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70 lines
2.9 KiB
70 lines
2.9 KiB
from __future__ import absolute_import
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import random
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import math
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import torch
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def _get_patch(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
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if per_pixel:
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return torch.empty(
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patch_size, dtype=dtype, device=device).normal_()
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elif rand_color:
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return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
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else:
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return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
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class RandomErasing:
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""" Randomly selects a rectangle region in an image and erases its pixels.
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'Random Erasing Data Augmentation' by Zhong et al.
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See https://arxiv.org/pdf/1708.04896.pdf
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This variant of RandomErasing is intended to be applied to either a batch
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or single image tensor after it has been normalized by dataset mean and std.
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Args:
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probability: The probability that the Random Erasing operation will be performed.
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sl: Minimum proportion of erased area against input image.
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sh: Maximum proportion of erased area against input image.
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min_aspect: Minimum aspect ratio of erased area.
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per_pixel: random value for each pixel in the erase region, precedence over rand_color
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rand_color: random color for whole erase region, 0 if neither this or per_pixel set
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"""
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def __init__(
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self,
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probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3,
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per_pixel=False, rand_color=False, device='cuda'):
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self.probability = probability
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self.sl = sl
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self.sh = sh
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self.min_aspect = min_aspect
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self.per_pixel = per_pixel # per pixel random, bounded by [pl, ph]
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self.rand_color = rand_color # per block random, bounded by [pl, ph]
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self.device = device
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def _erase(self, img, chan, img_h, img_w, dtype):
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if random.random() > self.probability:
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return
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area = img_h * img_w
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for attempt in range(100):
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target_area = random.uniform(self.sl, self.sh) * area
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aspect_ratio = random.uniform(self.min_aspect, 1 / self.min_aspect)
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h = int(round(math.sqrt(target_area * aspect_ratio)))
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w = int(round(math.sqrt(target_area / aspect_ratio)))
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if w < img_w and h < img_h:
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top = random.randint(0, img_h - h)
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left = random.randint(0, img_w - w)
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img[:, top:top + h, left:left + w] = _get_patch(
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self.per_pixel, self.rand_color, (chan, h, w), dtype=dtype, device=self.device)
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break
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def __call__(self, input):
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if len(input.size()) == 3:
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self._erase(input, *input.size(), input.dtype)
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else:
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batch_size, chan, img_h, img_w = input.size()
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for i in range(batch_size):
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self._erase(input[i], chan, img_h, img_w, input.dtype)
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return input
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