egg/zoo/emcom_as_ssl/data.py [36:64]:
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    train_sampler = None
    if is_distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset, shuffle=True, drop_last=True, seed=seed
        )

    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=batch_size,
        shuffle=(train_sampler is None),
        sampler=train_sampler,
        num_workers=num_workers,
        pin_memory=True,
        drop_last=True,
    )

    return train_loader


class GaussianBlur:
    """Gaussian blur augmentation as in SimCLR https://arxiv.org/abs/2002.05709"""

    def __init__(self, sigma=[0.1, 2.0]):
        self.sigma = sigma

    def __call__(self, x):
        sigma = random.uniform(self.sigma[0], self.sigma[1])
        x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
        return x
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egg/zoo/simclr/data.py [33:60]:
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    train_sampler = None
    if is_distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(
            train_dataset, shuffle=True, drop_last=True, seed=seed
        )

    train_loader = torch.utils.data.DataLoader(
        train_dataset,
        batch_size=batch_size,
        shuffle=(train_sampler is None),
        sampler=train_sampler,
        num_workers=num_workers,
        pin_memory=True,
        drop_last=True,
    )
    return train_loader


class GaussianBlur:
    """Gaussian blur augmentation in SimCLR https://arxiv.org/abs/2002.05709"""

    def __init__(self, sigma=[0.1, 2.0]):
        self.sigma = sigma

    def __call__(self, x):
        sigma = random.uniform(self.sigma[0], self.sigma[1])
        x = x.filter(ImageFilter.GaussianBlur(radius=sigma))
        return x
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