How to load a list of numpy arrays to pytorch dataset loader?

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. Hope it helps you.

Since you have images you probably want to perform transformations on them. So TensorDataset is not the best option here. Instead you can create your own Dataset. Something like this:

import torch
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
import numpy as np
from PIL import Image


class MyDataset(Dataset):
    def __init__(self, data, targets, transform=None):
        self.data = data
        self.targets = torch.LongTensor(targets)
        self.transform = transform
        
    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]
        
        if self.transform:
            x = Image.fromarray(self.data[index].astype(np.uint8).transpose(1,2,0))
            x = self.transform(x)
        
        return x, y
    
    def __len__(self):
        return len(self.data)

# Let's create 10 RGB images of size 128x128 and 10 labels {0, 1}
data = list(np.random.randint(0, 255, size=(10, 3, 128, 128)))
targets = list(np.random.randint(2, size=(10)))

transform = transforms.Compose([transforms.Resize(64), transforms.ToTensor()])
dataset = MyDataset(data, targets, transform=transform)
dataloader = DataLoader(dataset, batch_size=5)