Tensorflow CNN shape mismatch - deep-learning

Question

def load_data(data_path, batch_size, num_workers=2):
t_m = transforms.Compose(
[transforms.Grayscale(num_output_channels=1),
transforms.Resize((400,400)),
transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = torchvision.datasets.ImageFolder(root = data_path, transform=t_m)
# print (np.shape(dataset))
#split
train, test = torch.utils.data.random_split(dataset, [int( len(dataset) * 0.7 ), len(dataset) - int( len(dataset) * 0.7 ) ])
trainloader = torch.utils.data.DataLoader(train, batch_size=batch_size,
shuffle=True, num_workers=num_workers,drop_last = True)
testloader = torch.utils.data.DataLoader(test, batch_size=batch_size,
shuffle=False, num_workers=num_workers, drop_last = False)
return dataset,trainloader,testloader
import torch.nn as nn
model = torch.nn.Sequential(
nn.Conv2d(1, 32, kernel_size=5, padding=2),
nn.MaxPool2d(2, 2),
nn.Conv2d(32, 64, kernel_size=5, padding=2),
nn.MaxPool2d(2, 2),
nn.Linear ( 7 * 7 * 64, 1000),
nn.Linear(1000, 600),
nn.Linear(600, 200),
nn.Linear(200, 10)
)
#Training
total_epochs = 5
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adadelta(model.parameters())
for epoch in tqdm(range(total_epochs)):
#initialize
batch_count = 0
gc.collect()
loop_loss = 0.0
for img in (trainloader):
input_, label_ = img
# print (input_.shape)
out = model(input_)
out= nn.functional.relu(out)
loss = criterion(out, label_)
loss.backward()
optimizer.zero_grad()
optimizer.step()
loop_loss = loop_loss + loss.item()
batch_count = batch_count + 1
print('batch_loss: ', str(loss.item()))
print('Epochs completed:', epoch+1,'\n')
print('epoch_loss = ' + loop_loss/float(batch_count))
size mismatch, m1: [25600 x 100], m2: [3136 x 1000] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:41
Please explain where the shapes went wrong? How should I fix this?
I am new to this so this might not be a good question but any detail would help
The input images are resized to 400,400 and converted to gray from rgb

Answer 1

Your problem is in the first Linear layer. Always code like this so that you can figure out yourself.
class MyModel(nn.Module):
def __init__(self, params):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(...)
self.fc = nn.Linear(...)
def forward(self, x):
x = self.conv1(x)
import pdb; pdb.set_trace()
x = self.fc(x)
return x
This way you can put the pdb where you want and you can check the shapes using x.shape command. Your problem is in the mismatch between shape of output of conv layer and your first Linear layer.