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

Solution 1:

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.

Solution 2:

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)

Solution 3:

PyTorch DataLoader need a DataSet as you can check in the docs. The right way to do that is to use:

torch.utils.data.TensorDataset(*tensors)

Which is a Dataset for wrapping tensors, where each sample will be retrieved by indexing tensors along the first dimension. The parameters *tensors means tensors that have the same size of the first dimension.

The other class torch.utils.data.Dataset is an abstract class.

Here is how to convert numpy arrays to tensors:

import torch
import numpy as np
n = np.arange(10)
print(n) #[0 1 2 3 4 5 6 7 8 9]
t1 = torch.Tensor(n)  # as torch.float32
print(t1) #tensor([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
t2 = torch.from_numpy(n)  # as torch.int32
print(t2) #tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.int32)

The accepted answer used the torch.Tensor construct. If you have an image with pixels from 0-255 you may use this:

timg = torch.from_numpy(img).float()

Or torchvision to_tensor method, that converts a PIL Image or numpy.ndarray to tensor.

But here is a little trick you can put your numpy arrays directly.

x1 = np.array([1,2,3])
d1 = DataLoader( x1, batch_size=3)

This also works, but if you print d1.dataset type:

print(type(d1.dataset)) # <class 'numpy.ndarray'>

While we actually need Tensors for working with CUDA so it is better to use Tensors to feed the DataLoader.