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82 lines
3.4 KiB
82 lines
3.4 KiB
import random
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import math
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import torch
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def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
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# NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
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# paths, flip the order so normal is run on CPU if this becomes a problem
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# Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
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# will revert back to doing normal_() on GPU when it's in next release
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if per_pixel:
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return torch.empty(
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patch_size, dtype=dtype).normal_().to(device=device)
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elif rand_color:
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return torch.empty((patch_size[0], 1, 1), dtype=dtype).normal_().to(device=device)
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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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mode: pixel color mode, one of 'const', 'rand', or 'pixel'
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'const' - erase block is constant color of 0 for all channels
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'rand' - erase block is same per-cannel random (normal) color
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'pixel' - erase block is per-pixel random (normal) color
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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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mode='const', 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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mode = mode.lower()
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self.rand_color = False
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self.per_pixel = False
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if mode == 'rand':
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self.rand_color = True # per block random normal
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elif mode == 'pixel':
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self.per_pixel = True # per pixel random normal
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else:
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assert not mode or mode == 'const'
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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_pixels(
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self.per_pixel, self.rand_color, (chan, h, w),
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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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