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| import cv2 | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchvision.transforms import Compose | |
| from .dinov2 import DINOv2 | |
| from .util.blocks import FeatureFusionBlock, _make_scratch | |
| from .util.transform import Resize, NormalizeImage, PrepareForNet | |
| def _make_fusion_block(features, use_bn, size=None): | |
| return FeatureFusionBlock( | |
| features, | |
| nn.ReLU(False), | |
| deconv=False, | |
| bn=use_bn, | |
| expand=False, | |
| align_corners=True, | |
| size=size, | |
| ) | |
| class ConvBlock(nn.Module): | |
| def __init__(self, in_feature, out_feature): | |
| super().__init__() | |
| self.conv_block = nn.Sequential( | |
| nn.Conv2d(in_feature, out_feature, kernel_size=3, stride=1, padding=1), | |
| nn.BatchNorm2d(out_feature), | |
| nn.ReLU(True) | |
| ) | |
| def forward(self, x): | |
| return self.conv_block(x) | |
| class DPTHead(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| features=256, | |
| use_bn=False, | |
| out_channels=[256, 512, 1024, 1024], | |
| use_clstoken=False | |
| ): | |
| super(DPTHead, self).__init__() | |
| self.use_clstoken = use_clstoken | |
| self.projects = nn.ModuleList([ | |
| nn.Conv2d( | |
| in_channels=in_channels, | |
| out_channels=out_channel, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0, | |
| ) for out_channel in out_channels | |
| ]) | |
| self.resize_layers = nn.ModuleList([ | |
| nn.ConvTranspose2d( | |
| in_channels=out_channels[0], | |
| out_channels=out_channels[0], | |
| kernel_size=4, | |
| stride=4, | |
| padding=0), | |
| nn.ConvTranspose2d( | |
| in_channels=out_channels[1], | |
| out_channels=out_channels[1], | |
| kernel_size=2, | |
| stride=2, | |
| padding=0), | |
| nn.Identity(), | |
| nn.Conv2d( | |
| in_channels=out_channels[3], | |
| out_channels=out_channels[3], | |
| kernel_size=3, | |
| stride=2, | |
| padding=1) | |
| ]) | |
| if use_clstoken: | |
| self.readout_projects = nn.ModuleList() | |
| for _ in range(len(self.projects)): | |
| self.readout_projects.append( | |
| nn.Sequential( | |
| nn.Linear(2 * in_channels, in_channels), | |
| nn.GELU())) | |
| self.scratch = _make_scratch( | |
| out_channels, | |
| features, | |
| groups=1, | |
| expand=False, | |
| ) | |
| self.scratch.stem_transpose = None | |
| self.scratch.refinenet1 = _make_fusion_block(features, use_bn) | |
| self.scratch.refinenet2 = _make_fusion_block(features, use_bn) | |
| self.scratch.refinenet3 = _make_fusion_block(features, use_bn) | |
| self.scratch.refinenet4 = _make_fusion_block(features, use_bn) | |
| head_features_1 = features | |
| head_features_2 = 32 | |
| self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1) | |
| self.scratch.output_conv2 = nn.Sequential( | |
| nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1), | |
| nn.ReLU(True), | |
| nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0), | |
| nn.ReLU(True), | |
| nn.Identity(), | |
| ) | |
| def forward(self, out_features, patch_h, patch_w): | |
| out = [] | |
| for i, x in enumerate(out_features): | |
| if self.use_clstoken: | |
| x, cls_token = x[0], x[1] | |
| readout = cls_token.unsqueeze(1).expand_as(x) | |
| x = self.readout_projects[i](torch.cat((x, readout), -1)) | |
| else: | |
| x = x[0] | |
| x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w)) | |
| x = self.projects[i](x) | |
| x = self.resize_layers[i](x) | |
| out.append(x) | |
| layer_1, layer_2, layer_3, layer_4 = out | |
| layer_1_rn = self.scratch.layer1_rn(layer_1) | |
| layer_2_rn = self.scratch.layer2_rn(layer_2) | |
| layer_3_rn = self.scratch.layer3_rn(layer_3) | |
| layer_4_rn = self.scratch.layer4_rn(layer_4) | |
| path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:]) | |
| path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:]) | |
| path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:]) | |
| path_1 = self.scratch.refinenet1(path_2, layer_1_rn) | |
| out = self.scratch.output_conv1(path_1) | |
| out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True) | |
| out = self.scratch.output_conv2(out) | |
| return out | |
| class DepthAnythingV2(nn.Module): | |
| def __init__( | |
| self, | |
| encoder='vitl', | |
| features=256, | |
| out_channels=[256, 512, 1024, 1024], | |
| use_bn=False, | |
| use_clstoken=False | |
| ): | |
| super(DepthAnythingV2, self).__init__() | |
| self.intermediate_layer_idx = { | |
| 'vits': [2, 5, 8, 11], | |
| 'vitb': [2, 5, 8, 11], | |
| 'vitl': [4, 11, 17, 23], | |
| 'vitg': [9, 19, 29, 39] | |
| } | |
| self.encoder = encoder | |
| self.pretrained = DINOv2(model_name=encoder) | |
| self.depth_head = DPTHead(self.pretrained.embed_dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken) | |
| def forward(self, x): | |
| patch_h, patch_w = x.shape[-2] // 14, x.shape[-1] // 14 | |
| features = self.pretrained.get_intermediate_layers(x, self.intermediate_layer_idx[self.encoder], return_class_token=True) | |
| depth = self.depth_head(features, patch_h, patch_w) | |
| depth = F.relu(depth) | |
| return depth.squeeze(1) | |
| def infer_image(self, raw_image, input_size=518): | |
| image, (h, w) = self.image2tensor(raw_image, input_size) | |
| depth = self.forward(image) | |
| depth = F.interpolate(depth[:, None], (h, w), mode="bilinear", align_corners=True)[0, 0] | |
| return depth.cpu().numpy() | |
| def image2tensor(self, raw_image, input_size=518): | |
| transform = Compose([ | |
| Resize( | |
| width=input_size, | |
| height=input_size, | |
| resize_target=False, | |
| keep_aspect_ratio=True, | |
| ensure_multiple_of=14, | |
| resize_method='lower_bound', | |
| image_interpolation_method=cv2.INTER_CUBIC, | |
| ), | |
| NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| PrepareForNet(), | |
| ]) | |
| h, w = raw_image.shape[:2] | |
| image = cv2.cvtColor(raw_image, cv2.COLOR_BGR2RGB) / 255.0 | |
| image = transform({'image': image})['image'] | |
| image = torch.from_numpy(image).unsqueeze(0) | |
| DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu' | |
| image = image.to(DEVICE) | |
| return image, (h, w) | |