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ganimation/model.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import random
+
+from PIL import Image
+
+
+class BaseNetwork(nn.Module):
+    def __init__(self):
+        super(BaseNetwork, self).__init__()
+
+    def init_weights(self):
+        self.apply(self._weights_init_fn)
+
+    def _weights_init_fn(self, m):
+        classname = m.__class__.__name__
+        if classname.find('Conv') != -1:
+            m.weight.data.normal_(0.0, 0.02)
+            if hasattr(m.bias, 'data'):
+                m.bias.data.fill_(0)
+        elif classname.find('BatchNorm2d') != -1:
+            m.weight.data.normal_(1.0, 0.02)
+            m.bias.data.fill_(0)
+
+
+class ResidualBlock(BaseNetwork):
+    """Residual Block with instance normalization."""
+
+    def __init__(self, dim_in, dim_out):
+        super(ResidualBlock, self).__init__()
+        self.main = nn.Sequential(
+            nn.Conv2d(dim_in, dim_out, kernel_size=3,
+                      stride=1, padding=1, bias=False),
+            nn.InstanceNorm2d(dim_out, affine=True, track_running_stats=True),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(dim_out, dim_out, kernel_size=3,
+                      stride=1, padding=1, bias=False),
+            nn.InstanceNorm2d(dim_out, affine=True, track_running_stats=True))
+
+        self.init_weights()
+
+    def forward(self, x):
+        return x + self.main(x)
+
+
+class Generator(BaseNetwork):
+    """Generator network."""
+
+    def __init__(self, conv_dim=64, c_dim=5, repeat_num=6):
+        super(Generator, self).__init__()
+
+        layers = []
+        layers.append(nn.Conv2d(3+c_dim, conv_dim, kernel_size=7,
+                                stride=1, padding=3, bias=False))
+        layers.append(nn.InstanceNorm2d(
+            conv_dim, affine=True, track_running_stats=True))
+        layers.append(nn.ReLU(inplace=True))
+
+        self.debug1 = nn.Sequential(*layers)
+
+        # Down-sampling layers.
+        curr_dim = conv_dim
+        for i in range(2):
+            layers.append(nn.Conv2d(curr_dim, curr_dim*2,
+                                    kernel_size=4, stride=2, padding=1, bias=False))
+            layers.append(nn.InstanceNorm2d(
+                curr_dim*2, affine=True, track_running_stats=True))
+            layers.append(nn.ReLU(inplace=True))
+            curr_dim = curr_dim * 2
+
+        self.debug2 = nn.Sequential(*layers)
+
+        # Bottleneck layers.
+        for i in range(repeat_num):
+            layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))
+
+        self.debug3 = nn.Sequential(*layers)
+
+        # Up-sampling layers.
+        for i in range(2):
+            layers.append(nn.ConvTranspose2d(curr_dim, curr_dim //
+                                             2, kernel_size=4, stride=2, padding=1, bias=False))
+            layers.append(nn.InstanceNorm2d(
+                curr_dim//2, affine=True, track_running_stats=True))
+            layers.append(nn.ReLU(inplace=True))
+            curr_dim = curr_dim // 2
+
+        self.main = nn.Sequential(*layers)
+
+        self.debug4 = nn.Sequential(*layers)
+
+        # Same architecture for the color regression
+        layers = []
+        layers.append(nn.Conv2d(curr_dim, 3, kernel_size=7,
+                                stride=1, padding=3, bias=False))
+        layers.append(nn.Tanh())
+        self.im_reg = nn.Sequential(*layers)
+
+        # One Channel output and Sigmoid function for the attention layer
+        layers = []
+        layers.append(nn.Conv2d(curr_dim, 1, kernel_size=7,
+                                stride=1, padding=3, bias=False))
+        layers.append(nn.Sigmoid())  # Values between 0 and 1
+        self.im_att = nn.Sequential(*layers)
+
+        self.init_weights()
+
+    def forward(self, x, c):
+        # Replicate spatially and concatenate domain information.
+
+        c = c.unsqueeze(2).unsqueeze(3)
+        c = c.expand(c.size(0), c.size(1), x.size(2), x.size(3))
+
+        x = torch.cat([x, c], dim=1)
+        features = self.main(x)
+
+        reg = self.im_reg(features)
+        att = self.im_att(features)
+
+        return att, reg
+
+
+class Discriminator(BaseNetwork):
+    """Discriminator network with PatchGAN."""
+
+    def __init__(self, image_size=128, conv_dim=64, c_dim=5, repeat_num=6):
+        super(Discriminator, self).__init__()
+
+        layers = []
+        layers.append(
+            nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
+        layers.append(nn.LeakyReLU(0.01))
+
+        curr_dim = conv_dim
+        for i in range(1, repeat_num):
+            layers.append(nn.Conv2d(curr_dim, curr_dim*2,
+                                    kernel_size=4, stride=2, padding=1))
+            layers.append(nn.LeakyReLU(0.01))
+            curr_dim = curr_dim * 2
+
+        kernel_size = int(image_size / np.power(2, repeat_num))
+        self.main = nn.Sequential(*layers)
+        self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=3,
+                               stride=1, padding=1, bias=False)
+        self.conv2 = nn.Conv2d(
+            curr_dim, c_dim, kernel_size=kernel_size, bias=False)
+
+        self.init_weights()
+
+    def forward(self, x):
+        h = self.main(x)
+        out_src = self.conv1(h)
+        out_cls = self.conv2(h)
+
+        # out_cls.view(out_cls.size(0), out_cls.size(1))
+        return out_src.squeeze(), out_cls.squeeze()