init

.gitignore

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+
+FBPN_NEW/.DS_Store
+weights/.DS_Store
+ucf101_interp_ours/.DS_Store
+.DS_Store

autoencoder_v4.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class UNet(nn.Module):
+    def __init__(self, in_channels, out_classes):
+        super(UNet, self).__init__()
+        self.inc = inconv(in_channels, 64)
+        self.down1 = down(64, 128)
+        self.down2 = down(128, 256)
+        self.down3 = down(256, 512)
+        self.down4 = down(512, 1024)
+        self.down5 = down(1024, 1024)
+        self.up1 = up(2048, 512)
+        self.up2 = up(1024, 256)
+        self.up3 = up(512, 128)
+        self.up4 = up(256, 64)
+        self.up5 = up(128, 64)
+        self.outc = outconv(64, out_classes)
+
+    def forward(self, x):
+        x1 = self.inc(x)
+        x2 = self.down1(x1)
+        x3 = self.down2(x2)
+        x4 = self.down3(x3)
+        x5 = self.down4(x4)
+        x6 = self.down5(x5)
+        x = self.up1(x6, x5)
+        x = self.up2(x, x4)
+        x = self.up3(x, x3)
+        x = self.up4(x, x2)
+        x = self.up5(x, x1)
+        x = self.outc(x)
+        return torch.sigmoid(x)
+
+class double_conv(nn.Module):
+    '''(conv => BN => ReLU) * 2'''
+    def __init__(self, in_ch, out_ch):
+        super(double_conv, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_ch, out_ch, 3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(out_ch, out_ch, 3, padding=1),
+            nn.BatchNorm2d(out_ch),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+class inconv(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super(inconv, self).__init__()
+        self.conv = double_conv(in_ch, out_ch)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x
+
+
+class down(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super(down, self).__init__()
+        self.mpconv = nn.Sequential(
+            nn.MaxPool2d(2),
+            double_conv(in_ch, out_ch)
+        )
+
+    def forward(self, x):
+        x = self.mpconv(x)
+        return x
+
+
+class up(nn.Module):
+    def __init__(self, in_ch, out_ch, bilinear=True):
+        super(up, self).__init__()
+
+        #  would be a nice idea if the upsampling could be learned too,
+        #  but my machine do not have enough memory to handle all those weights
+        if bilinear:
+            self.up = nn.Upsample(scale_factor=2.0, mode='bilinear', align_corners=True)
+        else:
+            self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
+
+        self.conv = double_conv(in_ch, out_ch)
+
+    def forward(self, x1, x2):
+        x1 = self.up(x1)
+
+        # input is CHW
+        diffY = x2.size()[2] - x1.size()[2]
+        diffX = x2.size()[3] - x1.size()[3]
+
+        x1 = F.pad(x1, (diffX // 2, diffX - diffX//2,
+                        diffY // 2, diffY - diffY//2))
+
+        # for padding issues, see 
+        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
+        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
+
+        x = torch.cat([x2, x1], dim=1)
+        x = self.conv(x)
+        return x
+
+
+class outconv(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super(outconv, self).__init__()
+        self.conv = nn.Conv2d(in_ch, out_ch, 1)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return x