import torch import torch.nn as nn from models.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN import torch.nn.functional as F from trainer.networks import register_model from utils.util import checkpoint, opt_get 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 @register_model def register_discriminator_vgg_128(opt_net, opt): return Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=opt_net['image_size'] / 128, extra_conv=opt_net['extra_conv']) class Discriminator_VGG_128_GN(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, do_checkpointing=False, extra_conv=False): super(Discriminator_VGG_128_GN, self).__init__() self.do_checkpointing = do_checkpointing # [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.extra_conv = extra_conv if extra_conv: self.conv5_0 = nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False) self.bn5_0 = nn.GroupNorm(8, nf * 8, affine=True) self.conv5_1 = nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False) self.bn5_1 = nn.GroupNorm(8, nf * 8, affine=True) input_img_factor = input_img_factor / 2 final_nf = nf * 8 # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) self.linear1 = nn.Linear(int(final_nf * 4 * input_img_factor * 4 * input_img_factor), 100) self.linear2 = nn.Linear(100, 1) def compute_body(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))) return fea def forward(self, x): if self.do_checkpointing: fea = checkpoint(self.compute_body, x) else: fea = self.compute_body(x) fea = fea.contiguous().view(fea.size(0), -1) fea = self.lrelu(self.linear1(fea)) out = self.linear2(fea) return out @register_model def register_discriminator_vgg_128(opt_net, opt): return Discriminator_VGG_128_GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=opt_net['image_size'] / 128, extra_conv=opt_get(opt_net, ['extra_conv'], False), do_checkpointing=opt_get(opt_net, ['do_checkpointing'], False)) class DiscriminatorVGG448GN(nn.Module): # input_img_factor = multiplier to support images over 128x128. Only certain factors are supported. def __init__(self, in_nc, nf, do_checkpointing=False): super().__init__() self.do_checkpointing = do_checkpointing # 448x448 self.convn1_0 = nn.Conv2d(in_nc, nf // 2, 3, 1, 1, bias=True) self.convn1_1 = nn.Conv2d(nf // 2, nf // 2, 4, 2, 1, bias=False) self.bnn1_1 = nn.GroupNorm(8, nf // 2, affine=True) # 224x224 (new head) self.conv0_0_new = nn.Conv2d(nf // 2, nf, 3, 1, 1, bias=False) self.bn0_0 = nn.GroupNorm(8, nf, affine=True) # 224x224 (old head) self.conv0_0 = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True) # Unused. self.conv0_1 = nn.Conv2d(nf, nf, 4, 2, 1, bias=False) self.bn0_1 = nn.GroupNorm(8, nf, affine=True) # 112x112 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) # 56x56 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) # 28x28 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) # 14x14 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) # out: 7x7 # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) final_nf = nf * 8 self.linear1 = nn.Linear(int(final_nf * 7 * 7), 100) self.linear2 = nn.Linear(100, 1) # Assign all new heads to the new param group.2 for m in [self.convn1_0, self.convn1_1, self.bnn1_1, self.conv0_0_new, self.bn0_0]: for p in m.parameters(): p.PARAM_GROUP = 'new_head' def compute_body(self, x): fea = self.lrelu(self.convn1_0(x)) fea = self.lrelu(self.bnn1_1(self.convn1_1(fea))) fea = self.lrelu(self.bn0_0(self.conv0_0_new(fea))) # fea = self.lrelu(self.conv0_0(x)) <- replaced fea = self.lrelu(self.bn0_1(self.conv0_1(fea))) fea = self.lrelu(self.bn1_0(self.conv1_0(fea))) fea = self.lrelu(self.bn1_1(self.conv1_1(fea))) 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))) return fea def forward(self, x): if self.do_checkpointing: fea = checkpoint(self.compute_body, x) else: fea = self.compute_body(x) fea = fea.contiguous().view(fea.size(0), -1) fea = self.lrelu(self.linear1(fea)) out = self.linear2(fea) return out @register_model def register_discriminator_vgg_448(opt_net, opt): return DiscriminatorVGG448GN(in_nc=opt_net['in_nc'], nf=opt_net['nf'])