rm wrong file

edit net a bit

DataLoader.py

@@ -80,17 +80,17 @@ def get_image_loader(path: str, precision: np.float32 or np.float64):
                                  transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
                                                                      transforms.RandomVerticalFlip()]),
                                  precision=precision)
-
-    image_dataset_augmented = ImageDataset(path,
-                                           offsetrange=(0, 8),
-                                           spacingrange=(2, 6),
-                                           transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
-                                                                               transforms.RandomVerticalFlip(),
-                                                                               transforms.GaussianBlur(3, 4)]),
-                                           precision=precision)
+    #
+    # image_dataset_augmented = ImageDataset(path,
+    #                                        offsetrange=(0, 8),
+    #                                        spacingrange=(2, 6),
+    #                                        transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
+    #                                                                            transforms.RandomVerticalFlip(),
+    #                                                                            transforms.GaussianBlur(3, 4)]),
+    #                                        precision=precision)
 
     # merge different datasets here!
-    merged_dataset = torch.utils.data.ConcatDataset([image_dataset, image_dataset_augmented])
+    merged_dataset = torch.utils.data.ConcatDataset([image_dataset])
 
     totlen = len(merged_dataset)
     test_set_size = .1

ImageImpaint.py

@@ -48,13 +48,22 @@ def train_model():
         print(f"Epoch {epoch}/{n_epochs}\n")
         i = 0
         for input_tensor, mask, target_tensor in train_loader:
+            input_tensor = input_tensor.to(device)
+            mask = mask.to(device)
+            target_tensor = target_tensor.to(device)
+
             optimizer.zero_grad()  # reset gradients
             input_tensor = torch.cat((input_tensor, mask), 1)
 
-            output = nn(input_tensor.to(device))  # get model output (forward pass)
+            output = nn(input_tensor)  # get model output (forward pass)
 
-            loss = loss_function(output.to(device),
-                                 target_tensor.to(device))  # compute loss given model output and true target
+            output_flat = output * (1 - mask)
+            output_flat = output_flat[1 - mask > 0]
+
+            rest = target_tensor * (1 - mask)
+            rest = rest[1 - mask > 0]
+
+            loss = loss_function(output_flat, rest)  # compute loss given model output and true target
             loss.backward()  # compute gradients (backward pass)
             optimizer.step()  # perform gradient descent update step
 
@@ -101,7 +110,14 @@ def eval_model(model: torch.nn.Module, dataloader: torch.utils.data.DataLoader,
 
             input = torch.cat((input, mask), 1)
             out = model(input)
-            loss += loss_fn(out, target).item()
+
+            out = out * (1 - mask)
+            out = out[1 - mask > 0]
+
+            rest = target * (1 - mask)
+            rest = rest[1 - mask > 0]
+
+            loss += loss_fn(out, rest).item()
             print(f'\rEval prog[{i}/{len(dataloader) * dataloader.batch_size}]', end='')
             i += dataloader.batch_size
     print()
@@ -112,9 +128,9 @@ def eval_model(model: torch.nn.Module, dataloader: torch.utils.data.DataLoader,
 def plot(inputs, targets, predictions, path, update, epoch):
     """Plotting the inputs, targets and predictions to file `path`"""
     os.makedirs(path, exist_ok=True)
-    fig, axes = plt.subplots(ncols=3, figsize=(15, 5))
+    fig, axes = plt.subplots(ncols=4, figsize=(15, 5))
 
-    for ax, data, title in zip(axes, [inputs, targets, predictions], ["Input", "Target", "Prediction"]):
+    for ax, data, title in zip(axes, [inputs, targets, predictions, predictions-targets], ["Input", "Target", "Prediction", "diff"]):
         ax.clear()
         ax.set_title(title)
         ax.imshow(DataLoader.postprocess(np.transpose(data[:3, :, :], (1, 2, 0))), interpolation="none")

Net.py

@@ -7,18 +7,24 @@ from torch import nn
 
 class ImageNN(torch.nn.Module):
     def __init__(self, precision: np.float32 or np.float64, n_in_channels: int = 1, n_hidden_layers: int = 3,
-                 n_kernels: int = 32, kernel_size: int = 7):
+                 n_kernels: int = 32, kernel_size: int = 9):
         """Simple CNN with `n_hidden_layers`, `n_kernels`, and `kernel_size` as hyperparameters"""
         super().__init__()
 
+        ksize = kernel_size
+
         cnn = []
         for i in range(n_hidden_layers):
             cnn.append(torch.nn.Conv2d(
                 in_channels=n_in_channels,
                 out_channels=n_kernels,
-                kernel_size=kernel_size,
-                padding=int(kernel_size / 2)
+                kernel_size=ksize,
+                padding=int(ksize / 2),
+                padding_mode="replicate"
             ))
+
+            kernel_size -= 2
+
             cnn.append(torch.nn.ReLU())
             n_in_channels = n_kernels
         self.hidden_layers = torch.nn.Sequential(*cnn)

netio.py

@@ -36,7 +36,10 @@ def eval_evalset():
         piclen = len(b['input_arrays'])
         for input_array, known_array in zip(b['input_arrays'], b['known_arrays']):
             input_array = DataLoader.preprocess(input_array, precision=np.float32)
-            input_tensor = torch.cat((torch.from_numpy(input_array), torch.from_numpy(known_array)), 0)
+
+            input_array = np.expand_dims(input_array, 0)
+            known_array = np.expand_dims(known_array, 0)
+            input_tensor = torch.cat((torch.from_numpy(input_array), torch.from_numpy(known_array)), 1)
             out = model(input_tensor)
             out = DataLoader.postprocess(out.cpu().detach().numpy())