More Pythonic Syntax

train.py

@@ -112,37 +112,40 @@ def train_net(net,
 
                 # Evaluation round
                 division_step = (n_train // (10 * batch_size))
-                if division_step > 0:
-                    if global_step % division_step == 0:
-                        histograms = {}
-                        for tag, value in net.named_parameters():
-                            tag = tag.replace('/', '.')
-                            histograms['Weights/' + tag] = wandb.Histogram(value.data.cpu())
-                            histograms['Gradients/' + tag] = wandb.Histogram(value.grad.data.cpu())
-
-                        val_score = evaluate(net, val_loader, device)
-                        scheduler.step(val_score)
-
-                        logging.info('Validation Dice score: {}'.format(val_score))
-                        experiment.log({
-                            'learning rate': optimizer.param_groups[0]['lr'],
-                            'validation Dice': val_score,
-                            'images': wandb.Image(images[0].cpu()),
-                            'masks': {
-                                'true': wandb.Image(true_masks[0].float().cpu()),
-                                'pred': wandb.Image(masks_pred.argmax(dim=1)[0].float().cpu()),
-                            },
-                            'step': global_step,
-                            'epoch': epoch,
-                            **histograms
-                        })
+
+                if division_step > 0 and global_step % division_step == 0:
+                    histograms = {}
+                    for tag, value in net.named_parameters():
+                        tag = tag.replace('/', '.')
+                        histograms[f'Weights/{tag}'] = wandb.Histogram(value.data.cpu())
+                        histograms[f'Gradients/{tag}'] = wandb.Histogram(value.grad.data.cpu())
+
+                    val_score = evaluate(net, val_loader, device)
+                    scheduler.step(val_score)
+
+                    logging.info(f'Validation Dice score: {val_score}')
+                    experiment.log({
+                        'learning rate': optimizer.param_groups[0]['lr'],
+                        'validation Dice': val_score,
+                        'images': wandb.Image(images[0].cpu()),
+                        'masks': {
+                            'true': wandb.Image(true_masks[0].float().cpu()),
+                            'pred': wandb.Image(masks_pred.argmax(dim=1)[0].float().cpu()),
+                        },
+                        'step': global_step,
+                        'epoch': epoch,
+                        **histograms
+                    })
+
 
         if save_checkpoint:
             Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
+
             torch.save(net.state_dict(), str(dir_checkpoint / 'checkpoint_epoch{}.pth'.format(epoch)))
             logging.info(f'Checkpoint {epoch} saved!')
 
 
+
 def get_args():
     parser = argparse.ArgumentParser(description='Train the UNet on images and target masks')
     parser.add_argument('--epochs', '-e', metavar='E', type=int, default=5, help='Number of epochs')

unet/unet_model.py

@@ -32,5 +32,4 @@ def forward(self, x):
         x = self.up2(x, x3)
         x = self.up3(x, x2)
         x = self.up4(x, x1)
-        logits = self.outc(x)
-        return logits
+        return self.outc(x)

utils/data_loading.py

@@ -56,7 +56,7 @@ def load(filename):
     def __getitem__(self, idx):
         name = self.ids[idx]
         mask_file = list(self.masks_dir.glob(name + self.mask_suffix + '.*'))
-        img_file = list(self.images_dir.glob(name + '.*'))
+        img_file = list(self.images_dir.glob(f'{name}.*'))
 
         assert len(img_file) == 1, f'Either no image or multiple images found for the ID {name}: {img_file}'
         assert len(mask_file) == 1, f'Either no mask or multiple masks found for the ID {name}: {mask_file}'

utils/dice_score.py

@@ -17,18 +17,26 @@ def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False,
         return (2 * inter + epsilon) / (sets_sum + epsilon)
     else:
         # compute and average metric for each batch element
-        dice = 0
-        for i in range(input.shape[0]):
-            dice += dice_coeff(input[i, ...], target[i, ...])
+        dice = sum(
+            dice_coeff(input[i, ...], target[i, ...])
+            for i in range(input.shape[0])
+        )
+
         return dice / input.shape[0]
 
 
 def multiclass_dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):
     # Average of Dice coefficient for all classes
     assert input.size() == target.size()
-    dice = 0
-    for channel in range(input.shape[1]):
-        dice += dice_coeff(input[:, channel, ...], target[:, channel, ...], reduce_batch_first, epsilon)
+    dice = sum(
+        dice_coeff(
+            input[:, channel, ...],
+            target[:, channel, ...],
+            reduce_batch_first,
+            epsilon,
+        )
+        for channel in range(input.shape[1])
+    )
 
     return dice / input.shape[1]
 

utils/utils.py

@@ -11,7 +11,7 @@ def plot_img_and_mask(img, mask):
             ax[i + 1].set_title(f'Output mask (class {i + 1})')
             ax[i + 1].imshow(mask[i, :, :])
     else:
-        ax[1].set_title(f'Output mask')
+        ax[1].set_title('Output mask')
         ax[1].imshow(mask)
     plt.xticks([]), plt.yticks([])
     plt.show()