add patch d

model/image_translation/CycleGAN.py

@@ -1,5 +1,6 @@
 import torch.nn as nn
 
+from model import MODEL
 from model.base.module import Conv2dBlock, ResidualBlock
 
 
@@ -43,7 +44,7 @@ class Decoder(nn.Module):
     def __init__(self, in_channels, out_channels, num_up_sampling, num_residual_blocks,
                  activation_type="ReLU", padding_mode='reflect',
                  up_conv_kernel_size=5, up_conv_norm_type="LN",
-                 res_norm_type="AdaIN", pre_activation=False):
+                 res_norm_type="AdaIN", pre_activation=False, use_transpose_conv=False):
         super().__init__()
         self.residual_blocks = nn.ModuleList([
             ResidualBlock(
@@ -57,11 +58,21 @@ class Decoder(nn.Module):
 
         sequence = list()
         channels = in_channels
+        padding = (up_conv_kernel_size - 1) // 2
         for i in range(num_up_sampling):
+            if use_transpose_conv:
+                sequence.append(Conv2dBlock(
+                    channels, channels // 2, kernel_size=up_conv_kernel_size, stride=1,
+                    padding=padding, output_padding=padding,
+                    padding_mode=padding_mode,
+                    activation_type=activation_type, norm_type=up_conv_norm_type,
+                    use_transpose_conv=True
+                ))
+            else:
                 sequence.append(nn.Sequential(
                     nn.Upsample(scale_factor=2),
                     Conv2dBlock(channels, channels // 2, kernel_size=up_conv_kernel_size, stride=1,
-                            padding=int(up_conv_kernel_size / 2), padding_mode=padding_mode,
+                                padding=padding, padding_mode=padding_mode,
                                 activation_type=activation_type, norm_type=up_conv_norm_type),
                 ))
             channels = channels // 2
@@ -74,3 +85,61 @@ class Decoder(nn.Module):
         for i, blk in enumerate(self.residual_blocks):
             x = blk(x)
         return self.up_sequence(x)
+
+
+@MODEL.register_module("CycleGAN-Generator")
+class Generator(nn.Module):
+    def __init__(self, in_channels, out_channels, base_channels=64, num_blocks=9, activation_type="ReLU",
+                 padding_mode='reflect', norm_type="IN", pre_activation=True, use_transpose_conv=True):
+        super().__init__()
+        self.encoder = Encoder(in_channels, base_channels, num_conv=2, num_res=num_blocks,
+                               padding_mode=padding_mode, activation_type=activation_type,
+                               down_conv_norm_type=norm_type, res_norm_type=norm_type, pre_activation=pre_activation)
+        self.decoder = Decoder(self.encoder.out_channels, out_channels, num_up_sampling=2, num_residual_blocks=0,
+                               padding_mode=padding_mode, activation_type=activation_type,
+                               up_conv_kernel_size=3, up_conv_norm_type=norm_type,
+                               pre_activation=pre_activation, use_transpose_conv=use_transpose_conv)
+
+    def forward(self, x):
+        return self.decoder(self.encoder(x))
+
+
+@MODEL.register_module("PatchDiscriminator")
+class PatchDiscriminator(nn.Module):
+    def __int__(self, in_channels, base_channels=64, num_conv=4, need_intermediate_feature=False,
+                norm_type="IN", padding_mode='reflect', activation_type="LeakyReLU"):
+        super().__init__()
+        self.need_intermediate_feature = need_intermediate_feature
+        kernel_size = 4
+        padding = (kernel_size - 1) // 2
+        sequence = [Conv2dBlock(
+            in_channels, base_channels, kernel_size=kernel_size, stride=2, padding=padding, padding_mode=padding_mode,
+            activation_type=activation_type, norm_type=norm_type
+        )]
+
+        multiple_now = 1
+        for i in range(1, num_conv + 1):
+            multiple_prev = multiple_now
+            multiple_now = min(2 ** i, 2 ** 3)
+            stride = 1 if i == num_conv - 1 else 2
+            sequence.append(Conv2dBlock(
+                multiple_prev * base_channels, multiple_now * base_channels,
+                kernel_size=kernel_size, stride=stride, padding=padding, padding_mode=padding_mode,
+                activation_type=activation_type, norm_type=norm_type
+            ))
+        sequence.append(nn.Conv2d(
+            base_channels * multiple_now, 1, kernel_size, stride=1, padding=padding, padding_mode=padding_mode))
+        if self.need_intermediate_feature:
+            self.sequence = nn.ModuleList(sequence)
+        else:
+            self.sequence = nn.Sequential(*sequence)
+
+    def forward(self, x):
+        if self.need_intermediate_feature:
+            intermediate_feature = []
+            for layer in self.sequence:
+                x = layer(x)
+                intermediate_feature.append(x)
+            return tuple(intermediate_feature)
+        else:
+            return self.sequence(x)