score should be + # negative + loss_dict['real_score'] = real_pred.detach().mean() + loss_dict['fake_score'] = fake_pred.detach().mean() + l_d.backward() + + if current_iter % self.net_d_reg_every == 0: + self.real_img.requires_grad = True + real_pred = self.net_d(self.real_img) + l_d_r1 = r1_penalty(real_pred, self.real_img) + l_d_r1 = (self.r1_reg_weight / 2 * l_d_r1 * self.net_d_reg_every + 0 * real_pred[0]) + # TODO: why do we need to add 0 * real_pred, otherwise, a runtime + # error will arise: RuntimeError: Expected to have finished + # reduction in the prior iteration before starting a new one. + # This error indicates that your module has parameters that were + # not used in producing loss. + loss_dict['l_d_r1'] = l_d_r1.detach().mean() + l_d_r1.backward() + + self.optimizer_d.step() + + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + self.optimizer_g.zero_grad() + + noise = self.mixing_noise(batch, self.mixing_prob) + fake_img, _ = self.net_g(noise) + fake_pred = self.net_d(fake_img) + + # wgan loss with softplus (non-saturating loss) for generator + l_g = self.cri_gan(fake_pred, True, is_disc=False) + loss_dict['l_g'] = l_g + l_g.backward() + + if current_iter % self.net_g_reg_every == 0: + path_batch_size = max(1, batch // self.opt['train']['path_batch_shrink']) + noise = self.mixing_noise(path_batch_size, self.mixing_prob) + fake_img, latents = self.net_g(noise, return_latents=True) + l_g_path, path_lengths, self.mean_path_length = g_path_regularize(fake_img, latents, self.mean_path_length) + + l_g_path = (self.path_reg_weight * self.net_g_reg_every * l_g_path + 0 * fake_img[0, 0, 0, 0]) + # TODO: why do we need to add 0 * fake_img[0, 0, 0, 0] + l_g_path.backward() + loss_dict['l_g_path'] = l_g_path.detach().mean() + loss_dict['path_length'] = path_lengths + + self.optimizer_g.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + # EMA + self.model_ema(decay=0.5**(32 / (10 * 1000))) + + def test(self): + with torch.no_grad(): + self.net_g_ema.eval() + self.output, _ = self.net_g_ema([self.fixed_sample]) + + def dist_validation(self, dataloader, current_iter, tb_logger, save_img): + if self.opt['rank'] == 0: + self.nondist_validation(dataloader, current_iter, tb_logger, save_img) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + assert dataloader is None, 'Validation dataloader should be None.' + self.test() + result = tensor2img(self.output, min_max=(-1, 1)) + if self.opt['is_train']: + save_img_path = osp.join(self.opt['path']['visualization'], 'train', f'train_{current_iter}.png') + else: + save_img_path = osp.join(self.opt['path']['visualization'], 'test', f'test_{self.opt["name"]}.png') + imwrite(result, save_img_path) + # add sample images to tb_logger + result = (result / 255.).astype(np.float32) + result = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) + if tb_logger is not None: + tb_logger.add_image('samples', result, global_step=current_iter, dataformats='HWC') + + def save(self, epoch, current_iter): + self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) + self.save_network(self.net_d, 'net_d', current_iter) + self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/swinir_model.py b/basicsr/models/swinir_model.py new file mode 100644 index 0000000000000000000000000000000000000000..5ac182f23b4a300aff14b2b45fcdca8c00da90c1 --- /dev/null +++ b/basicsr/models/swinir_model.py @@ -0,0 +1,33 @@ +import torch +from torch.nn import functional as F + +from basicsr.utils.registry import MODEL_REGISTRY +from .sr_model import SRModel + + +@MODEL_REGISTRY.register() +class SwinIRModel(SRModel): + + def test(self): + # pad to multiplication of window_size + window_size = self.opt['network_g']['window_size'] + scale = self.opt.get('scale', 1) + mod_pad_h, mod_pad_w = 0, 0 + _, _, h, w = self.lq.size() + if h % window_size != 0: + mod_pad_h = window_size - h % window_size + if w % window_size != 0: + mod_pad_w = window_size - w % window_size + img = F.pad(self.lq, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + if hasattr(self, 'net_g_ema'): + self.net_g_ema.eval() + with torch.no_grad(): + self.output = self.net_g_ema(img) + else: + self.net_g.eval() + with torch.no_grad(): + self.output = self.net_g(img) + self.net_g.train() + + _, _, h, w = self.output.size() + self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale] diff --git a/basicsr/models/video_base_model.py b/basicsr/models/video_base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..9f7993a15e585526135d1ede094f4dcff47f64db --- /dev/null +++ b/basicsr/models/video_base_model.py @@ -0,0 +1,160 @@ +import torch +from collections import Counter +from os import path as osp +from torch import distributed as dist +from tqdm import tqdm + +from basicsr.metrics import calculate_metric +from basicsr.utils import get_root_logger, imwrite, tensor2img +from basicsr.utils.dist_util import get_dist_info +from basicsr.utils.registry import MODEL_REGISTRY +from .sr_model import SRModel + + +@MODEL_REGISTRY.register() +class VideoBaseModel(SRModel): + """Base video SR model.""" + + def dist_validation(self, dataloader, current_iter, tb_logger, save_img): + dataset = dataloader.dataset + dataset_name = dataset.opt['name'] + with_metrics = self.opt['val']['metrics'] is not None + # initialize self.metric_results + # It is a dict: { + # 'folder1': tensor (num_frame x len(metrics)), + # 'folder2': tensor (num_frame x len(metrics)) + # } + if with_metrics: + if not hasattr(self, 'metric_results'): # only execute in the first run + self.metric_results = {} + num_frame_each_folder = Counter(dataset.data_info['folder']) + for folder, num_frame in num_frame_each_folder.items(): + self.metric_results[folder] = torch.zeros( + num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') + # initialize the best metric results + self._initialize_best_metric_results(dataset_name) + # zero self.metric_results + rank, world_size = get_dist_info() + if with_metrics: + for _, tensor in self.metric_results.items(): + tensor.zero_() + + metric_data = dict() + # record all frames (border and center frames) + if rank == 0: + pbar = tqdm(total=len(dataset), unit='frame') + for idx in range(rank, len(dataset), world_size): + val_data = dataset[idx] + val_data['lq'].unsqueeze_(0) + val_data['gt'].unsqueeze_(0) + folder = val_data['folder'] + frame_idx, max_idx = val_data['idx'].split('/') + lq_path = val_data['lq_path'] + + self.feed_data(val_data) + self.test() + visuals = self.get_current_visuals() + result_img = tensor2img([visuals['result']]) + metric_data['img'] = result_img + if 'gt' in visuals: + gt_img = tensor2img([visuals['gt']]) + metric_data['img2'] = gt_img + del self.gt + + # tentative for out of GPU memory + del self.lq + del self.output + torch.cuda.empty_cache() + + if save_img: + if self.opt['is_train']: + raise NotImplementedError('saving image is not supported during training.') + else: + if 'vimeo' in dataset_name.lower(): # vimeo90k dataset + split_result = lq_path.split('/') + img_name = f'{split_result[-3]}_{split_result[-2]}_{split_result[-1].split(".")[0]}' + else: # other datasets, e.g., REDS, Vid4 + img_name = osp.splitext(osp.basename(lq_path))[0] + + if self.opt['val']['suffix']: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, + f'{img_name}_{self.opt["val"]["suffix"]}.png') + else: + save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, + f'{img_name}_{self.opt["name"]}.png') + imwrite(result_img, save_img_path) + + if with_metrics: + # calculate metrics + for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): + result = calculate_metric(metric_data, opt_) + self.metric_results[folder][int(frame_idx), metric_idx] += result + + # progress bar + if rank == 0: + for _ in range(world_size): + pbar.update(1) + pbar.set_description(f'Test {folder}: {int(frame_idx) + world_size}/{max_idx}') + if rank == 0: + pbar.close() + + if with_metrics: + if self.opt['dist']: + # collect data among GPUs + for _, tensor in self.metric_results.items(): + dist.reduce(tensor, 0) + dist.barrier() + else: + pass # assume use one gpu in non-dist testing + + if rank == 0: + self._log_validation_metric_values(current_iter, dataset_name, tb_logger) + + def nondist_validation(self, dataloader, current_iter, tb_logger, save_img): + logger = get_root_logger() + logger.warning('nondist_validation is not implemented. Run dist_validation.') + self.dist_validation(dataloader, current_iter, tb_logger, save_img) + + def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger): + # ----------------- calculate the average values for each folder, and for each metric ----------------- # + # average all frames for each sub-folder + # metric_results_avg is a dict:{ + # 'folder1': tensor (len(metrics)), + # 'folder2': tensor (len(metrics)) + # } + metric_results_avg = { + folder: torch.mean(tensor, dim=0).cpu() + for (folder, tensor) in self.metric_results.items() + } + # total_avg_results is a dict: { + # 'metric1': float, + # 'metric2': float + # } + total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()} + for folder, tensor in metric_results_avg.items(): + for idx, metric in enumerate(total_avg_results.keys()): + total_avg_results[metric] += metric_results_avg[folder][idx].item() + # average among folders + for metric in total_avg_results.keys(): + total_avg_results[metric] /= len(metric_results_avg) + # update the best metric result + self._update_best_metric_result(dataset_name, metric, total_avg_results[metric], current_iter) + + # ------------------------------------------ log the metric ------------------------------------------ # + log_str = f'Validation {dataset_name}\n' + for metric_idx, (metric, value) in enumerate(total_avg_results.items()): + log_str += f'\t # {metric}: {value:.4f}' + for folder, tensor in metric_results_avg.items(): + log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}' + if hasattr(self, 'best_metric_results'): + log_str += (f'\n\t Best: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ ' + f'{self.best_metric_results[dataset_name][metric]["iter"]} iter') + log_str += '\n' + + logger = get_root_logger() + logger.info(log_str) + if tb_logger: + for metric_idx, (metric, value) in enumerate(total_avg_results.items()): + tb_logger.add_scalar(f'metrics/{metric}', value, current_iter) + for folder, tensor in metric_results_avg.items(): + tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter) diff --git a/basicsr/models/video_gan_model.py b/basicsr/models/video_gan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7500cfd27da2195d69d10a5cbd21a611c2c9dcd6 --- /dev/null +++ b/basicsr/models/video_gan_model.py @@ -0,0 +1,17 @@ +from basicsr.utils.registry import MODEL_REGISTRY +from .srgan_model import SRGANModel +from .video_base_model import VideoBaseModel + + +@MODEL_REGISTRY.register() +class VideoGANModel(SRGANModel, VideoBaseModel): + """Video GAN model. + + Use multiple inheritance. + It will first use the functions of SRGANModel: + init_training_settings + setup_optimizers + optimize_parameters + save + Then find functions in VideoBaseModel. + """ diff --git a/basicsr/models/video_recurrent_gan_model.py b/basicsr/models/video_recurrent_gan_model.py new file mode 100644 index 0000000000000000000000000000000000000000..74cf81145c50ffafb220d22b51e56746dee5ba41 --- /dev/null +++ b/basicsr/models/video_recurrent_gan_model.py @@ -0,0 +1,180 @@ +import torch +from collections import OrderedDict + +from basicsr.archs import build_network +from basicsr.losses import build_loss +from basicsr.utils import get_root_logger +from basicsr.utils.registry import MODEL_REGISTRY +from .video_recurrent_model import VideoRecurrentModel + + +@MODEL_REGISTRY.register() +class VideoRecurrentGANModel(VideoRecurrentModel): + + def init_training_settings(self): + train_opt = self.opt['train'] + + self.ema_decay = train_opt.get('ema_decay', 0) + if self.ema_decay > 0: + logger = get_root_logger() + logger.info(f'Use Exponential Moving Average with decay: {self.ema_decay}') + # build network net_g with Exponential Moving Average (EMA) + # net_g_ema only used for testing on one GPU and saving. + # There is no need to wrap with DistributedDataParallel + self.net_g_ema = build_network(self.opt['network_g']).to(self.device) + # load pretrained model + load_path = self.opt['path'].get('pretrain_network_g', None) + if load_path is not None: + self.load_network(self.net_g_ema, load_path, self.opt['path'].get('strict_load_g', True), 'params_ema') + else: + self.model_ema(0) # copy net_g weight + self.net_g_ema.eval() + + # define network net_d + self.net_d = build_network(self.opt['network_d']) + self.net_d = self.model_to_device(self.net_d) + self.print_network(self.net_d) + + # load pretrained models + load_path = self.opt['path'].get('pretrain_network_d', None) + if load_path is not None: + param_key = self.opt['path'].get('param_key_d', 'params') + self.load_network(self.net_d, load_path, self.opt['path'].get('strict_load_d', True), param_key) + + self.net_g.train() + self.net_d.train() + + # define losses + if train_opt.get('pixel_opt'): + self.cri_pix = build_loss(train_opt['pixel_opt']).to(self.device) + else: + self.cri_pix = None + + if train_opt.get('perceptual_opt'): + self.cri_perceptual = build_loss(train_opt['perceptual_opt']).to(self.device) + else: + self.cri_perceptual = None + + if train_opt.get('gan_opt'): + self.cri_gan = build_loss(train_opt['gan_opt']).to(self.device) + + self.net_d_iters = train_opt.get('net_d_iters', 1) + self.net_d_init_iters = train_opt.get('net_d_init_iters', 0) + + # set up optimizers and schedulers + self.setup_optimizers() + self.setup_schedulers() + + def setup_optimizers(self): + train_opt = self.opt['train'] + if train_opt['fix_flow']: + normal_params = [] + flow_params = [] + for name, param in self.net_g.named_parameters(): + if 'spynet' in name: # The fix_flow now only works for spynet. + flow_params.append(param) + else: + normal_params.append(param) + + optim_params = [ + { # add flow params first + 'params': flow_params, + 'lr': train_opt['lr_flow'] + }, + { + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }, + ] + else: + optim_params = self.net_g.parameters() + + # optimizer g + optim_type = train_opt['optim_g'].pop('type') + self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) + self.optimizers.append(self.optimizer_g) + # optimizer d + optim_type = train_opt['optim_d'].pop('type') + self.optimizer_d = self.get_optimizer(optim_type, self.net_d.parameters(), **train_opt['optim_d']) + self.optimizers.append(self.optimizer_d) + + def optimize_parameters(self, current_iter): + logger = get_root_logger() + # optimize net_g + for p in self.net_d.parameters(): + p.requires_grad = False + + if self.fix_flow_iter: + if current_iter == 1: + logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') + for name, param in self.net_g.named_parameters(): + if 'spynet' in name or 'edvr' in name: + param.requires_grad_(False) + elif current_iter == self.fix_flow_iter: + logger.warning('Train all the parameters.') + self.net_g.requires_grad_(True) + + self.optimizer_g.zero_grad() + self.output = self.net_g(self.lq) + + _, _, c, h, w = self.output.size() + + l_g_total = 0 + loss_dict = OrderedDict() + if (current_iter % self.net_d_iters == 0 and current_iter > self.net_d_init_iters): + # pixel loss + if self.cri_pix: + l_g_pix = self.cri_pix(self.output, self.gt) + l_g_total += l_g_pix + loss_dict['l_g_pix'] = l_g_pix + # perceptual loss + if self.cri_perceptual: + l_g_percep, l_g_style = self.cri_perceptual(self.output.view(-1, c, h, w), self.gt.view(-1, c, h, w)) + if l_g_percep is not None: + l_g_total += l_g_percep + loss_dict['l_g_percep'] = l_g_percep + if l_g_style is not None: + l_g_total += l_g_style + loss_dict['l_g_style'] = l_g_style + # gan loss + fake_g_pred = self.net_d(self.output.view(-1, c, h, w)) + l_g_gan = self.cri_gan(fake_g_pred, True, is_disc=False) + l_g_total += l_g_gan + loss_dict['l_g_gan'] = l_g_gan + + l_g_total.backward() + self.optimizer_g.step() + + # optimize net_d + for p in self.net_d.parameters(): + p.requires_grad = True + + self.optimizer_d.zero_grad() + # real + # reshape to (b*n, c, h, w) + real_d_pred = self.net_d(self.gt.view(-1, c, h, w)) + l_d_real = self.cri_gan(real_d_pred, True, is_disc=True) + loss_dict['l_d_real'] = l_d_real + loss_dict['out_d_real'] = torch.mean(real_d_pred.detach()) + l_d_real.backward() + # fake + # reshape to (b*n, c, h, w) + fake_d_pred = self.net_d(self.output.view(-1, c, h, w).detach()) + l_d_fake = self.cri_gan(fake_d_pred, False, is_disc=True) + loss_dict['l_d_fake'] = l_d_fake + loss_dict['out_d_fake'] = torch.mean(fake_d_pred.detach()) + l_d_fake.backward() + self.optimizer_d.step() + + self.log_dict = self.reduce_loss_dict(loss_dict) + + if self.ema_decay > 0: + self.model_ema(decay=self.ema_decay) + + def save(self, epoch, current_iter): + if self.ema_decay > 0: + self.save_network([self.net_g, self.net_g_ema], 'net_g', current_iter, param_key=['params', 'params_ema']) + else: + self.save_network(self.net_g, 'net_g', current_iter) + self.save_network(self.net_d, 'net_d', current_iter) + self.save_training_state(epoch, current_iter) diff --git a/basicsr/models/video_recurrent_model.py b/basicsr/models/video_recurrent_model.py new file mode 100644 index 0000000000000000000000000000000000000000..796ee57d5aeb84e81fe8dc769facc8339798cc3e --- /dev/null +++ b/basicsr/models/video_recurrent_model.py @@ -0,0 +1,197 @@ +import torch +from collections import Counter +from os import path as osp +from torch import distributed as dist +from tqdm import tqdm + +from basicsr.metrics import calculate_metric +from basicsr.utils import get_root_logger, imwrite, tensor2img +from basicsr.utils.dist_util import get_dist_info +from basicsr.utils.registry import MODEL_REGISTRY +from .video_base_model import VideoBaseModel + + +@MODEL_REGISTRY.register() +class VideoRecurrentModel(VideoBaseModel): + + def __init__(self, opt): + super(VideoRecurrentModel, self).__init__(opt) + if self.is_train: + self.fix_flow_iter = opt['train'].get('fix_flow') + + def setup_optimizers(self): + train_opt = self.opt['train'] + flow_lr_mul = train_opt.get('flow_lr_mul', 1) + logger = get_root_logger() + logger.info(f'Multiple the learning rate for flow network with {flow_lr_mul}.') + if flow_lr_mul == 1: + optim_params = self.net_g.parameters() + else: # separate flow params and normal params for different lr + normal_params = [] + flow_params = [] + for name, param in self.net_g.named_parameters(): + if 'spynet' in name: + flow_params.append(param) + else: + normal_params.append(param) + optim_params = [ + { # add normal params first + 'params': normal_params, + 'lr': train_opt['optim_g']['lr'] + }, + { + 'params': flow_params, + 'lr': train_opt['optim_g']['lr'] * flow_lr_mul + }, + ] + + optim_type = train_opt['optim_g'].pop('type') + self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g']) + self.optimizers.append(self.optimizer_g) + + def optimize_parameters(self, current_iter): + if self.fix_flow_iter: + logger = get_root_logger() + if current_iter == 1: + logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.') + for name, param in self.net_g.named_parameters(): + if 'spynet' in name or 'edvr' in name: + param.requires_grad_(False) + elif current_iter == self.fix_flow_iter: + logger.warning('Train all the parameters.') + self.net_g.requires_grad_(True) + + super(VideoRecurrentModel, self).optimize_parameters(current_iter) + + def dist_validation(self, dataloader, current_iter, tb_logger, save_img): + dataset = dataloader.dataset + dataset_name = dataset.opt['name'] + with_metrics = self.opt['val']['metrics'] is not None + # initialize self.metric_results + # It is a dict: { + # 'folder1': tensor (num_frame x len(metrics)), + # 'folder2': tensor (num_frame x len(metrics)) + # } + if with_metrics: + if not hasattr(self, 'metric_results'): # only execute in the first run + self.metric_results = {} + num_frame_each_folder = Counter(dataset.data_info['folder']) + for folder, num_frame in num_frame_each_folder.items(): + self.metric_results[folder] = torch.zeros( + num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda') + # initialize the best metric results + self._initialize_best_metric_results(dataset_name) + # zero self.metric_results + rank, world_size = get_dist_info() + if with_metrics: + for _, tensor in self.metric_results.items(): + tensor.zero_() + + metric_data = dict() + num_folders = len(dataset) + num_pad = (world_size - (num_folders % world_size)) % world_size + if rank == 0: + pbar = tqdm(total=len(dataset), unit='folder') + # Will evaluate (num_folders + num_pad) times, but only the first num_folders results will be recorded. + # (To avoid wait-dead) + for i in range(rank, num_folders + num_pad, world_size): + idx = min(i, num_folders - 1) + val_data = dataset[idx] + folder = val_data['folder'] + + # compute outputs + val_data['lq'].unsqueeze_(0) + val_data['gt'].unsqueeze_(0) + self.feed_data(val_data) + val_data['lq'].squeeze_(0) + val_data['gt'].squeeze_(0) + + self.test() + visuals = self.get_current_visuals() + + # tentative for out of GPU memory + del self.lq + del self.output + if 'gt' in visuals: + del self.gt + torch.cuda.empty_cache() + + if self.center_frame_only: + visuals['result'] = visuals['result'].unsqueeze(1) + if 'gt' in visuals: + visuals['gt'] = visuals['gt'].unsqueeze(1) + + # evaluate + if i < num_folders: + for idx in range(visuals['result'].size(1)): + result = visuals['result'][0, idx, :, :, :] + result_img = tensor2img([result]) # uint8, bgr + metric_data['img'] = result_img + if 'gt' in visuals: + gt = visuals['gt'][0, idx, :, :, :] + gt_img = tensor2img([gt]) # uint8, bgr + metric_data['img2'] = gt_img + + if save_img: + if self.opt['is_train']: + raise NotImplementedError('saving image is not supported during training.') + else: + if self.center_frame_only: # vimeo-90k + clip_ = val_data['lq_path'].split('/')[-3] + seq_ = val_data['lq_path'].split('/')[-2] + name_ = f'{clip_}_{seq_}' + img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, + f"{name_}_{self.opt['name']}.png") + else: # others + img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder, + f"{idx:08d}_{self.opt['name']}.png") + # image name only for REDS dataset + imwrite(result_img, img_path) + + # calculate metrics + if with_metrics: + for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()): + result = calculate_metric(metric_data, opt_) + self.metric_results[folder][idx, metric_idx] += result + + # progress bar + if rank == 0: + for _ in range(world_size): + pbar.update(1) + pbar.set_description(f'Folder: {folder}') + + if rank == 0: + pbar.close() + + if with_metrics: + if self.opt['dist']: + # collect data among GPUs + for _, tensor in self.metric_results.items(): + dist.reduce(tensor, 0) + dist.barrier() + + if rank == 0: + self._log_validation_metric_values(current_iter, dataset_name, tb_logger) + + def test(self): + n = self.lq.size(1) + self.net_g.eval() + + flip_seq = self.opt['val'].get('flip_seq', False) + self.center_frame_only = self.opt['val'].get('center_frame_only', False) + + if flip_seq: + self.lq = torch.cat([self.lq, self.lq.flip(1)], dim=1) + + with torch.no_grad(): + self.output = self.net_g(self.lq) + + if flip_seq: + output_1 = self.output[:, :n, :, :, :] + output_2 = self.output[:, n:, :, :, :].flip(1) + self.output = 0.5 * (output_1 + output_2) + + if self.center_frame_only: + self.output = self.output[:, n // 2, :, :, :] + + self.net_g.train() diff --git a/basicsr/ops/__init__.py b/basicsr/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/basicsr/ops/__pycache__/__init__.cpython-38.pyc b/basicsr/ops/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ef7db9c36f4d7c6ffc940f71c20ab46895ca7e73 Binary files /dev/null and b/basicsr/ops/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/ops/dcn/__init__.py b/basicsr/ops/dcn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..32e3592f896d61b4127e09d0476381b9d55e32ff --- /dev/null +++ b/basicsr/ops/dcn/__init__.py @@ -0,0 +1,7 @@ +from .deform_conv import (DeformConv, DeformConvPack, ModulatedDeformConv, ModulatedDeformConvPack, deform_conv, + modulated_deform_conv) + +__all__ = [ + 'DeformConv', 'DeformConvPack', 'ModulatedDeformConv', 'ModulatedDeformConvPack', 'deform_conv', + 'modulated_deform_conv' +] diff --git a/basicsr/ops/dcn/__pycache__/__init__.cpython-38.pyc b/basicsr/ops/dcn/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..946369fc409ec42d9e7f3aafc0e3a0d4b6308935 Binary files /dev/null and b/basicsr/ops/dcn/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/ops/dcn/__pycache__/deform_conv.cpython-38.pyc b/basicsr/ops/dcn/__pycache__/deform_conv.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..69997145f2491d39e0c8a2f6b0d8e2fd26ab672b Binary files /dev/null and b/basicsr/ops/dcn/__pycache__/deform_conv.cpython-38.pyc differ diff --git a/basicsr/ops/dcn/deform_conv.py b/basicsr/ops/dcn/deform_conv.py new file mode 100644 index 0000000000000000000000000000000000000000..6268ca825d59ef4a30d4d2156c4438cbbe9b3c1e --- /dev/null +++ b/basicsr/ops/dcn/deform_conv.py @@ -0,0 +1,379 @@ +import math +import os +import torch +from torch import nn as nn +from torch.autograd import Function +from torch.autograd.function import once_differentiable +from torch.nn import functional as F +from torch.nn.modules.utils import _pair, _single + +BASICSR_JIT = os.getenv('BASICSR_JIT') +if BASICSR_JIT == 'True': + from torch.utils.cpp_extension import load + module_path = os.path.dirname(__file__) + deform_conv_ext = load( + 'deform_conv', + sources=[ + os.path.join(module_path, 'src', 'deform_conv_ext.cpp'), + os.path.join(module_path, 'src', 'deform_conv_cuda.cpp'), + os.path.join(module_path, 'src', 'deform_conv_cuda_kernel.cu'), + ], + ) +else: + try: + from . import deform_conv_ext + except ImportError: + pass + # avoid annoying print output + # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' + # '1. compile with BASICSR_EXT=True. or\n ' + # '2. set BASICSR_JIT=True during running') + + +class DeformConvFunction(Function): + + @staticmethod + def forward(ctx, + input, + offset, + weight, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + im2col_step=64): + if input is not None and input.dim() != 4: + raise ValueError(f'Expected 4D tensor as input, got {input.dim()}D tensor instead.') + ctx.stride = _pair(stride) + ctx.padding = _pair(padding) + ctx.dilation = _pair(dilation) + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.im2col_step = im2col_step + + ctx.save_for_backward(input, offset, weight) + + output = input.new_empty(DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride)) + + ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones + + if not input.is_cuda: + raise NotImplementedError + else: + cur_im2col_step = min(ctx.im2col_step, input.shape[0]) + assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' + deform_conv_ext.deform_conv_forward(input, weight, + offset, output, ctx.bufs_[0], ctx.bufs_[1], weight.size(3), + weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], + ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, + ctx.deformable_groups, cur_im2col_step) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + input, offset, weight = ctx.saved_tensors + + grad_input = grad_offset = grad_weight = None + + if not grad_output.is_cuda: + raise NotImplementedError + else: + cur_im2col_step = min(ctx.im2col_step, input.shape[0]) + assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize' + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + deform_conv_ext.deform_conv_backward_input(input, offset, grad_output, grad_input, + grad_offset, weight, ctx.bufs_[0], weight.size(3), + weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1], + ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups, + ctx.deformable_groups, cur_im2col_step) + + if ctx.needs_input_grad[2]: + grad_weight = torch.zeros_like(weight) + deform_conv_ext.deform_conv_backward_parameters(input, offset, grad_output, grad_weight, + ctx.bufs_[0], ctx.bufs_[1], weight.size(3), + weight.size(2), ctx.stride[1], ctx.stride[0], + ctx.padding[1], ctx.padding[0], ctx.dilation[1], + ctx.dilation[0], ctx.groups, ctx.deformable_groups, 1, + cur_im2col_step) + + return (grad_input, grad_offset, grad_weight, None, None, None, None, None) + + @staticmethod + def _output_size(input, weight, padding, dilation, stride): + channels = weight.size(0) + output_size = (input.size(0), channels) + for d in range(input.dim() - 2): + in_size = input.size(d + 2) + pad = padding[d] + kernel = dilation[d] * (weight.size(d + 2) - 1) + 1 + stride_ = stride[d] + output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) + if not all(map(lambda s: s > 0, output_size)): + raise ValueError(f'convolution input is too small (output would be {"x".join(map(str, output_size))})') + return output_size + + +class ModulatedDeformConvFunction(Function): + + @staticmethod + def forward(ctx, + input, + offset, + mask, + weight, + bias=None, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1): + ctx.stride = stride + ctx.padding = padding + ctx.dilation = dilation + ctx.groups = groups + ctx.deformable_groups = deformable_groups + ctx.with_bias = bias is not None + if not ctx.with_bias: + bias = input.new_empty(1) # fake tensor + if not input.is_cuda: + raise NotImplementedError + if weight.requires_grad or mask.requires_grad or offset.requires_grad or input.requires_grad: + ctx.save_for_backward(input, offset, mask, weight, bias) + output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight)) + ctx._bufs = [input.new_empty(0), input.new_empty(0)] + deform_conv_ext.modulated_deform_conv_forward(input, weight, bias, ctx._bufs[0], offset, mask, output, + ctx._bufs[1], weight.shape[2], weight.shape[3], ctx.stride, + ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, + ctx.groups, ctx.deformable_groups, ctx.with_bias) + return output + + @staticmethod + @once_differentiable + def backward(ctx, grad_output): + if not grad_output.is_cuda: + raise NotImplementedError + input, offset, mask, weight, bias = ctx.saved_tensors + grad_input = torch.zeros_like(input) + grad_offset = torch.zeros_like(offset) + grad_mask = torch.zeros_like(mask) + grad_weight = torch.zeros_like(weight) + grad_bias = torch.zeros_like(bias) + deform_conv_ext.modulated_deform_conv_backward(input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], + grad_input, grad_weight, grad_bias, grad_offset, grad_mask, + grad_output, weight.shape[2], weight.shape[3], ctx.stride, + ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation, + ctx.groups, ctx.deformable_groups, ctx.with_bias) + if not ctx.with_bias: + grad_bias = None + + return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None, None) + + @staticmethod + def _infer_shape(ctx, input, weight): + n = input.size(0) + channels_out = weight.size(0) + height, width = input.shape[2:4] + kernel_h, kernel_w = weight.shape[2:4] + height_out = (height + 2 * ctx.padding - (ctx.dilation * (kernel_h - 1) + 1)) // ctx.stride + 1 + width_out = (width + 2 * ctx.padding - (ctx.dilation * (kernel_w - 1) + 1)) // ctx.stride + 1 + return n, channels_out, height_out, width_out + + +deform_conv = DeformConvFunction.apply +modulated_deform_conv = ModulatedDeformConvFunction.apply + + +class DeformConv(nn.Module): + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=False): + super(DeformConv, self).__init__() + + assert not bias + assert in_channels % groups == 0, f'in_channels {in_channels} is not divisible by groups {groups}' + assert out_channels % groups == 0, f'out_channels {out_channels} is not divisible by groups {groups}' + + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = _pair(stride) + self.padding = _pair(padding) + self.dilation = _pair(dilation) + self.groups = groups + self.deformable_groups = deformable_groups + # enable compatibility with nn.Conv2d + self.transposed = False + self.output_padding = _single(0) + + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size)) + + self.reset_parameters() + + def reset_parameters(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + + def forward(self, x, offset): + # To fix an assert error in deform_conv_cuda.cpp:128 + # input image is smaller than kernel + input_pad = (x.size(2) < self.kernel_size[0] or x.size(3) < self.kernel_size[1]) + if input_pad: + pad_h = max(self.kernel_size[0] - x.size(2), 0) + pad_w = max(self.kernel_size[1] - x.size(3), 0) + x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() + offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() + out = deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, + self.deformable_groups) + if input_pad: + out = out[:, :, :out.size(2) - pad_h, :out.size(3) - pad_w].contiguous() + return out + + +class DeformConvPack(DeformConv): + """A Deformable Conv Encapsulation that acts as normal Conv layers. + + Args: + in_channels (int): Same as nn.Conv2d. + out_channels (int): Same as nn.Conv2d. + kernel_size (int or tuple[int]): Same as nn.Conv2d. + stride (int or tuple[int]): Same as nn.Conv2d. + padding (int or tuple[int]): Same as nn.Conv2d. + dilation (int or tuple[int]): Same as nn.Conv2d. + groups (int): Same as nn.Conv2d. + bias (bool or str): If specified as `auto`, it will be decided by the + norm_cfg. Bias will be set as True if norm_cfg is None, otherwise + False. + """ + + _version = 2 + + def __init__(self, *args, **kwargs): + super(DeformConvPack, self).__init__(*args, **kwargs) + + self.conv_offset = nn.Conv2d( + self.in_channels, + self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1], + kernel_size=self.kernel_size, + stride=_pair(self.stride), + padding=_pair(self.padding), + dilation=_pair(self.dilation), + bias=True) + self.init_offset() + + def init_offset(self): + self.conv_offset.weight.data.zero_() + self.conv_offset.bias.data.zero_() + + def forward(self, x): + offset = self.conv_offset(x) + return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation, self.groups, + self.deformable_groups) + + +class ModulatedDeformConv(nn.Module): + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deformable_groups=1, + bias=True): + super(ModulatedDeformConv, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = _pair(kernel_size) + self.stride = stride + self.padding = padding + self.dilation = dilation + self.groups = groups + self.deformable_groups = deformable_groups + self.with_bias = bias + # enable compatibility with nn.Conv2d + self.transposed = False + self.output_padding = _single(0) + + self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.register_parameter('bias', None) + self.init_weights() + + def init_weights(self): + n = self.in_channels + for k in self.kernel_size: + n *= k + stdv = 1. / math.sqrt(n) + self.weight.data.uniform_(-stdv, stdv) + if self.bias is not None: + self.bias.data.zero_() + + def forward(self, x, offset, mask): + return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, + self.groups, self.deformable_groups) + + +class ModulatedDeformConvPack(ModulatedDeformConv): + """A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers. + + Args: + in_channels (int): Same as nn.Conv2d. + out_channels (int): Same as nn.Conv2d. + kernel_size (int or tuple[int]): Same as nn.Conv2d. + stride (int or tuple[int]): Same as nn.Conv2d. + padding (int or tuple[int]): Same as nn.Conv2d. + dilation (int or tuple[int]): Same as nn.Conv2d. + groups (int): Same as nn.Conv2d. + bias (bool or str): If specified as `auto`, it will be decided by the + norm_cfg. Bias will be set as True if norm_cfg is None, otherwise + False. + """ + + _version = 2 + + def __init__(self, *args, **kwargs): + super(ModulatedDeformConvPack, self).__init__(*args, **kwargs) + + self.conv_offset = nn.Conv2d( + self.in_channels, + self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1], + kernel_size=self.kernel_size, + stride=_pair(self.stride), + padding=_pair(self.padding), + dilation=_pair(self.dilation), + bias=True) + self.init_weights() + + def init_weights(self): + super(ModulatedDeformConvPack, self).init_weights() + if hasattr(self, 'conv_offset'): + self.conv_offset.weight.data.zero_() + self.conv_offset.bias.data.zero_() + + def forward(self, x): + out = self.conv_offset(x) + o1, o2, mask = torch.chunk(out, 3, dim=1) + offset = torch.cat((o1, o2), dim=1) + mask = torch.sigmoid(mask) + return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, + self.groups, self.deformable_groups) diff --git a/basicsr/ops/dcn/src/deform_conv_cuda.cpp b/basicsr/ops/dcn/src/deform_conv_cuda.cpp new file mode 100644 index 0000000000000000000000000000000000000000..b465c493a3dd67d320b7a8997fbd501d2f89c807 --- /dev/null +++ b/basicsr/ops/dcn/src/deform_conv_cuda.cpp @@ -0,0 +1,685 @@ +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c + +#include +#include + +#include +#include + +void deformable_im2col(const at::Tensor data_im, const at::Tensor data_offset, + const int channels, const int height, const int width, + const int ksize_h, const int ksize_w, const int pad_h, + const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, + at::Tensor data_col); + +void deformable_col2im(const at::Tensor data_col, const at::Tensor data_offset, + const int channels, const int height, const int width, + const int ksize_h, const int ksize_w, const int pad_h, + const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, + at::Tensor grad_im); + +void deformable_col2im_coord( + const at::Tensor data_col, const at::Tensor data_im, + const at::Tensor data_offset, const int channels, const int height, + const int width, const int ksize_h, const int ksize_w, const int pad_h, + const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int parallel_imgs, + const int deformable_group, at::Tensor grad_offset); + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, const at::Tensor data_offset, + const at::Tensor data_mask, const int batch_size, const int channels, + const int height_im, const int width_im, const int height_col, + const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int deformable_group, + at::Tensor data_col); + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, const at::Tensor data_offset, + const at::Tensor data_mask, const int batch_size, const int channels, + const int height_im, const int width_im, const int height_col, + const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int deformable_group, + at::Tensor grad_im); + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, const at::Tensor data_im, + const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, + const int width_im, const int height_col, const int width_col, + const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, + const int stride_h, const int stride_w, const int dilation_h, + const int dilation_w, const int deformable_group, at::Tensor grad_offset, + at::Tensor grad_mask); + +void shape_check(at::Tensor input, at::Tensor offset, at::Tensor *gradOutput, + at::Tensor weight, int kH, int kW, int dH, int dW, int padH, + int padW, int dilationH, int dilationW, int group, + int deformable_group) { + TORCH_CHECK(weight.ndimension() == 4, + "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, " + "but got: %s", + weight.ndimension()); + + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + + TORCH_CHECK(kW > 0 && kH > 0, + "kernel size should be greater than zero, but got kH: %d kW: %d", kH, + kW); + + TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), + "kernel size should be consistent with weight, ", + "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH, + kW, weight.size(2), weight.size(3)); + + TORCH_CHECK(dW > 0 && dH > 0, + "stride should be greater than zero, but got dH: %d dW: %d", dH, dW); + + TORCH_CHECK( + dilationW > 0 && dilationH > 0, + "dilation should be greater than 0, but got dilationH: %d dilationW: %d", + dilationH, dilationW); + + int ndim = input.ndimension(); + int dimf = 0; + int dimh = 1; + int dimw = 2; + + if (ndim == 4) { + dimf++; + dimh++; + dimw++; + } + + TORCH_CHECK(ndim == 3 || ndim == 4, "3D or 4D input tensor expected but got: %s", + ndim); + + long nInputPlane = weight.size(1) * group; + long inputHeight = input.size(dimh); + long inputWidth = input.size(dimw); + long nOutputPlane = weight.size(0); + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + + TORCH_CHECK(nInputPlane % deformable_group == 0, + "input channels must divide deformable group size"); + + if (outputWidth < 1 || outputHeight < 1) + AT_ERROR( + "Given input size: (%ld x %ld x %ld). " + "Calculated output size: (%ld x %ld x %ld). Output size is too small", + nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, + outputWidth); + + TORCH_CHECK(input.size(1) == nInputPlane, + "invalid number of input planes, expected: %d, but got: %d", + nInputPlane, input.size(1)); + + TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), + "input image is smaller than kernel"); + + TORCH_CHECK((offset.size(2) == outputHeight && offset.size(3) == outputWidth), + "invalid spatial size of offset, expected height: %d width: %d, but " + "got height: %d width: %d", + outputHeight, outputWidth, offset.size(2), offset.size(3)); + + TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), + "invalid number of channels of offset"); + + if (gradOutput != NULL) { + TORCH_CHECK(gradOutput->size(dimf) == nOutputPlane, + "invalid number of gradOutput planes, expected: %d, but got: %d", + nOutputPlane, gradOutput->size(dimf)); + + TORCH_CHECK((gradOutput->size(dimh) == outputHeight && + gradOutput->size(dimw) == outputWidth), + "invalid size of gradOutput, expected height: %d width: %d , but " + "got height: %d width: %d", + outputHeight, outputWidth, gradOutput->size(dimh), + gradOutput->size(dimw)); + } +} + +int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, + at::Tensor offset, at::Tensor output, + at::Tensor columns, at::Tensor ones, int kW, + int kH, int dW, int dH, int padW, int padH, + int dilationW, int dilationH, int group, + int deformable_group, int im2col_step) { + // todo: resize columns to include im2col: done + // todo: add im2col_step as input + // todo: add new output buffer and transpose it to output (or directly + // transpose output) todo: possibly change data indexing because of + // parallel_imgs + + shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, padW, + dilationH, dilationW, group, deformable_group); + at::DeviceGuard guard(input.device()); + + input = input.contiguous(); + offset = offset.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input.unsqueeze_(0); + offset.unsqueeze_(0); + } + + // todo: assert batchsize dividable by im2col_step + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, + outputHeight, outputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < outputHeight * outputWidth) { + ones = at::ones({outputHeight, outputWidth}, input.options()); + } + + input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + offset = + offset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + at::Tensor output_buffer = + at::zeros({batchSize / im2col_step, nOutputPlane, + im2col_step * outputHeight, outputWidth}, + output.options()); + + output_buffer = output_buffer.view( + {output_buffer.size(0), group, output_buffer.size(1) / group, + output_buffer.size(2), output_buffer.size(3)}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, + inputWidth, kH, kW, padH, padW, dH, dW, dilationH, + dilationW, im2col_step, deformable_group, columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + + for (int g = 0; g < group; g++) { + output_buffer[elt][g] = output_buffer[elt][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output_buffer[elt][g]); + } + } + + output_buffer = output_buffer.view( + {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), + output_buffer.size(3), output_buffer.size(4)}); + + output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, + im2col_step, outputHeight, outputWidth}); + output_buffer.transpose_(1, 2); + output.copy_(output_buffer); + output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + output = output.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, + at::Tensor gradOutput, at::Tensor gradInput, + at::Tensor gradOffset, at::Tensor weight, + at::Tensor columns, int kW, int kH, int dW, + int dH, int padW, int padH, int dilationW, + int dilationH, int group, + int deformable_group, int im2col_step) { + shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, padH, padW, + dilationH, dilationW, group, deformable_group); + at::DeviceGuard guard(input.device()); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + weight = weight.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view({1, input.size(0), input.size(1), input.size(2)}); + offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = weight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + // change order of grad output + gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, + nOutputPlane, outputHeight, outputWidth}); + gradOutput.transpose_(1, 2); + + gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, + outputWidth}); + offset = + offset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + // divide into groups + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), group, gradOutput.size(1) / group, + gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); + + for (int g = 0; g < group; g++) { + columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), + gradOutput[elt][g].flatten(1), 0.0f, 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradOutput = gradOutput.view( + {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), + gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); + + deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, + inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, + dilationH, dilationW, im2col_step, deformable_group, + gradOffset[elt]); + + deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, + inputWidth, kH, kW, padH, padW, dH, dW, dilationH, + dilationW, im2col_step, deformable_group, gradInput[elt]); + } + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + gradOffset = gradOffset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); + offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); + gradOffset = + gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); + } + + return 1; +} + +int deform_conv_backward_parameters_cuda( + at::Tensor input, at::Tensor offset, at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, + int padW, int padH, int dilationW, int dilationH, int group, + int deformable_group, float scale, int im2col_step) { + // todo: transpose and reshape outGrad + // todo: reshape columns + // todo: add im2col_step as input + + shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, dW, padH, + padW, dilationH, dilationW, group, deformable_group); + at::DeviceGuard guard(input.device()); + + input = input.contiguous(); + offset = offset.contiguous(); + gradOutput = gradOutput.contiguous(); + + int batch = 1; + + if (input.ndimension() == 3) { + // Force batch + batch = 0; + input = input.view( + at::IntList({1, input.size(0), input.size(1), input.size(2)})); + gradOutput = gradOutput.view( + {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); + } + + long batchSize = input.size(0); + long nInputPlane = input.size(1); + long inputHeight = input.size(2); + long inputWidth = input.size(3); + + long nOutputPlane = gradWeight.size(0); + + long outputWidth = + (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; + long outputHeight = + (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; + + TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); + + columns = at::zeros( + {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, + input.options()); + + gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, + nOutputPlane, outputHeight, outputWidth}); + gradOutput.transpose_(1, 2); + + at::Tensor gradOutputBuffer = at::zeros_like(gradOutput); + gradOutputBuffer = + gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, + outputHeight, outputWidth}); + gradOutputBuffer.copy_(gradOutput); + gradOutputBuffer = + gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, + im2col_step * outputHeight, outputWidth}); + + gradOutput.transpose_(1, 2); + gradOutput = + gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); + + input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, + inputHeight, inputWidth}); + offset = + offset.view({batchSize / im2col_step, im2col_step, + deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + for (int elt = 0; elt < batchSize / im2col_step; elt++) { + deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, + inputWidth, kH, kW, padH, padW, dH, dW, dilationH, + dilationW, im2col_step, deformable_group, columns); + + // divide into group + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, + gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + gradWeight = + gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), + gradWeight.size(2), gradWeight.size(3)}); + + for (int g = 0; g < group; g++) { + gradWeight[g] = gradWeight[g] + .flatten(1) + .addmm_(gradOutputBuffer[elt][g].flatten(1), + columns[g].transpose(1, 0), 1.0, scale) + .view_as(gradWeight[g]); + } + gradOutputBuffer = gradOutputBuffer.view( + {gradOutputBuffer.size(0), + gradOutputBuffer.size(1) * gradOutputBuffer.size(2), + gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), + gradWeight.size(2), gradWeight.size(3), + gradWeight.size(4)}); + } + + input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); + offset = offset.view( + {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); + + if (batch == 0) { + gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); + input = input.view({nInputPlane, inputHeight, inputWidth}); + } + + return 1; +} + +void modulated_deform_conv_cuda_forward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, + int kernel_h, int kernel_w, const int stride_h, const int stride_w, + const int pad_h, const int pad_w, const int dilation_h, + const int dilation_w, const int group, const int deformable_group, + const bool with_bias) { + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + at::DeviceGuard guard(input.device()); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_out = weight.size(0); + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", + kernel_h_, kernel_w, kernel_h_, kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", + channels, channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + // resize output + output = output.view({batch, channels_out, height_out, width_out}).zero_(); + // resize temporary columns + columns = + at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, + input.options()); + + output = output.view({output.size(0), group, output.size(1) / group, + output.size(2), output.size(3)}); + + for (int b = 0; b < batch; b++) { + modulated_deformable_im2col_cuda( + input[b], offset[b], mask[b], 1, channels, height, width, height_out, + width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, columns); + + // divide into group + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + + for (int g = 0; g < group; g++) { + output[b][g] = output[b][g] + .flatten(1) + .addmm_(weight[g].flatten(1), columns[g]) + .view_as(output[b][g]); + } + + weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), + weight.size(3), weight.size(4)}); + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + } + + output = output.view({output.size(0), output.size(1) * output.size(2), + output.size(3), output.size(4)}); + + if (with_bias) { + output += bias.view({1, bias.size(0), 1, 1}); + } +} + +void modulated_deform_conv_cuda_backward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor columns, + at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, + at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, + int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, + int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, + const bool with_bias) { + TORCH_CHECK(input.is_contiguous(), "input tensor has to be contiguous"); + TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); + at::DeviceGuard guard(input.device()); + + const int batch = input.size(0); + const int channels = input.size(1); + const int height = input.size(2); + const int width = input.size(3); + + const int channels_kernel = weight.size(1); + const int kernel_h_ = weight.size(2); + const int kernel_w_ = weight.size(3); + if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) + AT_ERROR("Input shape and kernel shape won't match: (%d x %d vs %d x %d).", + kernel_h_, kernel_w, kernel_h_, kernel_w_); + if (channels != channels_kernel * group) + AT_ERROR("Input shape and kernel channels won't match: (%d vs %d).", + channels, channels_kernel * group); + + const int height_out = + (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; + const int width_out = + (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; + + if (ones.ndimension() != 2 || + ones.size(0) * ones.size(1) < height_out * width_out) { + // Resize plane and fill with ones... + ones = at::ones({height_out, width_out}, input.options()); + } + + grad_input = grad_input.view({batch, channels, height, width}); + columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, + input.options()); + + grad_output = + grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, + grad_output.size(2), grad_output.size(3)}); + + for (int b = 0; b < batch; b++) { + // divide int group + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + weight = weight.view({group, weight.size(0) / group, weight.size(1), + weight.size(2), weight.size(3)}); + + for (int g = 0; g < group; g++) { + columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), + grad_output[b][g].flatten(1), 0.0f, 1.0f); + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), + weight.size(3), weight.size(4)}); + + // gradient w.r.t. input coordinate data + modulated_deformable_col2im_coord_cuda( + columns, input[b], offset[b], mask[b], 1, channels, height, width, + height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, + stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], + grad_mask[b]); + // gradient w.r.t. input data + modulated_deformable_col2im_cuda( + columns, offset[b], mask[b], 1, channels, height, width, height_out, + width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, grad_input[b]); + + // gradient w.r.t. weight, dWeight should accumulate across the batch and + // group + modulated_deformable_im2col_cuda( + input[b], offset[b], mask[b], 1, channels, height, width, height_out, + width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, deformable_group, columns); + + columns = columns.view({group, columns.size(0) / group, columns.size(1)}); + grad_weight = grad_weight.view({group, grad_weight.size(0) / group, + grad_weight.size(1), grad_weight.size(2), + grad_weight.size(3)}); + if (with_bias) + grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); + + for (int g = 0; g < group; g++) { + grad_weight[g] = + grad_weight[g] + .flatten(1) + .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) + .view_as(grad_weight[g]); + if (with_bias) { + grad_bias[g] = + grad_bias[g] + .view({-1, 1}) + .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) + .view(-1); + } + } + + columns = + columns.view({columns.size(0) * columns.size(1), columns.size(2)}); + grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), + grad_weight.size(2), grad_weight.size(3), + grad_weight.size(4)}); + if (with_bias) + grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); + } + grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), + grad_output.size(2), grad_output.size(3), + grad_output.size(4)}); +} diff --git a/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..98752dccf8c58817ca1a952554dd3f33188a2d34 --- /dev/null +++ b/basicsr/ops/dcn/src/deform_conv_cuda_kernel.cu @@ -0,0 +1,867 @@ +/*! + ******************* BEGIN Caffe Copyright Notice and Disclaimer **************** + * + * COPYRIGHT + * + * All contributions by the University of California: + * Copyright (c) 2014-2017 The Regents of the University of California (Regents) + * All rights reserved. + * + * All other contributions: + * Copyright (c) 2014-2017, the respective contributors + * All rights reserved. + * + * Caffe uses a shared copyright model: each contributor holds copyright over + * their contributions to Caffe. The project versioning records all such + * contribution and copyright details. If a contributor wants to further mark + * their specific copyright on a particular contribution, they should indicate + * their copyright solely in the commit message of the change when it is + * committed. + * + * LICENSE + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR + * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + * CONTRIBUTION AGREEMENT + * + * By contributing to the BVLC/caffe repository through pull-request, comment, + * or otherwise, the contributor releases their content to the + * license and copyright terms herein. + * + ***************** END Caffe Copyright Notice and Disclaimer ******************** + * + * Copyright (c) 2018 Microsoft + * Licensed under The MIT License [see LICENSE for details] + * \file modulated_deformable_im2col.cuh + * \brief Function definitions of converting an image to + * column matrix based on kernel, padding, dilation, and offset. + * These functions are mainly used in deformable convolution operators. + * \ref: https://arxiv.org/abs/1703.06211 + * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng + */ + +// modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu + +#include +#include +#include +#include +#include +#include + +using namespace at; + +#define CUDA_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +const int CUDA_NUM_THREADS = 1024; +const int kMaxGridNum = 65535; + +inline int GET_BLOCKS(const int N) +{ + return std::min(kMaxGridNum, (N + CUDA_NUM_THREADS - 1) / CUDA_NUM_THREADS); +} + +template +__device__ scalar_t deformable_im2col_bilinear(const scalar_t *bottom_data, const int data_width, + const int height, const int width, scalar_t h, scalar_t w) +{ + + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, + const int h, const int w, const int height, const int width) +{ + + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, + const int height, const int width, const scalar_t *im_data, + const int data_width, const int bp_dir) +{ + + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void deformable_im2col_gpu_kernel(const int n, const scalar_t *data_im, const scalar_t *data_offset, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + //const scalar_t* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const scalar_t map_h = i * dilation_h + offset_h; + //const scalar_t map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = deformable_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = deformable_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val; + data_col_ptr += batch_size * height_col * width_col; + } + } + } +} + +void deformable_im2col( + const at::Tensor data_im, const at::Tensor data_offset, const int channels, + const int height, const int width, const int ksize_h, const int ksize_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, const int parallel_imgs, + const int deformable_group, at::Tensor data_col) +{ + // num_axes should be smaller than block size + // todo: check parallel_imgs is correctly passed in + int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "deformable_im2col_gpu", ([&] { + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + scalar_t *data_col_ = data_col.data_ptr(); + + deformable_im2col_gpu_kernel<<>>( + num_kernels, data_im_, data_offset_, height, width, ksize_h, ksize_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, + channel_per_deformable_group, parallel_imgs, channels, deformable_group, + height_col, width_col, data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in deformable_im2col: %s\n", cudaGetErrorString(err)); + } +} + +template +__global__ void deformable_col2im_gpu_kernel( + const int n, const scalar_t *data_col, const scalar_t *data_offset, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + scalar_t *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * + 2 * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index]; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +void deformable_col2im( + const at::Tensor data_col, const at::Tensor data_offset, const int channels, + const int height, const int width, const int ksize_h, + const int ksize_w, const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, + at::Tensor grad_im) +{ + + // todo: make sure parallel_imgs is passed in correctly + int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; + int channel_per_deformable_group = channels / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + scalar_t *grad_im_ = grad_im.data_ptr(); + + deformable_col2im_gpu_kernel<<>>( + num_kernels, data_col_, data_offset_, channels, height, width, ksize_h, + ksize_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + parallel_imgs, deformable_group, height_col, width_col, grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in deformable_col2im: %s\n", cudaGetErrorString(err)); + } +} + +template +__global__ void deformable_col2im_coord_gpu_kernel(const int n, const scalar_t *data_col, + const scalar_t *data_im, const scalar_t *data_offset, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, scalar_t *grad_offset) +{ + CUDA_KERNEL_LOOP(index, n) + { + scalar_t val = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * + batch_size * width_col * height_col; + const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * + channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * + kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + const scalar_t weight = get_coordinate_weight( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos]; + cnt += 1; + } + + grad_offset[index] = val; + } +} + +void deformable_col2im_coord( + const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, + const int channels, const int height, const int width, const int ksize_h, + const int ksize_w, const int pad_h, const int pad_w, const int stride_h, + const int stride_w, const int dilation_h, const int dilation_w, + const int parallel_imgs, const int deformable_group, at::Tensor grad_offset) +{ + + int height_col = (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; + int width_col = (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; + int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * deformable_group * parallel_imgs; + int channel_per_deformable_group = channels * ksize_h * ksize_w / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + scalar_t *grad_offset_ = grad_offset.data_ptr(); + + deformable_col2im_coord_gpu_kernel<<>>( + num_kernels, data_col_, data_im_, data_offset_, channels, height, width, + ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + parallel_imgs, 2 * ksize_h * ksize_w * deformable_group, deformable_group, + height_col, width_col, grad_offset_); + })); +} + +template +__device__ scalar_t dmcn_im2col_bilinear(const scalar_t *bottom_data, const int data_width, + const int height, const int width, scalar_t h, scalar_t w) +{ + int h_low = floor(h); + int w_low = floor(w); + int h_high = h_low + 1; + int w_high = w_low + 1; + + scalar_t lh = h - h_low; + scalar_t lw = w - w_low; + scalar_t hh = 1 - lh, hw = 1 - lw; + + scalar_t v1 = 0; + if (h_low >= 0 && w_low >= 0) + v1 = bottom_data[h_low * data_width + w_low]; + scalar_t v2 = 0; + if (h_low >= 0 && w_high <= width - 1) + v2 = bottom_data[h_low * data_width + w_high]; + scalar_t v3 = 0; + if (h_high <= height - 1 && w_low >= 0) + v3 = bottom_data[h_high * data_width + w_low]; + scalar_t v4 = 0; + if (h_high <= height - 1 && w_high <= width - 1) + v4 = bottom_data[h_high * data_width + w_high]; + + scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; + + scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); + return val; +} + +template +__device__ scalar_t dmcn_get_gradient_weight(scalar_t argmax_h, scalar_t argmax_w, + const int h, const int w, const int height, const int width) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + if (h == argmax_h_low && w == argmax_w_low) + weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); + if (h == argmax_h_low && w == argmax_w_high) + weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); + if (h == argmax_h_high && w == argmax_w_low) + weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); + if (h == argmax_h_high && w == argmax_w_high) + weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); + return weight; +} + +template +__device__ scalar_t dmcn_get_coordinate_weight(scalar_t argmax_h, scalar_t argmax_w, + const int height, const int width, const scalar_t *im_data, + const int data_width, const int bp_dir) +{ + if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || argmax_w >= width) + { + //empty + return 0; + } + + int argmax_h_low = floor(argmax_h); + int argmax_w_low = floor(argmax_w); + int argmax_h_high = argmax_h_low + 1; + int argmax_w_high = argmax_w_low + 1; + + scalar_t weight = 0; + + if (bp_dir == 0) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += -1 * (argmax_w - argmax_w_low) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += (argmax_w_low + 1 - argmax_w) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_w - argmax_w_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + else if (bp_dir == 1) + { + if (argmax_h_low >= 0 && argmax_w_low >= 0) + weight += -1 * (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_low]; + if (argmax_h_low >= 0 && argmax_w_high <= width - 1) + weight += (argmax_h_low + 1 - argmax_h) * im_data[argmax_h_low * data_width + argmax_w_high]; + if (argmax_h_high <= height - 1 && argmax_w_low >= 0) + weight += -1 * (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_low]; + if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) + weight += (argmax_h - argmax_h_low) * im_data[argmax_h_high * data_width + argmax_w_high]; + } + + return weight; +} + +template +__global__ void modulated_deformable_im2col_gpu_kernel(const int n, + const scalar_t *data_im, const scalar_t *data_offset, const scalar_t *data_mask, + const int height, const int width, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int num_channels, const int deformable_group, + const int height_col, const int width_col, + scalar_t *data_col) +{ + CUDA_KERNEL_LOOP(index, n) + { + // index index of output matrix + const int w_col = index % width_col; + const int h_col = (index / width_col) % height_col; + const int b_col = (index / width_col / height_col) % batch_size; + const int c_im = (index / width_col / height_col) / batch_size; + const int c_col = c_im * kernel_h * kernel_w; + + // compute deformable group index + const int deformable_group_index = c_im / channel_per_deformable_group; + + const int h_in = h_col * stride_h - pad_h; + const int w_in = w_col * stride_w - pad_w; + + scalar_t *data_col_ptr = data_col + ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; + //const float* data_im_ptr = data_im + ((b_col * num_channels + c_im) * height + h_in) * width + w_in; + const scalar_t *data_im_ptr = data_im + (b_col * num_channels + c_im) * height * width; + const scalar_t *data_offset_ptr = data_offset + (b_col * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + + const scalar_t *data_mask_ptr = data_mask + (b_col * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + for (int i = 0; i < kernel_h; ++i) + { + for (int j = 0; j < kernel_w; ++j) + { + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + w_col; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t val = static_cast(0); + const scalar_t h_im = h_in + i * dilation_h + offset_h; + const scalar_t w_im = w_in + j * dilation_w + offset_w; + //if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { + if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) + { + //const float map_h = i * dilation_h + offset_h; + //const float map_w = j * dilation_w + offset_w; + //const int cur_height = height - h_in; + //const int cur_width = width - w_in; + //val = dmcn_im2col_bilinear(data_im_ptr, width, cur_height, cur_width, map_h, map_w); + val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, w_im); + } + *data_col_ptr = val * mask; + data_col_ptr += batch_size * height_col * width_col; + //data_col_ptr += height_col * width_col; + } + } + } +} + +template +__global__ void modulated_deformable_col2im_gpu_kernel(const int n, + const scalar_t *data_col, const scalar_t *data_offset, const scalar_t *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int deformable_group, + const int height_col, const int width_col, + scalar_t *grad_im) +{ + CUDA_KERNEL_LOOP(index, n) + { + const int j = (index / width_col / height_col / batch_size) % kernel_w; + const int i = (index / width_col / height_col / batch_size / kernel_w) % kernel_h; + const int c = index / width_col / height_col / batch_size / kernel_w / kernel_h; + // compute the start and end of the output + + const int deformable_group_index = c / channel_per_deformable_group; + + int w_out = index % width_col; + int h_out = (index / width_col) % height_col; + int b = (index / width_col / height_col) % batch_size; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + const int data_offset_h_ptr = ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; + const int data_offset_w_ptr = ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; + const int data_mask_hw_ptr = ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + const scalar_t cur_inv_h_data = h_in + i * dilation_h + offset_h; + const scalar_t cur_inv_w_data = w_in + j * dilation_w + offset_w; + + const scalar_t cur_top_grad = data_col[index] * mask; + const int cur_h = (int)cur_inv_h_data; + const int cur_w = (int)cur_inv_w_data; + for (int dy = -2; dy <= 2; dy++) + { + for (int dx = -2; dx <= 2; dx++) + { + if (cur_h + dy >= 0 && cur_h + dy < height && + cur_w + dx >= 0 && cur_w + dx < width && + abs(cur_inv_h_data - (cur_h + dy)) < 1 && + abs(cur_inv_w_data - (cur_w + dx)) < 1) + { + int cur_bottom_grad_pos = ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; + scalar_t weight = dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, cur_h + dy, cur_w + dx, height, width); + atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); + } + } + } + } +} + +template +__global__ void modulated_deformable_col2im_coord_gpu_kernel(const int n, + const scalar_t *data_col, const scalar_t *data_im, + const scalar_t *data_offset, const scalar_t *data_mask, + const int channels, const int height, const int width, + const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, + const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int channel_per_deformable_group, + const int batch_size, const int offset_channels, const int deformable_group, + const int height_col, const int width_col, + scalar_t *grad_offset, scalar_t *grad_mask) +{ + CUDA_KERNEL_LOOP(index, n) + { + scalar_t val = 0, mval = 0; + int w = index % width_col; + int h = (index / width_col) % height_col; + int c = (index / width_col / height_col) % offset_channels; + int b = (index / width_col / height_col) / offset_channels; + // compute the start and end of the output + + const int deformable_group_index = c / (2 * kernel_h * kernel_w); + const int col_step = kernel_h * kernel_w; + int cnt = 0; + const scalar_t *data_col_ptr = data_col + deformable_group_index * channel_per_deformable_group * batch_size * width_col * height_col; + const scalar_t *data_im_ptr = data_im + (b * deformable_group + deformable_group_index) * channel_per_deformable_group / kernel_h / kernel_w * height * width; + const scalar_t *data_offset_ptr = data_offset + (b * deformable_group + deformable_group_index) * 2 * kernel_h * kernel_w * height_col * width_col; + const scalar_t *data_mask_ptr = data_mask + (b * deformable_group + deformable_group_index) * kernel_h * kernel_w * height_col * width_col; + + const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; + + for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; col_c += col_step) + { + const int col_pos = (((col_c * batch_size + b) * height_col) + h) * width_col + w; + const int bp_dir = offset_c % 2; + + int j = (col_pos / width_col / height_col / batch_size) % kernel_w; + int i = (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; + int w_out = col_pos % width_col; + int h_out = (col_pos / width_col) % height_col; + int w_in = w_out * stride_w - pad_w; + int h_in = h_out * stride_h - pad_h; + const int data_offset_h_ptr = (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); + const int data_offset_w_ptr = (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out); + const int data_mask_hw_ptr = (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); + const scalar_t offset_h = data_offset_ptr[data_offset_h_ptr]; + const scalar_t offset_w = data_offset_ptr[data_offset_w_ptr]; + const scalar_t mask = data_mask_ptr[data_mask_hw_ptr]; + scalar_t inv_h = h_in + i * dilation_h + offset_h; + scalar_t inv_w = w_in + j * dilation_w + offset_w; + if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) + { + inv_h = inv_w = -2; + } + else + { + mval += data_col_ptr[col_pos] * dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, height, width, inv_h, inv_w); + } + const scalar_t weight = dmcn_get_coordinate_weight( + inv_h, inv_w, + height, width, data_im_ptr + cnt * height * width, width, bp_dir); + val += weight * data_col_ptr[col_pos] * mask; + cnt += 1; + } + // KERNEL_ASSIGN(grad_offset[index], offset_req, val); + grad_offset[index] = val; + if (offset_c % 2 == 0) + // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w], mask_req, mval); + grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * height_col + h) * width_col + w] = mval; + } +} + +void modulated_deformable_im2col_cuda( + const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kenerl_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, at::Tensor data_col) +{ + // num_axes should be smaller than block size + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * batch_size * height_col * width_col; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + const scalar_t *data_mask_ = data_mask.data_ptr(); + scalar_t *data_col_ = data_col.data_ptr(); + + modulated_deformable_im2col_gpu_kernel<<>>( + num_kernels, data_im_, data_offset_, data_mask_, height_im, width_im, kernel_h, kenerl_w, + pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, channel_per_deformable_group, + batch_size, channels, deformable_group, height_col, width_col, data_col_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_im2col_cuda: %s\n", cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_cuda( + const at::Tensor data_col, const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, at::Tensor grad_im) +{ + + const int channel_per_deformable_group = channels / deformable_group; + const int num_kernels = channels * kernel_h * kernel_w * batch_size * height_col * width_col; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + const scalar_t *data_mask_ = data_mask.data_ptr(); + scalar_t *grad_im_ = grad_im.data_ptr(); + + modulated_deformable_col2im_gpu_kernel<<>>( + num_kernels, data_col_, data_offset_, data_mask_, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, deformable_group, height_col, width_col, grad_im_); + })); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_cuda: %s\n", cudaGetErrorString(err)); + } +} + +void modulated_deformable_col2im_coord_cuda( + const at::Tensor data_col, const at::Tensor data_im, const at::Tensor data_offset, const at::Tensor data_mask, + const int batch_size, const int channels, const int height_im, const int width_im, + const int height_col, const int width_col, const int kernel_h, const int kernel_w, + const int pad_h, const int pad_w, const int stride_h, const int stride_w, + const int dilation_h, const int dilation_w, + const int deformable_group, + at::Tensor grad_offset, at::Tensor grad_mask) +{ + const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * kernel_w * deformable_group; + const int channel_per_deformable_group = channels * kernel_h * kernel_w / deformable_group; + + AT_DISPATCH_FLOATING_TYPES_AND_HALF( + data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { + const scalar_t *data_col_ = data_col.data_ptr(); + const scalar_t *data_im_ = data_im.data_ptr(); + const scalar_t *data_offset_ = data_offset.data_ptr(); + const scalar_t *data_mask_ = data_mask.data_ptr(); + scalar_t *grad_offset_ = grad_offset.data_ptr(); + scalar_t *grad_mask_ = grad_mask.data_ptr(); + + modulated_deformable_col2im_coord_gpu_kernel<<>>( + num_kernels, data_col_, data_im_, data_offset_, data_mask_, channels, height_im, width_im, + kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, + dilation_h, dilation_w, channel_per_deformable_group, + batch_size, 2 * kernel_h * kernel_w * deformable_group, deformable_group, height_col, width_col, + grad_offset_, grad_mask_); + })); + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) + { + printf("error in modulated_deformable_col2im_coord_cuda: %s\n", cudaGetErrorString(err)); + } +} diff --git a/basicsr/ops/dcn/src/deform_conv_ext.cpp b/basicsr/ops/dcn/src/deform_conv_ext.cpp new file mode 100644 index 0000000000000000000000000000000000000000..41c6df6f721bd95a525fd6a03dd9882e863de042 --- /dev/null +++ b/basicsr/ops/dcn/src/deform_conv_ext.cpp @@ -0,0 +1,164 @@ +// modify from +// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda.c + +#include +#include + +#include +#include + +#define WITH_CUDA // always use cuda +#ifdef WITH_CUDA +int deform_conv_forward_cuda(at::Tensor input, at::Tensor weight, + at::Tensor offset, at::Tensor output, + at::Tensor columns, at::Tensor ones, int kW, + int kH, int dW, int dH, int padW, int padH, + int dilationW, int dilationH, int group, + int deformable_group, int im2col_step); + +int deform_conv_backward_input_cuda(at::Tensor input, at::Tensor offset, + at::Tensor gradOutput, at::Tensor gradInput, + at::Tensor gradOffset, at::Tensor weight, + at::Tensor columns, int kW, int kH, int dW, + int dH, int padW, int padH, int dilationW, + int dilationH, int group, + int deformable_group, int im2col_step); + +int deform_conv_backward_parameters_cuda( + at::Tensor input, at::Tensor offset, at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, + int padW, int padH, int dilationW, int dilationH, int group, + int deformable_group, float scale, int im2col_step); + +void modulated_deform_conv_cuda_forward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, + int kernel_h, int kernel_w, const int stride_h, const int stride_w, + const int pad_h, const int pad_w, const int dilation_h, + const int dilation_w, const int group, const int deformable_group, + const bool with_bias); + +void modulated_deform_conv_cuda_backward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor columns, + at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, + at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, + int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, + int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, + const bool with_bias); +#endif + +int deform_conv_forward(at::Tensor input, at::Tensor weight, + at::Tensor offset, at::Tensor output, + at::Tensor columns, at::Tensor ones, int kW, + int kH, int dW, int dH, int padW, int padH, + int dilationW, int dilationH, int group, + int deformable_group, int im2col_step) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_conv_forward_cuda(input, weight, offset, output, columns, + ones, kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, + deformable_group, im2col_step); +#else + AT_ERROR("deform conv is not compiled with GPU support"); +#endif + } + AT_ERROR("deform conv is not implemented on CPU"); +} + +int deform_conv_backward_input(at::Tensor input, at::Tensor offset, + at::Tensor gradOutput, at::Tensor gradInput, + at::Tensor gradOffset, at::Tensor weight, + at::Tensor columns, int kW, int kH, int dW, + int dH, int padW, int padH, int dilationW, + int dilationH, int group, + int deformable_group, int im2col_step) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_conv_backward_input_cuda(input, offset, gradOutput, + gradInput, gradOffset, weight, columns, kW, kH, dW, dH, padW, padH, + dilationW, dilationH, group, deformable_group, im2col_step); +#else + AT_ERROR("deform conv is not compiled with GPU support"); +#endif + } + AT_ERROR("deform conv is not implemented on CPU"); +} + +int deform_conv_backward_parameters( + at::Tensor input, at::Tensor offset, at::Tensor gradOutput, + at::Tensor gradWeight, // at::Tensor gradBias, + at::Tensor columns, at::Tensor ones, int kW, int kH, int dW, int dH, + int padW, int padH, int dilationW, int dilationH, int group, + int deformable_group, float scale, int im2col_step) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return deform_conv_backward_parameters_cuda(input, offset, gradOutput, + gradWeight, columns, ones, kW, kH, dW, dH, padW, padH, dilationW, + dilationH, group, deformable_group, scale, im2col_step); +#else + AT_ERROR("deform conv is not compiled with GPU support"); +#endif + } + AT_ERROR("deform conv is not implemented on CPU"); +} + +void modulated_deform_conv_forward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor output, at::Tensor columns, + int kernel_h, int kernel_w, const int stride_h, const int stride_w, + const int pad_h, const int pad_w, const int dilation_h, + const int dilation_w, const int group, const int deformable_group, + const bool with_bias) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return modulated_deform_conv_cuda_forward(input, weight, bias, ones, + offset, mask, output, columns, kernel_h, kernel_w, stride_h, + stride_w, pad_h, pad_w, dilation_h, dilation_w, group, + deformable_group, with_bias); +#else + AT_ERROR("modulated deform conv is not compiled with GPU support"); +#endif + } + AT_ERROR("modulated deform conv is not implemented on CPU"); +} + +void modulated_deform_conv_backward( + at::Tensor input, at::Tensor weight, at::Tensor bias, at::Tensor ones, + at::Tensor offset, at::Tensor mask, at::Tensor columns, + at::Tensor grad_input, at::Tensor grad_weight, at::Tensor grad_bias, + at::Tensor grad_offset, at::Tensor grad_mask, at::Tensor grad_output, + int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, + int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, + const bool with_bias) { + if (input.device().is_cuda()) { +#ifdef WITH_CUDA + return modulated_deform_conv_cuda_backward(input, weight, bias, ones, + offset, mask, columns, grad_input, grad_weight, grad_bias, grad_offset, + grad_mask, grad_output, kernel_h, kernel_w, stride_h, stride_w, + pad_h, pad_w, dilation_h, dilation_w, group, deformable_group, + with_bias); +#else + AT_ERROR("modulated deform conv is not compiled with GPU support"); +#endif + } + AT_ERROR("modulated deform conv is not implemented on CPU"); +} + + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("deform_conv_forward", &deform_conv_forward, + "deform forward"); + m.def("deform_conv_backward_input", &deform_conv_backward_input, + "deform_conv_backward_input"); + m.def("deform_conv_backward_parameters", + &deform_conv_backward_parameters, + "deform_conv_backward_parameters"); + m.def("modulated_deform_conv_forward", + &modulated_deform_conv_forward, + "modulated deform conv forward"); + m.def("modulated_deform_conv_backward", + &modulated_deform_conv_backward, + "modulated deform conv backward"); +} diff --git a/basicsr/ops/fused_act/__init__.py b/basicsr/ops/fused_act/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..241dc0754fae7d88dbbd9a02e665ca30a73c7422 --- /dev/null +++ b/basicsr/ops/fused_act/__init__.py @@ -0,0 +1,3 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu + +__all__ = ['FusedLeakyReLU', 'fused_leaky_relu'] diff --git a/basicsr/ops/fused_act/__pycache__/__init__.cpython-38.pyc b/basicsr/ops/fused_act/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..aed564947c0267716204c1a9ad6e6158bdf4bf48 Binary files /dev/null and b/basicsr/ops/fused_act/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/ops/fused_act/__pycache__/fused_act.cpython-38.pyc b/basicsr/ops/fused_act/__pycache__/fused_act.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e66c8139939f53631c7a13c7e87e926f280dfad8 Binary files /dev/null and b/basicsr/ops/fused_act/__pycache__/fused_act.cpython-38.pyc differ diff --git a/basicsr/ops/fused_act/fused_act.py b/basicsr/ops/fused_act/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..88edc445484b71119dc22a258e83aef49ce39b07 --- /dev/null +++ b/basicsr/ops/fused_act/fused_act.py @@ -0,0 +1,95 @@ +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 + +import os +import torch +from torch import nn +from torch.autograd import Function + +BASICSR_JIT = os.getenv('BASICSR_JIT') +if BASICSR_JIT == 'True': + from torch.utils.cpp_extension import load + module_path = os.path.dirname(__file__) + fused_act_ext = load( + 'fused', + sources=[ + os.path.join(module_path, 'src', 'fused_bias_act.cpp'), + os.path.join(module_path, 'src', 'fused_bias_act_kernel.cu'), + ], + ) +else: + try: + from . import fused_act_ext + except ImportError: + pass + # avoid annoying print output + # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' + # '1. compile with BASICSR_EXT=True. or\n ' + # '2. set BASICSR_JIT=True during running') + + +class FusedLeakyReLUFunctionBackward(Function): + + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused_act_ext.fused_bias_act(grad_output, empty, out, 3, 1, negative_slope, scale) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + out, = ctx.saved_tensors + gradgrad_out = fused_act_ext.fused_bias_act(gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, + ctx.scale) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused_act_ext.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + out, = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(grad_output, out, ctx.negative_slope, ctx.scale) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + + def __init__(self, channel, negative_slope=0.2, scale=2**0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/basicsr/ops/fused_act/src/fused_bias_act.cpp b/basicsr/ops/fused_act/src/fused_bias_act.cpp new file mode 100644 index 0000000000000000000000000000000000000000..85ed0a79fb9c75f83470ac834090f03608d998ee --- /dev/null +++ b/basicsr/ops/fused_act/src/fused_bias_act.cpp @@ -0,0 +1,26 @@ +// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act.cpp +#include + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, + const torch::Tensor& bias, + const torch::Tensor& refer, + int act, int grad, float alpha, float scale); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor fused_bias_act(const torch::Tensor& input, + const torch::Tensor& bias, + const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + CHECK_CUDA(input); + CHECK_CUDA(bias); + + return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); +} diff --git a/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..54c7ff53ce8306db2b3c582ec7fa6696a38b4df0 --- /dev/null +++ b/basicsr/ops/fused_act/src/fused_bias_act_kernel.cu @@ -0,0 +1,100 @@ +// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_bias_act_kernel.cu +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +template +static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, + int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { + int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; + + scalar_t zero = 0.0; + + for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { + scalar_t x = p_x[xi]; + + if (use_bias) { + x += p_b[(xi / step_b) % size_b]; + } + + scalar_t ref = use_ref ? p_ref[xi] : zero; + + scalar_t y; + + switch (act * 10 + grad) { + default: + case 10: y = x; break; + case 11: y = x; break; + case 12: y = 0.0; break; + + case 30: y = (x > 0.0) ? x : x * alpha; break; + case 31: y = (ref > 0.0) ? x : x * alpha; break; + case 32: y = 0.0; break; + } + + out[xi] = y * scale; + } +} + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + auto x = input.contiguous(); + auto b = bias.contiguous(); + auto ref = refer.contiguous(); + + int use_bias = b.numel() ? 1 : 0; + int use_ref = ref.numel() ? 1 : 0; + + int size_x = x.numel(); + int size_b = b.numel(); + int step_b = 1; + + for (int i = 1 + 1; i < x.dim(); i++) { + step_b *= x.size(i); + } + + int loop_x = 4; + int block_size = 4 * 32; + int grid_size = (size_x - 1) / (loop_x * block_size) + 1; + + auto y = torch::empty_like(x); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { + fused_bias_act_kernel<<>>( + y.data_ptr(), + x.data_ptr(), + b.data_ptr(), + ref.data_ptr(), + act, + grad, + alpha, + scale, + loop_x, + size_x, + step_b, + size_b, + use_bias, + use_ref + ); + }); + + return y; +} diff --git a/basicsr/ops/upfirdn2d/__init__.py b/basicsr/ops/upfirdn2d/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..397e85bea063e97fc4c12ad4d3e15669b69290bd --- /dev/null +++ b/basicsr/ops/upfirdn2d/__init__.py @@ -0,0 +1,3 @@ +from .upfirdn2d import upfirdn2d + +__all__ = ['upfirdn2d'] diff --git a/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-38.pyc b/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..50c737b03aa10deb893012f1bf416ca925cb2092 Binary files /dev/null and b/basicsr/ops/upfirdn2d/__pycache__/__init__.cpython-38.pyc differ diff --git a/basicsr/ops/upfirdn2d/__pycache__/upfirdn2d.cpython-38.pyc b/basicsr/ops/upfirdn2d/__pycache__/upfirdn2d.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d3d3e3e68c7aac185d5158af239b10a5934a7ad5 Binary files /dev/null and b/basicsr/ops/upfirdn2d/__pycache__/upfirdn2d.cpython-38.pyc differ diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp b/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp new file mode 100644 index 0000000000000000000000000000000000000000..43d0b6783a5b512b55815a291fcac2bebeea31e0 --- /dev/null +++ b/basicsr/ops/upfirdn2d/src/upfirdn2d.cpp @@ -0,0 +1,24 @@ +// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.cpp +#include + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + CHECK_CUDA(input); + CHECK_CUDA(kernel); + + return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +} diff --git a/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..8870063bae4468deab2e721f0978fe9facfb01b1 --- /dev/null +++ b/basicsr/ops/upfirdn2d/src/upfirdn2d_kernel.cu @@ -0,0 +1,370 @@ +// from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d_kernel.cu +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + +static __host__ __device__ __forceinline__ int floor_div(int a, int b) { + int c = a / b; + + if (c * b > a) { + c--; + } + + return c; +} + +struct UpFirDn2DKernelParams { + int up_x; + int up_y; + int down_x; + int down_y; + int pad_x0; + int pad_x1; + int pad_y0; + int pad_y1; + + int major_dim; + int in_h; + int in_w; + int minor_dim; + int kernel_h; + int kernel_w; + int out_h; + int out_w; + int loop_major; + int loop_x; +}; + +template +__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, + const scalar_t *kernel, + const UpFirDn2DKernelParams p) { + int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; + int out_y = minor_idx / p.minor_dim; + minor_idx -= out_y * p.minor_dim; + int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; + int major_idx_base = blockIdx.z * p.loop_major; + + if (out_x_base >= p.out_w || out_y >= p.out_h || + major_idx_base >= p.major_dim) { + return; + } + + int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; + int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); + int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; + int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; + + for (int loop_major = 0, major_idx = major_idx_base; + loop_major < p.loop_major && major_idx < p.major_dim; + loop_major++, major_idx++) { + for (int loop_x = 0, out_x = out_x_base; + loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { + int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; + int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); + int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; + int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; + + const scalar_t *x_p = + &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + + minor_idx]; + const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; + int x_px = p.minor_dim; + int k_px = -p.up_x; + int x_py = p.in_w * p.minor_dim; + int k_py = -p.up_y * p.kernel_w; + + scalar_t v = 0.0f; + + for (int y = 0; y < h; y++) { + for (int x = 0; x < w; x++) { + v += static_cast(*x_p) * static_cast(*k_p); + x_p += x_px; + k_p += k_px; + } + + x_p += x_py - w * x_px; + k_p += k_py - w * k_px; + } + + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + + minor_idx] = v; + } + } +} + +template +__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, + const scalar_t *kernel, + const UpFirDn2DKernelParams p) { + const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; + const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; + + __shared__ volatile float sk[kernel_h][kernel_w]; + __shared__ volatile float sx[tile_in_h][tile_in_w]; + + int minor_idx = blockIdx.x; + int tile_out_y = minor_idx / p.minor_dim; + minor_idx -= tile_out_y * p.minor_dim; + tile_out_y *= tile_out_h; + int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; + int major_idx_base = blockIdx.z * p.loop_major; + + if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | + major_idx_base >= p.major_dim) { + return; + } + + for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; + tap_idx += blockDim.x) { + int ky = tap_idx / kernel_w; + int kx = tap_idx - ky * kernel_w; + scalar_t v = 0.0; + + if (kx < p.kernel_w & ky < p.kernel_h) { + v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; + } + + sk[ky][kx] = v; + } + + for (int loop_major = 0, major_idx = major_idx_base; + loop_major < p.loop_major & major_idx < p.major_dim; + loop_major++, major_idx++) { + for (int loop_x = 0, tile_out_x = tile_out_x_base; + loop_x < p.loop_x & tile_out_x < p.out_w; + loop_x++, tile_out_x += tile_out_w) { + int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; + int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; + int tile_in_x = floor_div(tile_mid_x, up_x); + int tile_in_y = floor_div(tile_mid_y, up_y); + + __syncthreads(); + + for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; + in_idx += blockDim.x) { + int rel_in_y = in_idx / tile_in_w; + int rel_in_x = in_idx - rel_in_y * tile_in_w; + int in_x = rel_in_x + tile_in_x; + int in_y = rel_in_y + tile_in_y; + + scalar_t v = 0.0; + + if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { + v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * + p.minor_dim + + minor_idx]; + } + + sx[rel_in_y][rel_in_x] = v; + } + + __syncthreads(); + for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; + out_idx += blockDim.x) { + int rel_out_y = out_idx / tile_out_w; + int rel_out_x = out_idx - rel_out_y * tile_out_w; + int out_x = rel_out_x + tile_out_x; + int out_y = rel_out_y + tile_out_y; + + int mid_x = tile_mid_x + rel_out_x * down_x; + int mid_y = tile_mid_y + rel_out_y * down_y; + int in_x = floor_div(mid_x, up_x); + int in_y = floor_div(mid_y, up_y); + int rel_in_x = in_x - tile_in_x; + int rel_in_y = in_y - tile_in_y; + int kernel_x = (in_x + 1) * up_x - mid_x - 1; + int kernel_y = (in_y + 1) * up_y - mid_y - 1; + + scalar_t v = 0.0; + +#pragma unroll + for (int y = 0; y < kernel_h / up_y; y++) +#pragma unroll + for (int x = 0; x < kernel_w / up_x; x++) + v += sx[rel_in_y + y][rel_in_x + x] * + sk[kernel_y + y * up_y][kernel_x + x * up_x]; + + if (out_x < p.out_w & out_y < p.out_h) { + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + + minor_idx] = v; + } + } + } + } +} + +torch::Tensor upfirdn2d_op(const torch::Tensor &input, + const torch::Tensor &kernel, int up_x, int up_y, + int down_x, int down_y, int pad_x0, int pad_x1, + int pad_y0, int pad_y1) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + UpFirDn2DKernelParams p; + + auto x = input.contiguous(); + auto k = kernel.contiguous(); + + p.major_dim = x.size(0); + p.in_h = x.size(1); + p.in_w = x.size(2); + p.minor_dim = x.size(3); + p.kernel_h = k.size(0); + p.kernel_w = k.size(1); + p.up_x = up_x; + p.up_y = up_y; + p.down_x = down_x; + p.down_y = down_y; + p.pad_x0 = pad_x0; + p.pad_x1 = pad_x1; + p.pad_y0 = pad_y0; + p.pad_y1 = pad_y1; + + p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / + p.down_y; + p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / + p.down_x; + + auto out = + at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); + + int mode = -1; + + int tile_out_h = -1; + int tile_out_w = -1; + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 1; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 3 && p.kernel_w <= 3) { + mode = 2; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 3; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 4; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 5; + tile_out_h = 8; + tile_out_w = 32; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && + p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 6; + tile_out_h = 8; + tile_out_w = 32; + } + + dim3 block_size; + dim3 grid_size; + + if (tile_out_h > 0 && tile_out_w > 0) { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 1; + block_size = dim3(32 * 8, 1, 1); + grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, + (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } else { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 4; + block_size = dim3(4, 32, 1); + grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, + (p.out_w - 1) / (p.loop_x * block_size.y) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { + switch (mode) { + case 1: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 2: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 3: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 4: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 5: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 6: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + default: + upfirdn2d_kernel_large<<>>( + out.data_ptr(), x.data_ptr(), + k.data_ptr(), p); + } + }); + + return out; +} diff --git a/basicsr/ops/upfirdn2d/upfirdn2d.py b/basicsr/ops/upfirdn2d/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..d6122d59aa32fd52e956bd36200ba79af4a17b17 --- /dev/null +++ b/basicsr/ops/upfirdn2d/upfirdn2d.py @@ -0,0 +1,192 @@ +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 + +import os +import torch +from torch.autograd import Function +from torch.nn import functional as F + +BASICSR_JIT = os.getenv('BASICSR_JIT') +if BASICSR_JIT == 'True': + from torch.utils.cpp_extension import load + module_path = os.path.dirname(__file__) + upfirdn2d_ext = load( + 'upfirdn2d', + sources=[ + os.path.join(module_path, 'src', 'upfirdn2d.cpp'), + os.path.join(module_path, 'src', 'upfirdn2d_kernel.cu'), + ], + ) +else: + try: + from . import upfirdn2d_ext + except ImportError: + pass + # avoid annoying print output + # print(f'Cannot import deform_conv_ext. Error: {error}. You may need to: \n ' + # '1. compile with BASICSR_EXT=True. or\n ' + # '2. set BASICSR_JIT=True during running') + + +class UpFirDn2dBackward(Function): + + @staticmethod + def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size): + + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_ext.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + kernel, = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_ext.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], + # ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + _, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_ext.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + if input.device.type == 'cpu': + out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]) + else: + out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])) + + return out + + +def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]) + out = out[:, max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0), max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0), :, ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) diff --git a/basicsr/test.py b/basicsr/test.py new file mode 100644 index 0000000000000000000000000000000000000000..53cb3b7aa860c90518e15ba76e1a55fdf404bcc2 --- /dev/null +++ b/basicsr/test.py @@ -0,0 +1,45 @@ +import logging +import torch +from os import path as osp + +from basicsr.data import build_dataloader, build_dataset +from basicsr.models import build_model +from basicsr.utils import get_env_info, get_root_logger, get_time_str, make_exp_dirs +from basicsr.utils.options import dict2str, parse_options + + +def test_pipeline(root_path): + # parse options, set distributed setting, set ramdom seed + opt, _ = parse_options(root_path, is_train=False) + + torch.backends.cudnn.benchmark = True + # torch.backends.cudnn.deterministic = True + + # mkdir and initialize loggers + make_exp_dirs(opt) + log_file = osp.join(opt['path']['log'], f"test_{opt['name']}_{get_time_str()}.log") + logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) + logger.info(get_env_info()) + logger.info(dict2str(opt)) + + # create test dataset and dataloader + test_loaders = [] + for _, dataset_opt in sorted(opt['datasets'].items()): + test_set = build_dataset(dataset_opt) + test_loader = build_dataloader( + test_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) + logger.info(f"Number of test images in {dataset_opt['name']}: {len(test_set)}") + test_loaders.append(test_loader) + + # create model + model = build_model(opt) + + for test_loader in test_loaders: + test_set_name = test_loader.dataset.opt['name'] + logger.info(f'Testing {test_set_name}...') + model.validation(test_loader, current_iter=opt['name'], tb_logger=None, save_img=opt['val']['save_img']) + + +if __name__ == '__main__': + root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) + test_pipeline(root_path) diff --git a/basicsr/train.py b/basicsr/train.py new file mode 100644 index 0000000000000000000000000000000000000000..e02d98fe07f8c2924dda5b49f95adfa21990fa91 --- /dev/null +++ b/basicsr/train.py @@ -0,0 +1,215 @@ +import datetime +import logging +import math +import time +import torch +from os import path as osp + +from basicsr.data import build_dataloader, build_dataset +from basicsr.data.data_sampler import EnlargedSampler +from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher +from basicsr.models import build_model +from basicsr.utils import (AvgTimer, MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, + init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, scandir) +from basicsr.utils.options import copy_opt_file, dict2str, parse_options + + +def init_tb_loggers(opt): + # initialize wandb logger before tensorboard logger to allow proper sync + if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') + is not None) and ('debug' not in opt['name']): + assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb') + init_wandb_logger(opt) + tb_logger = None + if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']: + tb_logger = init_tb_logger(log_dir=osp.join(opt['root_path'], 'tb_logger', opt['name'])) + return tb_logger + + +def create_train_val_dataloader(opt, logger): + # create train and val dataloaders + train_loader, val_loaders = None, [] + for phase, dataset_opt in opt['datasets'].items(): + if phase == 'train': + dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1) + train_set = build_dataset(dataset_opt) + train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio) + train_loader = build_dataloader( + train_set, + dataset_opt, + num_gpu=opt['num_gpu'], + dist=opt['dist'], + sampler=train_sampler, + seed=opt['manual_seed']) + + num_iter_per_epoch = math.ceil( + len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size'])) + total_iters = int(opt['train']['total_iter']) + total_epochs = math.ceil(total_iters / (num_iter_per_epoch)) + logger.info('Training statistics:' + f'\n\tNumber of train images: {len(train_set)}' + f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}' + f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}' + f'\n\tWorld size (gpu number): {opt["world_size"]}' + f'\n\tRequire iter number per epoch: {num_iter_per_epoch}' + f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.') + elif phase.split('_')[0] == 'val': + val_set = build_dataset(dataset_opt) + val_loader = build_dataloader( + val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed']) + logger.info(f'Number of val images/folders in {dataset_opt["name"]}: {len(val_set)}') + val_loaders.append(val_loader) + else: + raise ValueError(f'Dataset phase {phase} is not recognized.') + + return train_loader, train_sampler, val_loaders, total_epochs, total_iters + + +def load_resume_state(opt): + resume_state_path = None + if opt['auto_resume']: + state_path = osp.join('experiments', opt['name'], 'training_states') + if osp.isdir(state_path): + states = list(scandir(state_path, suffix='state', recursive=False, full_path=False)) + if len(states) != 0: + states = [float(v.split('.state')[0]) for v in states] + resume_state_path = osp.join(state_path, f'{max(states):.0f}.state') + opt['path']['resume_state'] = resume_state_path + else: + if opt['path'].get('resume_state'): + resume_state_path = opt['path']['resume_state'] + + if resume_state_path is None: + resume_state = None + else: + device_id = torch.cuda.current_device() + resume_state = torch.load(resume_state_path, map_location=lambda storage, loc: storage.cuda(device_id)) + check_resume(opt, resume_state['iter']) + return resume_state + + +def train_pipeline(root_path): + # parse options, set distributed setting, set random seed + opt, args = parse_options(root_path, is_train=True) + opt['root_path'] = root_path + + torch.backends.cudnn.benchmark = True + # torch.backends.cudnn.deterministic = True + + # load resume states if necessary + resume_state = load_resume_state(opt) + # mkdir for experiments and logger + if resume_state is None: + make_exp_dirs(opt) + if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name'] and opt['rank'] == 0: + mkdir_and_rename(osp.join(opt['root_path'], 'tb_logger', opt['name'])) + + # copy the yml file to the experiment root + copy_opt_file(args.opt, opt['path']['experiments_root']) + + # WARNING: should not use get_root_logger in the above codes, including the called functions + # Otherwise the logger will not be properly initialized + log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log") + logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file) + logger.info(get_env_info()) + logger.info(dict2str(opt)) + # initialize wandb and tb loggers + tb_logger = init_tb_loggers(opt) + + # create train and validation dataloaders + result = create_train_val_dataloader(opt, logger) + train_loader, train_sampler, val_loaders, total_epochs, total_iters = result + + # create model + model = build_model(opt) + if resume_state: # resume training + model.resume_training(resume_state) # handle optimizers and schedulers + logger.info(f"Resuming training from epoch: {resume_state['epoch']}, iter: {resume_state['iter']}.") + start_epoch = resume_state['epoch'] + current_iter = resume_state['iter'] + else: + start_epoch = 0 + current_iter = 0 + + # create message logger (formatted outputs) + msg_logger = MessageLogger(opt, current_iter, tb_logger) + + # dataloader prefetcher + prefetch_mode = opt['datasets']['train'].get('prefetch_mode') + if prefetch_mode is None or prefetch_mode == 'cpu': + prefetcher = CPUPrefetcher(train_loader) + elif prefetch_mode == 'cuda': + prefetcher = CUDAPrefetcher(train_loader, opt) + logger.info(f'Use {prefetch_mode} prefetch dataloader') + if opt['datasets']['train'].get('pin_memory') is not True: + raise ValueError('Please set pin_memory=True for CUDAPrefetcher.') + else: + raise ValueError(f"Wrong prefetch_mode {prefetch_mode}. Supported ones are: None, 'cuda', 'cpu'.") + + # training + logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter}') + data_timer, iter_timer = AvgTimer(), AvgTimer() + start_time = time.time() + + for epoch in range(start_epoch, total_epochs + 1): + train_sampler.set_epoch(epoch) + prefetcher.reset() + train_data = prefetcher.next() + + while train_data is not None: + data_timer.record() + + current_iter += 1 + if current_iter > total_iters: + break + # update learning rate + model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1)) + # training + model.feed_data(train_data) + model.optimize_parameters(current_iter) + iter_timer.record() + if current_iter == 1: + # reset start time in msg_logger for more accurate eta_time + # not work in resume mode + msg_logger.reset_start_time() + # log + if current_iter % opt['logger']['print_freq'] == 0: + log_vars = {'epoch': epoch, 'iter': current_iter} + log_vars.update({'lrs': model.get_current_learning_rate()}) + log_vars.update({'time': iter_timer.get_avg_time(), 'data_time': data_timer.get_avg_time()}) + log_vars.update(model.get_current_log()) + msg_logger(log_vars) + + # save models and training states + if current_iter % opt['logger']['save_checkpoint_freq'] == 0: + logger.info('Saving models and training states.') + model.save(epoch, current_iter) + + # validation + if opt.get('val') is not None and (current_iter % opt['val']['val_freq'] == 0): + if len(val_loaders) > 1: + logger.warning('Multiple validation datasets are *only* supported by SRModel.') + for val_loader in val_loaders: + model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) + + data_timer.start() + iter_timer.start() + train_data = prefetcher.next() + # end of iter + + # end of epoch + + consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time))) + logger.info(f'End of training. Time consumed: {consumed_time}') + logger.info('Save the latest model.') + model.save(epoch=-1, current_iter=-1) # -1 stands for the latest + if opt.get('val') is not None: + for val_loader in val_loaders: + model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img']) + if tb_logger: + tb_logger.close() + + +if __name__ == '__main__': + root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir)) + train_pipeline(root_path) diff --git a/basicsr/utils/__init__.py b/basicsr/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e4e999c8eddc6a5f9623863ce85232e58984e138 --- /dev/null +++ b/basicsr/utils/__init__.py @@ -0,0 +1,44 @@ +from .color_util import bgr2ycbcr, rgb2ycbcr, rgb2ycbcr_pt, ycbcr2bgr, ycbcr2rgb +from .diffjpeg import DiffJPEG +from .file_client import FileClient +from .img_process_util import USMSharp, usm_sharp +from .img_util import crop_border, imfrombytes, img2tensor, imwrite, tensor2img +from .logger import AvgTimer, MessageLogger, get_env_info, get_root_logger, init_tb_logger, init_wandb_logger +from .misc import check_resume, get_time_str, make_exp_dirs, mkdir_and_rename, scandir, set_random_seed, sizeof_fmt + +__all__ = [ + # color_util.py + 'bgr2ycbcr', + 'rgb2ycbcr', + 'rgb2ycbcr_pt', + 'ycbcr2bgr', + 'ycbcr2rgb', + # file_client.py + 'FileClient', + # img_util.py + 'img2tensor', + 'tensor2img', + 'imfrombytes', + 'imwrite', + 'crop_border', + # logger.py + 'MessageLogger', + 'AvgTimer', + 'init_tb_logger', + 'init_wandb_logger', + 'get_root_logger', + 'get_env_info', + # misc.py + 'set_random_seed', + 'get_time_str', + 'mkdir_and_rename', + 'make_exp_dirs', + 'scandir', + 'check_resume', + 'sizeof_fmt', + # diffjpeg + 'DiffJPEG', + # img_process_util + 'USMSharp', + 'usm_sharp' +] diff --git a/basicsr/utils/__pycache__/__init__.cpython-38.pyc b/basicsr/utils/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 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See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + y_only (bool): Whether to only return Y channel. Default: False. + + Returns: + ndarray: The converted YCbCr image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) + if y_only: + out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 + else: + out_img = np.matmul( + img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]) + [16, 128, 128] + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def bgr2ycbcr(img, y_only=False): + """Convert a BGR image to YCbCr image. + + The bgr version of rgb2ycbcr. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + y_only (bool): Whether to only return Y channel. Default: False. + + Returns: + ndarray: The converted YCbCr image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) + if y_only: + out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 + else: + out_img = np.matmul( + img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], [65.481, -37.797, 112.0]]) + [16, 128, 128] + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def ycbcr2rgb(img): + """Convert a YCbCr image to RGB image. + + This function produces the same results as Matlab's ycbcr2rgb function. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + + Returns: + ndarray: The converted RGB image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) * 255 + out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071], + [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836] # noqa: E126 + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def ycbcr2bgr(img): + """Convert a YCbCr image to BGR image. + + The bgr version of ycbcr2rgb. + It implements the ITU-R BT.601 conversion for standard-definition + television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. + In OpenCV, it implements a JPEG conversion. See more details in + https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + + Returns: + ndarray: The converted BGR image. The output image has the same type + and range as input image. + """ + img_type = img.dtype + img = _convert_input_type_range(img) * 255 + out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0.00791071, -0.00153632, 0], + [0, -0.00318811, 0.00625893]]) * 255.0 + [-276.836, 135.576, -222.921] # noqa: E126 + out_img = _convert_output_type_range(out_img, img_type) + return out_img + + +def _convert_input_type_range(img): + """Convert the type and range of the input image. + + It converts the input image to np.float32 type and range of [0, 1]. + It is mainly used for pre-processing the input image in colorspace + conversion functions such as rgb2ycbcr and ycbcr2rgb. + + Args: + img (ndarray): The input image. It accepts: + 1. np.uint8 type with range [0, 255]; + 2. np.float32 type with range [0, 1]. + + Returns: + (ndarray): The converted image with type of np.float32 and range of + [0, 1]. + """ + img_type = img.dtype + img = img.astype(np.float32) + if img_type == np.float32: + pass + elif img_type == np.uint8: + img /= 255. + else: + raise TypeError(f'The img type should be np.float32 or np.uint8, but got {img_type}') + return img + + +def _convert_output_type_range(img, dst_type): + """Convert the type and range of the image according to dst_type. + + It converts the image to desired type and range. If `dst_type` is np.uint8, + images will be converted to np.uint8 type with range [0, 255]. If + `dst_type` is np.float32, it converts the image to np.float32 type with + range [0, 1]. + It is mainly used for post-processing images in colorspace conversion + functions such as rgb2ycbcr and ycbcr2rgb. + + Args: + img (ndarray): The image to be converted with np.float32 type and + range [0, 255]. + dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it + converts the image to np.uint8 type with range [0, 255]. If + dst_type is np.float32, it converts the image to np.float32 type + with range [0, 1]. + + Returns: + (ndarray): The converted image with desired type and range. + """ + if dst_type not in (np.uint8, np.float32): + raise TypeError(f'The dst_type should be np.float32 or np.uint8, but got {dst_type}') + if dst_type == np.uint8: + img = img.round() + else: + img /= 255. + return img.astype(dst_type) + + +def rgb2ycbcr_pt(img, y_only=False): + """Convert RGB images to YCbCr images (PyTorch version). + + It implements the ITU-R BT.601 conversion for standard-definition television. See more details in + https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. + + Args: + img (Tensor): Images with shape (n, 3, h, w), the range [0, 1], float, RGB format. + y_only (bool): Whether to only return Y channel. Default: False. + + Returns: + (Tensor): converted images with the shape (n, 3/1, h, w), the range [0, 1], float. + """ + if y_only: + weight = torch.tensor([[65.481], [128.553], [24.966]]).to(img) + out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + 16.0 + else: + weight = torch.tensor([[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], [24.966, 112.0, -18.214]]).to(img) + bias = torch.tensor([16, 128, 128]).view(1, 3, 1, 1).to(img) + out_img = torch.matmul(img.permute(0, 2, 3, 1), weight).permute(0, 3, 1, 2) + bias + + out_img = out_img / 255. + return out_img diff --git a/basicsr/utils/diffjpeg.py b/basicsr/utils/diffjpeg.py new file mode 100644 index 0000000000000000000000000000000000000000..65f96b44f9e7f3f8a589668f0003adf328cc5742 --- /dev/null +++ b/basicsr/utils/diffjpeg.py @@ -0,0 +1,515 @@ +""" +Modified from https://github.com/mlomnitz/DiffJPEG + +For images not divisible by 8 +https://dsp.stackexchange.com/questions/35339/jpeg-dct-padding/35343#35343 +""" +import itertools +import numpy as np +import torch +import torch.nn as nn +from torch.nn import functional as F + +# ------------------------ utils ------------------------# +y_table = np.array( + [[16, 11, 10, 16, 24, 40, 51, 61], [12, 12, 14, 19, 26, 58, 60, 55], [14, 13, 16, 24, 40, 57, 69, 56], + [14, 17, 22, 29, 51, 87, 80, 62], [18, 22, 37, 56, 68, 109, 103, 77], [24, 35, 55, 64, 81, 104, 113, 92], + [49, 64, 78, 87, 103, 121, 120, 101], [72, 92, 95, 98, 112, 100, 103, 99]], + dtype=np.float32).T +y_table = nn.Parameter(torch.from_numpy(y_table)) +c_table = np.empty((8, 8), dtype=np.float32) +c_table.fill(99) +c_table[:4, :4] = np.array([[17, 18, 24, 47], [18, 21, 26, 66], [24, 26, 56, 99], [47, 66, 99, 99]]).T +c_table = nn.Parameter(torch.from_numpy(c_table)) + + +def diff_round(x): + """ Differentiable rounding function + """ + return torch.round(x) + (x - torch.round(x))**3 + + +def quality_to_factor(quality): + """ Calculate factor corresponding to quality + + Args: + quality(float): Quality for jpeg compression. + + Returns: + float: Compression factor. + """ + if quality < 50: + quality = 5000. / quality + else: + quality = 200. - quality * 2 + return quality / 100. + + +# ------------------------ compression ------------------------# +class RGB2YCbCrJpeg(nn.Module): + """ Converts RGB image to YCbCr + """ + + def __init__(self): + super(RGB2YCbCrJpeg, self).__init__() + matrix = np.array([[0.299, 0.587, 0.114], [-0.168736, -0.331264, 0.5], [0.5, -0.418688, -0.081312]], + dtype=np.float32).T + self.shift = nn.Parameter(torch.tensor([0., 128., 128.])) + self.matrix = nn.Parameter(torch.from_numpy(matrix)) + + def forward(self, image): + """ + Args: + image(Tensor): batch x 3 x height x width + + Returns: + Tensor: batch x height x width x 3 + """ + image = image.permute(0, 2, 3, 1) + result = torch.tensordot(image, self.matrix, dims=1) + self.shift + return result.view(image.shape) + + +class ChromaSubsampling(nn.Module): + """ Chroma subsampling on CbCr channels + """ + + def __init__(self): + super(ChromaSubsampling, self).__init__() + + def forward(self, image): + """ + Args: + image(tensor): batch x height x width x 3 + + Returns: + y(tensor): batch x height x width + cb(tensor): batch x height/2 x width/2 + cr(tensor): batch x height/2 x width/2 + """ + image_2 = image.permute(0, 3, 1, 2).clone() + cb = F.avg_pool2d(image_2[:, 1, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) + cr = F.avg_pool2d(image_2[:, 2, :, :].unsqueeze(1), kernel_size=2, stride=(2, 2), count_include_pad=False) + cb = cb.permute(0, 2, 3, 1) + cr = cr.permute(0, 2, 3, 1) + return image[:, :, :, 0], cb.squeeze(3), cr.squeeze(3) + + +class BlockSplitting(nn.Module): + """ Splitting image into patches + """ + + def __init__(self): + super(BlockSplitting, self).__init__() + self.k = 8 + + def forward(self, image): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x h*w/64 x h x w + """ + height, _ = image.shape[1:3] + batch_size = image.shape[0] + image_reshaped = image.view(batch_size, height // self.k, self.k, -1, self.k) + image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) + return image_transposed.contiguous().view(batch_size, -1, self.k, self.k) + + +class DCT8x8(nn.Module): + """ Discrete Cosine Transformation + """ + + def __init__(self): + super(DCT8x8, self).__init__() + tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) + for x, y, u, v in itertools.product(range(8), repeat=4): + tensor[x, y, u, v] = np.cos((2 * x + 1) * u * np.pi / 16) * np.cos((2 * y + 1) * v * np.pi / 16) + alpha = np.array([1. / np.sqrt(2)] + [1] * 7) + self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) + self.scale = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha) * 0.25).float()) + + def forward(self, image): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x height x width + """ + image = image - 128 + result = self.scale * torch.tensordot(image, self.tensor, dims=2) + result.view(image.shape) + return result + + +class YQuantize(nn.Module): + """ JPEG Quantization for Y channel + + Args: + rounding(function): rounding function to use + """ + + def __init__(self, rounding): + super(YQuantize, self).__init__() + self.rounding = rounding + self.y_table = y_table + + def forward(self, image, factor=1): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x height x width + """ + if isinstance(factor, (int, float)): + image = image.float() / (self.y_table * factor) + else: + b = factor.size(0) + table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) + image = image.float() / table + image = self.rounding(image) + return image + + +class CQuantize(nn.Module): + """ JPEG Quantization for CbCr channels + + Args: + rounding(function): rounding function to use + """ + + def __init__(self, rounding): + super(CQuantize, self).__init__() + self.rounding = rounding + self.c_table = c_table + + def forward(self, image, factor=1): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x height x width + """ + if isinstance(factor, (int, float)): + image = image.float() / (self.c_table * factor) + else: + b = factor.size(0) + table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) + image = image.float() / table + image = self.rounding(image) + return image + + +class CompressJpeg(nn.Module): + """Full JPEG compression algorithm + + Args: + rounding(function): rounding function to use + """ + + def __init__(self, rounding=torch.round): + super(CompressJpeg, self).__init__() + self.l1 = nn.Sequential(RGB2YCbCrJpeg(), ChromaSubsampling()) + self.l2 = nn.Sequential(BlockSplitting(), DCT8x8()) + self.c_quantize = CQuantize(rounding=rounding) + self.y_quantize = YQuantize(rounding=rounding) + + def forward(self, image, factor=1): + """ + Args: + image(tensor): batch x 3 x height x width + + Returns: + dict(tensor): Compressed tensor with batch x h*w/64 x 8 x 8. + """ + y, cb, cr = self.l1(image * 255) + components = {'y': y, 'cb': cb, 'cr': cr} + for k in components.keys(): + comp = self.l2(components[k]) + if k in ('cb', 'cr'): + comp = self.c_quantize(comp, factor=factor) + else: + comp = self.y_quantize(comp, factor=factor) + + components[k] = comp + + return components['y'], components['cb'], components['cr'] + + +# ------------------------ decompression ------------------------# + + +class YDequantize(nn.Module): + """Dequantize Y channel + """ + + def __init__(self): + super(YDequantize, self).__init__() + self.y_table = y_table + + def forward(self, image, factor=1): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x height x width + """ + if isinstance(factor, (int, float)): + out = image * (self.y_table * factor) + else: + b = factor.size(0) + table = self.y_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) + out = image * table + return out + + +class CDequantize(nn.Module): + """Dequantize CbCr channel + """ + + def __init__(self): + super(CDequantize, self).__init__() + self.c_table = c_table + + def forward(self, image, factor=1): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x height x width + """ + if isinstance(factor, (int, float)): + out = image * (self.c_table * factor) + else: + b = factor.size(0) + table = self.c_table.expand(b, 1, 8, 8) * factor.view(b, 1, 1, 1) + out = image * table + return out + + +class iDCT8x8(nn.Module): + """Inverse discrete Cosine Transformation + """ + + def __init__(self): + super(iDCT8x8, self).__init__() + alpha = np.array([1. / np.sqrt(2)] + [1] * 7) + self.alpha = nn.Parameter(torch.from_numpy(np.outer(alpha, alpha)).float()) + tensor = np.zeros((8, 8, 8, 8), dtype=np.float32) + for x, y, u, v in itertools.product(range(8), repeat=4): + tensor[x, y, u, v] = np.cos((2 * u + 1) * x * np.pi / 16) * np.cos((2 * v + 1) * y * np.pi / 16) + self.tensor = nn.Parameter(torch.from_numpy(tensor).float()) + + def forward(self, image): + """ + Args: + image(tensor): batch x height x width + + Returns: + Tensor: batch x height x width + """ + image = image * self.alpha + result = 0.25 * torch.tensordot(image, self.tensor, dims=2) + 128 + result.view(image.shape) + return result + + +class BlockMerging(nn.Module): + """Merge patches into image + """ + + def __init__(self): + super(BlockMerging, self).__init__() + + def forward(self, patches, height, width): + """ + Args: + patches(tensor) batch x height*width/64, height x width + height(int) + width(int) + + Returns: + Tensor: batch x height x width + """ + k = 8 + batch_size = patches.shape[0] + image_reshaped = patches.view(batch_size, height // k, width // k, k, k) + image_transposed = image_reshaped.permute(0, 1, 3, 2, 4) + return image_transposed.contiguous().view(batch_size, height, width) + + +class ChromaUpsampling(nn.Module): + """Upsample chroma layers + """ + + def __init__(self): + super(ChromaUpsampling, self).__init__() + + def forward(self, y, cb, cr): + """ + Args: + y(tensor): y channel image + cb(tensor): cb channel + cr(tensor): cr channel + + Returns: + Tensor: batch x height x width x 3 + """ + + def repeat(x, k=2): + height, width = x.shape[1:3] + x = x.unsqueeze(-1) + x = x.repeat(1, 1, k, k) + x = x.view(-1, height * k, width * k) + return x + + cb = repeat(cb) + cr = repeat(cr) + return torch.cat([y.unsqueeze(3), cb.unsqueeze(3), cr.unsqueeze(3)], dim=3) + + +class YCbCr2RGBJpeg(nn.Module): + """Converts YCbCr image to RGB JPEG + """ + + def __init__(self): + super(YCbCr2RGBJpeg, self).__init__() + + matrix = np.array([[1., 0., 1.402], [1, -0.344136, -0.714136], [1, 1.772, 0]], dtype=np.float32).T + self.shift = nn.Parameter(torch.tensor([0, -128., -128.])) + self.matrix = nn.Parameter(torch.from_numpy(matrix)) + + def forward(self, image): + """ + Args: + image(tensor): batch x height x width x 3 + + Returns: + Tensor: batch x 3 x height x width + """ + result = torch.tensordot(image + self.shift, self.matrix, dims=1) + return result.view(image.shape).permute(0, 3, 1, 2) + + +class DeCompressJpeg(nn.Module): + """Full JPEG decompression algorithm + + Args: + rounding(function): rounding function to use + """ + + def __init__(self, rounding=torch.round): + super(DeCompressJpeg, self).__init__() + self.c_dequantize = CDequantize() + self.y_dequantize = YDequantize() + self.idct = iDCT8x8() + self.merging = BlockMerging() + self.chroma = ChromaUpsampling() + self.colors = YCbCr2RGBJpeg() + + def forward(self, y, cb, cr, imgh, imgw, factor=1): + """ + Args: + compressed(dict(tensor)): batch x h*w/64 x 8 x 8 + imgh(int) + imgw(int) + factor(float) + + Returns: + Tensor: batch x 3 x height x width + """ + components = {'y': y, 'cb': cb, 'cr': cr} + for k in components.keys(): + if k in ('cb', 'cr'): + comp = self.c_dequantize(components[k], factor=factor) + height, width = int(imgh / 2), int(imgw / 2) + else: + comp = self.y_dequantize(components[k], factor=factor) + height, width = imgh, imgw + comp = self.idct(comp) + components[k] = self.merging(comp, height, width) + # + image = self.chroma(components['y'], components['cb'], components['cr']) + image = self.colors(image) + + image = torch.min(255 * torch.ones_like(image), torch.max(torch.zeros_like(image), image)) + return image / 255 + + +# ------------------------ main DiffJPEG ------------------------ # + + +class DiffJPEG(nn.Module): + """This JPEG algorithm result is slightly different from cv2. + DiffJPEG supports batch processing. + + Args: + differentiable(bool): If True, uses custom differentiable rounding function, if False, uses standard torch.round + """ + + def __init__(self, differentiable=True): + super(DiffJPEG, self).__init__() + if differentiable: + rounding = diff_round + else: + rounding = torch.round + + self.compress = CompressJpeg(rounding=rounding) + self.decompress = DeCompressJpeg(rounding=rounding) + + def forward(self, x, quality): + """ + Args: + x (Tensor): Input image, bchw, rgb, [0, 1] + quality(float): Quality factor for jpeg compression scheme. + """ + factor = quality + if isinstance(factor, (int, float)): + factor = quality_to_factor(factor) + else: + for i in range(factor.size(0)): + factor[i] = quality_to_factor(factor[i]) + h, w = x.size()[-2:] + h_pad, w_pad = 0, 0 + # why should use 16 + if h % 16 != 0: + h_pad = 16 - h % 16 + if w % 16 != 0: + w_pad = 16 - w % 16 + x = F.pad(x, (0, w_pad, 0, h_pad), mode='constant', value=0) + + y, cb, cr = self.compress(x, factor=factor) + recovered = self.decompress(y, cb, cr, (h + h_pad), (w + w_pad), factor=factor) + recovered = recovered[:, :, 0:h, 0:w] + return recovered + + +if __name__ == '__main__': + import cv2 + + from basicsr.utils import img2tensor, tensor2img + + img_gt = cv2.imread('test.png') / 255. + + # -------------- cv2 -------------- # + encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 20] + _, encimg = cv2.imencode('.jpg', img_gt * 255., encode_param) + img_lq = np.float32(cv2.imdecode(encimg, 1)) + cv2.imwrite('cv2_JPEG_20.png', img_lq) + + # -------------- DiffJPEG -------------- # + jpeger = DiffJPEG(differentiable=False).cuda() + img_gt = img2tensor(img_gt) + img_gt = torch.stack([img_gt, img_gt]).cuda() + quality = img_gt.new_tensor([20, 40]) + out = jpeger(img_gt, quality=quality) + + cv2.imwrite('pt_JPEG_20.png', tensor2img(out[0])) + cv2.imwrite('pt_JPEG_40.png', tensor2img(out[1])) diff --git a/basicsr/utils/dist_util.py b/basicsr/utils/dist_util.py new file mode 100644 index 0000000000000000000000000000000000000000..0fab887b2cb1ce8533d2e8fdee72ae0c24f68fd0 --- /dev/null +++ b/basicsr/utils/dist_util.py @@ -0,0 +1,82 @@ +# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501 +import functools +import os +import subprocess +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + + +def init_dist(launcher, backend='nccl', **kwargs): + if mp.get_start_method(allow_none=True) is None: + mp.set_start_method('spawn') + if launcher == 'pytorch': + _init_dist_pytorch(backend, **kwargs) + elif launcher == 'slurm': + _init_dist_slurm(backend, **kwargs) + else: + raise ValueError(f'Invalid launcher type: {launcher}') + + +def _init_dist_pytorch(backend, **kwargs): + rank = int(os.environ['RANK']) + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) + + +def _init_dist_slurm(backend, port=None): + """Initialize slurm distributed training environment. + + If argument ``port`` is not specified, then the master port will be system + environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system + environment variable, then a default port ``29500`` will be used. + + Args: + backend (str): Backend of torch.distributed. + port (int, optional): Master port. Defaults to None. + """ + proc_id = int(os.environ['SLURM_PROCID']) + ntasks = int(os.environ['SLURM_NTASKS']) + node_list = os.environ['SLURM_NODELIST'] + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(proc_id % num_gpus) + addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1') + # specify master port + if port is not None: + os.environ['MASTER_PORT'] = str(port) + elif 'MASTER_PORT' in os.environ: + pass # use MASTER_PORT in the environment variable + else: + # 29500 is torch.distributed default port + os.environ['MASTER_PORT'] = '29500' + os.environ['MASTER_ADDR'] = addr + os.environ['WORLD_SIZE'] = str(ntasks) + os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) + os.environ['RANK'] = str(proc_id) + dist.init_process_group(backend=backend) + + +def get_dist_info(): + if dist.is_available(): + initialized = dist.is_initialized() + else: + initialized = False + if initialized: + rank = dist.get_rank() + world_size = dist.get_world_size() + else: + rank = 0 + world_size = 1 + return rank, world_size + + +def master_only(func): + + @functools.wraps(func) + def wrapper(*args, **kwargs): + rank, _ = get_dist_info() + if rank == 0: + return func(*args, **kwargs) + + return wrapper diff --git a/basicsr/utils/download_util.py b/basicsr/utils/download_util.py new file mode 100644 index 0000000000000000000000000000000000000000..6adda71320625242b0107f77d328e7afa236aee6 --- /dev/null +++ b/basicsr/utils/download_util.py @@ -0,0 +1,99 @@ +import math +import os +import requests +from torch.hub import download_url_to_file, get_dir +from tqdm import tqdm +from urllib.parse import urlparse + +from .misc import sizeof_fmt + + +def download_file_from_google_drive(file_id, save_path): + """Download files from google drive. + + Ref: + https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 + + Args: + file_id (str): File id. + save_path (str): Save path. + """ + + session = requests.Session() + URL = 'https://docs.google.com/uc?export=download' + params = {'id': file_id} + + response = session.get(URL, params=params, stream=True) + token = get_confirm_token(response) + if token: + params['confirm'] = token + response = session.get(URL, params=params, stream=True) + + # get file size + response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'}) + if 'Content-Range' in response_file_size.headers: + file_size = int(response_file_size.headers['Content-Range'].split('/')[1]) + else: + file_size = None + + save_response_content(response, save_path, file_size) + + +def get_confirm_token(response): + for key, value in response.cookies.items(): + if key.startswith('download_warning'): + return value + return None + + +def save_response_content(response, destination, file_size=None, chunk_size=32768): + if file_size is not None: + pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk') + + readable_file_size = sizeof_fmt(file_size) + else: + pbar = None + + with open(destination, 'wb') as f: + downloaded_size = 0 + for chunk in response.iter_content(chunk_size): + downloaded_size += chunk_size + if pbar is not None: + pbar.update(1) + pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}') + if chunk: # filter out keep-alive new chunks + f.write(chunk) + if pbar is not None: + pbar.close() + + +def load_file_from_url(url, model_dir=None, progress=True, file_name=None): + """Load file form http url, will download models if necessary. + + Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py + + Args: + url (str): URL to be downloaded. + model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. + Default: None. + progress (bool): Whether to show the download progress. Default: True. + file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. + + Returns: + str: The path to the downloaded file. + """ + if model_dir is None: # use the pytorch hub_dir + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, 'checkpoints') + + os.makedirs(model_dir, exist_ok=True) + + parts = urlparse(url) + filename = os.path.basename(parts.path) + if file_name is not None: + filename = file_name + cached_file = os.path.abspath(os.path.join(model_dir, filename)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) + return cached_file diff --git a/basicsr/utils/face_util.py b/basicsr/utils/face_util.py new file mode 100644 index 0000000000000000000000000000000000000000..e6f2552ed459e7c15929c30b503347d7d8b9cbdb --- /dev/null +++ b/basicsr/utils/face_util.py @@ -0,0 +1,192 @@ +import cv2 +import numpy as np +import os +import torch +from skimage import transform as trans + +from basicsr.utils import imwrite + +try: + import dlib +except ImportError: + print('Please install dlib before testing face restoration.' 'Reference:　https://github.com/davisking/dlib') + + +class FaceRestorationHelper(object): + """Helper for the face restoration pipeline.""" + + def __init__(self, upscale_factor, face_size=512): + self.upscale_factor = upscale_factor + self.face_size = (face_size, face_size) + + # standard 5 landmarks for FFHQ faces with 1024 x 1024 + self.face_template = np.array([[686.77227723, 488.62376238], [586.77227723, 493.59405941], + [337.91089109, 488.38613861], [437.95049505, 493.51485149], + [513.58415842, 678.5049505]]) + self.face_template = self.face_template / (1024 // face_size) + # for estimation the 2D similarity transformation + self.similarity_trans = trans.SimilarityTransform() + + self.all_landmarks_5 = [] + self.all_landmarks_68 = [] + self.affine_matrices = [] + self.inverse_affine_matrices = [] + self.cropped_faces = [] + self.restored_faces = [] + self.save_png = True + + def init_dlib(self, detection_path, landmark5_path, landmark68_path): + """Initialize the dlib detectors and predictors.""" + self.face_detector = dlib.cnn_face_detection_model_v1(detection_path) + self.shape_predictor_5 = dlib.shape_predictor(landmark5_path) + self.shape_predictor_68 = dlib.shape_predictor(landmark68_path) + + def free_dlib_gpu_memory(self): + del self.face_detector + del self.shape_predictor_5 + del self.shape_predictor_68 + + def read_input_image(self, img_path): + # self.input_img is Numpy array, (h, w, c) with RGB order + self.input_img = dlib.load_rgb_image(img_path) + + def detect_faces(self, img_path, upsample_num_times=1, only_keep_largest=False): + """ + Args: + img_path (str): Image path. + upsample_num_times (int): Upsamples the image before running the + face detector + + Returns: + int: Number of detected faces. + """ + self.read_input_image(img_path) + det_faces = self.face_detector(self.input_img, upsample_num_times) + if len(det_faces) == 0: + print('No face detected. Try to increase upsample_num_times.') + else: + if only_keep_largest: + print('Detect several faces and only keep the largest.') + face_areas = [] + for i in range(len(det_faces)): + face_area = (det_faces[i].rect.right() - det_faces[i].rect.left()) * ( + det_faces[i].rect.bottom() - det_faces[i].rect.top()) + face_areas.append(face_area) + largest_idx = face_areas.index(max(face_areas)) + self.det_faces = [det_faces[largest_idx]] + else: + self.det_faces = det_faces + return len(self.det_faces) + + def get_face_landmarks_5(self): + for face in self.det_faces: + shape = self.shape_predictor_5(self.input_img, face.rect) + landmark = np.array([[part.x, part.y] for part in shape.parts()]) + self.all_landmarks_5.append(landmark) + return len(self.all_landmarks_5) + + def get_face_landmarks_68(self): + """Get 68 densemarks for cropped images. + + Should only have one face at most in the cropped image. + """ + num_detected_face = 0 + for idx, face in enumerate(self.cropped_faces): + # face detection + det_face = self.face_detector(face, 1) # TODO: can we remove it? + if len(det_face) == 0: + print(f'Cannot find faces in cropped image with index {idx}.') + self.all_landmarks_68.append(None) + else: + if len(det_face) > 1: + print('Detect several faces in the cropped face. Use the ' + ' largest one. Note that it will also cause overlap ' + 'during paste_faces_to_input_image.') + face_areas = [] + for i in range(len(det_face)): + face_area = (det_face[i].rect.right() - det_face[i].rect.left()) * ( + det_face[i].rect.bottom() - det_face[i].rect.top()) + face_areas.append(face_area) + largest_idx = face_areas.index(max(face_areas)) + face_rect = det_face[largest_idx].rect + else: + face_rect = det_face[0].rect + shape = self.shape_predictor_68(face, face_rect) + landmark = np.array([[part.x, part.y] for part in shape.parts()]) + self.all_landmarks_68.append(landmark) + num_detected_face += 1 + + return num_detected_face + + def warp_crop_faces(self, save_cropped_path=None, save_inverse_affine_path=None): + """Get affine matrix, warp and cropped faces. + + Also get inverse affine matrix for post-processing. + """ + for idx, landmark in enumerate(self.all_landmarks_5): + # use 5 landmarks to get affine matrix + self.similarity_trans.estimate(landmark, self.face_template) + affine_matrix = self.similarity_trans.params[0:2, :] + self.affine_matrices.append(affine_matrix) + # warp and crop faces + cropped_face = cv2.warpAffine(self.input_img, affine_matrix, self.face_size) + self.cropped_faces.append(cropped_face) + # save the cropped face + if save_cropped_path is not None: + path, ext = os.path.splitext(save_cropped_path) + if self.save_png: + save_path = f'{path}_{idx:02d}.png' + else: + save_path = f'{path}_{idx:02d}{ext}' + + imwrite(cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR), save_path) + + # get inverse affine matrix + self.similarity_trans.estimate(self.face_template, landmark * self.upscale_factor) + inverse_affine = self.similarity_trans.params[0:2, :] + self.inverse_affine_matrices.append(inverse_affine) + # save inverse affine matrices + if save_inverse_affine_path is not None: + path, _ = os.path.splitext(save_inverse_affine_path) + save_path = f'{path}_{idx:02d}.pth' + torch.save(inverse_affine, save_path) + + def add_restored_face(self, face): + self.restored_faces.append(face) + + def paste_faces_to_input_image(self, save_path): + # operate in the BGR order + input_img = cv2.cvtColor(self.input_img, cv2.COLOR_RGB2BGR) + h, w, _ = input_img.shape + h_up, w_up = h * self.upscale_factor, w * self.upscale_factor + # simply resize the background + upsample_img = cv2.resize(input_img, (w_up, h_up)) + assert len(self.restored_faces) == len( + self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') + for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): + inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) + mask = np.ones((*self.face_size, 3), dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) + # remove the black borders + inv_mask_erosion = cv2.erode(inv_mask, np.ones((2 * self.upscale_factor, 2 * self.upscale_factor), + np.uint8)) + inv_restored_remove_border = inv_mask_erosion * inv_restored + total_face_area = np.sum(inv_mask_erosion) // 3 + # compute the fusion edge based on the area of face + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + upsample_img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * upsample_img + if self.save_png: + save_path = save_path.replace('.jpg', '.png').replace('.jpeg', '.png') + imwrite(upsample_img.astype(np.uint8), save_path) + + def clean_all(self): + self.all_landmarks_5 = [] + self.all_landmarks_68 = [] + self.restored_faces = [] + self.affine_matrices = [] + self.cropped_faces = [] + self.inverse_affine_matrices = [] diff --git a/basicsr/utils/file_client.py b/basicsr/utils/file_client.py new file mode 100644 index 0000000000000000000000000000000000000000..89d83ab9e0d4314f8cdf2393908a561c6d1dca92 --- /dev/null +++ b/basicsr/utils/file_client.py @@ -0,0 +1,167 @@ +# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py # noqa: E501 +from abc import ABCMeta, abstractmethod + + +class BaseStorageBackend(metaclass=ABCMeta): + """Abstract class of storage backends. + + All backends need to implement two apis: ``get()`` and ``get_text()``. + ``get()`` reads the file as a byte stream and ``get_text()`` reads the file + as texts. + """ + + @abstractmethod + def get(self, filepath): + pass + + @abstractmethod + def get_text(self, filepath): + pass + + +class MemcachedBackend(BaseStorageBackend): + """Memcached storage backend. + + Attributes: + server_list_cfg (str): Config file for memcached server list. + client_cfg (str): Config file for memcached client. + sys_path (str | None): Additional path to be appended to `sys.path`. + Default: None. + """ + + def __init__(self, server_list_cfg, client_cfg, sys_path=None): + if sys_path is not None: + import sys + sys.path.append(sys_path) + try: + import mc + except ImportError: + raise ImportError('Please install memcached to enable MemcachedBackend.') + + self.server_list_cfg = server_list_cfg + self.client_cfg = client_cfg + self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg) + # mc.pyvector servers as a point which points to a memory cache + self._mc_buffer = mc.pyvector() + + def get(self, filepath): + filepath = str(filepath) + import mc + self._client.Get(filepath, self._mc_buffer) + value_buf = mc.ConvertBuffer(self._mc_buffer) + return value_buf + + def get_text(self, filepath): + raise NotImplementedError + + +class HardDiskBackend(BaseStorageBackend): + """Raw hard disks storage backend.""" + + def get(self, filepath): + filepath = str(filepath) + with open(filepath, 'rb') as f: + value_buf = f.read() + return value_buf + + def get_text(self, filepath): + filepath = str(filepath) + with open(filepath, 'r') as f: + value_buf = f.read() + return value_buf + + +class LmdbBackend(BaseStorageBackend): + """Lmdb storage backend. + + Args: + db_paths (str | list[str]): Lmdb database paths. + client_keys (str | list[str]): Lmdb client keys. Default: 'default'. + readonly (bool, optional): Lmdb environment parameter. If True, + disallow any write operations. Default: True. + lock (bool, optional): Lmdb environment parameter. If False, when + concurrent access occurs, do not lock the database. Default: False. + readahead (bool, optional): Lmdb environment parameter. If False, + disable the OS filesystem readahead mechanism, which may improve + random read performance when a database is larger than RAM. + Default: False. + + Attributes: + db_paths (list): Lmdb database path. + _client (list): A list of several lmdb envs. + """ + + def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs): + try: + import lmdb + except ImportError: + raise ImportError('Please install lmdb to enable LmdbBackend.') + + if isinstance(client_keys, str): + client_keys = [client_keys] + + if isinstance(db_paths, list): + self.db_paths = [str(v) for v in db_paths] + elif isinstance(db_paths, str): + self.db_paths = [str(db_paths)] + assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, ' + f'but received {len(client_keys)} and {len(self.db_paths)}.') + + self._client = {} + for client, path in zip(client_keys, self.db_paths): + self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs) + + def get(self, filepath, client_key): + """Get values according to the filepath from one lmdb named client_key. + + Args: + filepath (str | obj:`Path`): Here, filepath is the lmdb key. + client_key (str): Used for distinguishing different lmdb envs. + """ + filepath = str(filepath) + assert client_key in self._client, (f'client_key {client_key} is not in lmdb clients.') + client = self._client[client_key] + with client.begin(write=False) as txn: + value_buf = txn.get(filepath.encode('ascii')) + return value_buf + + def get_text(self, filepath): + raise NotImplementedError + + +class FileClient(object): + """A general file client to access files in different backend. + + The client loads a file or text in a specified backend from its path + and return it as a binary file. it can also register other backend + accessor with a given name and backend class. + + Attributes: + backend (str): The storage backend type. Options are "disk", + "memcached" and "lmdb". + client (:obj:`BaseStorageBackend`): The backend object. + """ + + _backends = { + 'disk': HardDiskBackend, + 'memcached': MemcachedBackend, + 'lmdb': LmdbBackend, + } + + def __init__(self, backend='disk', **kwargs): + if backend not in self._backends: + raise ValueError(f'Backend {backend} is not supported. Currently supported ones' + f' are {list(self._backends.keys())}') + self.backend = backend + self.client = self._backends[backend](**kwargs) + + def get(self, filepath, client_key='default'): + # client_key is used only for lmdb, where different fileclients have + # different lmdb environments. + if self.backend == 'lmdb': + return self.client.get(filepath, client_key) + else: + return self.client.get(filepath) + + def get_text(self, filepath): + return self.client.get_text(filepath) diff --git a/basicsr/utils/flow_util.py b/basicsr/utils/flow_util.py new file mode 100644 index 0000000000000000000000000000000000000000..3d7180b4e9b5c8f2eb36a9a0e4ff6affdaae84b8 --- /dev/null +++ b/basicsr/utils/flow_util.py @@ -0,0 +1,170 @@ +# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py # noqa: E501 +import cv2 +import numpy as np +import os + + +def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs): + """Read an optical flow map. + + Args: + flow_path (ndarray or str): Flow path. + quantize (bool): whether to read quantized pair, if set to True, + remaining args will be passed to :func:`dequantize_flow`. + concat_axis (int): The axis that dx and dy are concatenated, + can be either 0 or 1. Ignored if quantize is False. + + Returns: + ndarray: Optical flow represented as a (h, w, 2) numpy array + """ + if quantize: + assert concat_axis in [0, 1] + cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED) + if cat_flow.ndim != 2: + raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.') + assert cat_flow.shape[concat_axis] % 2 == 0 + dx, dy = np.split(cat_flow, 2, axis=concat_axis) + flow = dequantize_flow(dx, dy, *args, **kwargs) + else: + with open(flow_path, 'rb') as f: + try: + header = f.read(4).decode('utf-8') + except Exception: + raise IOError(f'Invalid flow file: {flow_path}') + else: + if header != 'PIEH': + raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH') + + w = np.fromfile(f, np.int32, 1).squeeze() + h = np.fromfile(f, np.int32, 1).squeeze() + flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2)) + + return flow.astype(np.float32) + + +def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): + """Write optical flow to file. + + If the flow is not quantized, it will be saved as a .flo file losslessly, + otherwise a jpeg image which is lossy but of much smaller size. (dx and dy + will be concatenated horizontally into a single image if quantize is True.) + + Args: + flow (ndarray): (h, w, 2) array of optical flow. + filename (str): Output filepath. + quantize (bool): Whether to quantize the flow and save it to 2 jpeg + images. If set to True, remaining args will be passed to + :func:`quantize_flow`. + concat_axis (int): The axis that dx and dy are concatenated, + can be either 0 or 1. Ignored if quantize is False. + """ + if not quantize: + with open(filename, 'wb') as f: + f.write('PIEH'.encode('utf-8')) + np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) + flow = flow.astype(np.float32) + flow.tofile(f) + f.flush() + else: + assert concat_axis in [0, 1] + dx, dy = quantize_flow(flow, *args, **kwargs) + dxdy = np.concatenate((dx, dy), axis=concat_axis) + os.makedirs(os.path.dirname(filename), exist_ok=True) + cv2.imwrite(filename, dxdy) + + +def quantize_flow(flow, max_val=0.02, norm=True): + """Quantize flow to [0, 255]. + + After this step, the size of flow will be much smaller, and can be + dumped as jpeg images. + + Args: + flow (ndarray): (h, w, 2) array of optical flow. + max_val (float): Maximum value of flow, values beyond + [-max_val, max_val] will be truncated. + norm (bool): Whether to divide flow values by image width/height. + + Returns: + tuple[ndarray]: Quantized dx and dy. + """ + h, w, _ = flow.shape + dx = flow[..., 0] + dy = flow[..., 1] + if norm: + dx = dx / w # avoid inplace operations + dy = dy / h + # use 255 levels instead of 256 to make sure 0 is 0 after dequantization. + flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]] + return tuple(flow_comps) + + +def dequantize_flow(dx, dy, max_val=0.02, denorm=True): + """Recover from quantized flow. + + Args: + dx (ndarray): Quantized dx. + dy (ndarray): Quantized dy. + max_val (float): Maximum value used when quantizing. + denorm (bool): Whether to multiply flow values with width/height. + + Returns: + ndarray: Dequantized flow. + """ + assert dx.shape == dy.shape + assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1) + + dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]] + + if denorm: + dx *= dx.shape[1] + dy *= dx.shape[0] + flow = np.dstack((dx, dy)) + return flow + + +def quantize(arr, min_val, max_val, levels, dtype=np.int64): + """Quantize an array of (-inf, inf) to [0, levels-1]. + + Args: + arr (ndarray): Input array. + min_val (scalar): Minimum value to be clipped. + max_val (scalar): Maximum value to be clipped. + levels (int): Quantization levels. + dtype (np.type): The type of the quantized array. + + Returns: + tuple: Quantized array. + """ + if not (isinstance(levels, int) and levels > 1): + raise ValueError(f'levels must be a positive integer, but got {levels}') + if min_val >= max_val: + raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') + + arr = np.clip(arr, min_val, max_val) - min_val + quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1) + + return quantized_arr + + +def dequantize(arr, min_val, max_val, levels, dtype=np.float64): + """Dequantize an array. + + Args: + arr (ndarray): Input array. + min_val (scalar): Minimum value to be clipped. + max_val (scalar): Maximum value to be clipped. + levels (int): Quantization levels. + dtype (np.type): The type of the dequantized array. + + Returns: + tuple: Dequantized array. + """ + if not (isinstance(levels, int) and levels > 1): + raise ValueError(f'levels must be a positive integer, but got {levels}') + if min_val >= max_val: + raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') + + dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val + + return dequantized_arr diff --git a/basicsr/utils/img_process_util.py b/basicsr/utils/img_process_util.py new file mode 100644 index 0000000000000000000000000000000000000000..52e02f09930dbf13bcd12bbe16b76e4fce52578e --- /dev/null +++ b/basicsr/utils/img_process_util.py @@ -0,0 +1,83 @@ +import cv2 +import numpy as np +import torch +from torch.nn import functional as F + + +def filter2D(img, kernel): + """PyTorch version of cv2.filter2D + + Args: + img (Tensor): (b, c, h, w) + kernel (Tensor): (b, k, k) + """ + k = kernel.size(-1) + b, c, h, w = img.size() + if k % 2 == 1: + img = F.pad(img, (k // 2, k // 2, k // 2, k // 2), mode='reflect') + else: + raise ValueError('Wrong kernel size') + + ph, pw = img.size()[-2:] + + if kernel.size(0) == 1: + # apply the same kernel to all batch images + img = img.view(b * c, 1, ph, pw) + kernel = kernel.view(1, 1, k, k) + return F.conv2d(img, kernel, padding=0).view(b, c, h, w) + else: + img = img.view(1, b * c, ph, pw) + kernel = kernel.view(b, 1, k, k).repeat(1, c, 1, 1).view(b * c, 1, k, k) + return F.conv2d(img, kernel, groups=b * c).view(b, c, h, w) + + +def usm_sharp(img, weight=0.5, radius=50, threshold=10): + """USM sharpening. + + Input image: I; Blurry image: B. + 1. sharp = I + weight * (I - B) + 2. Mask = 1 if abs(I - B) > threshold, else: 0 + 3. Blur mask: + 4. Out = Mask * sharp + (1 - Mask) * I + + + Args: + img (Numpy array): Input image, HWC, BGR; float32, [0, 1]. + weight (float): Sharp weight. Default: 1. + radius (float): Kernel size of Gaussian blur. Default: 50. + threshold (int): + """ + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + sharp = img + weight * residual + sharp = np.clip(sharp, 0, 1) + return soft_mask * sharp + (1 - soft_mask) * img + + +class USMSharp(torch.nn.Module): + + def __init__(self, radius=50, sigma=0): + super(USMSharp, self).__init__() + if radius % 2 == 0: + radius += 1 + self.radius = radius + kernel = cv2.getGaussianKernel(radius, sigma) + kernel = torch.FloatTensor(np.dot(kernel, kernel.transpose())).unsqueeze_(0) + self.register_buffer('kernel', kernel) + + def forward(self, img, weight=0.5, threshold=10): + blur = filter2D(img, self.kernel) + residual = img - blur + + mask = torch.abs(residual) * 255 > threshold + mask = mask.float() + soft_mask = filter2D(mask, self.kernel) + sharp = img + weight * residual + sharp = torch.clip(sharp, 0, 1) + return soft_mask * sharp + (1 - soft_mask) * img diff --git a/basicsr/utils/img_util.py b/basicsr/utils/img_util.py new file mode 100644 index 0000000000000000000000000000000000000000..3a5f1da0911d9b12f9c6164df6c6e14e3c1aef88 --- /dev/null +++ b/basicsr/utils/img_util.py @@ -0,0 +1,172 @@ +import cv2 +import math +import numpy as np +import os +import torch +from torchvision.utils import make_grid + + +def img2tensor(imgs, bgr2rgb=True, float32=True): + """Numpy array to tensor. + + Args: + imgs (list[ndarray] | ndarray): Input images. + bgr2rgb (bool): Whether to change bgr to rgb. + float32 (bool): Whether to change to float32. + + Returns: + list[tensor] | tensor: Tensor images. If returned results only have + one element, just return tensor. + """ + + def _totensor(img, bgr2rgb, float32): + if img.shape[2] == 3 and bgr2rgb: + if img.dtype == 'float64': + img = img.astype('float32') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = torch.from_numpy(img.transpose(2, 0, 1)) + if float32: + img = img.float() + return img + + if isinstance(imgs, list): + return [_totensor(img, bgr2rgb, float32) for img in imgs] + else: + return _totensor(imgs, bgr2rgb, float32) + + +def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): + """Convert torch Tensors into image numpy arrays. + + After clamping to [min, max], values will be normalized to [0, 1]. + + Args: + tensor (Tensor or list[Tensor]): Accept shapes: + 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); + 2) 3D Tensor of shape (3/1 x H x W); + 3) 2D Tensor of shape (H x W). + Tensor channel should be in RGB order. + rgb2bgr (bool): Whether to change rgb to bgr. + out_type (numpy type): output types. If ``np.uint8``, transform outputs + to uint8 type with range [0, 255]; otherwise, float type with + range [0, 1]. Default: ``np.uint8``. + min_max (tuple[int]): min and max values for clamp. + + Returns: + (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of + shape (H x W). The channel order is BGR. + """ + if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): + raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}') + + if torch.is_tensor(tensor): + tensor = [tensor] + result = [] + for _tensor in tensor: + _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) + _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0]) + + n_dim = _tensor.dim() + if n_dim == 4: + img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy() + img_np = img_np.transpose(1, 2, 0) + if rgb2bgr: + img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) + elif n_dim == 3: + img_np = _tensor.numpy() + img_np = img_np.transpose(1, 2, 0) + if img_np.shape[2] == 1: # gray image + img_np = np.squeeze(img_np, axis=2) + else: + if rgb2bgr: + img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) + elif n_dim == 2: + img_np = _tensor.numpy() + else: + raise TypeError(f'Only support 4D, 3D or 2D tensor. But received with dimension: {n_dim}') + if out_type == np.uint8: + # Unlike MATLAB, numpy.unit8() WILL NOT round by default. + img_np = (img_np * 255.0).round() + img_np = img_np.astype(out_type) + result.append(img_np) + if len(result) == 1: + result = result[0] + return result + + +def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)): + """This implementation is slightly faster than tensor2img. + It now only supports torch tensor with shape (1, c, h, w). + + Args: + tensor (Tensor): Now only support torch tensor with (1, c, h, w). + rgb2bgr (bool): Whether to change rgb to bgr. Default: True. + min_max (tuple[int]): min and max values for clamp. + """ + output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0) + output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255 + output = output.type(torch.uint8).cpu().numpy() + if rgb2bgr: + output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) + return output + + +def imfrombytes(content, flag='color', float32=False): + """Read an image from bytes. + + Args: + content (bytes): Image bytes got from files or other streams. + flag (str): Flags specifying the color type of a loaded image, + candidates are `color`, `grayscale` and `unchanged`. + float32 (bool): Whether to change to float32., If True, will also norm + to [0, 1]. Default: False. + + Returns: + ndarray: Loaded image array. + """ + img_np = np.frombuffer(content, np.uint8) + imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED} + img = cv2.imdecode(img_np, imread_flags[flag]) + if float32: + img = img.astype(np.float32) / 255. + return img + + +def imwrite(img, file_path, params=None, auto_mkdir=True): + """Write image to file. + + Args: + img (ndarray): Image array to be written. + file_path (str): Image file path. + params (None or list): Same as opencv's :func:`imwrite` interface. + auto_mkdir (bool): If the parent folder of `file_path` does not exist, + whether to create it automatically. + + Returns: + bool: Successful or not. + """ + if auto_mkdir: + dir_name = os.path.abspath(os.path.dirname(file_path)) + os.makedirs(dir_name, exist_ok=True) + ok = cv2.imwrite(file_path, img, params) + if not ok: + raise IOError('Failed in writing images.') + + +def crop_border(imgs, crop_border): + """Crop borders of images. + + Args: + imgs (list[ndarray] | ndarray): Images with shape (h, w, c). + crop_border (int): Crop border for each end of height and weight. + + Returns: + list[ndarray]: Cropped images. + """ + if crop_border == 0: + return imgs + else: + if isinstance(imgs, list): + return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs] + else: + return imgs[crop_border:-crop_border, crop_border:-crop_border, ...] diff --git a/basicsr/utils/lmdb_util.py b/basicsr/utils/lmdb_util.py new file mode 100644 index 0000000000000000000000000000000000000000..e0a10f60ffca2e36ac5f5564aafd70e79d06a723 --- /dev/null +++ b/basicsr/utils/lmdb_util.py @@ -0,0 +1,196 @@ +import cv2 +import lmdb +import sys +from multiprocessing import Pool +from os import path as osp +from tqdm import tqdm + + +def make_lmdb_from_imgs(data_path, + lmdb_path, + img_path_list, + keys, + batch=5000, + compress_level=1, + multiprocessing_read=False, + n_thread=40, + map_size=None): + """Make lmdb from images. + + Contents of lmdb. The file structure is: + example.lmdb + ├── data.mdb + ├── lock.mdb + ├── meta_info.txt + + The data.mdb and lock.mdb are standard lmdb files and you can refer to + https://lmdb.readthedocs.io/en/release/ for more details. + + The meta_info.txt is a specified txt file to record the meta information + of our datasets. It will be automatically created when preparing + datasets by our provided dataset tools. + Each line in the txt file records 1)image name (with extension), + 2)image shape, and 3)compression level, separated by a white space. + + For example, the meta information could be: + `000_00000000.png (720,1280,3) 1`, which means: + 1) image name (with extension): 000_00000000.png; + 2) image shape: (720,1280,3); + 3) compression level: 1 + + We use the image name without extension as the lmdb key. + + If `multiprocessing_read` is True, it will read all the images to memory + using multiprocessing. Thus, your server needs to have enough memory. + + Args: + data_path (str): Data path for reading images. + lmdb_path (str): Lmdb save path. + img_path_list (str): Image path list. + keys (str): Used for lmdb keys. + batch (int): After processing batch images, lmdb commits. + Default: 5000. + compress_level (int): Compress level when encoding images. Default: 1. + multiprocessing_read (bool): Whether use multiprocessing to read all + the images to memory. Default: False. + n_thread (int): For multiprocessing. + map_size (int | None): Map size for lmdb env. If None, use the + estimated size from images. Default: None + """ + + assert len(img_path_list) == len(keys), ('img_path_list and keys should have the same length, ' + f'but got {len(img_path_list)} and {len(keys)}') + print(f'Create lmdb for {data_path}, save to {lmdb_path}...') + print(f'Totoal images: {len(img_path_list)}') + if not lmdb_path.endswith('.lmdb'): + raise ValueError("lmdb_path must end with '.lmdb'.") + if osp.exists(lmdb_path): + print(f'Folder {lmdb_path} already exists. Exit.') + sys.exit(1) + + if multiprocessing_read: + # read all the images to memory (multiprocessing) + dataset = {} # use dict to keep the order for multiprocessing + shapes = {} + print(f'Read images with multiprocessing, #thread: {n_thread} ...') + pbar = tqdm(total=len(img_path_list), unit='image') + + def callback(arg): + """get the image data and update pbar.""" + key, dataset[key], shapes[key] = arg + pbar.update(1) + pbar.set_description(f'Read {key}') + + pool = Pool(n_thread) + for path, key in zip(img_path_list, keys): + pool.apply_async(read_img_worker, args=(osp.join(data_path, path), key, compress_level), callback=callback) + pool.close() + pool.join() + pbar.close() + print(f'Finish reading {len(img_path_list)} images.') + + # create lmdb environment + if map_size is None: + # obtain data size for one image + img = cv2.imread(osp.join(data_path, img_path_list[0]), cv2.IMREAD_UNCHANGED) + _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) + data_size_per_img = img_byte.nbytes + print('Data size per image is: ', data_size_per_img) + data_size = data_size_per_img * len(img_path_list) + map_size = data_size * 10 + + env = lmdb.open(lmdb_path, map_size=map_size) + + # write data to lmdb + pbar = tqdm(total=len(img_path_list), unit='chunk') + txn = env.begin(write=True) + txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w') + for idx, (path, key) in enumerate(zip(img_path_list, keys)): + pbar.update(1) + pbar.set_description(f'Write {key}') + key_byte = key.encode('ascii') + if multiprocessing_read: + img_byte = dataset[key] + h, w, c = shapes[key] + else: + _, img_byte, img_shape = read_img_worker(osp.join(data_path, path), key, compress_level) + h, w, c = img_shape + + txn.put(key_byte, img_byte) + # write meta information + txt_file.write(f'{key}.png ({h},{w},{c}) {compress_level}\n') + if idx % batch == 0: + txn.commit() + txn = env.begin(write=True) + pbar.close() + txn.commit() + env.close() + txt_file.close() + print('\nFinish writing lmdb.') + + +def read_img_worker(path, key, compress_level): + """Read image worker. + + Args: + path (str): Image path. + key (str): Image key. + compress_level (int): Compress level when encoding images. + + Returns: + str: Image key. + byte: Image byte. + tuple[int]: Image shape. + """ + + img = cv2.imread(path, cv2.IMREAD_UNCHANGED) + if img.ndim == 2: + h, w = img.shape + c = 1 + else: + h, w, c = img.shape + _, img_byte = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compress_level]) + return (key, img_byte, (h, w, c)) + + +class LmdbMaker(): + """LMDB Maker. + + Args: + lmdb_path (str): Lmdb save path. + map_size (int): Map size for lmdb env. Default: 1024 ** 4, 1TB. + batch (int): After processing batch images, lmdb commits. + Default: 5000. + compress_level (int): Compress level when encoding images. Default: 1. + """ + + def __init__(self, lmdb_path, map_size=1024**4, batch=5000, compress_level=1): + if not lmdb_path.endswith('.lmdb'): + raise ValueError("lmdb_path must end with '.lmdb'.") + if osp.exists(lmdb_path): + print(f'Folder {lmdb_path} already exists. Exit.') + sys.exit(1) + + self.lmdb_path = lmdb_path + self.batch = batch + self.compress_level = compress_level + self.env = lmdb.open(lmdb_path, map_size=map_size) + self.txn = self.env.begin(write=True) + self.txt_file = open(osp.join(lmdb_path, 'meta_info.txt'), 'w') + self.counter = 0 + + def put(self, img_byte, key, img_shape): + self.counter += 1 + key_byte = key.encode('ascii') + self.txn.put(key_byte, img_byte) + # write meta information + h, w, c = img_shape + self.txt_file.write(f'{key}.png ({h},{w},{c}) {self.compress_level}\n') + if self.counter % self.batch == 0: + self.txn.commit() + self.txn = self.env.begin(write=True) + + def close(self): + self.txn.commit() + self.env.close() + self.txt_file.close() diff --git a/basicsr/utils/logger.py b/basicsr/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..73553dc664781a061737e94880ea1c6788c09043 --- /dev/null +++ b/basicsr/utils/logger.py @@ -0,0 +1,213 @@ +import datetime +import logging +import time + +from .dist_util import get_dist_info, master_only + +initialized_logger = {} + + +class AvgTimer(): + + def __init__(self, window=200): + self.window = window # average window + self.current_time = 0 + self.total_time = 0 + self.count = 0 + self.avg_time = 0 + self.start() + + def start(self): + self.start_time = self.tic = time.time() + + def record(self): + self.count += 1 + self.toc = time.time() + self.current_time = self.toc - self.tic + self.total_time += self.current_time + # calculate average time + self.avg_time = self.total_time / self.count + + # reset + if self.count > self.window: + self.count = 0 + self.total_time = 0 + + self.tic = time.time() + + def get_current_time(self): + return self.current_time + + def get_avg_time(self): + return self.avg_time + + +class MessageLogger(): + """Message logger for printing. + + Args: + opt (dict): Config. It contains the following keys: + name (str): Exp name. + logger (dict): Contains 'print_freq' (str) for logger interval. + train (dict): Contains 'total_iter' (int) for total iters. + use_tb_logger (bool): Use tensorboard logger. + start_iter (int): Start iter. Default: 1. + tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None. + """ + + def __init__(self, opt, start_iter=1, tb_logger=None): + self.exp_name = opt['name'] + self.interval = opt['logger']['print_freq'] + self.start_iter = start_iter + self.max_iters = opt['train']['total_iter'] + self.use_tb_logger = opt['logger']['use_tb_logger'] + self.tb_logger = tb_logger + self.start_time = time.time() + self.logger = get_root_logger() + + def reset_start_time(self): + self.start_time = time.time() + + @master_only + def __call__(self, log_vars): + """Format logging message. + + Args: + log_vars (dict): It contains the following keys: + epoch (int): Epoch number. + iter (int): Current iter. + lrs (list): List for learning rates. + + time (float): Iter time. + data_time (float): Data time for each iter. + """ + # epoch, iter, learning rates + epoch = log_vars.pop('epoch') + current_iter = log_vars.pop('iter') + lrs = log_vars.pop('lrs') + + message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, iter:{current_iter:8,d}, lr:(') + for v in lrs: + message += f'{v:.3e},' + message += ')] ' + + # time and estimated time + if 'time' in log_vars.keys(): + iter_time = log_vars.pop('time') + data_time = log_vars.pop('data_time') + + total_time = time.time() - self.start_time + time_sec_avg = total_time / (current_iter - self.start_iter + 1) + eta_sec = time_sec_avg * (self.max_iters - current_iter - 1) + eta_str = str(datetime.timedelta(seconds=int(eta_sec))) + message += f'[eta: {eta_str}, ' + message += f'time (data): {iter_time:.3f} ({data_time:.3f})] ' + + # other items, especially losses + for k, v in log_vars.items(): + message += f'{k}: {v:.4e} ' + # tensorboard logger + if self.use_tb_logger and 'debug' not in self.exp_name: + if k.startswith('l_'): + self.tb_logger.add_scalar(f'losses/{k}', v, current_iter) + else: + self.tb_logger.add_scalar(k, v, current_iter) + self.logger.info(message) + + +@master_only +def init_tb_logger(log_dir): + from torch.utils.tensorboard import SummaryWriter + tb_logger = SummaryWriter(log_dir=log_dir) + return tb_logger + + +@master_only +def init_wandb_logger(opt): + """We now only use wandb to sync tensorboard log.""" + import wandb + logger = get_root_logger() + + project = opt['logger']['wandb']['project'] + resume_id = opt['logger']['wandb'].get('resume_id') + if resume_id: + wandb_id = resume_id + resume = 'allow' + logger.warning(f'Resume wandb logger with id={wandb_id}.') + else: + wandb_id = wandb.util.generate_id() + resume = 'never' + + wandb.init(id=wandb_id, resume=resume, name=opt['name'], config=opt, project=project, sync_tensorboard=True) + + logger.info(f'Use wandb logger with id={wandb_id}; project={project}.') + + +def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None): + """Get the root logger. + + The logger will be initialized if it has not been initialized. By default a + StreamHandler will be added. If `log_file` is specified, a FileHandler will + also be added. + + Args: + logger_name (str): root logger name. Default: 'basicsr'. + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the root logger. + log_level (int): The root logger level. Note that only the process of + rank 0 is affected, while other processes will set the level to + "Error" and be silent most of the time. + + Returns: + logging.Logger: The root logger. + """ + logger = logging.getLogger(logger_name) + # if the logger has been initialized, just return it + if logger_name in initialized_logger: + return logger + + format_str = '%(asctime)s %(levelname)s: %(message)s' + stream_handler = logging.StreamHandler() + stream_handler.setFormatter(logging.Formatter(format_str)) + logger.addHandler(stream_handler) + logger.propagate = False + rank, _ = get_dist_info() + if rank != 0: + logger.setLevel('ERROR') + elif log_file is not None: + logger.setLevel(log_level) + # add file handler + file_handler = logging.FileHandler(log_file, 'w') + file_handler.setFormatter(logging.Formatter(format_str)) + file_handler.setLevel(log_level) + logger.addHandler(file_handler) + initialized_logger[logger_name] = True + return logger + + +def get_env_info(): + """Get environment information. + + Currently, only log the software version. + """ + import torch + import torchvision + + from basicsr.version import __version__ + msg = r""" + ____ _ _____ ____ + / __ ) ____ _ _____ (_)_____/ ___/ / __ \ + / __ |/ __ `// ___// // ___/\__ \ / /_/ / + / /_/ // /_/ /(__ )/ // /__ ___/ // _, _/ + /_____/ \__,_//____//_/ \___//____//_/ |_| + ______ __ __ __ __ + / ____/____ ____ ____/ / / / __ __ _____ / /__ / / + / / __ / __ \ / __ \ / __ / / / / / / // ___// //_/ / / + / /_/ // /_/ // /_/ // /_/ / / /___/ /_/ // /__ / /< /_/ + \____/ \____/ \____/ \____/ /_____/\____/ \___//_/|_| (_) + """ + msg += ('\nVersion Information: ' + f'\n\tBasicSR: {__version__}' + f'\n\tPyTorch: {torch.__version__}' + f'\n\tTorchVision: {torchvision.__version__}') + return msg diff --git a/basicsr/utils/matlab_functions.py b/basicsr/utils/matlab_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..a201f79aaf030cdba710dd97c28af1b29a93ed2a --- /dev/null +++ b/basicsr/utils/matlab_functions.py @@ -0,0 +1,178 @@ +import math +import numpy as np +import torch + + +def cubic(x): + """cubic function used for calculate_weights_indices.""" + absx = torch.abs(x) + absx2 = absx**2 + absx3 = absx**3 + return (1.5 * absx3 - 2.5 * absx2 + 1) * ( + (absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx + 2) * (((absx > 1) * + (absx <= 2)).type_as(absx)) + + +def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing): + """Calculate weights and indices, used for imresize function. + + Args: + in_length (int): Input length. + out_length (int): Output length. + scale (float): Scale factor. + kernel_width (int): Kernel width. + antialisaing (bool): Whether to apply anti-aliasing when downsampling. + """ + + if (scale < 1) and antialiasing: + # Use a modified kernel (larger kernel width) to simultaneously + # interpolate and antialias + kernel_width = kernel_width / scale + + # Output-space coordinates + x = torch.linspace(1, out_length, out_length) + + # Input-space coordinates. Calculate the inverse mapping such that 0.5 + # in output space maps to 0.5 in input space, and 0.5 + scale in output + # space maps to 1.5 in input space. + u = x / scale + 0.5 * (1 - 1 / scale) + + # What is the left-most pixel that can be involved in the computation? + left = torch.floor(u - kernel_width / 2) + + # What is the maximum number of pixels that can be involved in the + # computation? Note: it's OK to use an extra pixel here; if the + # corresponding weights are all zero, it will be eliminated at the end + # of this function. + p = math.ceil(kernel_width) + 2 + + # The indices of the input pixels involved in computing the k-th output + # pixel are in row k of the indices matrix. + indices = left.view(out_length, 1).expand(out_length, p) + torch.linspace(0, p - 1, p).view(1, p).expand( + out_length, p) + + # The weights used to compute the k-th output pixel are in row k of the + # weights matrix. + distance_to_center = u.view(out_length, 1).expand(out_length, p) - indices + + # apply cubic kernel + if (scale < 1) and antialiasing: + weights = scale * cubic(distance_to_center * scale) + else: + weights = cubic(distance_to_center) + + # Normalize the weights matrix so that each row sums to 1. + weights_sum = torch.sum(weights, 1).view(out_length, 1) + weights = weights / weights_sum.expand(out_length, p) + + # If a column in weights is all zero, get rid of it. only consider the + # first and last column. + weights_zero_tmp = torch.sum((weights == 0), 0) + if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6): + indices = indices.narrow(1, 1, p - 2) + weights = weights.narrow(1, 1, p - 2) + if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6): + indices = indices.narrow(1, 0, p - 2) + weights = weights.narrow(1, 0, p - 2) + weights = weights.contiguous() + indices = indices.contiguous() + sym_len_s = -indices.min() + 1 + sym_len_e = indices.max() - in_length + indices = indices + sym_len_s - 1 + return weights, indices, int(sym_len_s), int(sym_len_e) + + +@torch.no_grad() +def imresize(img, scale, antialiasing=True): + """imresize function same as MATLAB. + + It now only supports bicubic. + The same scale applies for both height and width. + + Args: + img (Tensor | Numpy array): + Tensor: Input image with shape (c, h, w), [0, 1] range. + Numpy: Input image with shape (h, w, c), [0, 1] range. + scale (float): Scale factor. The same scale applies for both height + and width. + antialisaing (bool): Whether to apply anti-aliasing when downsampling. + Default: True. + + Returns: + Tensor: Output image with shape (c, h, w), [0, 1] range, w/o round. + """ + squeeze_flag = False + if type(img).__module__ == np.__name__: # numpy type + numpy_type = True + if img.ndim == 2: + img = img[:, :, None] + squeeze_flag = True + img = torch.from_numpy(img.transpose(2, 0, 1)).float() + else: + numpy_type = False + if img.ndim == 2: + img = img.unsqueeze(0) + squeeze_flag = True + + in_c, in_h, in_w = img.size() + out_h, out_w = math.ceil(in_h * scale), math.ceil(in_w * scale) + kernel_width = 4 + kernel = 'cubic' + + # get weights and indices + weights_h, indices_h, sym_len_hs, sym_len_he = calculate_weights_indices(in_h, out_h, scale, kernel, kernel_width, + antialiasing) + weights_w, indices_w, sym_len_ws, sym_len_we = calculate_weights_indices(in_w, out_w, scale, kernel, kernel_width, + antialiasing) + # process H dimension + # symmetric copying + img_aug = torch.FloatTensor(in_c, in_h + sym_len_hs + sym_len_he, in_w) + img_aug.narrow(1, sym_len_hs, in_h).copy_(img) + + sym_patch = img[:, :sym_len_hs, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, 0, sym_len_hs).copy_(sym_patch_inv) + + sym_patch = img[:, -sym_len_he:, :] + inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(1, inv_idx) + img_aug.narrow(1, sym_len_hs + in_h, sym_len_he).copy_(sym_patch_inv) + + out_1 = torch.FloatTensor(in_c, out_h, in_w) + kernel_width = weights_h.size(1) + for i in range(out_h): + idx = int(indices_h[i][0]) + for j in range(in_c): + out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_h[i]) + + # process W dimension + # symmetric copying + out_1_aug = torch.FloatTensor(in_c, out_h, in_w + sym_len_ws + sym_len_we) + out_1_aug.narrow(2, sym_len_ws, in_w).copy_(out_1) + + sym_patch = out_1[:, :, :sym_len_ws] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, 0, sym_len_ws).copy_(sym_patch_inv) + + sym_patch = out_1[:, :, -sym_len_we:] + inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long() + sym_patch_inv = sym_patch.index_select(2, inv_idx) + out_1_aug.narrow(2, sym_len_ws + in_w, sym_len_we).copy_(sym_patch_inv) + + out_2 = torch.FloatTensor(in_c, out_h, out_w) + kernel_width = weights_w.size(1) + for i in range(out_w): + idx = int(indices_w[i][0]) + for j in range(in_c): + out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_w[i]) + + if squeeze_flag: + out_2 = out_2.squeeze(0) + if numpy_type: + out_2 = out_2.numpy() + if not squeeze_flag: + out_2 = out_2.transpose(1, 2, 0) + + return out_2 diff --git a/basicsr/utils/misc.py b/basicsr/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..c8d4a1403509672e85e74ac476e028cefb6dbb62 --- /dev/null +++ b/basicsr/utils/misc.py @@ -0,0 +1,141 @@ +import numpy as np +import os +import random +import time +import torch +from os import path as osp + +from .dist_util import master_only + + +def set_random_seed(seed): + """Set random seeds.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def get_time_str(): + return time.strftime('%Y%m%d_%H%M%S', time.localtime()) + + +def mkdir_and_rename(path): + """mkdirs. If path exists, rename it with timestamp and create a new one. + + Args: + path (str): Folder path. + """ + if osp.exists(path): + new_name = path + '_archived_' + get_time_str() + print(f'Path already exists. Rename it to {new_name}', flush=True) + os.rename(path, new_name) + os.makedirs(path, exist_ok=True) + + +@master_only +def make_exp_dirs(opt): + """Make dirs for experiments.""" + path_opt = opt['path'].copy() + if opt['is_train']: + mkdir_and_rename(path_opt.pop('experiments_root')) + else: + mkdir_and_rename(path_opt.pop('results_root')) + for key, path in path_opt.items(): + if ('strict_load' in key) or ('pretrain_network' in key) or ('resume' in key) or ('param_key' in key): + continue + else: + os.makedirs(path, exist_ok=True) + + +def scandir(dir_path, suffix=None, recursive=False, full_path=False): + """Scan a directory to find the interested files. + + Args: + dir_path (str): Path of the directory. + suffix (str | tuple(str), optional): File suffix that we are + interested in. Default: None. + recursive (bool, optional): If set to True, recursively scan the + directory. Default: False. + full_path (bool, optional): If set to True, include the dir_path. + Default: False. + + Returns: + A generator for all the interested files with relative paths. + """ + + if (suffix is not None) and not isinstance(suffix, (str, tuple)): + raise TypeError('"suffix" must be a string or tuple of strings') + + root = dir_path + + def _scandir(dir_path, suffix, recursive): + for entry in os.scandir(dir_path): + if not entry.name.startswith('.') and entry.is_file(): + if full_path: + return_path = entry.path + else: + return_path = osp.relpath(entry.path, root) + + if suffix is None: + yield return_path + elif return_path.endswith(suffix): + yield return_path + else: + if recursive: + yield from _scandir(entry.path, suffix=suffix, recursive=recursive) + else: + continue + + return _scandir(dir_path, suffix=suffix, recursive=recursive) + + +def check_resume(opt, resume_iter): + """Check resume states and pretrain_network paths. + + Args: + opt (dict): Options. + resume_iter (int): Resume iteration. + """ + if opt['path']['resume_state']: + # get all the networks + networks = [key for key in opt.keys() if key.startswith('network_')] + flag_pretrain = False + for network in networks: + if opt['path'].get(f'pretrain_{network}') is not None: + flag_pretrain = True + if flag_pretrain: + print('pretrain_network path will be ignored during resuming.') + # set pretrained model paths + for network in networks: + name = f'pretrain_{network}' + basename = network.replace('network_', '') + if opt['path'].get('ignore_resume_networks') is None or (network + not in opt['path']['ignore_resume_networks']): + opt['path'][name] = osp.join(opt['path']['models'], f'net_{basename}_{resume_iter}.pth') + print(f"Set {name} to {opt['path'][name]}") + + # change param_key to params in resume + param_keys = [key for key in opt['path'].keys() if key.startswith('param_key')] + for param_key in param_keys: + if opt['path'][param_key] == 'params_ema': + opt['path'][param_key] = 'params' + print(f'Set {param_key} to params') + + +def sizeof_fmt(size, suffix='B'): + """Get human readable file size. + + Args: + size (int): File size. + suffix (str): Suffix. Default: 'B'. + + Return: + str: Formatted file size. + """ + for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: + if abs(size) < 1024.0: + return f'{size:3.1f} {unit}{suffix}' + size /= 1024.0 + return f'{size:3.1f} Y{suffix}' diff --git a/basicsr/utils/options.py b/basicsr/utils/options.py new file mode 100644 index 0000000000000000000000000000000000000000..09bfa5a5b68bec82902f65179883474f470c8de9 --- /dev/null +++ b/basicsr/utils/options.py @@ -0,0 +1,194 @@ +import argparse +import random +import torch +import yaml +from collections import OrderedDict +from os import path as osp + +from basicsr.utils import set_random_seed +from basicsr.utils.dist_util import get_dist_info, init_dist, master_only + + +def ordered_yaml(): + """Support OrderedDict for yaml. + + Returns: + yaml Loader and Dumper. + """ + try: + from yaml import CDumper as Dumper + from yaml import CLoader as Loader + except ImportError: + from yaml import Dumper, Loader + + _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG + + def dict_representer(dumper, data): + return dumper.represent_dict(data.items()) + + def dict_constructor(loader, node): + return OrderedDict(loader.construct_pairs(node)) + + Dumper.add_representer(OrderedDict, dict_representer) + Loader.add_constructor(_mapping_tag, dict_constructor) + return Loader, Dumper + + +def dict2str(opt, indent_level=1): + """dict to string for printing options. + + Args: + opt (dict): Option dict. + indent_level (int): Indent level. Default: 1. + + Return: + (str): Option string for printing. + """ + msg = '\n' + for k, v in opt.items(): + if isinstance(v, dict): + msg += ' ' * (indent_level * 2) + k + ':[' + msg += dict2str(v, indent_level + 1) + msg += ' ' * (indent_level * 2) + ']\n' + else: + msg += ' ' * (indent_level * 2) + k + ': ' + str(v) + '\n' + return msg + + +def _postprocess_yml_value(value): + # None + if value == '~' or value.lower() == 'none': + return None + # bool + if value.lower() == 'true': + return True + elif value.lower() == 'false': + return False + # !!float number + if value.startswith('!!float'): + return float(value.replace('!!float', '')) + # number + if value.isdigit(): + return int(value) + elif value.replace('.', '', 1).isdigit() and value.count('.') < 2: + return float(value) + # list + if value.startswith('['): + return eval(value) + # str + return value + + +def parse_options(root_path, is_train=True): + parser = argparse.ArgumentParser() + parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.') + parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher') + parser.add_argument('--auto_resume', action='store_true') + parser.add_argument('--debug', action='store_true') + parser.add_argument('--local_rank', type=int, default=0) + parser.add_argument( + '--force_yml', nargs='+', default=None, help='Force to update yml files. Examples: train:ema_decay=0.999') + args = parser.parse_args() + + # parse yml to dict + with open(args.opt, mode='r') as f: + opt = yaml.load(f, Loader=ordered_yaml()[0]) + + # distributed settings + if args.launcher == 'none': + opt['dist'] = False + print('Disable distributed.', flush=True) + else: + opt['dist'] = True + if args.launcher == 'slurm' and 'dist_params' in opt: + init_dist(args.launcher, **opt['dist_params']) + else: + init_dist(args.launcher) + opt['rank'], opt['world_size'] = get_dist_info() + + # random seed + seed = opt.get('manual_seed') + if seed is None: + seed = random.randint(1, 10000) + opt['manual_seed'] = seed + set_random_seed(seed + opt['rank']) + + # force to update yml options + if args.force_yml is not None: + for entry in args.force_yml: + # now do not support creating new keys + keys, value = entry.split('=') + keys, value = keys.strip(), value.strip() + value = _postprocess_yml_value(value) + eval_str = 'opt' + for key in keys.split(':'): + eval_str += f'["{key}"]' + eval_str += '=value' + # using exec function + exec(eval_str) + + opt['auto_resume'] = args.auto_resume + opt['is_train'] = is_train + + # debug setting + if args.debug and not opt['name'].startswith('debug'): + opt['name'] = 'debug_' + opt['name'] + + if opt['num_gpu'] == 'auto': + opt['num_gpu'] = torch.cuda.device_count() + + # datasets + for phase, dataset in opt['datasets'].items(): + # for multiple datasets, e.g., val_1, val_2; test_1, test_2 + phase = phase.split('_')[0] + dataset['phase'] = phase + if 'scale' in opt: + dataset['scale'] = opt['scale'] + if dataset.get('dataroot_gt') is not None: + dataset['dataroot_gt'] = osp.expanduser(dataset['dataroot_gt']) + if dataset.get('dataroot_lq') is not None: + dataset['dataroot_lq'] = osp.expanduser(dataset['dataroot_lq']) + + # paths + for key, val in opt['path'].items(): + if (val is not None) and ('resume_state' in key or 'pretrain_network' in key): + opt['path'][key] = osp.expanduser(val) + + if is_train: + experiments_root = osp.join(root_path, 'experiments', opt['name']) + opt['path']['experiments_root'] = experiments_root + opt['path']['models'] = osp.join(experiments_root, 'models') + opt['path']['training_states'] = osp.join(experiments_root, 'training_states') + opt['path']['log'] = experiments_root + opt['path']['visualization'] = osp.join(experiments_root, 'visualization') + + # change some options for debug mode + if 'debug' in opt['name']: + if 'val' in opt: + opt['val']['val_freq'] = 8 + opt['logger']['print_freq'] = 1 + opt['logger']['save_checkpoint_freq'] = 8 + else: # test + results_root = osp.join(root_path, 'results', opt['name']) + opt['path']['results_root'] = results_root + opt['path']['log'] = results_root + opt['path']['visualization'] = osp.join(results_root, 'visualization') + + return opt, args + + +@master_only +def copy_opt_file(opt_file, experiments_root): + # copy the yml file to the experiment root + import sys + import time + from shutil import copyfile + cmd = ' '.join(sys.argv) + filename = osp.join(experiments_root, osp.basename(opt_file)) + copyfile(opt_file, filename) + + with open(filename, 'r+') as f: + lines = f.readlines() + lines.insert(0, f'# GENERATE TIME: {time.asctime()}\n# CMD:\n# {cmd}\n\n') + f.seek(0) + f.writelines(lines) diff --git a/basicsr/utils/plot_util.py b/basicsr/utils/plot_util.py new file mode 100644 index 0000000000000000000000000000000000000000..c5e5a3ffad2cc851d5755d5d62efc290320901b6 --- /dev/null +++ b/basicsr/utils/plot_util.py @@ -0,0 +1,84 @@ +import re + + +def read_data_from_tensorboard(log_path, tag): + """Get raw data (steps and values) from tensorboard events. + + Args: + log_path (str): Path to the tensorboard log. + tag (str): tag to be read. + """ + from tensorboard.backend.event_processing.event_accumulator import EventAccumulator + + # tensorboard event + event_acc = EventAccumulator(log_path) + event_acc.Reload() + scalar_list = event_acc.Tags()['scalars'] + print('tag list: ', scalar_list) + steps = [int(s.step) for s in event_acc.Scalars(tag)] + values = [s.value for s in event_acc.Scalars(tag)] + return steps, values + + +def read_data_from_txt_2v(path, pattern, step_one=False): + """Read data from txt with 2 returned values (usually [step, value]). + + Args: + path (str): path to the txt file. + pattern (str): re (regular expression) pattern. + step_one (bool): add 1 to steps. Default: False. + """ + with open(path) as f: + lines = f.readlines() + lines = [line.strip() for line in lines] + steps = [] + values = [] + + pattern = re.compile(pattern) + for line in lines: + match = pattern.match(line) + if match: + steps.append(int(match.group(1))) + values.append(float(match.group(2))) + if step_one: + steps = [v + 1 for v in steps] + return steps, values + + +def read_data_from_txt_1v(path, pattern): + """Read data from txt with 1 returned values. + + Args: + path (str): path to the txt file. + pattern (str): re (regular expression) pattern. + """ + with open(path) as f: + lines = f.readlines() + lines = [line.strip() for line in lines] + data = [] + + pattern = re.compile(pattern) + for line in lines: + match = pattern.match(line) + if match: + data.append(float(match.group(1))) + return data + + +def smooth_data(values, smooth_weight): + """ Smooth data using 1st-order IIR low-pass filter (what tensorflow does). + + Ref: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/\ + tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 + + Args: + values (list): A list of values to be smoothed. + smooth_weight (float): Smooth weight. + """ + values_sm = [] + last_sm_value = values[0] + for value in values: + value_sm = last_sm_value * smooth_weight + (1 - smooth_weight) * value + values_sm.append(value_sm) + last_sm_value = value_sm + return values_sm diff --git a/basicsr/utils/registry.py b/basicsr/utils/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..5e72ef7ff21b94f50e6caa8948f69ca0b04bc968 --- /dev/null +++ b/basicsr/utils/registry.py @@ -0,0 +1,88 @@ +# Modified from: https://github.com/facebookresearch/fvcore/blob/master/fvcore/common/registry.py # noqa: E501 + + +class Registry(): + """ + The registry that provides name -> object mapping, to support third-party + users' custom modules. + + To create a registry (e.g. a backbone registry): + + .. code-block:: python + + BACKBONE_REGISTRY = Registry('BACKBONE') + + To register an object: + + .. code-block:: python + + @BACKBONE_REGISTRY.register() + class MyBackbone(): + ... + + Or: + + .. code-block:: python + + BACKBONE_REGISTRY.register(MyBackbone) + """ + + def __init__(self, name): + """ + Args: + name (str): the name of this registry + """ + self._name = name + self._obj_map = {} + + def _do_register(self, name, obj, suffix=None): + if isinstance(suffix, str): + name = name + '_' + suffix + + assert (name not in self._obj_map), (f"An object named '{name}' was already registered " + f"in '{self._name}' registry!") + self._obj_map[name] = obj + + def register(self, obj=None, suffix=None): + """ + Register the given object under the the name `obj.__name__`. + Can be used as either a decorator or not. + See docstring of this class for usage. + """ + if obj is None: + # used as a decorator + def deco(func_or_class): + name = func_or_class.__name__ + self._do_register(name, func_or_class, suffix) + return func_or_class + + return deco + + # used as a function call + name = obj.__name__ + self._do_register(name, obj, suffix) + + def get(self, name, suffix='basicsr'): + ret = self._obj_map.get(name) + if ret is None: + ret = self._obj_map.get(name + '_' + suffix) + print(f'Name {name} is not found, use name: {name}_{suffix}!') + if ret is None: + raise KeyError(f"No object named '{name}' found in '{self._name}' registry!") + return ret + + def __contains__(self, name): + return name in self._obj_map + + def __iter__(self): + return iter(self._obj_map.items()) + + def keys(self): + return self._obj_map.keys() + + +DATASET_REGISTRY = Registry('dataset') +ARCH_REGISTRY = Registry('arch') +MODEL_REGISTRY = Registry('model') +LOSS_REGISTRY = Registry('loss') +METRIC_REGISTRY = Registry('metric') diff --git a/checkpoints/.DS_Store b/checkpoints/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/checkpoints/.DS_Store differ diff --git a/checkpoints/esrgan_max.pth b/checkpoints/esrgan_max.pth new file mode 100644 index 0000000000000000000000000000000000000000..683b40d58c1d97ebbf63f2d535a9754a5dcd62f8 --- /dev/null +++ b/checkpoints/esrgan_max.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d6ac08f5b01a02db10f90f49826b25c9788e2e4ba5ee96981edcbeae56d5e1c0 +size 67040989 diff --git a/checkpoints/face_segmentation.pth b/checkpoints/face_segmentation.pth new file mode 100644 index 0000000000000000000000000000000000000000..ca57f3257ca7715bc340d065764bc249d985c287 --- /dev/null +++ b/checkpoints/face_segmentation.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:468e13ca13a9b43cc0881a9f99083a430e9c0a38abd935431d1c28ee94b26567 +size 53289463 diff --git a/checkpoints/pretrained_state.pth b/checkpoints/pretrained_state.pth new file mode 100644 index 0000000000000000000000000000000000000000..9575a40a9f3534189b1649d8279cb9048827a0f8 --- /dev/null +++ b/checkpoints/pretrained_state.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e8d76d5f8af3ba33332067ed572edb55f3aa868310f6601abf73bfda2e6e208 +size 310688649 diff --git a/checkpoints/wav2lip_gan.pth b/checkpoints/wav2lip_gan.pth new file mode 100644 index 0000000000000000000000000000000000000000..0b8907735b1aef6adb297f2f6dcbcf8432823de4 --- /dev/null +++ b/checkpoints/wav2lip_gan.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ca9ab7b7b812c0e80a6e70a5977c545a1e8a365a6c49d5e533023c034d7ac3d8 +size 435801865 diff --git a/esrgan/.DS_Store b/esrgan/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..be6c4c779219ee8042832b1ff2c7dfd412073e36 Binary files /dev/null and b/esrgan/.DS_Store differ diff --git a/esrgan/__pycache__/upsample.cpython-38.pyc b/esrgan/__pycache__/upsample.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7f9218f40f7b84b3196b1d5bd39636992c5a4d5f Binary files /dev/null and b/esrgan/__pycache__/upsample.cpython-38.pyc differ diff --git a/esrgan/upsample.py b/esrgan/upsample.py new file mode 100644 index 0000000000000000000000000000000000000000..f9a6d1c26bc5b77c2ece7f66511391a0f82dd1f6 --- /dev/null +++ b/esrgan/upsample.py @@ -0,0 +1,84 @@ +import cv2 +import glob +import os +import sys +from basicsr.archs.rrdbnet_arch import RRDBNet +from basicsr.utils.download_util import load_file_from_url +import numpy as np +import torch +from gfpgan import GFPGANer +from realesrgan import RealESRGANer +from realesrgan.archs.srvgg_arch import SRVGGNetCompact +from basicsr.utils import imwrite, img2tensor, tensor2img +from torchvision.transforms.functional import normalize +from basicsr.utils.registry import ARCH_REGISTRY + +def load_sr(model_path, device, face): + if not face=='codeformer': + model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4) #alter to match dims as needed + netscale = 4 + model_path = os.path.normpath(model_path) + if not os.path.isfile(model_path): + model_path = load_file_from_url( + url='https://github.com/GucciFlipFlops1917/wav2lip-hq-updated-ESRGAN/releases/download/v0.0.1/4x_BigFace_v3_Clear.pth', + model_dir='weights', progress=True, file_name=None) + upsampler = RealESRGANer( + scale=netscale, + model_path=model_path, + dni_weight=None, + model=model, + tile=0, + tile_pad=10, + pre_pad=0, + half=True, + gpu_id=0) + if face==None: + run_params=upsampler + else: + gfp = GFPGANer( + model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth', + upscale=2, + arch='clean', + channel_multiplier=2, + bg_upsampler=upsampler) + run_params=gfp + else: + run_params = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, + connect_list=['32', '64', '128', '256']).to(device) + ckpt_path = load_file_from_url(url='https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth', + model_dir='weights/CodeFormer', progress=True, file_name=None) + checkpoint = torch.load(ckpt_path)['params_ema'] + run_params.load_state_dict(checkpoint) + run_params.eval() + return run_params + + +def upscale(image, face, properties): + try: + if face==1: ## GFP-GAN + _, _, output = properties.enhance(image, has_aligned=False, only_center_face=False, paste_back=True) + elif face==2: ## CODEFORMER + net = properties[0] + device = properties[1] + w = properties[2] + image = cv2.resize(image, (512, 512), interpolation=cv2.INTER_LINEAR) + cropped_face_t = img2tensor(image / 255., bgr2rgb=True, float32=True) + normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + cropped_face_t = cropped_face_t.unsqueeze(0).to(device) + try: + with torch.no_grad(): + cropped_face_t = net(cropped_face_t, w=w, adain=True)[0] + restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) + del cropped_face_t + torch.cuda.empty_cache() + except Exception as error: + print(f'\tFailed inference for CodeFormer: {error}') + restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) + output = restored_face.astype('uint8') + elif face==0: ## ESRGAN + img = image.astype(np.float32) / 255. + output, _ = properties.enhance(image, outscale=4) + except RuntimeError as error: + print('Error', error) + print('If you encounter CUDA out of memory, try to set --tile with a smaller number.') + return output diff --git a/face_detection/.DS_Store b/face_detection/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..b2410f5a74ae9d0fcc10cb26b7e726b9f7837a2c Binary files /dev/null and b/face_detection/.DS_Store differ diff --git a/face_detection/__init__.py b/face_detection/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4bae29fd5f85b41e4669302bd2603bc6924eddc7 --- /dev/null +++ b/face_detection/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- + +__author__ = """Adrian Bulat""" +__email__ = 'adrian.bulat@nottingham.ac.uk' +__version__ = '1.0.1' + +from .api import FaceAlignment, LandmarksType, NetworkSize diff --git a/face_detection/__pycache__/__init__.cpython-310.pyc b/face_detection/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5c7ac81565bbcb6f083d22ffb659ed922fc6c2e9 Binary files /dev/null and b/face_detection/__pycache__/__init__.cpython-310.pyc differ diff --git a/face_detection/__pycache__/__init__.cpython-38.pyc b/face_detection/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..12bddca7b1babd9cc32cca71d516845d156fc002 Binary files /dev/null and b/face_detection/__pycache__/__init__.cpython-38.pyc differ diff --git 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0000000000000000000000000000000000000000..cb02d5252db5362b9985687a992e128a522e5b63 --- /dev/null +++ b/face_detection/api.py @@ -0,0 +1,79 @@ +from __future__ import print_function +import os +import torch +from torch.utils.model_zoo import load_url +from enum import Enum +import numpy as np +import cv2 +try: + import urllib.request as request_file +except BaseException: + import urllib as request_file + +from .models import FAN, ResNetDepth +from .utils import * + + +class LandmarksType(Enum): + """Enum class defining the type of landmarks to detect. + + ``_2D`` - the detected points ``(x,y)`` are detected in a 2D space and follow the visible contour of the face + ``_2halfD`` - this points represent the projection of the 3D points into 3D + ``_3D`` - detect the points ``(x,y,z)``` in a 3D space + + """ + _2D = 1 + _2halfD = 2 + _3D = 3 + + +class NetworkSize(Enum): + # TINY = 1 + # SMALL = 2 + # MEDIUM = 3 + LARGE = 4 + + def __new__(cls, value): + member = object.__new__(cls) + member._value_ = value + return member + + def __int__(self): + return self.value + +ROOT = os.path.dirname(os.path.abspath(__file__)) + +class FaceAlignment: + def __init__(self, landmarks_type, network_size=NetworkSize.LARGE, + device='cuda', flip_input=False, face_detector='sfd', verbose=False): + self.device = device + self.flip_input = flip_input + self.landmarks_type = landmarks_type + self.verbose = verbose + + network_size = int(network_size) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + # Get the face detector + face_detector_module = __import__('face_detection.detection.' + face_detector, + globals(), locals(), [face_detector], 0) + self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose) + + def get_detections_for_batch(self, images): + images = images[..., ::-1] + detected_faces = self.face_detector.detect_from_batch(images.copy()) + results = [] + + for i, d in enumerate(detected_faces): + if len(d) == 0: + results.append(None) + continue + d = d[0] + d = np.clip(d, 0, None) + + x1, y1, x2, y2 = map(int, d[:-1]) + results.append((x1, y1, x2, y2)) + + return results \ No newline at end of file diff --git a/face_detection/detection/__init__.py b/face_detection/detection/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1a6b0402dae864a3cc5dc2a90a412fd842a0efc7 --- /dev/null +++ b/face_detection/detection/__init__.py @@ -0,0 +1 @@ +from .core import FaceDetector \ No newline at end of file diff --git a/face_detection/detection/__pycache__/__init__.cpython-38.pyc b/face_detection/detection/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bb8dce8250c4981669296155f4b44d6eba21546f Binary files /dev/null and b/face_detection/detection/__pycache__/__init__.cpython-38.pyc differ diff --git a/face_detection/detection/__pycache__/core.cpython-38.pyc b/face_detection/detection/__pycache__/core.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0dfa727b2ad5e562f2a12201397b3d1ad11d72c5 Binary files /dev/null and b/face_detection/detection/__pycache__/core.cpython-38.pyc differ diff --git a/face_detection/detection/core.py b/face_detection/detection/core.py new file mode 100644 index 0000000000000000000000000000000000000000..0f8275e8e53143f66298f75f0517c234a68778cd --- /dev/null +++ b/face_detection/detection/core.py @@ -0,0 +1,130 @@ +import logging +import glob +from tqdm import tqdm +import numpy as np +import torch +import cv2 + + +class FaceDetector(object): + """An abstract class representing a face detector. + + Any other face detection implementation must subclass it. All subclasses + must implement ``detect_from_image``, that return a list of detected + bounding boxes. Optionally, for speed considerations detect from path is + recommended. + """ + + def __init__(self, device, verbose): + self.device = device + self.verbose = verbose + + if verbose: + if 'cpu' in device: + logger = logging.getLogger(__name__) + logger.warning("Detection running on CPU, this may be potentially slow.") + + if 'cpu' not in device and 'cuda' not in device: + if verbose: + logger.error("Expected values for device are: {cpu, cuda} but got: %s", device) + raise ValueError + + def detect_from_image(self, tensor_or_path): + """Detects faces in a given image. + + This function detects the faces present in a provided BGR(usually) + image. The input can be either the image itself or the path to it. + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path + to an image or the image itself. + + Example:: + + >>> path_to_image = 'data/image_01.jpg' + ... detected_faces = detect_from_image(path_to_image) + [A list of bounding boxes (x1, y1, x2, y2)] + >>> image = cv2.imread(path_to_image) + ... detected_faces = detect_from_image(image) + [A list of bounding boxes (x1, y1, x2, y2)] + + """ + raise NotImplementedError + + def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): + """Detects faces from all the images present in a given directory. + + Arguments: + path {string} -- a string containing a path that points to the folder containing the images + + Keyword Arguments: + extensions {list} -- list of string containing the extensions to be + consider in the following format: ``.extension_name`` (default: + {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the + folder recursively (default: {False}) show_progress_bar {bool} -- + display a progressbar (default: {True}) + + Example: + >>> directory = 'data' + ... detected_faces = detect_from_directory(directory) + {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]} + + """ + if self.verbose: + logger = logging.getLogger(__name__) + + if len(extensions) == 0: + if self.verbose: + logger.error("Expected at list one extension, but none was received.") + raise ValueError + + if self.verbose: + logger.info("Constructing the list of images.") + additional_pattern = '/**/*' if recursive else '/*' + files = [] + for extension in extensions: + files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive)) + + if self.verbose: + logger.info("Finished searching for images. %s images found", len(files)) + logger.info("Preparing to run the detection.") + + predictions = {} + for image_path in tqdm(files, disable=not show_progress_bar): + if self.verbose: + logger.info("Running the face detector on image: %s", image_path) + predictions[image_path] = self.detect_from_image(image_path) + + if self.verbose: + logger.info("The detector was successfully run on all %s images", len(files)) + + return predictions + + @property + def reference_scale(self): + raise NotImplementedError + + @property + def reference_x_shift(self): + raise NotImplementedError + + @property + def reference_y_shift(self): + raise NotImplementedError + + @staticmethod + def tensor_or_path_to_ndarray(tensor_or_path, rgb=True): + """Convert path (represented as a string) or torch.tensor to a numpy.ndarray + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself + """ + if isinstance(tensor_or_path, str): + return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1] + elif torch.is_tensor(tensor_or_path): + # Call cpu in case its coming from cuda + return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy() + elif isinstance(tensor_or_path, np.ndarray): + return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path + else: + raise TypeError diff --git a/face_detection/detection/sfd/__init__.py b/face_detection/detection/sfd/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5a63ecd45658f22e66c171ada751fb33764d4559 --- /dev/null +++ b/face_detection/detection/sfd/__init__.py @@ -0,0 +1 @@ +from .sfd_detector import SFDDetector as FaceDetector \ No newline at end of file diff --git a/face_detection/detection/sfd/__pycache__/__init__.cpython-38.pyc b/face_detection/detection/sfd/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8b9011739d2fcce0394145059d53858fa10fc8f8 Binary files /dev/null and b/face_detection/detection/sfd/__pycache__/__init__.cpython-38.pyc differ diff --git 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b/face_detection/detection/sfd/__pycache__/net_s3fd.cpython-38.pyc differ diff --git a/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-38.pyc b/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d96e5732a0b2f8d2ec91aac24a7a6b16bbb17ffe Binary files /dev/null and b/face_detection/detection/sfd/__pycache__/sfd_detector.cpython-38.pyc differ diff --git a/face_detection/detection/sfd/bbox.py b/face_detection/detection/sfd/bbox.py new file mode 100644 index 0000000000000000000000000000000000000000..4bd7222e5e5f78a51944cbeed3cccbacddc46bed --- /dev/null +++ b/face_detection/detection/sfd/bbox.py @@ -0,0 +1,129 @@ +from __future__ import print_function +import os +import sys +import cv2 +import random +import datetime +import time +import math +import argparse +import numpy as np +import torch + +try: + from iou import IOU +except BaseException: + # IOU cython speedup 10x + def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): + sa = abs((ax2 - ax1) * (ay2 - ay1)) + sb = abs((bx2 - bx1) * (by2 - by1)) + x1, y1 = max(ax1, bx1), max(ay1, by1) + x2, y2 = min(ax2, bx2), min(ay2, by2) + w = x2 - x1 + h = y2 - y1 + if w < 0 or h < 0: + return 0.0 + else: + return 1.0 * w * h / (sa + sb - w * h) + + +def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh): + xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1 + dx, dy = (xc - axc) / aww, (yc - ayc) / ahh + dw, dh = math.log(ww / aww), math.log(hh / ahh) + return dx, dy, dw, dh + + +def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh): + xc, yc = dx * aww + axc, dy * ahh + ayc + ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh + x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2 + return x1, y1, x2, y2 + + +def nms(dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) + + w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def batch_decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes diff --git a/face_detection/detection/sfd/detect.py b/face_detection/detection/sfd/detect.py new file mode 100644 index 0000000000000000000000000000000000000000..4e1cd814603a3d722c2f544d611850f92d4827b5 --- /dev/null +++ b/face_detection/detection/sfd/detect.py @@ -0,0 +1,112 @@ +import torch +import torch.nn.functional as F + +import os +import sys +import cv2 +import random +import datetime +import math +import argparse +import numpy as np + +import scipy.io as sio +import zipfile +from .net_s3fd import s3fd +from .bbox import * + + +def detect(net, img, device): + img = img - np.array([104, 117, 123]) + img = img.transpose(2, 0, 1) + img = img.reshape((1,) + img.shape) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + img = torch.from_numpy(img.copy()).to(device, dtype=torch.float32) + BB, CC, HH, WW = img.size() + with torch.no_grad(): + olist = net(img) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[0, 1, hindex, windex] + loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) + variances = [0.1, 0.2] + box = decode(loc, priors, variances) + x1, y1, x2, y2 = box[0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append([x1, y1, x2, y2, score]) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, 5)) + + return bboxlist + +def batch_detect(net, imgs, device): + imgs = imgs - np.array([104, 117, 123]) + imgs = imgs.transpose(0, 3, 1, 2) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + imgs = torch.from_numpy(imgs.copy()).to(device, dtype=torch.float32) + BB, CC, HH, WW = imgs.size() + with torch.no_grad(): + olist = net(imgs) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[:, 1, hindex, windex] + loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) + variances = [0.1, 0.2] + box = batch_decode(loc, priors, variances) + box = box[:, 0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, BB, 5)) + + return bboxlist + +def flip_detect(net, img, device): + img = cv2.flip(img, 1) + b = detect(net, img, device) + + bboxlist = np.zeros(b.shape) + bboxlist[:, 0] = img.shape[1] - b[:, 2] + bboxlist[:, 1] = b[:, 1] + bboxlist[:, 2] = img.shape[1] - b[:, 0] + bboxlist[:, 3] = b[:, 3] + bboxlist[:, 4] = b[:, 4] + return bboxlist + + +def pts_to_bb(pts): + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + return np.array([min_x, min_y, max_x, max_y]) diff --git a/face_detection/detection/sfd/net_s3fd.py b/face_detection/detection/sfd/net_s3fd.py new file mode 100644 index 0000000000000000000000000000000000000000..fc64313c277ab594d0257585c70f147606693452 --- /dev/null +++ b/face_detection/detection/sfd/net_s3fd.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class L2Norm(nn.Module): + def __init__(self, n_channels, scale=1.0): + super(L2Norm, self).__init__() + self.n_channels = n_channels + self.scale = scale + self.eps = 1e-10 + self.weight = nn.Parameter(torch.Tensor(self.n_channels)) + self.weight.data *= 0.0 + self.weight.data += self.scale + + def forward(self, x): + norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps + x = x / norm * self.weight.view(1, -1, 1, 1) + return x + + +class s3fd(nn.Module): + def __init__(self): + super(s3fd, self).__init__() + self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) + + self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) + self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) + + self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) + self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + + self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) + self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3) + self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0) + + self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) + self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) + + self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0) + self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + + self.conv3_3_norm = L2Norm(256, scale=10) + self.conv4_3_norm = L2Norm(512, scale=8) + self.conv5_3_norm = L2Norm(512, scale=5) + + self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + + self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1) + self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + h = F.relu(self.conv1_1(x)) + h = F.relu(self.conv1_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv2_1(h)) + h = F.relu(self.conv2_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv3_1(h)) + h = F.relu(self.conv3_2(h)) + h = F.relu(self.conv3_3(h)) + f3_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv4_1(h)) + h = F.relu(self.conv4_2(h)) + h = F.relu(self.conv4_3(h)) + f4_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv5_1(h)) + h = F.relu(self.conv5_2(h)) + h = F.relu(self.conv5_3(h)) + f5_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.fc6(h)) + h = F.relu(self.fc7(h)) + ffc7 = h + h = F.relu(self.conv6_1(h)) + h = F.relu(self.conv6_2(h)) + f6_2 = h + h = F.relu(self.conv7_1(h)) + h = F.relu(self.conv7_2(h)) + f7_2 = h + + f3_3 = self.conv3_3_norm(f3_3) + f4_3 = self.conv4_3_norm(f4_3) + f5_3 = self.conv5_3_norm(f5_3) + + cls1 = self.conv3_3_norm_mbox_conf(f3_3) + reg1 = self.conv3_3_norm_mbox_loc(f3_3) + cls2 = self.conv4_3_norm_mbox_conf(f4_3) + reg2 = self.conv4_3_norm_mbox_loc(f4_3) + cls3 = self.conv5_3_norm_mbox_conf(f5_3) + reg3 = self.conv5_3_norm_mbox_loc(f5_3) + cls4 = self.fc7_mbox_conf(ffc7) + reg4 = self.fc7_mbox_loc(ffc7) + cls5 = self.conv6_2_mbox_conf(f6_2) + reg5 = self.conv6_2_mbox_loc(f6_2) + cls6 = self.conv7_2_mbox_conf(f7_2) + reg6 = self.conv7_2_mbox_loc(f7_2) + + # max-out background label + chunk = torch.chunk(cls1, 4, 1) + bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2]) + cls1 = torch.cat([bmax, chunk[3]], dim=1) + + return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] diff --git a/face_detection/detection/sfd/s3fd.pth b/face_detection/detection/sfd/s3fd.pth new file mode 100644 index 0000000000000000000000000000000000000000..895538e7fb6df3ad6e0e80d6d48b3c4e60cd9e6c --- /dev/null +++ b/face_detection/detection/sfd/s3fd.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:619a31681264d3f7f7fc7a16a42cbbe8b23f31a256f75a366e5a1bcd59b33543 +size 89843225 diff --git a/face_detection/detection/sfd/sfd_detector.py b/face_detection/detection/sfd/sfd_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..8fbce15253251d403754ab4348f93ae85a6ba2fb --- /dev/null +++ b/face_detection/detection/sfd/sfd_detector.py @@ -0,0 +1,59 @@ +import os +import cv2 +from torch.utils.model_zoo import load_url + +from ..core import FaceDetector + +from .net_s3fd import s3fd +from .bbox import * +from .detect import * + +models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', +} + + +class SFDDetector(FaceDetector): + def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False): + super(SFDDetector, self).__init__(device, verbose) + + # Initialise the face detector + if not os.path.isfile(path_to_detector): + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + + self.face_detector = s3fd() + self.face_detector.load_state_dict(model_weights) + self.face_detector.to(device) + self.face_detector.eval() + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = detect(self.face_detector, image, device=self.device) + keep = nms(bboxlist, 0.3) + bboxlist = bboxlist[keep, :] + bboxlist = [x for x in bboxlist if x[-1] > 0.5] + + return bboxlist + + def detect_from_batch(self, images): + bboxlists = batch_detect(self.face_detector, images, device=self.device) + keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])] + bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)] + bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists] + + return bboxlists + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/face_detection/models.py b/face_detection/models.py new file mode 100644 index 0000000000000000000000000000000000000000..ee2dde32bdf72c25a4600e48efa73ffc0d4a3893 --- /dev/null +++ b/face_detection/models.py @@ -0,0 +1,261 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + + +def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, + stride=strd, padding=padding, bias=bias) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, out_planes): + super(ConvBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = conv3x3(in_planes, int(out_planes / 2)) + self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) + self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) + self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) + self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) + + if in_planes != out_planes: + self.downsample = nn.Sequential( + nn.BatchNorm2d(in_planes), + nn.ReLU(True), + nn.Conv2d(in_planes, out_planes, + kernel_size=1, stride=1, bias=False), + ) + else: + self.downsample = None + + def forward(self, x): + residual = x + + out1 = self.bn1(x) + out1 = F.relu(out1, True) + out1 = self.conv1(out1) + + out2 = self.bn2(out1) + out2 = F.relu(out2, True) + out2 = self.conv2(out2) + + out3 = self.bn3(out2) + out3 = F.relu(out3, True) + out3 = self.conv3(out3) + + out3 = torch.cat((out1, out2, out3), 1) + + if self.downsample is not None: + residual = self.downsample(residual) + + out3 += residual + + return out3 + + +class Bottleneck(nn.Module): + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HourGlass(nn.Module): + def __init__(self, num_modules, depth, num_features): + super(HourGlass, self).__init__() + self.num_modules = num_modules + self.depth = depth + self.features = num_features + + self._generate_network(self.depth) + + def _generate_network(self, level): + self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) + + if level > 1: + self._generate_network(level - 1) + else: + self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) + + def _forward(self, level, inp): + # Upper branch + up1 = inp + up1 = self._modules['b1_' + str(level)](up1) + + # Lower branch + low1 = F.avg_pool2d(inp, 2, stride=2) + low1 = self._modules['b2_' + str(level)](low1) + + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = low1 + low2 = self._modules['b2_plus_' + str(level)](low2) + + low3 = low2 + low3 = self._modules['b3_' + str(level)](low3) + + up2 = F.interpolate(low3, scale_factor=2, mode='nearest') + + return up1 + up2 + + def forward(self, x): + return self._forward(self.depth, x) + + +class FAN(nn.Module): + + def __init__(self, num_modules=1): + super(FAN, self).__init__() + self.num_modules = num_modules + + # Base part + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.conv2 = ConvBlock(64, 128) + self.conv3 = ConvBlock(128, 128) + self.conv4 = ConvBlock(128, 256) + + # Stacking part + for hg_module in range(self.num_modules): + self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) + self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) + self.add_module('conv_last' + str(hg_module), + nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) + self.add_module('l' + str(hg_module), nn.Conv2d(256, + 68, kernel_size=1, stride=1, padding=0)) + + if hg_module < self.num_modules - 1: + self.add_module( + 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('al' + str(hg_module), nn.Conv2d(68, + 256, kernel_size=1, stride=1, padding=0)) + + def forward(self, x): + x = F.relu(self.bn1(self.conv1(x)), True) + x = F.avg_pool2d(self.conv2(x), 2, stride=2) + x = self.conv3(x) + x = self.conv4(x) + + previous = x + + outputs = [] + for i in range(self.num_modules): + hg = self._modules['m' + str(i)](previous) + + ll = hg + ll = self._modules['top_m_' + str(i)](ll) + + ll = F.relu(self._modules['bn_end' + str(i)] + (self._modules['conv_last' + str(i)](ll)), True) + + # Predict heatmaps + tmp_out = self._modules['l' + str(i)](ll) + outputs.append(tmp_out) + + if i < self.num_modules - 1: + ll = self._modules['bl' + str(i)](ll) + tmp_out_ = self._modules['al' + str(i)](tmp_out) + previous = previous + ll + tmp_out_ + + return outputs + + +class ResNetDepth(nn.Module): + + def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): + self.inplanes = 64 + super(ResNetDepth, self).__init__() + self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x diff --git a/face_detection/utils.py b/face_detection/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc4cf3e328efaa227cbcfdd969e1056688adad5 --- /dev/null +++ b/face_detection/utils.py @@ -0,0 +1,313 @@ +from __future__ import print_function +import os +import sys +import time +import torch +import math +import numpy as np +import cv2 + + +def _gaussian( + size=3, sigma=0.25, amplitude=1, normalize=False, width=None, + height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5, + mean_vert=0.5): + # handle some defaults + if width is None: + width = size + if height is None: + height = size + if sigma_horz is None: + sigma_horz = sigma + if sigma_vert is None: + sigma_vert = sigma + center_x = mean_horz * width + 0.5 + center_y = mean_vert * height + 0.5 + gauss = np.empty((height, width), dtype=np.float32) + # generate kernel + for i in range(height): + for j in range(width): + gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / ( + sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0)) + if normalize: + gauss = gauss / np.sum(gauss) + return gauss + + +def draw_gaussian(image, point, sigma): + # Check if the gaussian is inside + ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)] + br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)] + if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1): + return image + size = 6 * sigma + 1 + g = _gaussian(size) + g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))] + g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))] + img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))] + img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))] + assert (g_x[0] > 0 and g_y[1] > 0) + image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1] + ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]] + image[image > 1] = 1 + return image + + +def transform(point, center, scale, resolution, invert=False): + """Generate and affine transformation matrix. + + Given a set of points, a center, a scale and a targer resolution, the + function generates and affine transformation matrix. If invert is ``True`` + it will produce the inverse transformation. + + Arguments: + point {torch.tensor} -- the input 2D point + center {torch.tensor or numpy.array} -- the center around which to perform the transformations + scale {float} -- the scale of the face/object + resolution {float} -- the output resolution + + Keyword Arguments: + invert {bool} -- define wherever the function should produce the direct or the + inverse transformation matrix (default: {False}) + """ + _pt = torch.ones(3) + _pt[0] = point[0] + _pt[1] = point[1] + + h = 200.0 * scale + t = torch.eye(3) + t[0, 0] = resolution / h + t[1, 1] = resolution / h + t[0, 2] = resolution * (-center[0] / h + 0.5) + t[1, 2] = resolution * (-center[1] / h + 0.5) + + if invert: + t = torch.inverse(t) + + new_point = (torch.matmul(t, _pt))[0:2] + + return new_point.int() + + +def crop(image, center, scale, resolution=256.0): + """Center crops an image or set of heatmaps + + Arguments: + image {numpy.array} -- an rgb image + center {numpy.array} -- the center of the object, usually the same as of the bounding box + scale {float} -- scale of the face + + Keyword Arguments: + resolution {float} -- the size of the output cropped image (default: {256.0}) + + Returns: + [type] -- [description] + """ # Crop around the center point + """ Crops the image around the center. Input is expected to be an np.ndarray """ + ul = transform([1, 1], center, scale, resolution, True) + br = transform([resolution, resolution], center, scale, resolution, True) + # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0) + if image.ndim > 2: + newDim = np.array([br[1] - ul[1], br[0] - ul[0], + image.shape[2]], dtype=np.int32) + newImg = np.zeros(newDim, dtype=np.uint8) + else: + newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int) + newImg = np.zeros(newDim, dtype=np.uint8) + ht = image.shape[0] + wd = image.shape[1] + newX = np.array( + [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32) + newY = np.array( + [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32) + oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32) + oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32) + newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] + ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :] + newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), + interpolation=cv2.INTER_LINEAR) + return newImg + + +def get_preds_fromhm(hm, center=None, scale=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the center + and the scale is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + center {torch.tensor} -- the center of the bounding box (default: {None}) + scale {float} -- face scale (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if center is not None and scale is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], center, scale, hm.size(2), True) + + return preds, preds_orig + +def get_preds_fromhm_batch(hm, centers=None, scales=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the centers + and the scales is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + centers {torch.tensor} -- the centers of the bounding box (default: {None}) + scales {float} -- face scales (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if centers is not None and scales is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], centers[i], scales[i], hm.size(2), True) + + return preds, preds_orig + +def shuffle_lr(parts, pairs=None): + """Shuffle the points left-right according to the axis of symmetry + of the object. + + Arguments: + parts {torch.tensor} -- a 3D or 4D object containing the + heatmaps. + + Keyword Arguments: + pairs {list of integers} -- [order of the flipped points] (default: {None}) + """ + if pairs is None: + pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, + 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, + 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, + 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, + 62, 61, 60, 67, 66, 65] + if parts.ndimension() == 3: + parts = parts[pairs, ...] + else: + parts = parts[:, pairs, ...] + + return parts + + +def flip(tensor, is_label=False): + """Flip an image or a set of heatmaps left-right + + Arguments: + tensor {numpy.array or torch.tensor} -- [the input image or heatmaps] + + Keyword Arguments: + is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False}) + """ + if not torch.is_tensor(tensor): + tensor = torch.from_numpy(tensor) + + if is_label: + tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1) + else: + tensor = tensor.flip(tensor.ndimension() - 1) + + return tensor + +# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py) + + +def appdata_dir(appname=None, roaming=False): + """ appdata_dir(appname=None, roaming=False) + + Get the path to the application directory, where applications are allowed + to write user specific files (e.g. configurations). For non-user specific + data, consider using common_appdata_dir(). + If appname is given, a subdir is appended (and created if necessary). + If roaming is True, will prefer a roaming directory (Windows Vista/7). + """ + + # Define default user directory + userDir = os.getenv('FACEALIGNMENT_USERDIR', None) + if userDir is None: + userDir = os.path.expanduser('~') + if not os.path.isdir(userDir): # pragma: no cover + userDir = '/var/tmp' # issue #54 + + # Get system app data dir + path = None + if sys.platform.startswith('win'): + path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA') + path = (path2 or path1) if roaming else (path1 or path2) + elif sys.platform.startswith('darwin'): + path = os.path.join(userDir, 'Library', 'Application Support') + # On Linux and as fallback + if not (path and os.path.isdir(path)): + path = userDir + + # Maybe we should store things local to the executable (in case of a + # portable distro or a frozen application that wants to be portable) + prefix = sys.prefix + if getattr(sys, 'frozen', None): + prefix = os.path.abspath(os.path.dirname(sys.executable)) + for reldir in ('settings', '../settings'): + localpath = os.path.abspath(os.path.join(prefix, reldir)) + if os.path.isdir(localpath): # pragma: no cover + try: + open(os.path.join(localpath, 'test.write'), 'wb').close() + os.remove(os.path.join(localpath, 'test.write')) + except IOError: + pass # We cannot write in this directory + else: + path = localpath + break + + # Get path specific for this app + if appname: + if path == userDir: + appname = '.' + appname.lstrip('.') # Make it a hidden directory + path = os.path.join(path, appname) + if not os.path.isdir(path): # pragma: no cover + os.mkdir(path) + + # Done + return path diff --git a/face_parsing/.DS_Store b/face_parsing/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..6ed0065c833ae0ba322350d0f35f96fae8db3024 Binary files /dev/null and b/face_parsing/.DS_Store differ diff --git a/face_parsing/__init__.py b/face_parsing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed087a4fc05eda34ea6ddb92c53889f3903c9f9b --- /dev/null +++ b/face_parsing/__init__.py @@ -0,0 +1 @@ +from .swap import init_parser, swap_regions \ No newline at end of file diff --git a/face_parsing/__pycache__/__init__.cpython-310.pyc b/face_parsing/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a445bc684382dfff946a7fbecab8abf46b898be3 Binary files /dev/null and b/face_parsing/__pycache__/__init__.cpython-310.pyc differ diff --git a/face_parsing/__pycache__/__init__.cpython-38.pyc b/face_parsing/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6d8a8c9ec36f2d53fe29587bc06784c6e4d86a5c Binary files /dev/null and b/face_parsing/__pycache__/__init__.cpython-38.pyc differ diff --git a/face_parsing/__pycache__/model.cpython-38.pyc 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0000000000000000000000000000000000000000..31ab7a8afc2db0a336361ca4a86853486f97a900 Binary files /dev/null and b/face_parsing/__pycache__/swap.cpython-38.pyc differ diff --git a/face_parsing/model.py b/face_parsing/model.py new file mode 100644 index 0000000000000000000000000000000000000000..5119e751c3ae18e4dc1eecde7bfcb5bf9c62fb92 --- /dev/null +++ b/face_parsing/model.py @@ -0,0 +1,283 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from .resnet import Resnet18 +# from modules.bn import InPlaceABNSync as BatchNorm2d + + +class ConvBNReLU(nn.Module): + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d(in_chan, + out_chan, + kernel_size = ks, + stride = stride, + padding = padding, + bias = False) + self.bn = nn.BatchNorm2d(out_chan) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = F.relu(self.bn(x)) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + +class BiSeNetOutput(nn.Module): + def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): + super(BiSeNetOutput, self).__init__() + self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) + self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = self.conv_out(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class AttentionRefinementModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(AttentionRefinementModule, self).__init__() + self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) + self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) + self.bn_atten = nn.BatchNorm2d(out_chan) + self.sigmoid_atten = nn.Sigmoid() + self.init_weight() + + def forward(self, x): + feat = self.conv(x) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv_atten(atten) + atten = self.bn_atten(atten) + atten = self.sigmoid_atten(atten) + out = torch.mul(feat, atten) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class ContextPath(nn.Module): + def __init__(self, *args, **kwargs): + super(ContextPath, self).__init__() + self.resnet = Resnet18() + self.arm16 = AttentionRefinementModule(256, 128) + self.arm32 = AttentionRefinementModule(512, 128) + self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) + + self.init_weight() + + def forward(self, x): + H0, W0 = x.size()[2:] + feat8, feat16, feat32 = self.resnet(x) + H8, W8 = feat8.size()[2:] + H16, W16 = feat16.size()[2:] + H32, W32 = feat32.size()[2:] + + avg = F.avg_pool2d(feat32, feat32.size()[2:]) + avg = self.conv_avg(avg) + avg_up = F.interpolate(avg, (H32, W32), mode='nearest') + + feat32_arm = self.arm32(feat32) + feat32_sum = feat32_arm + avg_up + feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest') + feat32_up = self.conv_head32(feat32_up) + + feat16_arm = self.arm16(feat16) + feat16_sum = feat16_arm + feat32_up + feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest') + feat16_up = self.conv_head16(feat16_up) + + return feat8, feat16_up, feat32_up # x8, x8, x16 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +### This is not used, since I replace this with the resnet feature with the same size +class SpatialPath(nn.Module): + def __init__(self, *args, **kwargs): + super(SpatialPath, self).__init__() + self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) + self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) + self.init_weight() + + def forward(self, x): + feat = self.conv1(x) + feat = self.conv2(feat) + feat = self.conv3(feat) + feat = self.conv_out(feat) + return feat + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class FeatureFusionModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(FeatureFusionModule, self).__init__() + self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) + self.conv1 = nn.Conv2d(out_chan, + out_chan//4, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.conv2 = nn.Conv2d(out_chan//4, + out_chan, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + self.init_weight() + + def forward(self, fsp, fcp): + fcat = torch.cat([fsp, fcp], dim=1) + feat = self.convblk(fcat) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv1(atten) + atten = self.relu(atten) + atten = self.conv2(atten) + atten = self.sigmoid(atten) + feat_atten = torch.mul(feat, atten) + feat_out = feat_atten + feat + return feat_out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class BiSeNet(nn.Module): + def __init__(self, n_classes, *args, **kwargs): + super(BiSeNet, self).__init__() + self.cp = ContextPath() + ## here self.sp is deleted + self.ffm = FeatureFusionModule(256, 256) + self.conv_out = BiSeNetOutput(256, 256, n_classes) + self.conv_out16 = BiSeNetOutput(128, 64, n_classes) + self.conv_out32 = BiSeNetOutput(128, 64, n_classes) + self.init_weight() + + def forward(self, x): + H, W = x.size()[2:] + feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature + feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature + feat_fuse = self.ffm(feat_sp, feat_cp8) + + feat_out = self.conv_out(feat_fuse) + feat_out16 = self.conv_out16(feat_cp8) + feat_out32 = self.conv_out32(feat_cp16) + + feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True) + feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True) + feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True) + return feat_out, feat_out16, feat_out32 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] + for name, child in self.named_children(): + child_wd_params, child_nowd_params = child.get_params() + if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): + lr_mul_wd_params += child_wd_params + lr_mul_nowd_params += child_nowd_params + else: + wd_params += child_wd_params + nowd_params += child_nowd_params + return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params + + +if __name__ == "__main__": + net = BiSeNet(19) + net.cuda() + net.eval() + in_ten = torch.randn(16, 3, 640, 480).cuda() + out, out16, out32 = net(in_ten) + print(out.shape) + + net.get_params() diff --git a/face_parsing/resnet.py b/face_parsing/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..aa2bf95130e9815ba378cb6f73207068b81a04b9 --- /dev/null +++ b/face_parsing/resnet.py @@ -0,0 +1,109 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +# from modules.bn import InPlaceABNSync as BatchNorm2d + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = F.relu(self.bn1(residual)) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = F.relu(self.bn1(x)) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +if __name__ == "__main__": + net = Resnet18() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + net.get_params() diff --git a/face_parsing/swap.py b/face_parsing/swap.py new file mode 100644 index 0000000000000000000000000000000000000000..19bdacdabd6fb05cb46eaa14c455ebc0eab64dfd --- /dev/null +++ b/face_parsing/swap.py @@ -0,0 +1,50 @@ +import torch +import torchvision.transforms as transforms +import cv2 +import numpy as np + +from .model import BiSeNet + + +def init_parser(pth_path): + n_classes = 19 + net = BiSeNet(n_classes=n_classes) + net.cuda() + net.load_state_dict(torch.load(pth_path)) + net.eval() + return net + + +def image_to_parsing(img, net): + img = cv2.resize(img, (512, 512)) + img = img[:,:,::-1] + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) + ]) + img = transform(img.copy()) + img = torch.unsqueeze(img, 0) + + with torch.no_grad(): + img = img.cuda() + out = net(img)[0] + parsing = out.squeeze(0).cpu().numpy().argmax(0) + return parsing + + +def get_mask(parsing, classes): + res = parsing == classes[0] + for val in classes[1:]: + res += parsing == val + return res + + +def swap_regions(source, target, net): + parsing = image_to_parsing(source, net) + face_classes = [1, 11, 12, 13] + + mask = get_mask(parsing, face_classes) + mask = np.repeat(np.expand_dims(mask, axis=2), 3, 2) + result = (1 - mask) * cv2.resize(source, (512, 512)) + mask * cv2.resize(target, (512, 512)) + result = cv2.resize(result.astype("float32"), (source.shape[1], source.shape[0])) + return result diff --git a/hparams.py b/hparams.py new file mode 100644 index 0000000000000000000000000000000000000000..1c019046279f497e4eae3f839f683bc0b1193c6b --- /dev/null +++ b/hparams.py @@ -0,0 +1,101 @@ +from glob import glob +import os + +def get_image_list(data_root, split): + filelist = [] + + with open('filelists/{}.txt'.format(split)) as f: + for line in f: + line = line.strip() + if ' ' in line: line = line.split()[0] + filelist.append(os.path.join(data_root, line)) + + return filelist + +class HParams: + def __init__(self, **kwargs): + self.data = {} + + for key, value in kwargs.items(): + self.data[key] = value + + def __getattr__(self, key): + if key not in self.data: + raise AttributeError("'HParams' object has no attribute %s" % key) + return self.data[key] + + def set_hparam(self, key, value): + self.data[key] = value + + +# Default hyperparameters +hparams = HParams( + num_mels=80, # Number of mel-spectrogram channels and local conditioning dimensionality + # network + rescale=True, # Whether to rescale audio prior to preprocessing + rescaling_max=0.9, # Rescaling value + + # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction + # It"s preferred to set True to use with https://github.com/r9y9/wavenet_vocoder + # Does not work if n_ffit is not multiple of hop_size!! + use_lws=False, + + n_fft=800, # Extra window size is filled with 0 paddings to match this parameter + hop_size=200, # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate) + win_size=800, # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate) + sample_rate=16000, # 16000Hz (corresponding to librispeech) (sox --i ) + + frame_shift_ms=None, # Can replace hop_size parameter. (Recommended: 12.5) + + # Mel and Linear spectrograms normalization/scaling and clipping + signal_normalization=True, + # Whether to normalize mel spectrograms to some predefined range (following below parameters) + allow_clipping_in_normalization=True, # Only relevant if mel_normalization = True + symmetric_mels=True, + # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, + # faster and cleaner convergence) + max_abs_value=4., + # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not + # be too big to avoid gradient explosion, + # not too small for fast convergence) + # Contribution by @begeekmyfriend + # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude + # levels. Also allows for better G&L phase reconstruction) + preemphasize=True, # whether to apply filter + preemphasis=0.97, # filter coefficient. + + # Limits + min_level_db=-100, + ref_level_db=20, + fmin=55, + # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To + # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525]) + fmax=7600, # To be increased/reduced depending on data. + + ###################### Our training parameters ################################# + img_size=96, + fps=25, + + batch_size=16, + initial_learning_rate=1e-4, + nepochs=200000000000000000, ### ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs + num_workers=16, + checkpoint_interval=3000, + eval_interval=3000, + save_optimizer_state=True, + + syncnet_wt=0.0, # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence. + syncnet_batch_size=64, + syncnet_lr=1e-4, + syncnet_eval_interval=10000, + syncnet_checkpoint_interval=10000, + + disc_wt=0.07, + disc_initial_learning_rate=1e-4, +) + + +def hparams_debug_string(): + values = hparams.values() + hp = [" %s: %s" % (name, values[name]) for name in sorted(values) if name != "sentences"] + return "Hyperparameters:\n" + "\n".join(hp) diff --git a/images/comparison.png b/images/comparison.png new file mode 100644 index 0000000000000000000000000000000000000000..bb8de60f6b5d898a885367c2ba07fd9d4e16d38d --- /dev/null +++ b/images/comparison.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d328c5c5ea8423344a17fa9c0fb820303833c3cb7f9b73bef1941de98de59a7a +size 1507434 diff --git a/inference.py b/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..878267991629c6f4a1a8a2b4e2a410efa0e96a1e --- /dev/null +++ b/inference.py @@ -0,0 +1,362 @@ +from os import listdir, path +import numpy as np +import scipy, cv2, os, sys, argparse, audio +import json, subprocess, random, string +from tqdm import tqdm +from glob import glob +import torch, face_detection +from wav2lip_models import Wav2Lip +import platform +from face_parsing import init_parser, swap_regions +from esrgan.upsample import upscale +from esrgan.upsample import load_sr +from basicsr.archs.rrdbnet_arch import RRDBNet +from basicsr.utils.download_util import load_file_from_url + +parser = argparse.ArgumentParser(description='Inference code to lip-sync videos in the wild using Wav2Lip models') + +parser.add_argument('--checkpoint_path', type=str, default="checkpoints/wav2lip_gan.pth", + help='Name of saved checkpoint to load weights from', required=False) + +parser.add_argument('--segmentation_path', type=str, default="checkpoints/face_segmentation.pth", + help='Name of saved checkpoint of segmentation network', required=False) + +parser.add_argument('--sr_path', type=str, default='weights/4x_BigFace_v3_Clear.pth', + help='Name of saved checkpoint of super-resolution network', required=False) + +parser.add_argument('--face', type=str, + help='Filepath of video/image that contains faces to use', required=True) +parser.add_argument('--audio', type=str, + help='Filepath of video/audio file to use as raw audio source', required=True) +parser.add_argument('--outfile', type=str, help='Video path to save result. See default for an e.g.', + default='results/result_voice.mp4') + + +parser.add_argument('--static', type=bool, + help='If True, then use only first video frame for inference', default=False) +parser.add_argument('--fps', type=float, help='Can be specified only if input is a static image (default: 25)', + default=25., required=False) + +parser.add_argument('--pads', nargs='+', type=int, default=[0, 10, 0, 0], + help='Padding (top, bottom, left, right). Please adjust to include chin at least') + +parser.add_argument('--face_det_batch_size', type=int, + help='Batch size for face detection', default=16) +parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip model(s)', default=128) + +parser.add_argument('--resize_factor', default=1, type=int, + help='Reduce the resolution by this factor. Sometimes, best results are obtained at 480p or 720p') + +parser.add_argument('--crop', nargs='+', type=int, default=[0, -1, 0, -1], + help='Crop video to a smaller region (top, bottom, left, right). Applied after resize_factor and rotate arg. ' + 'Useful if multiple face present. -1 implies the value will be auto-inferred based on height, width') + +parser.add_argument('--box', nargs='+', type=int, default=[-1, -1, -1, -1], + help='Specify a constant bounding box for the face. Use only as a last resort if the face is not detected.' + 'Also, might work only if the face is not moving around much. Syntax: (top, bottom, left, right).') + +parser.add_argument('--rotate', default=False, action='store_true', + help='Sometimes videos taken from a phone can be flipped 90deg. If true, will flip video right by 90deg.' + 'Use if you get a flipped result, despite feeding a normal looking video') + +parser.add_argument('--nosmooth', default=False, action='store_true', + help='Prevent smoothing face detections over a short temporal window') +parser.add_argument('--no_seg', default=False, action='store_true', + help='Prevent using face segmentation') +parser.add_argument('--no_sr', default=False, action='store_true', + help='Prevent using super resolution') +parser.add_argument('--enhance_face', default=None, choices=['gfpgan','codeformer'], + help='Use GFP-GAN to enhance facial details.') +parser.add_argument('-w', '--fidelity_weight', type=float, default=0.75, + help='Balance the quality and fidelity. Default: 0.75') +parser.add_argument('--save_frames', default=False, action='store_true', + help='Save each frame as an image. Use with caution') +parser.add_argument('--gt_path', type=str, + help='Where to store saved ground truth frames', required=False) +parser.add_argument('--pred_path', type=str, + help='Where to store frames produced by algorithm', required=False) +parser.add_argument('--save_as_video', action="store_true", default=False, + help='Whether to save frames as video', required=False) +parser.add_argument('--image_prefix', type=str, default="", + help='Prefix to save frames with', required=False) + +args = parser.parse_args() +args.img_size = 96 + +if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: + args.static = True + +def get_smoothened_boxes(boxes, T): + for i in range(len(boxes)): + if i + T > len(boxes): + window = boxes[len(boxes) - T:] + else: + window = boxes[i : i + T] + boxes[i] = np.mean(window, axis=0) + return boxes + +def face_detect(images): + detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, + flip_input=False, device=device) + + batch_size = args.face_det_batch_size + + while 1: + predictions = [] + try: + for i in range(0, len(images), batch_size): + predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size]))) + except RuntimeError: + if batch_size == 1: + raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument') + batch_size //= 2 + print('Recovering from OOM error; New batch size: {}'.format(batch_size)) + continue + break + + results = [] + pady1, pady2, padx1, padx2 = args.pads + for rect, image in zip(predictions, images): + if rect is None: + continue + y1 = max(0, rect[1] - pady1) + y2 = min(image.shape[0], rect[3] + pady2) + x1 = max(0, rect[0] - padx1) + x2 = min(image.shape[1], rect[2] + padx2) + + results.append([x1, y1, x2, y2]) + + boxes = np.array(results) + if not args.nosmooth: boxes = get_smoothened_boxes(boxes, T=5) + results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2)] for image, (x1, y1, x2, y2) in zip(images, boxes)] + + del detector + return results + +def datagen(mels): + img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] + + # Uncommented code removed for clarity + + reader = read_frames() + + for i, m in enumerate(mels): + try: + frame_to_save = next(reader) + except StopIteration: + reader = read_frames() + frame_to_save = next(reader, None) + + if frame_to_save is not None: + face_detect_result = face_detect([frame_to_save]) + if len(face_detect_result) > 0: # Check if face detection was successful + face, coords = face_detect_result[0] + face = cv2.resize(face, (args.img_size, args.img_size)) + img_batch.append(face) + mel_batch.append(m) + frame_batch.append(frame_to_save) + coords_batch.append(coords) + + if len(img_batch) >= args.wav2lip_batch_size: + img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, args.img_size // 2:] = 0 + + img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. + mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch, frame_batch, coords_batch + img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] + + if len(img_batch) > 0: + img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) + + img_masked = img_batch.copy() + img_masked[:, args.img_size // 2:] = 0 + + img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. + mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) + + yield img_batch, mel_batch, frame_batch, coords_batch + +mel_step_size = 16 +device = 'cuda' if torch.cuda.is_available() else 'cpu' +print('Using {} for inference.'.format(device)) + +def _load(checkpoint_path): + if device == 'cuda': + checkpoint = torch.load(checkpoint_path) + else: + checkpoint = torch.load(checkpoint_path, + map_location=lambda storage, loc: storage) + return checkpoint + +def load_model(path): + model = Wav2Lip() + print("Load checkpoint from: {}".format(path)) + checkpoint = _load(path) + s = checkpoint["state_dict"] + new_s = {} + for k, v in s.items(): + new_s[k.replace('module.', '')] = v + model.load_state_dict(new_s) + + model = model.to(device) + return model.eval() + +def read_frames(): + if args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: + face = cv2.imread(args.face) + while 1: + yield face + + video_stream = cv2.VideoCapture(args.face) + fps = video_stream.get(cv2.CAP_PROP_FPS) + + print('Reading video frames from start...') + + while 1: + still_reading, frame = video_stream.read() + if not still_reading: + video_stream.release() + break + if args.resize_factor > 1: + frame = cv2.resize(frame, (frame.shape[1]//args.resize_factor, frame.shape[0]//args.resize_factor)) + + if args.rotate: + frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE) + + y1, y2, x1, x2 = args.crop + if x2 == -1: x2 = frame.shape[1] + if y2 == -1: y2 = frame.shape[0] + + frame = frame[y1:y2, x1:x2] + + yield frame + +def main(): + if not os.path.isfile(args.face): + raise ValueError('--face argument must be a valid path to video/image file') + + elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']: + fps = args.fps + else: + video_stream = cv2.VideoCapture(args.face) + fps = video_stream.get(cv2.CAP_PROP_FPS) + video_stream.release() + + + if not args.audio.endswith('.wav'): + print('Extracting raw audio...') + command = 'ffmpeg -y -i {} -strict -2 {}'.format(args.audio, 'temp/temp.wav') + + subprocess.call(command, shell=True) + args.audio = 'temp/temp.wav' + + wav = audio.load_wav(args.audio, 16000) + mel = audio.melspectrogram(wav) + print(mel.shape) + + if np.isnan(mel.reshape(-1)).sum() > 0: + raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again') + + mel_chunks = [] + mel_idx_multiplier = 80./fps + i = 0 + while 1: + start_idx = int(i * mel_idx_multiplier) + if start_idx + mel_step_size > len(mel[0]): + mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:]) + break + mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size]) + i += 1 + + print("Length of mel chunks: {}".format(len(mel_chunks))) + + batch_size = args.wav2lip_batch_size + gen = datagen(mel_chunks) + + + + if args.save_as_video: + gt_out = cv2.VideoWriter("temp/gt.avi", cv2.VideoWriter_fourcc(*'DIVX'), fps, (384, 384)) + pred_out = cv2.VideoWriter("temp/pred.avi", cv2.VideoWriter_fourcc(*'DIVX'), fps, (96, 96)) + + abs_idx = 0 + for i, (img_batch, mel_batch, frames, coords) in enumerate(tqdm(gen, + total=int(np.ceil(float(len(mel_chunks))/batch_size)))): + if i == 0: + if not args.no_seg==True: + print("Loading segmentation network...") + seg_net = load_file_from_url( + url='https://github.com/GucciFlipFlops1917/wav2lip-hq-updated-ESRGAN/releases/download/v0.0.1/face_segmentation.pth', + model_dir='checkpoints', progress=True, file_name=None) + seg_net = init_parser(args.segmentation_path) + if not args.no_sr==True: + print("Loading super resolution model...") + run_params = load_sr(args.sr_path, device, args.enhance_face) + + model_path = load_file_from_url( + url='https://github.com/GucciFlipFlops1917/wav2lip-hq-updated-ESRGAN/releases/download/v0.0.1/wav2lip_gan.pth', + model_dir='checkpoints', progress=True, file_name=None) + model = load_model(args.checkpoint_path) + print ("Model loaded") + + frame_h, frame_w = next(read_frames()).shape[:-1] + out = cv2.VideoWriter('temp/result.avi', + cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h)) + + img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device) + mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device) + + with torch.no_grad(): + pred = model(mel_batch, img_batch) + + pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. + + for p, f, c in zip(pred, frames, coords): + y1, y2, x1, x2 = c + + if args.save_frames: + if args.save_as_video: + pred_out.write(p.astype(np.uint8)) + gt_out.write(cv2.resize(f[y1:y2, x1:x2], (384, 384))) + else: + cv2.imwrite(f"{args.gt_path}/{args.image_prefix}{abs_idx}.png", f[y1:y2, x1:x2]) + cv2.imwrite(f"{args.pred_path}/{args.image_prefix}{abs_idx}.png", p) + abs_idx += 1 + + if not args.no_sr: + if args.enhance_face==None: + p = upscale(p, 0, run_params) + elif args.enhance_face=='codeformer': + p = upscale(p, 2, [run_params, device, args.fidelity_weight]) + elif args.enhance_face=='gfpgan': + p = upscale(p, 1, run_params) + p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1)) + + if args.no_seg==False: + p = swap_regions(f[y1:y2, x1:x2], p, seg_net) + + f[y1:y2, x1:x2] = p + out.write(f) + + out.release() + + command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/result.avi', args.outfile) + subprocess.call(command, shell=platform.system() != 'Windows') + + if args.save_frames and args.save_as_video: + gt_out.release() + pred_out.release() + + command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/gt.avi', args.gt_path) + subprocess.call(command, shell=platform.system() != 'Windows') + + command = 'ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/pred.avi', args.pred_path) + subprocess.call(command, shell=platform.system() != 'Windows') + + +if __name__ == '__main__': + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..268bb2f88e08a1914095814c4a0691e5c5c8584a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,20 @@ +addict +future +librosa==0.7.0 +lmdb +numba==0.48 +numpy==1.21.1 +opencv-contrib-python>=4.2.0.34 +opencv-python +Pillow +pyyaml +requests +scikit-image +scipy +tb-nightly +torch>=1.3 +torchvision +tqdm +yapf +realesrgan +gfpgan diff --git a/results/README.md b/results/README.md new file mode 100644 index 0000000000000000000000000000000000000000..bcda506bb66017995df8d830735e1b162aad1742 --- /dev/null +++ b/results/README.md @@ -0,0 +1 @@ +This is a folder for result videos. \ No newline at end of file diff --git a/temp/.DS_Store b/temp/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/temp/.DS_Store differ diff --git a/train_basicsr.yml b/train_basicsr.yml new file mode 100644 index 0000000000000000000000000000000000000000..eeab508392069455960d3dd2ddcb4315d3a97ca0 --- /dev/null +++ b/train_basicsr.yml @@ -0,0 +1,120 @@ +# general settings +name: 001_ESRGAN_x4_f64b23_custom16k_500k_B16G1_wandb +model_type: ESRGANModel +scale: 4 +num_gpu: 1 # set num_gpu: 0 for cpu mode +manual_seed: 0 + +# dataset and data loader settings +datasets: + train: + name: face_dataset + type: PairedImageDataset + dataroot_gt: data/hq + dataroot_lq: data/lq + filename_tmpl: '{}' + io_backend: + type: disk + + gt_size: 384 + use_flip: true + use_rot: true + + # data loader + use_shuffle: true + num_worker_per_gpu: 1 + batch_size_per_gpu: 4 + dataset_enlarge_ratio: 1 + prefetch_mode: ~ + +# network structures +network_g: + type: RRDBNet + num_in_ch: 3 + num_out_ch: 3 + num_feat: 64 + num_block: 23 + +network_d: + type: VGGStyleDiscriminator128 + num_in_ch: 3 + num_feat: 64 + +# path +path: + pretrain_network_g: ~ + strict_load_g: true + resume_state: checkpoints/pretrained.state + +# training settings +train: + optim_g: + type: Adam + lr: !!float 1e-4 + weight_decay: 0 + betas: [0.9, 0.99] + optim_d: + type: Adam + lr: !!float 1e-4 + weight_decay: 0 + betas: [0.9, 0.99] + + scheduler: + type: MultiStepLR + milestones: [50000, 100000, 200000, 300000] + gamma: 0.5 + + total_iter: 150000 + warmup_iter: -1 # no warm up + + # losses + pixel_opt: + type: L1Loss + loss_weight: !!float 1e-2 + reduction: mean + perceptual_opt: + type: PerceptualLoss + layer_weights: + 'conv5_4': 1 # before relu + vgg_type: vgg19 + use_input_norm: true + range_norm: false + perceptual_weight: 1.0 + style_weight: 0 + criterion: l1 + gan_opt: + type: GANLoss + gan_type: vanilla + real_label_val: 1.0 + fake_label_val: 0.0 + loss_weight: !!float 5e-3 + + net_d_iters: 1 + net_d_init_iters: 0 + +# validation settings +val: + val_freq: !!float 25e2 + save_img: true + + metrics: + psnr: # metric name, can be arbitrary + type: calculate_psnr + crop_border: 4 + test_y_channel: false + +# logging settings +logger: + print_freq: 100 + save_checkpoint_freq: !!float 25e2 + use_tb_logger: true + wandb: + project: ~ + resume_id: ~ + +# dist training settings +dist_params: + backend: nccl + port: 29500 + + diff --git a/videos/.DS_Store b/videos/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/videos/.DS_Store differ diff --git a/wav2lip_models/.DS_Store b/wav2lip_models/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..41c1a0c5bb57ef81966ba49aa234c73a2c7466fb Binary files /dev/null and b/wav2lip_models/.DS_Store differ diff --git a/wav2lip_models/__init__.py b/wav2lip_models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4374370494b65f10b76c70a2d4f731c238cfa54c --- /dev/null +++ b/wav2lip_models/__init__.py @@ -0,0 +1,2 @@ +from .wav2lip import Wav2Lip, Wav2Lip_disc_qual +from .syncnet import SyncNet_color \ No newline at end of file diff --git a/wav2lip_models/__pycache__/__init__.cpython-310.pyc b/wav2lip_models/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..57f3eda3f41d59d40889ca7bcc1f974dc46bb60e Binary files /dev/null and b/wav2lip_models/__pycache__/__init__.cpython-310.pyc differ diff --git a/wav2lip_models/__pycache__/__init__.cpython-38.pyc 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0000000000000000000000000000000000000000..91164349edc3bef18235525e54c03dd5c8ef3631 Binary files /dev/null and b/wav2lip_models/__pycache__/wav2lip.cpython-38.pyc differ diff --git a/wav2lip_models/conv.py b/wav2lip_models/conv.py new file mode 100644 index 0000000000000000000000000000000000000000..ed83da00cb199e027ef217fd360352d91a7891ff --- /dev/null +++ b/wav2lip_models/conv.py @@ -0,0 +1,44 @@ +import torch +from torch import nn +from torch.nn import functional as F + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out += x + return self.act(out) + +class nonorm_Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.Conv2d(cin, cout, kernel_size, stride, padding), + ) + self.act = nn.LeakyReLU(0.01, inplace=True) + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) + +class Conv2dTranspose(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential( + nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding), + nn.BatchNorm2d(cout) + ) + self.act = nn.ReLU() + + def forward(self, x): + out = self.conv_block(x) + return self.act(out) diff --git a/wav2lip_models/syncnet.py b/wav2lip_models/syncnet.py new file mode 100644 index 0000000000000000000000000000000000000000..e773cdca675236745a379a776b7c07d7d353f590 --- /dev/null +++ b/wav2lip_models/syncnet.py @@ -0,0 +1,66 @@ +import torch +from torch import nn +from torch.nn import functional as F + +from .conv import Conv2d + +class SyncNet_color(nn.Module): + def __init__(self): + super(SyncNet_color, self).__init__() + + self.face_encoder = nn.Sequential( + Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3), + + Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=2, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=2, padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(512, 512, kernel_size=3, stride=2, padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T) + face_embedding = self.face_encoder(face_sequences) + audio_embedding = self.audio_encoder(audio_sequences) + + audio_embedding = audio_embedding.view(audio_embedding.size(0), -1) + face_embedding = face_embedding.view(face_embedding.size(0), -1) + + audio_embedding = F.normalize(audio_embedding, p=2, dim=1) + face_embedding = F.normalize(face_embedding, p=2, dim=1) + + + return audio_embedding, face_embedding diff --git a/wav2lip_models/wav2lip.py b/wav2lip_models/wav2lip.py new file mode 100644 index 0000000000000000000000000000000000000000..ae5d6919169ec497f0f0815184f5db8ba9108fbd --- /dev/null +++ b/wav2lip_models/wav2lip.py @@ -0,0 +1,184 @@ +import torch +from torch import nn +from torch.nn import functional as F +import math + +from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d + +class Wav2Lip(nn.Module): + def __init__(self): + super(Wav2Lip, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3)), # 96,96 + + nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1), # 48,48 + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1), # 24,24 + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1), # 12,12 + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1), # 6,6 + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)), + + nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(64, 128, kernel_size=3, stride=3, padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + + Conv2d(256, 512, kernel_size=3, stride=1, padding=0), + Conv2d(512, 512, kernel_size=1, stride=1, padding=0),) + + self.face_decoder_blocks = nn.ModuleList([ + nn.Sequential(Conv2d(512, 512, kernel_size=1, stride=1, padding=0),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=1, padding=0), # 3,3 + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), + + nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),), # 6, 6 + + nn.Sequential(Conv2dTranspose(768, 384, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(384, 384, kernel_size=3, stride=1, padding=1, residual=True),), # 12, 12 + + nn.Sequential(Conv2dTranspose(512, 256, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),), # 24, 24 + + nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),), # 48, 48 + + nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),),]) # 96,96 + + self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1), + nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0), + nn.Sigmoid()) + + def forward(self, audio_sequences, face_sequences): + # audio_sequences = (B, T, 1, 80, 16) + B = audio_sequences.size(0) + + input_dim_size = len(face_sequences.size()) + if input_dim_size > 4: + audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + + audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1 + + feats = [] + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + feats.append(x) + + x = audio_embedding + for f in self.face_decoder_blocks: + x = f(x) + try: + x = torch.cat((x, feats[-1]), dim=1) + except Exception as e: + print(x.size()) + print(feats[-1].size()) + raise e + + feats.pop() + + x = self.output_block(x) + + if input_dim_size > 4: + x = torch.split(x, B, dim=0) # [(B, C, H, W)] + outputs = torch.stack(x, dim=2) # (B, C, T, H, W) + + else: + outputs = x + + return outputs + +class Wav2Lip_disc_qual(nn.Module): + def __init__(self): + super(Wav2Lip_disc_qual, self).__init__() + + self.face_encoder_blocks = nn.ModuleList([ + nn.Sequential(nonorm_Conv2d(3, 32, kernel_size=7, stride=1, padding=3)), # 48,96 + + nn.Sequential(nonorm_Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=2), # 48,48 + nonorm_Conv2d(64, 64, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(64, 128, kernel_size=5, stride=2, padding=2), # 24,24 + nonorm_Conv2d(128, 128, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(128, 256, kernel_size=5, stride=2, padding=2), # 12,12 + nonorm_Conv2d(256, 256, kernel_size=5, stride=1, padding=2)), + + nn.Sequential(nonorm_Conv2d(256, 512, kernel_size=3, stride=2, padding=1), # 6,6 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1)), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=2, padding=1), # 3,3 + nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=1),), + + nn.Sequential(nonorm_Conv2d(512, 512, kernel_size=3, stride=1, padding=0), # 1, 1 + nonorm_Conv2d(512, 512, kernel_size=1, stride=1, padding=0)),]) + + self.binary_pred = nn.Sequential(nn.Conv2d(512, 1, kernel_size=1, stride=1, padding=0), nn.Sigmoid()) + self.label_noise = .0 + + def get_lower_half(self, face_sequences): + return face_sequences[:, :, face_sequences.size(2)//2:] + + def to_2d(self, face_sequences): + B = face_sequences.size(0) + face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0) + return face_sequences + + def perceptual_forward(self, false_face_sequences): + false_face_sequences = self.to_2d(false_face_sequences) + false_face_sequences = self.get_lower_half(false_face_sequences) + + false_feats = false_face_sequences + for f in self.face_encoder_blocks: + false_feats = f(false_feats) + + false_pred_loss = F.binary_cross_entropy(self.binary_pred(false_feats).view(len(false_feats), -1), + torch.ones((len(false_feats), 1)).cuda()) + + return false_pred_loss + + def forward(self, face_sequences): + face_sequences = self.to_2d(face_sequences) + face_sequences = self.get_lower_half(face_sequences) + + x = face_sequences + for f in self.face_encoder_blocks: + x = f(x) + + return self.binary_pred(x).view(len(x), -1) diff --git a/weights/.DS_Store b/weights/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 Binary files /dev/null and b/weights/.DS_Store differ