Hi, I’m new to pytorch, I have read the pytorch tutorials and doc document, So I try to program some code to test. But there is a error that my “input” doesn’t have the same dimension as tensor for “result”, I’ve tried to change the size  of input and reduce the kernel_size but none of them work.
here is my code, I use cifar10 dataset and make a AlexNet model.
import torch.nn as nn
import torch.nn.functional as F 
import argparse
import torch
import torch.optim as optim
from torchvision import datasets, transforms

class alexnet(nn.Module):

    def __init__(self):
        super(alexnet,self).__init__()
    
        self.conv_block1 = nn.Sequential(
            nn.Conv2d(3,96,kernel_size=11,stride=4),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3,2,ceil_mode=True),
            nn.BatchNorm2d(96,affine=False),
        )

        self.conv_block2 = nn.Sequential(
            nn.Conv2d(96,256,kernel_size=11,padding=2,groups=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3,2,ceil_mode=True),
            nn.BatchNorm2d(256,affine=False),
        )

        self.conv_block3 = nn.Sequential(
            nn.Conv2d(256,384,kernel_size=3,padding=1),
            nn.ReLU(inplace=True),
        )

        self.conv_block4 = nn.Sequential(
            nn.Conv2d(384,384,kernel_size=3,padding=1,groups=2),
            nn.ReLU(inplace=True),
        )

        self.conv_block5 = nn.Sequential(
            nn.Conv2d(384,256,kernel_size=3,padding=1,groups=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3,2),
        )

        self.classifier = nn.Sequential(
            nn.Linear(6*6*256,4096),
            nn.ReLU(True),
            nn.Linear(4096,4096),
            nn.ReLU(True),
            nn.Dropout(),
            nn.Linear(4096,10),
        )

    def forward(self,x):

        conv1 = self.conv_block1(x)
        conv2 = self.conv_block2(conv1)
        conv3 = self.conv_block3(conv2)
        conv4 = self.conv_block4(conv3)
        conv5 = self.conv_block5(conv4)
        score = self.classifier(conv5)

        return score

def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interbal == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()
            ))

def test(args, model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, size_average=False).item()
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    print ('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} (:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)
    ))

def main():
    parser = argparse.ArgumentParser(description='PyTorch AlexNet Try')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    args = parser.parse_args()

    if torch.cuda.is_available():    
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")
    
    torch.manual_seed(args.seed)

    kwargs = {'num_workers': 1, 'pin_memory': True}

    transform = transforms.Compose(
        [
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ])

    train_loader = torch.utils.data.DataLoader(
        datasets.CIFAR10('./alextry', train=True, download=True,
                        transform=transform), 
        batch_size = args.batch_size, shuffle=True, **kwargs
    )
    test_loader = torch.utils.data.DataLoader(
        datasets.CIFAR10('./alextry', train=False, download=True,
                        transform=transform),
        batch_size=args.test_batch_size, shuffle=True, **kwargs
    )

    model = alexnet().to(device)
    optimizer = optim.SGD(model.parameters(), lr = args.lr, momentum=args.momentum)

    for epoch in range(1, args.epochs+1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(args, model, device, test_loader)


if __name__ == '__main__':
    main()

thanks for your help