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| # temporal patch GAN to maintain the temporal consecutive of the flows | |
| import torch | |
| import torch.nn as nn | |
| from .BaseNetwork import BaseNetwork | |
| class Discriminator(BaseNetwork): | |
| def __init__(self, in_channels, conv_type, dist_cnum, use_sigmoid=False, use_spectral_norm=True, init_weights=True): | |
| """ | |
| Args: | |
| in_channels: The input channels of the discriminator | |
| use_sigmoid: Whether to use sigmoid for the base network (true for the nsgan) | |
| use_spectral_norm: The usage of the spectral norm: always be true for the stability of GAN | |
| init_weights: always be True | |
| """ | |
| super(Discriminator, self).__init__(conv_type) | |
| self.use_sigmoid = use_sigmoid | |
| nf = dist_cnum | |
| self.conv = nn.Sequential( | |
| spectral_norm( | |
| nn.Conv3d(in_channels=in_channels, out_channels=nf * 1, kernel_size=(3, 5, 5), stride=(1, 2, 2), | |
| padding=(1, 2, 2), | |
| bias=not use_spectral_norm), use_spectral_norm), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| spectral_norm( | |
| nn.Conv3d(in_channels=nf * 1, out_channels=nf * 2, kernel_size=(3, 5, 5), stride=(1, 2, 2), | |
| padding=(1, 2, 2), | |
| bias=not use_spectral_norm), use_spectral_norm), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| spectral_norm( | |
| nn.Conv3d(in_channels=nf * 2, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), | |
| padding=(1, 2, 2), | |
| bias=not use_spectral_norm), use_spectral_norm), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| spectral_norm( | |
| nn.Conv3d(in_channels=nf * 4, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), | |
| padding=(1, 2, 2), | |
| bias=not use_spectral_norm), use_spectral_norm), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| spectral_norm( | |
| nn.Conv3d(in_channels=nf * 4, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), | |
| padding=(1, 2, 2), | |
| bias=not use_spectral_norm), use_spectral_norm), | |
| nn.LeakyReLU(0.2, inplace=True), | |
| nn.Conv3d(in_channels=nf * 4, out_channels=nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), | |
| padding=(1, 2, 2)) | |
| ) | |
| if init_weights: | |
| self.init_weights() | |
| def forward(self, xs, t): | |
| """ | |
| Args: | |
| xs: Input feature, with shape of [bt, c, h, w] | |
| Returns: The discriminative map from the GAN | |
| """ | |
| bt, c, h, w = xs.shape | |
| b = bt // t | |
| xs = xs.view(b, t, c, h, w).permute(0, 2, 1, 3, 4).contiguous() | |
| feat = self.conv(xs) | |
| if self.use_sigmoid: | |
| feat = torch.sigmoid(feat) | |
| out = torch.transpose(feat, 1, 2) # [b, t, c, h, w] | |
| return out | |
| def spectral_norm(module, mode=True): | |
| if mode: | |
| return nn.utils.spectral_norm(module) | |
| return module | |