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"""Modified from https://github.com/chaofengc/PSFRGAN |
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""" |
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import numpy as np |
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import torch.nn as nn |
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from torch.nn import functional as F |
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class NormLayer(nn.Module): |
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"""Normalization Layers. |
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Args: |
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channels: input channels, for batch norm and instance norm. |
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input_size: input shape without batch size, for layer norm. |
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""" |
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def __init__(self, channels, normalize_shape=None, norm_type='bn'): |
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super(NormLayer, self).__init__() |
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norm_type = norm_type.lower() |
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self.norm_type = norm_type |
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if norm_type == 'bn': |
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self.norm = nn.BatchNorm2d(channels, affine=True) |
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elif norm_type == 'in': |
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self.norm = nn.InstanceNorm2d(channels, affine=False) |
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elif norm_type == 'gn': |
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self.norm = nn.GroupNorm(32, channels, affine=True) |
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elif norm_type == 'pixel': |
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self.norm = lambda x: F.normalize(x, p=2, dim=1) |
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elif norm_type == 'layer': |
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self.norm = nn.LayerNorm(normalize_shape) |
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elif norm_type == 'none': |
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self.norm = lambda x: x * 1.0 |
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else: |
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assert 1 == 0, f'Norm type {norm_type} not support.' |
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def forward(self, x, ref=None): |
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if self.norm_type == 'spade': |
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return self.norm(x, ref) |
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else: |
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return self.norm(x) |
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class ReluLayer(nn.Module): |
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"""Relu Layer. |
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Args: |
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relu type: type of relu layer, candidates are |
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- ReLU |
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- LeakyReLU: default relu slope 0.2 |
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- PRelu |
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- SELU |
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- none: direct pass |
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""" |
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def __init__(self, channels, relu_type='relu'): |
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super(ReluLayer, self).__init__() |
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relu_type = relu_type.lower() |
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if relu_type == 'relu': |
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self.func = nn.ReLU(True) |
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elif relu_type == 'leakyrelu': |
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self.func = nn.LeakyReLU(0.2, inplace=True) |
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elif relu_type == 'prelu': |
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self.func = nn.PReLU(channels) |
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elif relu_type == 'selu': |
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self.func = nn.SELU(True) |
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elif relu_type == 'none': |
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self.func = lambda x: x * 1.0 |
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else: |
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assert 1 == 0, f'Relu type {relu_type} not support.' |
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def forward(self, x): |
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return self.func(x) |
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class ConvLayer(nn.Module): |
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def __init__(self, |
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in_channels, |
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out_channels, |
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kernel_size=3, |
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scale='none', |
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norm_type='none', |
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relu_type='none', |
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use_pad=True, |
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bias=True): |
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super(ConvLayer, self).__init__() |
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self.use_pad = use_pad |
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self.norm_type = norm_type |
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if norm_type in ['bn']: |
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bias = False |
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stride = 2 if scale == 'down' else 1 |
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self.scale_func = lambda x: x |
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if scale == 'up': |
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self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest') |
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self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2))) |
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self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias) |
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self.relu = ReluLayer(out_channels, relu_type) |
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self.norm = NormLayer(out_channels, norm_type=norm_type) |
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def forward(self, x): |
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out = self.scale_func(x) |
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if self.use_pad: |
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out = self.reflection_pad(out) |
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out = self.conv2d(out) |
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out = self.norm(out) |
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out = self.relu(out) |
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return out |
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class ResidualBlock(nn.Module): |
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""" |
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Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html |
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""" |
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def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'): |
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super(ResidualBlock, self).__init__() |
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if scale == 'none' and c_in == c_out: |
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self.shortcut_func = lambda x: x |
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else: |
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self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) |
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scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']} |
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scale_conf = scale_config_dict[scale] |
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self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) |
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self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none') |
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def forward(self, x): |
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identity = self.shortcut_func(x) |
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res = self.conv1(x) |
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res = self.conv2(res) |
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return identity + res |
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class ParseNet(nn.Module): |
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def __init__(self, |
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in_size=128, |
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out_size=128, |
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min_feat_size=32, |
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base_ch=64, |
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parsing_ch=19, |
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res_depth=10, |
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relu_type='LeakyReLU', |
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norm_type='bn', |
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ch_range=[32, 256]): |
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super().__init__() |
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self.res_depth = res_depth |
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act_args = {'norm_type': norm_type, 'relu_type': relu_type} |
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min_ch, max_ch = ch_range |
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ch_clip = lambda x: max(min_ch, min(x, max_ch)) |
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min_feat_size = min(in_size, min_feat_size) |
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down_steps = int(np.log2(in_size // min_feat_size)) |
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up_steps = int(np.log2(out_size // min_feat_size)) |
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self.encoder = [] |
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self.encoder.append(ConvLayer(3, base_ch, 3, 1)) |
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head_ch = base_ch |
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for i in range(down_steps): |
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cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) |
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self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) |
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head_ch = head_ch * 2 |
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self.body = [] |
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for i in range(res_depth): |
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self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) |
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self.decoder = [] |
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for i in range(up_steps): |
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cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) |
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self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) |
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head_ch = head_ch // 2 |
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self.encoder = nn.Sequential(*self.encoder) |
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self.body = nn.Sequential(*self.body) |
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self.decoder = nn.Sequential(*self.decoder) |
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self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) |
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self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) |
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def forward(self, x): |
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feat = self.encoder(x) |
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x = feat + self.body(feat) |
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x = self.decoder(x) |
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out_img = self.out_img_conv(x) |
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out_mask = self.out_mask_conv(x) |
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return out_mask, out_img |
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