How to resolve runtime error due to size mismatch in PyTorch?

Question

I am trying to implement a simple autoencoder using PyTorch. My dataset consists of 256 x 256 x 3 images. I have built a torch.utils.data.dataloader.DataLoade

Answer 1

Whenever you have:

RuntimeError: size mismatch, m1: [a x b], m2: [c x d]

all you have to care is b=c and you are done:

m1 is [a x b] which is [batch size x in features]

m2 is [c x d] which is [in features x out features]

Answer 2

If your input is 3 x 256 x 256, then you need to convert it to B x N to pass it through the linear layer: nn.Linear(3*256*256, 128) where B is the batch_size and N is the linear layer input size. If you are giving one image at a time, you can convert your input tensor of shape 3 x 256 x 256 to 1 x (3*256*256) as follows.

img = img.view(1, -1) # converts [3 x 256 x 256] to 1 x 196608
output = model(img)

Answer 3

Your error:

size mismatch, m1: [76800 x 256], m2: [784 x 128]

says that previous layer output shape is not equal to next layer input shape

[76800 x 256], m2: [784 x 128] # Incorrect!
[76800 x 256], m2: [256 x 128] # Correct!