Spaces:
Running
on
Zero
Running
on
Zero
| """ | |
| Ported from Paella | |
| """ | |
| import torch | |
| from torch import nn | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.models.modeling_utils import ModelMixin | |
| import functools | |
| # import torch.nn as nn | |
| from taming.modules.util import ActNorm | |
| # Discriminator model ported from Paella https://github.com/dome272/Paella/blob/main/src_distributed/vqgan.py | |
| class Discriminator(ModelMixin, ConfigMixin): | |
| def __init__(self, in_channels=3, cond_channels=0, hidden_channels=512, depth=6): | |
| super().__init__() | |
| d = max(depth - 3, 3) | |
| layers = [ | |
| nn.utils.spectral_norm( | |
| nn.Conv2d(in_channels, hidden_channels // (2**d), kernel_size=3, stride=2, padding=1) | |
| ), | |
| nn.LeakyReLU(0.2), | |
| ] | |
| for i in range(depth - 1): | |
| c_in = hidden_channels // (2 ** max((d - i), 0)) | |
| c_out = hidden_channels // (2 ** max((d - 1 - i), 0)) | |
| layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1))) | |
| layers.append(nn.InstanceNorm2d(c_out)) | |
| layers.append(nn.LeakyReLU(0.2)) | |
| self.encoder = nn.Sequential(*layers) | |
| self.shuffle = nn.Conv2d( | |
| (hidden_channels + cond_channels) if cond_channels > 0 else hidden_channels, 1, kernel_size=1 | |
| ) | |
| # self.logits = nn.Sigmoid() | |
| def forward(self, x, cond=None): | |
| x = self.encoder(x) | |
| if cond is not None: | |
| cond = cond.view( | |
| cond.size(0), | |
| cond.size(1), | |
| 1, | |
| 1, | |
| ).expand(-1, -1, x.size(-2), x.size(-1)) | |
| x = torch.cat([x, cond], dim=1) | |
| x = self.shuffle(x) | |
| # x = self.logits(x) | |
| return x | |
| def weights_init(m): | |
| classname = m.__class__.__name__ | |
| if classname.find('Conv') != -1: | |
| nn.init.normal_(m.weight.data, 0.0, 0.02) | |
| elif classname.find('BatchNorm') != -1: | |
| nn.init.normal_(m.weight.data, 1.0, 0.02) | |
| nn.init.constant_(m.bias.data, 0) | |
| class NLayerDiscriminator(nn.Module): | |
| """Defines a PatchGAN discriminator as in Pix2Pix | |
| --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py | |
| """ | |
| def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False): | |
| """Construct a PatchGAN discriminator | |
| Parameters: | |
| input_nc (int) -- the number of channels in input images | |
| ndf (int) -- the number of filters in the last conv layer | |
| n_layers (int) -- the number of conv layers in the discriminator | |
| norm_layer -- normalization layer | |
| """ | |
| super(NLayerDiscriminator, self).__init__() | |
| if not use_actnorm: | |
| # norm_layer = nn.BatchNorm2d | |
| norm_layer = nn.InstanceNorm2d | |
| else: | |
| norm_layer = ActNorm | |
| if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters | |
| # use_bias = norm_layer.func != nn.BatchNorm2d | |
| use_bias = norm_layer.func != nn.InstanceNorm2d | |
| else: | |
| # use_bias = norm_layer != nn.BatchNorm2d | |
| use_bias = norm_layer != nn.InstanceNorm2d | |
| kw = 4 | |
| padw = 1 | |
| sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)] | |
| nf_mult = 1 | |
| nf_mult_prev = 1 | |
| for n in range(1, n_layers): # gradually increase the number of filters | |
| nf_mult_prev = nf_mult | |
| nf_mult = min(2 ** n, 8) | |
| sequence += [ | |
| nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), | |
| norm_layer(ndf * nf_mult), | |
| nn.LeakyReLU(0.2, False) | |
| ] | |
| nf_mult_prev = nf_mult | |
| nf_mult = min(2 ** n_layers, 8) | |
| sequence += [ | |
| nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), | |
| norm_layer(ndf * nf_mult), | |
| nn.LeakyReLU(0.2, False) | |
| ] | |
| sequence += [ | |
| nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map | |
| self.main = nn.Sequential(*sequence) | |
| def forward(self, input): | |
| """Standard forward.""" | |
| return self.main(input) | |