Notebook 03: Different shape outputs between PyTorch 1.10, 1.11, 1.12 about pytorch-deep-learning HOT 4 OPEN

So, I just ran into the issue and spent a bit of time figuring out what was going on. Turns out, it's relatively simple, but it became simple only after banging my head against the keyboard a hundred times LOL! Basically, the key thing to understand is that the model is designed to work only with BATCHES! So, if you feed it a batch, basically a tensor of shape [<batch_size>, <color_channels>, <height>, <width>], e.g., [32, 1, 28, 28], everything just works, and the output of the model is a tensor of shape [<batch_size>, <number_of_classes>], e.g., [32, 10] in this notebook. Now, if you want to do a prediction on a single image, i.e. a tensor of shape [<color_channels>, <height>, <width>], it's not going to work. So, the trick is to artificially add a dimension to our image tensor before feeding it to our model. Basically, we just turn our image tensor into a batch of 1 image, i.e. a tensor of shape [1, 1, 28, 28]. We do that with:

image.unsqueeze(0)

Now, the output of the model will be a batch containing 1 prediction, i.e. a tensor of shape [1, 10]. In inference mode, use squeeze() to remove the first dimension. So, here is the code:

model_X.to(device)
model_X.eval()
with torch.inference_mode():
  prediction = model_X(image.unsqueeze(0).to(device)).squeeze()
prediction

And the output will be a tensor of shape [10], and you can use argmax(dim=1) to get the index of the class with the highest probability.

Hopefully, this clarifies things. I think this issue can be closed (unless I missed something obvious, of course) but I do think that the course material may need to be updated to clarify this a bit.

from pytorch-deep-learning.

I've run into something similar in 03. Excersises - Running single dummy tensors torch.rand(size=(1, 28, 28)).unsqueeze(dim=0) as you did in the solutions caused shape errors for me (again 49 vs 490) but did work in the training/test loops. Unsure where this comes from.. Could also be me misunderstanding something with shapes? still learning after all.

Note: model architecture was completely the same, even when running your code it prompted shape error on single tensors.

from pytorch-deep-learning.

Hello,
I ran into same issue, then I gave model.unsqueeze(dim=0) as suggested by aronvandepol, can someone explain what is the reason of such a problem?
Thanks.

from pytorch-deep-learning.

So, I just ran into the issue and spent a bit of time figuring out what was going on. Turns out, it's relatively simple, but it became simple only after banging my head against the keyboard a hundred times LOL! Basically, the key thing to understand is that the model is designed to work only with BATCHES! So, if you feed it a batch, basically a tensor of shape [<batch_size>, <color_channels>, <height>, <width>], e.g., [32, 1, 28, 28], everything just works, and the output of the model is a tensor of shape [<batch_size>, <number_of_classes>], e.g., [32, 10] in this notebook. Now, if you want to do a prediction on a single image, i.e. a tensor of shape [<color_channels>, <height>, <width>], it's not going to work. So, the trick is to artificially add a dimension to our image tensor before feeding it to our model. Basically, we just turn our image tensor into a batch of 1 image, i.e. a tensor of shape [1, 1, 28, 28]. We do that with:

image.unsqueeze(0)

Now, the output of the model will be a batch containing 1 prediction, i.e. a tensor of shape [1, 10]. In inference mode, use squeeze() to remove the first dimension. So, here is the code:

model_X.to(device)
model_X.eval()
with torch.inference_mode():
  prediction = model_X(image.unsqueeze(0).to(device)).squeeze()
prediction

And the output will be a tensor of shape [10], and you can use argmax(dim=1) to get the index of the class with the highest probability.

Hopefully, this clarifies things. I think this issue can be closed (unless I missed something obvious, of course) but I do think that the course material may need to be updated to clarify this a bit.

Thank you for making things simple as i was about to post the same question.

from pytorch-deep-learning.