pytorch change input image size
I am new to pytorch and I am following a tutorial but when i try to modify the code to use 64x64x3 images instead of 32x32x3 images, i get a buch of errors. Here is the code from the tutorial:
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Resize(32),
     transforms.RandomCrop(32),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

trainset = ImageFolder(&quot;Train&quot;, transform=transform)
trainloader = DataLoader(trainset, shuffle=True, batch_size=batch_size, num_workers=0)

classes = ('Dog', 'Cat')

import matplotlib.pyplot as plt
import numpy as np

# functions to show an image


def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()


# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()

# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))

import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

print(&quot;training started&quot;)

from tqdm import tqdm

for epoch in range(5):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in tqdm(enumerate(trainloader, 0), desc=f&quot;epoch: {epoch + 1}&quot;):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

PATH = './net.pth'
torch.save(net.state_dict(), PATH)

If I change the ´transforms.Resize(32)´ and ´transforms.RandomCrop(32)´ to 64 (to get 64x64x3 images), I get this error
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
~\Documents\pyth\classifier\train_classifier.py in &lt;module&gt;
     86 
     87         # forward + backward + optimize
---&gt; 88         outputs = net(inputs)
     89         loss = criterion(outputs, labels)
     90         loss.backward()

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~\Documents\pyth\classifier\train_classifier.py in forward(self, x)
     57         x = self.pool(F.relu(self.conv1(x)))
     58         x = self.pool(F.relu(self.conv2(x)))
---&gt; 59         x = x.view(-1, 10816+1)
     60         x = F.relu(self.fc1(x))
     61         x = F.relu(self.fc2(x))

RuntimeError: shape '[-1, 10817]' is invalid for input of size 10816
´´´

and if i try to change the parameters of ´x.view(...)´ i get this error

´´´
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
~\Documents\pyth\classifier\train_classifier.py in &lt;module&gt;
     86 
     87         # forward + backward + optimize
---&gt; 88         outputs = net(inputs)
     89         loss = criterion(outputs, labels)
     90         loss.backward()

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~\Documents\pyth\classifier\train_classifier.py in forward(self, x)
     58         x = self.pool(F.relu(self.conv2(x)))
     59         x = x.view(-1, 16 * 2 * 5 * 5)
---&gt; 60         x = F.relu(self.fc1(x))
     61         x = F.relu(self.fc2(x))
     62         x = self.fc3(x)

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--&gt; 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~\Anaconda3\lib\site-packages\torch\nn\modules\linear.py in forward(self, input)
     92 
     93     def forward(self, input: Tensor) -&gt; Tensor:
---&gt; 94         return F.linear(input, self.weight, self.bias)
     95 
     96     def extra_repr(self) -&gt; str:

~\Anaconda3\lib\site-packages\torch\nn\functional.py in linear(input, weight, bias)
   1751     if has_torch_function_variadic(input, weight):
   1752         return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
-&gt; 1753     return torch._C._nn.linear(input, weight, bias)
   1754 
   1755 

RuntimeError: mat1 and mat2 shapes cannot be multiplied (2x800 and 400x120)
´´´


I think this should work because after performing 2nd Pooling operation the output feature map is coming N x C x 13 x 13

self.fc1 = nn.Linear(16 * 13 * 13, 120)


x = x.view(-1, 16 * 13 * 13)

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 13 * 13, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 13 * 13)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

