mrnet now ready

.gitignore

@@ -5,4 +5,6 @@ data/masks/*
 data/output/*
 data/train_imgs/*
 data/train_masks/*
+data/train_imgs_32/*
 .ipynb_checkpoints/
+runs

mrnet/mrnet_module.py

@@ -14,7 +14,7 @@ class MultiUnet(nn.Module):
         self.bilinear = bilinear
 
         self.inconv = nn.Sequential(
-            nn.Conv2d(n_channels, 8, kernel_size = 3, stride = 1, padding_mode = 'same'),
+            nn.Conv2d(n_channels, 8, kernel_size = 3, stride = 1, padding = 1),
             nn.BatchNorm2d(8),
             nn.ReLU(inplace = True)
         )
@@ -41,7 +41,7 @@ class MultiUnet(nn.Module):
 
         self.pool = nn.MaxPool2d(2)
 
-        self.outconv = nn.Conv2d(self.res9.outc, n_classes, kernel_size = 3, padding_mode = 'same')
+        self.outconv = nn.Conv2d(self.res9.outc, n_classes, kernel_size = 3, padding = 1)
 
     def forward(self, x):
         x = self.inconv(x)

mrnet/mrnet_parts.py

@@ -16,8 +16,8 @@ class MultiResBlock(nn.Module):
 
     def conv(self, in_channel, out_channel, kernel_size):
         return nn.Sequential(
-            nn.Conv2d(in_channels = in_channel, out_channels = out_channel, kernel_size = kernel_size, stride = 1,padding = 1,
-                      padding_mode = 'same'), nn.BatchNorm2d(out_channel), nn.ReLU(inplace = True))
+            nn.Conv2d(in_channels = in_channel, out_channels = out_channel, kernel_size = kernel_size, stride = 1,padding = 1),
+            nn.BatchNorm2d(out_channel), nn.ReLU(inplace = True))
 
     def forward(self, x):
         shortcut = nn.Sequential(
@@ -41,9 +41,9 @@ class TransCompose(nn.Module):
         self.inc = in_channels
         self.outc = out_channels
 
-    def forward(self,x):
+    def forward(self, x):
         return nn.Sequential(
-            nn.ConvTranspose2d(in_channels = self.inc, out_channels = self.outc, kernel_size = 3, stride = 2),
+            nn.ConvTranspose2d(in_channels = self.inc, out_channels = self.outc, kernel_size = 2, stride = 2),
             nn.BatchNorm2d(self.outc)
         )(x)
 
@@ -61,7 +61,8 @@ class ResPath(nn.Module):
             nn.BatchNorm2d(self.outc))(x)
         conv = nn.Sequential(
             nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1, padding = 1),
-            nn.BatchNorm2d(out_channels), nn.ReLU(inplace = True))(x)
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace = True))(x)
 
         result = torch.add(conv, shortcut)
         return nn.Sequential(nn.ReLU(inplace = True), nn.BatchNorm2d(out_channels))(result)

mrnet/train.py

@@ -18,7 +18,7 @@ dir_mask = 'data/train_masks/'
 dir_checkpoint = 'checkpoint'
 
 
-def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0.1, save_cp = True, img_scale = 0.5):
+def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0.1, img_scale = 0.5):
     dataset = BasicDataset(dir_img, dir_mask, img_scale)
     n_val = int(len(dataset) * val_percent)
     n_train = len(dataset) - n_val
@@ -27,7 +27,7 @@ def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0
     val_loader = DataLoader(val, batch_size = batch_size, shuffle = False, num_workers = 8, pin_memory = True)
 
     optimizer = optim.Adam(net.parameters(), lr = lr)
-    criterion = nn.BCELoss()
+    criterion = nn.BCEWithLogitsLoss()
 
     for epoch in tqdm(range(epochs)):
         net.train()

train.py

@@ -40,14 +40,13 @@ if __name__ == '__main__':
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     logging.info(f'Using device {device}')
 
-    # Change here to adapt to your data
     # n_channels=3 for RGB images
     # n_classes is the number of probabilities you want to get per pixel
     #   - For 1 class and background, use n_classes=1
     #   - For 2 classes, use n_classes=1
     #   - For N > 2 classes, use n_classes=N
 
-    #net = UNet(n_channels = 1, n_classes = 1)
+    # net = UNet(n_channels = 1, n_classes = 1)
     net = MultiUnet(n_channels = 1, n_classes = 1)
 
     logging.info(f'Network:\n'
@@ -64,7 +63,7 @@ if __name__ == '__main__':
     # cudnn.benchmark = True
 
     try:
-        mrnet.train_net(net = net, device=device, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr,
+        mrnet.train_net(net = net, device = device, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr,
                         img_scale = args.scale, val_percent = args.val / 100)
     except KeyboardInterrupt:
         torch.save(net.state_dict(), 'INTERRUPTED.pth')

utils/dataset.py

@@ -23,11 +23,10 @@ class BasicDataset(Dataset):
         return len(self.ids)
 
     @classmethod
-    def preprocess(cls, pil_img, scale):
-        w, h = pil_img.size
-        newW, newH = 256,256 #int(scale * w), int(scale * h)
-        assert newW > 0 and newH > 0, 'Scale is too small'
-        pil_img = pil_img.resize((newW, newH))
+    def preprocess(cls, pil_img):
+        #newW, newH = 256,256 #int(scale * w), int(scale * h)
+        #assert newW > 0 and newH > 0, 'Scale is too small'
+        pil_img = pil_img.resize((256, 256))
 
         img_nd = np.array(pil_img)
 
@@ -56,7 +55,7 @@ class BasicDataset(Dataset):
         assert img.size == mask.size, \
             f'Image and mask {idx} should be the same size, but are {img.size} and {mask.size}'
 
-        img = self.preprocess(img, self.scale)
-        mask = self.preprocess(mask, self.scale)
+        img = self.preprocess(img)
+        mask = self.preprocess(mask)
 
         return {'image': torch.from_numpy(img), 'mask': torch.from_numpy(mask)}