Dubhe/dubhe-tadl/network_morphism/datasets.py

import numpy as np
import torch
import torchvision.transforms as transforms
from utils import Constant

class Cutout:
    """Randomly mask out one or more patches from an image.
    Args:
        n_holes (int): Number of patches to cut out of each image.
        length (int): The length (in pixels) of each square patch.
    """

    def __init__(self, n_holes, length):
        self.n_holes = n_holes
        self.length = length

    def __call__(self, img):
        """
        Args:
            img (Tensor): Tensor image of size (C, H, W).
        Returns:
            Tensor: Image with n_holes of dimension length x length cut out of it.
        """
        h, w = img.size(1), img.size(2)
        mask = np.ones((h, w), np.float32)

        for _ in range(self.n_holes):
            y = np.random.randint(h)
            x = np.random.randint(w)

            y1 = np.clip(y - self.length // 2, 0, h)
            y2 = np.clip(y + self.length // 2, 0, h)
            x1 = np.clip(x - self.length // 2, 0, w)
            x2 = np.clip(x + self.length // 2, 0, w)

            mask[y1:y2, x1:x2] = 0.0

        mask = torch.from_numpy(mask)
        mask = mask.expand_as(img)
        img *= mask
        return img

def data_transforms_cifar10():
    """ data_transforms for cifar10 dataset
    """

    cifar_mean = [0.49139968, 0.48215827, 0.44653124]
    cifar_std = [0.24703233, 0.24348505, 0.26158768]

    train_transform = transforms.Compose(
        [
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize(cifar_mean, cifar_std),
            Cutout(n_holes=Constant.CUTOUT_HOLES,
                    length=int(32 * Constant.CUTOUT_RATIO))
        ]
    )

    valid_transform = transforms.Compose(
        [transforms.ToTensor(), transforms.Normalize(cifar_mean, cifar_std)]
    )
    return train_transform, valid_transform