Helllo @ptrblck . I trained Resnet50 on my dataset. At the end of training I got following error. Can you please let me know for solving this issue?
Thank you

Screenshot 2022-01-25 at 15.24.34744×512 42.5 KB

This is my code

Just normalization for validation
data_transforms = {
‘train’: transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
‘val’: transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
 'test': transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),

}
data_dir = ‘/content/drive/MyDrive/raphcatr’
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in [‘train’, ‘val’, ‘test’]}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=16,
shuffle=True, num_workers=4)
for x in [‘train’, ‘val’, ‘test’]}
dataset_sizes = {x: len(image_datasets[x]) for x in [‘train’, ‘val’, ‘test’]}
class_names = image_datasets[‘train’].classes
device = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”)
def train_model(model, criterion, optimizer,  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('Epoch {}/{}'.format(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)
        

        epoch_loss = running_loss / dataset_sizes[phase]
        epoch_acc = running_corrects.double() / dataset_sizes[phase]

        print('{} Loss: {:.4f} Acc: {:.4f}'.format(
            phase, epoch_loss, epoch_acc))

        # 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('Training complete in {:.0f}m {:.0f}s'.format(
    time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))

# load best model weights
model.load_state_dict(best_model_wts)
return model

model = models.resnet50(pretrained=True)
# Freeze training for all “features” layers
for param in model.parameters():
param.requires_grad = False
classifier = nn.Sequential(
nn.Linear(2048,512),
nn.ReLU(),
nn.Dropout(p=0.5),
nn.Linear(512, 3))
model.fc = classifier
class_weights = torch.FloatTensor([1./127, 1./141, 1./257]) * 100
criterion = nn.CrossEntropyLoss(weight = class_weights).to(device)

Only train the classifier parameters, feature parameters are frozen
optimizer = optim.Adam(model.fc.parameters(), lr=0.0002)
from PIL import Image, ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES=True
resnet_model, history = train_model(model, criterion, optimizer,
num_epochs=25)