Hi,
I need to freeze everything except the last layer.
I do this:
for param in model.parameters():
        param.requires_grad = False
    # Replace the last fully-connected layer
    # Parameters of newly constructed modules have requires_grad=True by default
    model.fc = nn.Linear(64, 10)

But i have this error:
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn.
Another thing is that I want to use this code or something similar, since I also need to freeze the layers I want for other tests.
https://spandan-madan.github.io/A-Collection-of-important-tasks-in-pytorch/ 1
The problem is that when I use the children function I have like all the layers put in Resnet () and therefore it only returns 1 child, and in the link it returns 9 childrens.
My net:
children 0 is -
ResNet(
  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (1): BasicBlock(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (2): BasicBlock(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (3): BasicBlock(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (4): BasicBlock(
      (conv1): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): LambdaLayer()
    )
    (1): BasicBlock(
      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (2): BasicBlock(
      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (3): BasicBlock(
      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (4): BasicBlock(
      (conv1): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): LambdaLayer()
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (2): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (3): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
    (4): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (shortcut): Sequential()
    )
  )
  (linear): Linear(in_features=64, out_features=10, bias=True)
)

Link’s net:
child 0 is -
Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
 child 1 is -
BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
 child 2 is -
ReLU (inplace)
 child 3 is -
MaxPool2d (size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1))
 child 4 is -
Sequential (
  (0): BasicBlock (
    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
  )
  (1): BasicBlock (
    (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
  )
)
 child 5 is -
Sequential (
  (0): BasicBlock (
    (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    (downsample): Sequential (
      (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
      (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (1): BasicBlock (
    (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
  )
)
 child 6 is -
Sequential (
  (0): BasicBlock (
    (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    (downsample): Sequential (
      (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
      (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (1): BasicBlock (
    (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
  )
)
 child 7 is -
Sequential (
  (0): BasicBlock (
    (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
    (downsample): Sequential (
      (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
      (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
    )
  )
  (1): BasicBlock (
    (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
    (relu): ReLU (inplace)
    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
  )
)
 child 8 is -
AvgPool2d (
)
 child 9 is -
Linear (512 -> 1000)

Here  its my main code:
def main():
    global args, best_prec1
    args = parser.parse_args()
    loss_train_epoch = []
    loss_test_epoch = []
    acc_train_per_epoch = []
    acc_test_per_epoch = []


    # Check the save_dir exists or not
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    model = torch.nn.DataParallel(resnet.__dict__[args.arch]())
    model.cuda()

    checkpoint_path = "/content/drive/My Drive/CIFAR10_pruebas/pytorch_resnet_cifar10/save_resnet32/checkpoint3_1.th"

    # optionally resume from a checkpoint
    if checkpoint_path:
        if os.path.isfile(checkpoint_path):
            print("=> loading checkpoint '{}'".format(checkpoint_path))
            checkpoint = torch.load(checkpoint_path)
            #args.start_epoch = checkpoint['epoch']
            #best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            print("=> loaded checkpoint")# '{}' (epoch {})"
                  #.format(args.evaluate))#, checkpoint['epoch']))
            #print("BEST_ACC:",best_prec1)
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    for param in model.parameters():
        param.requires_grad = False
    # Replace the last fully-connected layer
    # Parameters of newly constructed modules have requires_grad=True by default
    model.fc = nn.Linear(64, 10)

    cudnn.benchmark = True

    train_set = CifarDataset('./datasets', train=True, CONFIG=CONFIG, imbalance_factor=CONFIG['DATASET']['IMBALANCE'])
    val_set = CifarDataset('./datasets', train=False, CONFIG=CONFIG)

    train_loader =  torch.utils.data.DataLoader(dataset=train_set,
                                     batch_size=args.batch_size,
                                     shuffle=True,
                                     num_workers=args.workers,
                                     pin_memory=True)
    val_loader =  torch.utils.data.DataLoader(dataset=val_set,
                                     batch_size=args.batch_size,
                                     shuffle=False,
                                     num_workers=args.workers,
                                     pin_memory=True)

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda()

    if args.half:
        model.half()
        criterion.half()

    optimizer = torch.optim.SGD(model.fc.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    #lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
    #                                                    milestones=[100, 150], last_epoch=args.start_epoch - 1)
    #lr_scheduler.last_epoch = args.start_epoch - 1   #CUANDO CARGUEMOS CHECKPOINT.

    if args.arch in ['resnet1202', 'resnet110']:
        # for resnet1202 original paper uses lr=0.01 for first 400 minibatches for warm-up
        # then switch back. In this setup it will correspond for first epoch.
        for param_group in optimizer.param_groups:
            param_group['lr'] = args.lr*0.1


    if args.evaluate:
        validate(val_loader, model, criterion)
        return

    for epoch in range(args.start_epoch, args.epochs):

        # train for one epoch
        print('current lr {:.5e}'.format(optimizer.param_groups[0]['lr']))
        loss_per_epoch, top1_train_ac = train(train_loader, model, criterion, optimizer, epoch)
        loss_train_epoch += [loss_per_epoch]
        #lr_scheduler.step()

        # evaluate on validation set
        loss_per_epoch_test, prec1 = validate(val_loader, model, criterion)

        loss_test_epoch += [loss_per_epoch_test]
        acc_train_per_epoch += [top1_train_ac]
        acc_test_per_epoch += [prec1]

        # remember best prec@1 and save checkpoint
        is_best = prec1 > best_prec1
        best_prec1 = max(prec1, best_prec1)

        if epoch > 0 and epoch % args.save_every == 0:
            save_checkpoint({
                'epoch': epoch + 1,
                'state_dict': model.state_dict(),
                'best_prec1': best_prec1,
            }, is_best, filename=os.path.join(args.save_dir, 'checkpoint4.th'))

        save_checkpoint({
            'epoch' : epoch,
            'state_dict': model.state_dict(),
            'best_prec1': best_prec1,
        }, is_best, filename=os.path.join(args.save_dir, 'model4.th'))

        # save losses:
        np.save(res_path + '/' + 'LOSS_epoch_train.npy', np.asarray(loss_train_epoch))
        np.save(res_path + '/' + 'LOSS_epoch_val.npy', np.asarray(loss_test_epoch))

        # save accuracies:
        np.save(res_path + '/' + 'accuracy_per_epoch_train.npy', np.asarray(acc_train_per_epoch))
        np.save(res_path + '/' + 'accuracy_per_epoch_val.npy', np.asarray(acc_test_per_epoch))


If someone could help me I would be very grateful