Replace usage of copy.deepcopy() in Computer Vision Transfer Learning…

… Tutorial, resolves issue #2332 (#2404)

beginner_source/transfer_learning_tutorial.py

@@ -46,7 +46,7 @@
 import matplotlib.pyplot as plt
 import time
 import os
-import copy
+from tempfile import TemporaryDirectory
 
 cudnn.benchmark = True
 plt.ion()   # interactive mode
@@ -146,67 +146,71 @@ def imshow(inp, title=None):
 def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
     since = time.time()
 
-    best_model_wts = copy.deepcopy(model.state_dict())
-    best_acc = 0.0
-
-    for epoch in range(num_epochs):
-        print(f'Epoch {epoch}/{num_epochs - 1}')
-        print('-' * 10)
-
-        # Each epoch has a training and validation phase
-        for phase in ['train', 'val']:
-            if phase == 'train':
-                model.train()  # Set model to training mode
-            else:
-                model.eval()   # Set model to evaluate mode
-
-            running_loss = 0.0
-            running_corrects = 0
-
-            # Iterate over data.
-            for inputs, labels in dataloaders[phase]:
-                inputs = inputs.to(device)
-                labels = labels.to(device)
-
-                # zero the parameter gradients
-                optimizer.zero_grad()
-
-                # forward
-                # track history if only in train
-                with torch.set_grad_enabled(phase == 'train'):
-                    outputs = model(inputs)
-                    _, preds = torch.max(outputs, 1)
-                    loss = criterion(outputs, labels)
-
-                    # backward + optimize only if in training phase
-                    if phase == 'train':
-                        loss.backward()
-                        optimizer.step()
-
-                # statistics
-                running_loss += loss.item() * inputs.size(0)
-                running_corrects += torch.sum(preds == labels.data)
-            if phase == 'train':
-                scheduler.step()
-
-            epoch_loss = running_loss / dataset_sizes[phase]
-            epoch_acc = running_corrects.double() / dataset_sizes[phase]
-
-            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
-
-            # deep copy the model
-            if phase == 'val' and epoch_acc > best_acc:
-                best_acc = epoch_acc
-                best_model_wts = copy.deepcopy(model.state_dict())
-
-        print()
-
-    time_elapsed = time.time() - since
-    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
-    print(f'Best val Acc: {best_acc:4f}')
-
-    # load best model weights
-    model.load_state_dict(best_model_wts)
+    # Create a temporary directory to save training checkpoints
+    with TemporaryDirectory() as tempdir:
+        best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')
+
+        torch.save(model.state_dict(), best_model_params_path)
+        best_acc = 0.0
+
+        for epoch in range(num_epochs):
+            print(f'Epoch {epoch}/{num_epochs - 1}')
+            print('-' * 10)
+
+            # Each epoch has a training and validation phase
+            for phase in ['train', 'val']:
+                if phase == 'train':
+                    model.train()  # Set model to training mode
+                else:
+                    model.eval()   # Set model to evaluate mode
+
+                running_loss = 0.0
+                running_corrects = 0
+
+                # Iterate over data.
+                for inputs, labels in dataloaders[phase]:
+                    inputs = inputs.to(device)
+                    labels = labels.to(device)
+
+                    # zero the parameter gradients
+                    optimizer.zero_grad()
+
+                    # forward
+                    # track history if only in train
+                    with torch.set_grad_enabled(phase == 'train'):
+                        outputs = model(inputs)
+                        _, preds = torch.max(outputs, 1)
+                        loss = criterion(outputs, labels)
+
+                        # backward + optimize only if in training phase
+                        if phase == 'train':
+                            loss.backward()
+                            optimizer.step()
+
+                    # statistics
+                    running_loss += loss.item() * inputs.size(0)
+                    running_corrects += torch.sum(preds == labels.data)
+                if phase == 'train':
+                    scheduler.step()
+
+                epoch_loss = running_loss / dataset_sizes[phase]
+                epoch_acc = running_corrects.double() / dataset_sizes[phase]
+
+                print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
+
+                # deep copy the model
+                if phase == 'val' and epoch_acc > best_acc:
+                    best_acc = epoch_acc
+                    torch.save(model.state_dict(), best_model_params_path)
+
+            print()
+
+        time_elapsed = time.time() - since
+        print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
+        print(f'Best val Acc: {best_acc:4f}')
+
+        # load best model weights
+        model.load_state_dict(torch.load(best_model_params_path))
     return model