In this article, we are going to take a look at How to deal with Custom PyTorch Dataset. It is natural that we will develop our way of creating custom datasets while dealing with different Projects.
Custom datasets!! WHY??
Because you can shape it in a way you desire!!!
It is natural that we will develop our way of creating custom datasets while dealing with different Projects.
There are some official custom dataset examples on PyTorch Like here but it seemed a bit obscure to a beginner (like me, back then). The topics which we will discuss are as follows.
A dataset must contain the following functions to be used by DataLoader later on.
__init__()function, the initial logic happens here, like reading a CSV, assigning transforms, filtering data, etc.,
__getitem__()returns the data and the labels.
__len__()returns the count of samples your dataset has.
Now, the first part is to create a dataset class:
from torch.utils.data.dataset import Dataset class MyCustomDataset(Dataset): def __init__(self, ...): ## stuff def __getitem__(self, index): ## stuff return (img, label) def __len__(self): return count ## of how many examples(images?) you have
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