Using/Tesgin PyTorch SubSetRandSampler

test_sampler.py

import torch
from torch.utils.data import SubsetRandomSampler as SubSetRandSampler
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import matplotlib.pyplot as plt

my_sampler_size = 5000 # I'm going to randomly sample 5000 items from the dataset 
# Why using this instead of shuffle? Because I want to get less samples than the whole dataset 
# would using an if statement inside the validation function be good ... something like if i>my_sampler_size: break 

 
val_transforms = transforms.Compose([
        # transforms.Resize(image_size, interpolation=Image.BICUBIC),
        # transforms.CenterCrop(crop_size),
        transforms.ToTensor(),
        # normalize,
    ])

val_dataset = datasets.CIFAR10('../data', train= False, download=True,
                        transform = val_transforms, 
                        )                                

validate_significance_loader = torch.utils.data.DataLoader(
        val_dataset,
        batch_size=100,         
        num_workers=0,         
        sampler = SubSetRandSampler(torch.randint(0, len(val_dataset), (my_sampler_size,)) ), # SubSetRandSampler(range(1000)),
        shuffle= False, 
        pin_memory=True)
    
# Let's check it out
sum=0
for i, (images, target) in enumerate(validate_significance_loader):
    sum += len(target)    
    if i<2:
        plt.imshow(images[i,:].permute(1, 2, 0)  ); plt.show() # to see the image
        print(target[i])
        break
print(sum)