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from typing import List, Tuple, Union, Optional |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from saicinpainting.training.modules.base import get_conv_block_ctor, get_activation |
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from saicinpainting.training.modules.pix2pixhd import ResnetBlock |
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class ResNetHead(nn.Module): |
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def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, |
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padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)): |
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assert (n_blocks >= 0) |
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super(ResNetHead, self).__init__() |
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conv_layer = get_conv_block_ctor(conv_kind) |
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model = [nn.ReflectionPad2d(3), |
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conv_layer(input_nc, ngf, kernel_size=7, padding=0), |
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norm_layer(ngf), |
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activation] |
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for i in range(n_downsampling): |
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mult = 2 ** i |
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model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1), |
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norm_layer(ngf * mult * 2), |
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activation] |
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mult = 2 ** n_downsampling |
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for i in range(n_blocks): |
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model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, |
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conv_kind=conv_kind)] |
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self.model = nn.Sequential(*model) |
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def forward(self, input): |
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return self.model(input) |
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class ResNetTail(nn.Module): |
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def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d, |
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padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), |
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up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0, |
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add_in_proj=None): |
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assert (n_blocks >= 0) |
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super(ResNetTail, self).__init__() |
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mult = 2 ** n_downsampling |
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model = [] |
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if add_in_proj is not None: |
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model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1)) |
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for i in range(n_blocks): |
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model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer, |
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conv_kind=conv_kind)] |
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for i in range(n_downsampling): |
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mult = 2 ** (n_downsampling - i) |
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model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, |
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output_padding=1), |
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up_norm_layer(int(ngf * mult / 2)), |
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up_activation] |
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self.model = nn.Sequential(*model) |
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out_layers = [] |
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for _ in range(out_extra_layers_n): |
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out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0), |
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up_norm_layer(ngf), |
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up_activation] |
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out_layers += [nn.ReflectionPad2d(3), |
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nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)] |
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if add_out_act: |
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out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act)) |
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self.out_proj = nn.Sequential(*out_layers) |
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def forward(self, input, return_last_act=False): |
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features = self.model(input) |
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out = self.out_proj(features) |
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if return_last_act: |
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return out, features |
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else: |
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return out |
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class MultiscaleResNet(nn.Module): |
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def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3, |
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norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True), |
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up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0, |
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out_cumulative=False, return_only_hr=False): |
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super().__init__() |
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self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling, |
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n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type, |
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conv_kind=conv_kind, activation=activation) |
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for i in range(n_scales)]) |
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tail_in_feats = ngf * (2 ** n_downsampling) + ngf |
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self.tails = nn.ModuleList([ResNetTail(output_nc, |
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ngf=ngf, n_downsampling=n_downsampling, |
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n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type, |
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conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer, |
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up_activation=up_activation, add_out_act=add_out_act, |
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out_extra_layers_n=out_extra_layers_n, |
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add_in_proj=None if (i == n_scales - 1) else tail_in_feats) |
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for i in range(n_scales)]) |
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self.out_cumulative = out_cumulative |
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self.return_only_hr = return_only_hr |
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@property |
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def num_scales(self): |
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return len(self.heads) |
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def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \ |
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-> Union[torch.Tensor, List[torch.Tensor]]: |
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""" |
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:param ms_inputs: List of inputs of different resolutions from HR to LR |
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:param smallest_scales_num: int or None, number of smallest scales to take at input |
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:return: Depending on return_only_hr: |
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True: Only the most HR output |
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False: List of outputs of different resolutions from HR to LR |
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""" |
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if smallest_scales_num is None: |
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assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num) |
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smallest_scales_num = len(self.heads) |
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else: |
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assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num) |
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cur_heads = self.heads[-smallest_scales_num:] |
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ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)] |
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all_outputs = [] |
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prev_tail_features = None |
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for i in range(len(ms_features)): |
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scale_i = -i - 1 |
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cur_tail_input = ms_features[-i - 1] |
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if prev_tail_features is not None: |
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if prev_tail_features.shape != cur_tail_input.shape: |
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prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:], |
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mode='bilinear', align_corners=False) |
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cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1) |
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cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True) |
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prev_tail_features = cur_tail_feats |
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all_outputs.append(cur_out) |
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if self.out_cumulative: |
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all_outputs_cum = [all_outputs[0]] |
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for i in range(1, len(ms_features)): |
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cur_out = all_outputs[i] |
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cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:], |
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mode='bilinear', align_corners=False) |
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all_outputs_cum.append(cur_out_cum) |
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all_outputs = all_outputs_cum |
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if self.return_only_hr: |
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return all_outputs[-1] |
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else: |
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return all_outputs[::-1] |
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class MultiscaleDiscriminatorSimple(nn.Module): |
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def __init__(self, ms_impl): |
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super().__init__() |
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self.ms_impl = nn.ModuleList(ms_impl) |
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@property |
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def num_scales(self): |
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return len(self.ms_impl) |
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def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \ |
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-> List[Tuple[torch.Tensor, List[torch.Tensor]]]: |
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""" |
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:param ms_inputs: List of inputs of different resolutions from HR to LR |
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:param smallest_scales_num: int or None, number of smallest scales to take at input |
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:return: List of pairs (prediction, features) for different resolutions from HR to LR |
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""" |
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if smallest_scales_num is None: |
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assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num) |
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smallest_scales_num = len(self.heads) |
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else: |
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assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \ |
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(len(self.ms_impl), len(ms_inputs), smallest_scales_num) |
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return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)] |
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class SingleToMultiScaleInputMixin: |
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def forward(self, x: torch.Tensor) -> List: |
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orig_height, orig_width = x.shape[2:] |
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factors = [2 ** i for i in range(self.num_scales)] |
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ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False) |
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for f in factors] |
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return super().forward(ms_inputs) |
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class GeneratorMultiToSingleOutputMixin: |
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def forward(self, x): |
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return super().forward(x)[0] |
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class DiscriminatorMultiToSingleOutputMixin: |
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def forward(self, x): |
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out_feat_tuples = super().forward(x) |
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return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist] |
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class DiscriminatorMultiToSingleOutputStackedMixin: |
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def __init__(self, *args, return_feats_only_levels=None, **kwargs): |
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super().__init__(*args, **kwargs) |
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self.return_feats_only_levels = return_feats_only_levels |
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def forward(self, x): |
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out_feat_tuples = super().forward(x) |
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outs = [out for out, _ in out_feat_tuples] |
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scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:], |
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mode='bilinear', align_corners=False) |
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for cur_out in outs[1:]] |
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out = torch.cat(scaled_outs, dim=1) |
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if self.return_feats_only_levels is not None: |
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feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels] |
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else: |
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feat_lists = [flist for _, flist in out_feat_tuples] |
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feats = [f for flist in feat_lists for f in flist] |
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return out, feats |
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class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple): |
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pass |
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class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet): |
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pass |
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