I am currently doing this 11. I really don’t know much about pytorch so I would like as much help as possible. The error is in the end. I am also using a Raspberry Pi.
These are the codes:
main.py:
import torch.utils.data
import torchvision.datasets as datasets

from StyleCNN import *
from utils import *

# CUDA Configurations
dtype = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor

# Content and style
style = image_loader("styles/starry_night.jpg").type(dtype)
#content = image_loader("contents/result.jpg").type(dtype)

#pastiche = image_loader("contents/result.jpg").type(dtype)
#pastiche.data = torch.randn(pastiche.data.size()).type(dtype)

num_epochs = 3
N = 4

def main():
    style_cnn = StyleCNN(style)

    # Contents
    coco = datasets.ImageFolder(root='data', transform=loader)
    content_loader = torch.utils.data.DataLoader(coco, batch_size=N, shuffle=True)

    for epoch in range(num_epochs):
        for i, content_batch in enumerate(content_loader):
          iteration = epoch * i + i
          content_loss, style_loss, pastiches = style_cnn.train(content_batch)

          if i % 10 == 0:
              print("Iteration: %d" % (iteration))
              print("Content loss: %f" % (content_loss.data[0]))
              print("Style loss: %f" % (style_loss.data[0]))

          if i % 500 == 0:
              path = "outputs/%d_" % (iteration)
              paths = [path + str(n) + ".png" for n in range(N)]
              save_images(pastiches, paths)

              path = "outputs/content_%d_" % (iteration)
              paths = [path + str(n) + ".png" for n in range(N)]
              save_images(content_batch, paths)
              style_cnn.save()

main()

StyleCNN.py:
import torch.optim as optim
import torchvision.models as models

from GramMatrix import *

class StyleCNN(object):
    def __init__(self, style):
        super(StyleCNN, self).__init__()
        
        self.style = style
        
        self.content_layers = ['conv_4']
        self.style_layers = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
        self.content_weight = 1
        self.style_weight = 1000
        
        self.transform_network = nn.Sequential(nn.ReflectionPad2d(40),
                            nn.Conv2d(3, 32, 9, stride=1, padding=4),
                            nn.Conv2d(32, 64, 3, stride=2, padding=1),
                            nn.Conv2d(64, 128, 3, stride=2, padding=1),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.Conv2d(128, 128, 3, stride=1, padding=0),
                            nn.ConvTranspose2d(128, 64, 3, stride=2, padding=1, output_padding=1),
                            nn.ConvTranspose2d(64, 32, 3, stride=2, padding=1, output_padding=1),
                            nn.Conv2d(32, 3, 9, stride=1, padding=4),
                            )
        self.gram = GramMatrix()
        self.loss = nn.MSELoss()
        self.optimizer = optim.Adam(self.transform_network.parameters(), lr=1e-3)
    
        self.use_cuda = torch.cuda.is_available()
        if self.use_cuda:
            self.loss_network.cuda()
            self.gram.cuda()


    def train(self, content):
        self.optimizer.zero_grad()

        content = content
        style = self.style
        pastiche = self.transform_network.forward(content)

        content_loss = 0
        style_loss = 0

        i = 1
        not_inplace = lambda layer: nn.ReLU(inplace=False) if isinstance(layer, nn.ReLU) else layer
        for layer in list(self.loss_network.features):
            layer = not_inplace(layer)
            if self.use_cuda:
                layer.cuda()

            pastiche, content, style = layer.forward(pastiche), layer.forward(content), layer.forward(style)

            if isinstance(layer, nn.Conv2d):
                name = "conv_" + str(i)

                if name in self.content_layers:
                    content_loss += self.loss(pastiche * self.content_weight, content.detach() * self.content_weight)
                if name in self.style_layers:
                    pastiche_g, style_g = self.gram.forward(pastiche), self.gram.forward(style)
                    style_loss += self.loss(pastiche_g * self.style_weight, style_g.detach() * self.style_weight)

            if isinstance(layer, nn.ReLU):
                i += 1

        total_loss = content_loss + style_loss
        total_loss.backward()

        self.optimizer.step()

        return self.pastiche

GramMatrix.py:
import torch
import torch.nn as nn

class GramMatrix(nn.Module):
    
    def forward(self, input):
        a, b, c, d = input.size()
        features = input.view(a * b, c * d)
        G = torch.mm(features, features.t())
        
        return G.div(a * b * c * d)

This is the error:
Traceback (most recent call last):
  File "main.py", line 47, in <module>
    main()
  File "main.py", line 30, in main
    content_loss, style_loss, pastiches = style_cnn.train(content_batch)
  File "/home/pi/Desktop/python_torch/StyleCNN.py", line 50, in train
    pastiche = self.transform_network.forward(content)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/container.py", line 97, in forward
    input = module(input)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 491, in __call__
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/padding.py", line 172, in forward
    return F.pad(input, self.padding, 'reflect')
  File "/usr/local/lib/python2.7/dist-packages/torch/nn/functional.py", line 2711, in pad
    assert len(pad) // 2 <= input.dim(), 'Padding length too large'
AttributeError: 'list' object has no attribute 'dim'