import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torchvision import datasets, models, transforms
import time
import copy

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('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'test']:
            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.
#*******************error came here***********************#
            for inputs, labels in dataloaders[phase]:
                print("---")
                # print("---", inputs, "---", labels)
                # print("---", inputs)
                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('{} 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

if __name__ == '__main__':
    nclass = 100
    # Data augmentation and normalization for training
    # Just normalization for validation
    data_transforms = {
        'train': transforms.Compose([
            [transforms.ToTensor(),
             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
        ]),
        'test': transforms.Compose([
            [transforms.ToTensor(),
             transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
        ]),
    }

    image_datasets = {x: datasets.CIFAR100(root='../data', train = ((lambda t:True if t =='train' else False)(x)),
                                              download=True, transform=data_transforms[x])
                      for x in ['train', 'test']}
    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                                 shuffle=True, num_workers=4)
                  for x in ['train', 'test']}
    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}
    class_names = image_datasets['train'].classes
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(device)
    ###############feature extractor#################
    model_conv = models.resnet18(pretrained=True)
    for param in model_conv.parameters():
        param.requires_grad = False

    # Parameters of newly constructed modules have requires_grad=True by default
    num_ftrs = model_conv.fc.in_features
    model_conv.fc = nn.Linear(num_ftrs, nclass)

    model_conv = model_conv.to(device)

    criterion = nn.CrossEntropyLoss()

    # Observe that only parameters of final layer are being optimized as
    # opposed to before.
    optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)

    # Decay LR by a factor of 0.1 every 7 epochs
    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)
    model_conv = train_model(model_conv, criterion, optimizer_conv,
                             exp_lr_scheduler, num_epochs=25)




image1067×742 99.7 KB

I’m using pretrained resnet-18 to train cifar-100.I have no idea why there came an error.![image|626x253]
image905×247 22.4 KB