from __future__ import absolute_import #from torchvision.transforms import * from PIL import Image import random import math import numpy as np import torch class RandomErasingNumpy: """ Randomly selects a rectangle region in an image and erases its pixels. 'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/pdf/1708.04896.pdf This 'Numpy' variant of RandomErasing is intended to be applied on a per image basis after transforming the image to uint8 numpy array in range 0-255 prior to tensor conversion and normalization Args: probability: The probability that the Random Erasing operation will be performed. sl: Minimum proportion of erased area against input image. sh: Maximum proportion of erased area against input image. r1: Minimum aspect ratio of erased area. mean: Erasing value. """ def __init__( self, probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3, per_pixel=False, rand_color=False, pl=0, ph=255, mean=[255 * 0.485, 255 * 0.456, 255 * 0.406], out_type=np.uint8): self.probability = probability if not per_pixel and not rand_color: self.mean = np.array(mean).round().astype(out_type) else: self.mean = None self.sl = sl self.sh = sh self.min_aspect = min_aspect self.pl = pl self.ph = ph self.per_pixel = per_pixel # per pixel random, bounded by [pl, ph] self.rand_color = rand_color # per block random, bounded by [pl, ph] self.out_type = out_type def __call__(self, img): if random.random() > self.probability: return img chan, img_h, img_w = img.shape area = img_h * img_w for attempt in range(100): target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.min_aspect, 1 / self.min_aspect) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if self.rand_color: c = np.random.randint(self.pl, self.ph + 1, (chan,), self.out_type) elif not self.per_pixel: c = self.mean[:chan] if w < img_w and h < img_h: top = random.randint(0, img_h - h) left = random.randint(0, img_w - w) if self.per_pixel: img[:, top:top + h, left:left + w] = np.random.randint( self.pl, self.ph + 1, (chan, h, w), self.out_type) else: img[:, top:top + h, left:left + w] = c return img return img class RandomErasingTorch: """ Randomly selects a rectangle region in an image and erases its pixels. 'Random Erasing Data Augmentation' by Zhong et al. See https://arxiv.org/pdf/1708.04896.pdf This 'Torch' variant of RandomErasing is intended to be applied to a full batch tensor after it has been normalized by dataset mean and std. Args: probability: The probability that the Random Erasing operation will be performed. sl: Minimum proportion of erased area against input image. sh: Maximum proportion of erased area against input image. r1: Minimum aspect ratio of erased area. """ def __init__( self, probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3, per_pixel=False, rand_color=False): self.probability = probability self.sl = sl self.sh = sh self.min_aspect = min_aspect self.per_pixel = per_pixel # per pixel random, bounded by [pl, ph] self.rand_color = rand_color # per block random, bounded by [pl, ph] def __call__(self, batch): batch_size, chan, img_h, img_w = batch.size() area = img_h * img_w for i in range(batch_size): if random.random() > self.probability: continue img = batch[i] for attempt in range(100): target_area = random.uniform(self.sl, self.sh) * area aspect_ratio = random.uniform(self.min_aspect, 1 / self.min_aspect) h = int(round(math.sqrt(target_area * aspect_ratio))) w = int(round(math.sqrt(target_area / aspect_ratio))) if self.rand_color: c = torch.empty((chan, 1, 1), dtype=batch.dtype).cuda().normal_() elif not self.per_pixel: c = torch.zeros((chan, 1, 1), dtype=batch.dtype).cuda() if w < img_w and h < img_h: top = random.randint(0, img_h - h) left = random.randint(0, img_w - w) if self.per_pixel: img[:, top:top + h, left:left + w] = torch.empty( (chan, h, w), dtype=batch.dtype).cuda().normal_() else: img[:, top:top + h, left:left + w] = c break return batch