Adding refine.py to 4.22Lite

4.22Lite/train_log/refine.py

@@ -0,0 +1,90 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.optim import AdamW
+import torch.optim as optim
+import itertools
+from model.warplayer import warp
+from torch.nn.parallel import DistributedDataParallel as DDP
+import torch.nn.functional as F
+
+device = torch.device("cuda")
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.LeakyReLU(0.2, True)
+        )
+
+def conv_woact(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        )
+
+def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
+    return nn.Sequential(
+        torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True),
+        nn.LeakyReLU(0.2, True)
+        )
+            
+class Conv2(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=2):
+        super(Conv2, self).__init__()
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        return x
+    
+c = 16
+class Contextnet(nn.Module):
+    def __init__(self):
+        super(Contextnet, self).__init__()
+        self.conv1 = Conv2(3, c)
+        self.conv2 = Conv2(c, 2*c)
+        self.conv3 = Conv2(2*c, 4*c)
+        self.conv4 = Conv2(4*c, 8*c)
+    
+    def forward(self, x, flow):
+        x = self.conv1(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f1 = warp(x, flow)        
+        x = self.conv2(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f2 = warp(x, flow)
+        x = self.conv3(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f3 = warp(x, flow)
+        x = self.conv4(x)
+        flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
+        f4 = warp(x, flow)
+        return [f1, f2, f3, f4]
+    
+class Unet(nn.Module):
+    def __init__(self):
+        super(Unet, self).__init__()
+        self.down0 = Conv2(17, 2*c)
+        self.down1 = Conv2(4*c, 4*c)
+        self.down2 = Conv2(8*c, 8*c)
+        self.down3 = Conv2(16*c, 16*c)
+        self.up0 = deconv(32*c, 8*c)
+        self.up1 = deconv(16*c, 4*c)
+        self.up2 = deconv(8*c, 2*c)
+        self.up3 = deconv(4*c, c)
+        self.conv = nn.Conv2d(c, 3, 3, 1, 1)
+
+    def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
+        s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
+        s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
+        s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
+        s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
+        x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
+        x = self.up1(torch.cat((x, s2), 1)) 
+        x = self.up2(torch.cat((x, s1), 1)) 
+        x = self.up3(torch.cat((x, s0), 1)) 
+        x = self.conv(x)
+        return torch.sigmoid(x)