TypeError: conv2d() received an invalid combination of arguments

Question

everyone! I am working on an object detection project using the VGG network in the PASCAL VOC dataset. I used custom dataset loading to load the PASCAL VOC dataset. And coded network from scratch. (They are similar to PyTorch's Vision method).

Currently, I'm getting the following error:

Traceback (most recent call last):
  File "/home/khushi/Documents/deep-learning/benchmarking-deep-neural-networks/vgg/main.py", line 59, in <module>
    main()
  File "/home/khushi/Documents/deep-learning/benchmarking-deep-neural-networks/vgg/main.py", line 55, in main
    train(data, model, num_epochs, criteria, optimizer)
  File "/home/khushi/Documents/deep-learning/benchmarking-deep-neural-networks/vgg/main.py", line 21, in train
    outputs = model(image)
  File "/home/khushi/.local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/khushi/Documents/deep-learning/benchmarking-deep-neural-networks/vgg/vgg_torch.py", line 39, in forward
    x = self.features(x)
  File "/home/khushi/.local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/khushi/.local/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward
    input = module(input)
  File "/home/khushi/.local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/khushi/.local/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 446, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/khushi/.local/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 442, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
TypeError: conv2d() received an invalid combination of arguments - got (Image, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (Image, Parameter, Parameter, tuple, tuple, tuple, int)
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (Image, Parameter, Parameter, tuple, tuple, tuple, int)

The code I am implementing:

import vgg_torch
import voc_loader

import time
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

num_epochs = 5
learning_rate = 0.01

# https://github.com/khushi-411/tutorials/pytorch
def train(data, model, num_epochs, criteria, optimizer):
    steps = len(data)
    for epochs in range(num_epochs):
        for i, (image, target) in enumerate(data):
            # forward pass
            # https://stackoverflow.com/questions/57237381
            #outputs = model(image[None, ...])
            outputs = model(image)
            loss = criteria(outputs, target)

            # backward pass and optimization
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            if (i+1) % 100 == 0:
                    print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epochs+1, num_epochs, i+1, steps, loss.item()))

def main():
    # give absolute path to dataset
    # https://stackoverflow.com/questions/56741108
    data = voc_loader.VOCDetection('/home/khushi/Documents/deep-learning/datasets/pascal-voc/')
    """, 
            transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.CenterCrop(224),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
            ])
        )
    """
    # Load model: vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn
    model = vgg_torch.vgg11()
    print(model)
    
    # Loss function and optimizer
    criteria = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    # training
    start = time.time()
    train(data, model, num_epochs, criteria, optimizer)
    print("Total time taken to train: ", time.time() - start)

if __name__ == "__main__":
    main()

Dependencies

• PyTorch: 1.10.0+cu102
• OS: Manjaro Distro
• RAM: 16GB

Will anyone please help me out to resolve this error? Thanks!