Update README.md

README.md

@@ -1,3 +1,109 @@
 ---
 license: mit
 ---
+Model weight for Fast Style Transfer
+
+```
+
+class TransformerNetwork(nn.Module):
+    """Feedforward Transformation Network without Tanh
+    reference: https://arxiv.org/abs/1603.08155 
+    exact architecture: https://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf
+    """
+    def __init__(self, tanh_multiplier=None):
+        super(TransformerNetwork, self).__init__()
+        self.ConvBlock = nn.Sequential(
+            ConvLayer(3, 32, 9, 1),
+            nn.ReLU(),
+            ConvLayer(32, 64, 3, 2),
+            nn.ReLU(),
+            ConvLayer(64, 128, 3, 2),
+            nn.ReLU()
+        )
+        self.ResidualBlock = nn.Sequential(
+            ResidualLayer(128, 3), 
+            ResidualLayer(128, 3), 
+            ResidualLayer(128, 3), 
+            ResidualLayer(128, 3), 
+            ResidualLayer(128, 3)
+        )
+        self.DeconvBlock = nn.Sequential(
+            DeconvLayer(128, 64, 3, 2, 1),
+            nn.ReLU(),
+            DeconvLayer(64, 32, 3, 2, 1),
+            nn.ReLU(),
+            ConvLayer(32, 3, 9, 1, norm="None")
+        )
+        self.tanh_multiplier = tanh_multiplier
+
+    def forward(self, x):
+        x = self.ConvBlock(x)
+        x = self.ResidualBlock(x)
+        x = self.DeconvBlock(x)
+        if isinstance(self.tanh_multiplier, int):
+            x = self.tanh_multiplier * F.tanh(x)
+        return x
+
+class ConvLayer(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, norm="instance"):
+        super(ConvLayer, self).__init__()
+        # Padding Layers
+        padding_size = kernel_size // 2
+        self.pad = nn.ReflectionPad2d(padding_size)
+
+        # Convolution Layer
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride)
+
+        # Normalization Layers
+        if norm == "instance":
+            self.norm = nn.InstanceNorm2d(out_channels, affine=True)
+        elif norm == "batch":
+            self.norm = nn.BatchNorm2d(out_channels, affine=True)
+        else:
+            self.norm = nn.Identity()
+
+    def forward(self, x):
+        x = self.pad(x)
+        x = self.conv(x)
+        x = self.norm(x)
+        return x
+
+class ResidualLayer(nn.Module):
+    """
+    Deep Residual Learning for Image Recognition
+    https://arxiv.org/abs/1512.03385
+    """
+    def __init__(self, channels=128, kernel_size=3):
+        super(ResidualLayer, self).__init__()
+        self.conv1 = ConvLayer(channels, channels, kernel_size, stride=1)
+        self.relu = nn.ReLU()
+        self.conv2 = ConvLayer(channels, channels, kernel_size, stride=1)
+
+    def forward(self, x):
+        identity = x                     # preserve residual
+        out = self.relu(self.conv1(x))   # 1st conv layer + activation
+        out = self.conv2(out)            # 2nd conv layer
+        out = out + identity             # add residual
+        return out
+
+class DeconvLayer(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, output_padding, norm="instance"):
+        super(DeconvLayer, self).__init__()
+
+        # Transposed Convolution 
+        padding_size = kernel_size // 2
+        self.conv_transpose = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride, padding_size, output_padding)
+
+        # Normalization Layers
+        if norm == "instance":
+            self.norm = nn.InstanceNorm2d(out_channels, affine=True)
+        elif norm == "batch":
+            self.norm = nn.BatchNorm2d(out_channels, affine=True)
+        else:
+            self.norm = nn.Identity()
+
+    def forward(self, x):
+        x = self.conv_transpose(x)
+        out = self.norm(x)
+        return out
+```