Memory leak on GaussianBlur

🐛 Describe the bug

Hello. When using num_workers > 0 for dataloader and GaussianBlur BEFORE the resize function in transforms (images in dataset are of different size) a memory leak appears. The larger num_workers used, the more the leak is (I ran out of 128 GB RAM in 300 iterations with batch_size of 32 and num_workers of 16). To reproduce (you should initialize images with array of filepaths to images):

import torch
import glob
from torchvision import transforms
from PIL import Image
class FramesDataset(torch.utils.data.Dataset):
    def __init__(self, images):
        self.images = images
        self.init_base_transform()

    def __len__(self):
        return len(self.images)

    def init_base_transform(self):
        self.tr_aug = transforms.Compose([transforms.GaussianBlur(7, (1, 5)),
                                          transforms.Resize((256, 256), antialias=True),
                                          transforms.ToTensor(),
                                          transforms.Normalize([0.5]*3, [0.5]*3) ])

    def __getitem__(self, idx):
        img = Image.open(self.images[idx]).convert('RGB')
        out = self.tr_aug(img)
        return out

dataset = TestDataset(images)
dl = torch.utils.data.DataLoader(dataset, batch_size = 16, num_workers = 8, pin_memory = False)
while True:
    for batch in dl:
        pass

Versions

torch: 1.12.0+cu116 torchvision: 0.13.0+cu116 PIL: 9.0.0 Ubuntu: 20.04.4 LTS

cc @vfdev-5 @datumbox

[GLivshits]

@vfdef-5 I’m just watching htop. The thing is that if your images are of the same size everything works ok (I also tried loading dataset of one image and there is no memory leak). But if there are multiple sizes of images - leak appears. It seems like there is some memory reserved for blurring operation in dataset, and if a tensor of some new size comes - leak does appear);

[GLivshits]

@vfdev-5 I’ve excluded every other augmentation and swapped the order of blur and resize and found matching leaky configuration. The code provided is already a localized version of a problem.