Hello everyone,
I am brand new to using PyTorch and just trying to implement some older computer vision white papers to get a hang of how this all works. I had two main questions which I will ask and then elaborate on below, any guidance would be much appreciated.
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How to use the PyTorch DataSet class and Data loader to train my network if the data set is a folder containing a few thousand images.
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How do the images get passed to the model class I create?
I am implemented the semantic segmentation model known as SegNet. I have been able to figure out how to build my model at least in terms of building the constructor and forward definition. I am now trying to build my train.py, but am really struggling with using the PyTorch data loader for image data sets contained in a folder directory and not a csv setup. Can anyone point me to an example that shows me how this works? And possibly an explanation? All the examples I find use CSV files where as I just have a few thousand .png files for training and testing.
I am also confused on how the images are passed to the forward function in my class “class SegNet(nn.Module):”. I never see a direct pass of the images to the network model in most examples and just wondered how they get there. What I mean is most examples I see call the class in the the train.py script and then load the data but is there some method to passing the data directly to the forward definition? I never see a directly call to the forward function where some variable storing the dataset is passed. I’m pretty new to all this so any help would be much appreciated cause I might be thinking about this all wrong.