["python","numpy","pytorch"]
How to load a list of numpy arrays to pytorch dataset loader?

I have a huge list of numpy arrays, where each array represents an image and I want to load it using torch.utils.data.Dataloader object. But the documentation of torch.utils.data.Dataloader mentions that it loads data directly from a folder. How do I modify it for my cause? I am new to pytorch and any help would be greatly appreciated. my numpy array for a single image looks something like this. The image is RBG image. [[[ 70 82 94] [ 67 81 93] [ 66 82 94] ..., [182 182 188] [183 183 189] [188 186 192]] [[ 66 80 92] [ 62 78 91] [ 64 79 95] ..., [176 176 182] [178 178 184] [180 180 186]] [[ 62 82 93] [ 62 81 96] [ 65 80 99] ..., [169 172 177] [173 173 179] [172 172 178]] ...,
I think what DataLoader actually requires is an input that subclasses Dataset. You can either write your own dataset class that subclasses Datasetor use TensorDataset as I have done below: import torch import numpy as np from torch.utils.data import TensorDataset, DataLoader my_x = [np.array([[1.0,2],[3,4]]),np.array([[5.,6],[7,8]])] # a list of numpy arrays my_y = [np.array([4.]), np.array([2.])] # another list of numpy arrays (targets) tensor_x = torch.Tensor(my_x) # transform to torch tensor tensor_y = torch.Tensor(my_y) my_dataset = TensorDataset(tensor_x,tensor_y) # create your datset my_dataloader = DataLoader(my_dataset) # create your dataloader Works for me.