数据增强实测之GridMask

GridMask是2020年arXiv上的一篇论文，可以认为是直接对标Hide_and_Seek方法。与之不同的是，GridMask采用了等间隔擦除patch的方式，有点类似空洞卷积，或许可以取名叫空洞擦除？

GridMask Data Augmentation

paper: https://arxiv.org/pdf/2001.04086

code: GitHub - dvlab-research/GridMask

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核心操作如下图所示，看一下基本就能明白。

实现代码相比于其他方法要复杂一些，如下：

import torch
import torch.nn as nn
import numpy as np
from PIL import Image
import pdb
import math


class Grid(object):
    def __init__(self, d1, d2, rotate=1, ratio=0.5, mode=0, prob=1.):
        self.d1 = d1
        self.d2 = d2
        self.rotate = rotate
        self.ratio = ratio
        self.mode = mode
        self.st_prob = self.prob = prob

    def set_prob(self, epoch, max_epoch):
        self.prob = self.st_prob * min(1, epoch / max_epoch)

    def __call__(self, img):
        if np.random.rand() > self.prob:
            return img
        h = img.size(1)
        w = img.size(2)

        # 1.5 * h, 1.5 * w works fine with the squared images
        # But w