import torch import torch.nn as nn import torchvision from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock import torch.nn.functional as F class Discriminator_VGG_128(nn.Module): # input_img_factor = multiplier to support images over 128x128. Only certain factors are supported. def __init__(self, in_nc, nf, input_img_factor=1, extra_conv=False): super(Discriminator_VGG_128, self).__init__() # [64, 128, 128] self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False) self.bn0_1 = nn.BatchNorm2d(nf, affine=True) # [64, 64, 64] self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False) self.bn1_0 = nn.BatchNorm2d(nf * 2, affine=True) self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False) self.bn1_1 = nn.BatchNorm2d(nf * 2, affine=True) # [128, 32, 32] self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False) self.bn2_0 = nn.BatchNorm2d(nf * 4, affine=True) self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False) self.bn2_1 = nn.BatchNorm2d(nf * 4, affine=True) # [256, 16, 16] self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False) self.bn3_0 = nn.BatchNorm2d(nf * 8, affine=True) self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False) self.bn3_1 = nn.BatchNorm2d(nf * 8, affine=True) # [512, 8, 8] self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False) self.bn4_0 = nn.BatchNorm2d(nf * 8, affine=True) self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False) self.bn4_1 = nn.BatchNorm2d(nf * 8, affine=True) final_nf = nf * 8 self.extra_conv = extra_conv if self.extra_conv: self.conv5_0 = nn.Conv2d(nf * 8, nf * 16, 3, 1, 1, bias=False) self.bn5_0 = nn.BatchNorm2d(nf * 16, affine=True) self.conv5_1 = nn.Conv2d(nf * 16, nf * 16, 4, 2, 1, bias=False) self.bn5_1 = nn.BatchNorm2d(nf * 16, affine=True) input_img_factor = input_img_factor // 2 final_nf = nf * 16 self.linear1 = nn.Linear(final_nf * 4 * input_img_factor * 4 * input_img_factor, 100) self.linear2 = nn.Linear(100, 1) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x): fea = self.lrelu(self.conv0_0(x)) fea = self.lrelu(self.bn0_1(self.conv0_1(fea))) #fea = torch.cat([fea, skip_med], dim=1) fea = self.lrelu(self.bn1_0(self.conv1_0(fea))) fea = self.lrelu(self.bn1_1(self.conv1_1(fea))) #fea = torch.cat([fea, skip_lo], dim=1) fea = self.lrelu(self.bn2_0(self.conv2_0(fea))) fea = self.lrelu(self.bn2_1(self.conv2_1(fea))) fea = self.lrelu(self.bn3_0(self.conv3_0(fea))) fea = self.lrelu(self.bn3_1(self.conv3_1(fea))) fea = self.lrelu(self.bn4_0(self.conv4_0(fea))) fea = self.lrelu(self.bn4_1(self.conv4_1(fea))) if self.extra_conv: fea = self.lrelu(self.bn5_0(self.conv5_0(fea))) fea = self.lrelu(self.bn5_1(self.conv5_1(fea))) fea = fea.contiguous().view(fea.size(0), -1) fea = self.lrelu(self.linear1(fea)) out = self.linear2(fea) return out class Discriminator_VGG_PixLoss(nn.Module): def __init__(self, in_nc, nf): super(Discriminator_VGG_PixLoss, self).__init__() # [64, 128, 128] self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False) self.bn0_1 = nn.GroupNorm(8, nf, affine=True) # [64, 64, 64] self.conv1_0 = nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False) self.bn1_0 = nn.GroupNorm(8, nf * 2, affine=True) self.conv1_1 = nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False) self.bn1_1 = nn.GroupNorm(8, nf * 2, affine=True) # [128, 32, 32] self.conv2_0 = nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False) self.bn2_0 = nn.GroupNorm(8, nf * 4, affine=True) self.conv2_1 = nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False) self.bn2_1 = nn.GroupNorm(8, nf * 4, affine=True) # [256, 16, 16] self.conv3_0 = nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False) self.bn3_0 = nn.GroupNorm(8, nf * 8, affine=True) self.conv3_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False) self.bn3_1 = nn.GroupNorm(8, nf * 8, affine=True) # [512, 8, 8] self.conv4_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False) self.bn4_0 = nn.GroupNorm(8, nf * 8, affine=True) self.conv4_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False) self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True) self.reduce_1 = ConvGnLelu(nf * 8, nf * 4, bias=False) self.pix_loss_collapse = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False) # Pyramid network: upsample with residuals and produce losses at multiple resolutions. self.up3_decimate = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=True, activation=False) self.up3_converge = ConvGnLelu(nf * 16, nf * 8, kernel_size=3, bias=False) self.up3_proc = ConvGnLelu(nf * 8, nf * 8, bias=False) self.up3_reduce = ConvGnLelu(nf * 8, nf * 4, bias=False) self.up3_pix = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False) self.up2_decimate = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, bias=True, activation=False) self.up2_converge = ConvGnLelu(nf * 8, nf * 4, kernel_size=3, bias=False) self.up2_proc = ConvGnLelu(nf * 4, nf * 4, bias=False) self.up2_reduce = ConvGnLelu(nf * 4, nf * 2, bias=False) self.up2_pix = ConvGnLelu(nf * 2, 1, bias=False, norm=False, activation=False) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, x, flatten=True): fea0 = self.lrelu(self.conv0_0(x)) fea0 = self.lrelu(self.bn0_1(self.conv0_1(fea0))) fea1 = self.lrelu(self.bn1_0(self.conv1_0(fea0))) fea1 = self.lrelu(self.bn1_1(self.conv1_1(fea1))) fea2 = self.lrelu(self.bn2_0(self.conv2_0(fea1))) fea2 = self.lrelu(self.bn2_1(self.conv2_1(fea2))) fea3 = self.lrelu(self.bn3_0(self.conv3_0(fea2))) fea3 = self.lrelu(self.bn3_1(self.conv3_1(fea3))) fea4 = self.lrelu(self.bn4_0(self.conv4_0(fea3))) fea4 = self.lrelu(self.bn4_1(self.conv4_1(fea4))) loss = self.reduce_1(fea4) # "Weight" all losses the same by interpolating them to the highest dimension. loss = self.pix_loss_collapse(loss) loss = F.interpolate(loss, scale_factor=4, mode="nearest") # And the pyramid network! dec3 = self.up3_decimate(F.interpolate(fea4, scale_factor=2, mode="nearest")) dec3 = torch.cat([dec3, fea3], dim=1) dec3 = self.up3_converge(dec3) dec3 = self.up3_proc(dec3) loss3 = self.up3_reduce(dec3) loss3 = self.up3_pix(loss3) loss3 = F.interpolate(loss3, scale_factor=2, mode="nearest") dec2 = self.up2_decimate(F.interpolate(dec3, scale_factor=2, mode="nearest")) dec2 = torch.cat([dec2, fea2], dim=1) dec2 = self.up2_converge(dec2) dec2 = self.up2_proc(dec2) dec2 = self.up2_reduce(dec2) loss2 = self.up2_pix(dec2) # Compress all of the loss values into the batch dimension. The actual loss attached to this output will # then know how to handle them. combined_losses = torch.cat([loss, loss3, loss2], dim=1) return combined_losses.view(-1, 1) def pixgan_parameters(self): return 3, 8 class Discriminator_UNet(nn.Module): def __init__(self, in_nc, nf): super(Discriminator_UNet, self).__init__() # [64, 128, 128] self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False) self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False) # [64, 64, 64] self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False) self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False) # [128, 32, 32] self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False) self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False) # [256, 16, 16] self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False) self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False) # [512, 8, 8] self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False) self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False) self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu) self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False) self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False) self.up2 = ExpansionBlock(nf * 8, nf * 4, block=ConvGnLelu) self.proc2 = ConvGnLelu(nf * 4, nf * 4, bias=False) self.collapse2 = ConvGnLelu(nf * 4, 1, bias=True, norm=False, activation=False) self.up3 = ExpansionBlock(nf * 4, nf * 2, block=ConvGnLelu) self.proc3 = ConvGnLelu(nf * 2, nf * 2, bias=False) self.collapse3 = ConvGnLelu(nf * 2, 1, bias=True, norm=False, activation=False) def forward(self, x, flatten=True): fea0 = self.conv0_0(x) fea0 = self.conv0_1(fea0) fea1 = self.conv1_0(fea0) fea1 = self.conv1_1(fea1) fea2 = self.conv2_0(fea1) fea2 = self.conv2_1(fea2) fea3 = self.conv3_0(fea2) fea3 = self.conv3_1(fea3) fea4 = self.conv4_0(fea3) fea4 = self.conv4_1(fea4) # And the pyramid network! u1 = self.up1(fea4, fea3) loss1 = self.collapse1(self.proc1(u1)) u2 = self.up2(u1, fea2) loss2 = self.collapse2(self.proc2(u2)) u3 = self.up3(u2, fea1) loss3 = self.collapse3(self.proc3(u3)) res = loss3.shape[2:] # Compress all of the loss values into the batch dimension. The actual loss attached to this output will # then know how to handle them. combined_losses = torch.cat([F.interpolate(loss1, scale_factor=4), F.interpolate(loss2, scale_factor=2), F.interpolate(loss3, scale_factor=1)], dim=1) return combined_losses.view(-1, 1) def pixgan_parameters(self): return 3, 4 import functools from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock, ConfigurableSwitchComputer, BareConvSwitch from switched_conv_util import save_attention_to_image from switched_conv import compute_attention_specificity, AttentionNorm class ExpandAndCollapse(nn.Module): def __init__(self, nf, nf_out, num_channels): super(ExpandAndCollapse, self).__init__() self.expand = ExpansionBlock(nf, nf_out, block=ConvGnLelu) self.collapse = ConvGnLelu(nf_out, num_channels, norm=False, bias=False, activation=False) def forward(self, x, passthrough): x = self.expand(x, passthrough) return self.collapse(x) # Differs from ConfigurableSwitchComputer in that the connections are not residual and the multiplexer is fed directly in. class ConfigurableLinearSwitchComputer(nn.Module): def __init__(self, out_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm, init_temp=20, add_scalable_noise_to_transforms=False): super(ConfigurableLinearSwitchComputer, self).__init__() self.multiplexer = multiplexer_net self.pre_transform = pre_transform_block self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)]) self.add_noise = add_scalable_noise_to_transforms self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3))) # And the switch itself, including learned scalars self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count) if attention_norm else None) self.post_switch_conv = ConvBnLelu(out_filters, out_filters, norm=False, bias=True) # The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not) # depending on its needs. self.psc_scale = nn.Parameter(torch.full((1,), float(.1))) def forward(self, x, passthrough, output_attention_weights=False, extra_arg=None): identity = x if self.add_noise: rand_feature = torch.randn_like(x) * self.noise_scale x = x + rand_feature x = self.pre_transform(x) xformed = [t.forward(x, passthrough) for t in self.transforms] m = self.multiplexer(identity, passthrough) outputs, attention = self.switch(xformed, m, True) outputs = self.post_switch_conv(outputs) if output_attention_weights: return outputs, attention else: return outputs def set_temperature(self, temp): self.switch.set_attention_temperature(temp) def create_switched_upsampler(nf, nf_out, num_channels, initial_temp=10): multiplx = ExpandAndCollapse(nf, nf_out, num_channels) pretransform = ConvGnLelu(nf, nf, norm=True, bias=False) transform_fn = functools.partial(ExpansionBlock, nf, nf_out, block=ConvGnLelu) return ConfigurableLinearSwitchComputer(nf_out, multiplx, pre_transform_block=pretransform, transform_block=transform_fn, attention_norm=True, transform_count=num_channels, init_temp=initial_temp, add_scalable_noise_to_transforms=False) class Discriminator_switched(nn.Module): def __init__(self, in_nc, nf, initial_temp=10, final_temperature_step=50000): super(Discriminator_switched, self).__init__() # [64, 128, 128] self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False) self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False) # [64, 64, 64] self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False) self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False) # [128, 32, 32] self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False) self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False) # [256, 16, 16] self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False) self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False) # [512, 8, 8] self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False) self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False) self.exp1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu) self.upsw2 = create_switched_upsampler(nf * 8, nf * 4, 8) self.upsw3 = create_switched_upsampler(nf * 4, nf * 2, 8) self.switches = [self.upsw2, self.upsw3] self.proc3 = ConvGnLelu(nf * 2, nf * 2, bias=False) self.collapse3 = ConvGnLelu(nf * 2, 1, bias=True, norm=False, activation=False) self.init_temperature = initial_temp self.final_temperature_step = final_temperature_step self.attentions = None def forward(self, x, flatten=True): fea0 = self.conv0_0(x) fea0 = self.conv0_1(fea0) fea1 = self.conv1_0(fea0) fea1 = self.conv1_1(fea1) fea2 = self.conv2_0(fea1) fea2 = self.conv2_1(fea2) fea3 = self.conv3_0(fea2) fea3 = self.conv3_1(fea3) fea4 = self.conv4_0(fea3) fea4 = self.conv4_1(fea4) u1 = self.exp1(fea4, fea3) u2, a1 = self.upsw2(u1, fea2, output_attention_weights=True) u3, a2 = self.upsw3(u2, fea1, output_attention_weights=True) self.attentions = [a1, a2] loss3 = self.collapse3(self.proc3(u3)) return loss3.view(-1, 1) def pixgan_parameters(self): return 1, 4 def set_temperature(self, temp): [sw.set_temperature(temp) for sw in self.switches] def update_for_step(self, step, experiments_path='.'): if self.attentions: for i, sw in enumerate(self.switches): temp_loss_per_step = (self.init_temperature - 1) / self.final_temperature_step sw.set_temperature(min(self.init_temperature, max(self.init_temperature - temp_loss_per_step * step, 1))) if step % 50 == 0: [save_attention_to_image(experiments_path, self.attentions[i], 8, step, "disc_a%i" % (i+1,), l_mult=10) for i in range(len(self.attentions))] def get_debug_values(self, step): temp = self.switches[0].switch.temperature mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions] means = [i[0] for i in mean_hists] hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists] val = {"disc_switch_temperature": temp} for i in range(len(means)): val["disc_switch_%i_specificity" % (i,)] = means[i] val["disc_switch_%i_histogram" % (i,)] = hists[i] return val class Discriminator_UNet_FeaOut(nn.Module): def __init__(self, in_nc, nf, feature_mode=False): super(Discriminator_UNet_FeaOut, self).__init__() # [64, 128, 128] self.conv0_0 = ConvGnLelu(in_nc, nf, kernel_size=3, bias=True, activation=False) self.conv0_1 = ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False) # [64, 64, 64] self.conv1_0 = ConvGnLelu(nf, nf * 2, kernel_size=3, bias=False) self.conv1_1 = ConvGnLelu(nf * 2, nf * 2, kernel_size=3, stride=2, bias=False) # [128, 32, 32] self.conv2_0 = ConvGnLelu(nf * 2, nf * 4, kernel_size=3, bias=False) self.conv2_1 = ConvGnLelu(nf * 4, nf * 4, kernel_size=3, stride=2, bias=False) # [256, 16, 16] self.conv3_0 = ConvGnLelu(nf * 4, nf * 8, kernel_size=3, bias=False) self.conv3_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False) # [512, 8, 8] self.conv4_0 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=False) self.conv4_1 = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, stride=2, bias=False) self.up1 = ExpansionBlock(nf * 8, nf * 8, block=ConvGnLelu) self.proc1 = ConvGnLelu(nf * 8, nf * 8, bias=False) self.fea_proc = ConvGnLelu(nf * 8, nf * 8, bias=True, norm=False, activation=False) self.collapse1 = ConvGnLelu(nf * 8, 1, bias=True, norm=False, activation=False) self.feature_mode = feature_mode def forward(self, x, output_feature_vector=False): fea0 = self.conv0_0(x) fea0 = self.conv0_1(fea0) fea1 = self.conv1_0(fea0) fea1 = self.conv1_1(fea1) fea2 = self.conv2_0(fea1) fea2 = self.conv2_1(fea2) fea3 = self.conv3_0(fea2) fea3 = self.conv3_1(fea3) fea4 = self.conv4_0(fea3) fea4 = self.conv4_1(fea4) # And the pyramid network! u1 = self.up1(fea4, fea3) loss1 = self.collapse1(self.proc1(u1)) fea_out = self.fea_proc(u1) combined_losses = F.interpolate(loss1, scale_factor=4) if output_feature_vector: return combined_losses.view(-1, 1), fea_out else: return combined_losses.view(-1, 1) def pixgan_parameters(self): return 1, 4