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model.py

@@ -218,10 +218,10 @@ class Net(nn.Module):
         self.conv4 = block(128, 256, (5, 5), (1, 1), (2, 2))
         self.conv4_1 = block(256, 256, (5, 5), (1, 1), (2, 2))
         self.conv5 = block(256, 128, (5, 5), (1, 1), (2, 2))
-        self.conv6 = block(128, 64, (3, 3), (1, 1), (1, 1))
+        self.conv6 = block(128, 64, (5, 5), (1, 1), (2, 2))
         self.conv7 = block(64, 32, (3, 3), (1, 1), (1, 1))
         self.conv7_1 = block(32, 16, (3, 3), (1, 1), (1, 1))
-        self.conv8 = nn.Conv2d(16, 3, (1, 1), (1, 1), (0, 0))
+        self.conv8 = nn.Conv2d(16, 3, (3, 3), (1, 1), (1, 1))
         #self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
         #self.relu = nn.ReLU(inplace=True)
         #self.upsample = nn.Upsample(scale_factor=2, mode='bicubic')

model_best.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:602198d11d0bbdc5140d258de0d027dbd48f0e82ed567857633a6f4aedbb7164
-size 67949739
+oid sha256:5053bfccec092aab411a17544cd048bf7f6ec15247b4aa0bf3bc9ed32d37a8b0
+size 69527211