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pytorch-image-models/timm/data/random_erasing.py

90 lines
3.9 KiB

import random
import math
import torch
def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
# NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
# paths, flip the order so normal is run on CPU if this becomes a problem
# Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
# will revert back to doing normal_() on GPU when it's in next release
if per_pixel:
return torch.empty(
patch_size, dtype=dtype).normal_().to(device=device)
elif rand_color:
return torch.empty((patch_size[0], 1, 1), dtype=dtype).normal_().to(device=device)
else:
return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
class RandomErasing:
""" 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 variant of RandomErasing is intended to be applied to either a batch
or single image 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.
min_aspect: Minimum aspect ratio of erased area.
mode: pixel color mode, one of 'const', 'rand', or 'pixel'
'const' - erase block is constant color of 0 for all channels
'rand' - erase block is same per-cannel random (normal) color
'pixel' - erase block is per-pixel random (normal) color
max_count: maximum number of erasing blocks per image, area per box is scaled by count.
per-image count is randomly chosen between 1 and this value.
"""
def __init__(
self,
probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3,
mode='const', max_count=1, device='cuda'):
self.probability = probability
self.sl = sl
self.sh = sh
self.min_aspect = min_aspect
self.min_count = 1
self.max_count = max_count
mode = mode.lower()
self.rand_color = False
self.per_pixel = False
if mode == 'rand':
self.rand_color = True # per block random normal
elif mode == 'pixel':
self.per_pixel = True # per pixel random normal
else:
assert not mode or mode == 'const'
self.device = device
def _erase(self, img, chan, img_h, img_w, dtype):
if random.random() > self.probability:
return
area = img_h * img_w
count = self.min_count if self.min_count == self.max_count else \
random.randint(self.min_count, self.max_count)
for _ in range(count):
for attempt in range(10):
target_area = random.uniform(self.sl, self.sh) * area / count
log_ratio = (math.log(self.min_aspect), math.log(1 / self.min_aspect))
aspect_ratio = math.exp(random.uniform(*log_ratio))
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img_w and h < img_h:
top = random.randint(0, img_h - h)
left = random.randint(0, img_w - w)
img[:, top:top + h, left:left + w] = _get_pixels(
self.per_pixel, self.rand_color, (chan, h, w),
dtype=dtype, device=self.device)
break
def __call__(self, input):
if len(input.size()) == 3:
self._erase(input, *input.size(), input.dtype)
else:
batch_size, chan, img_h, img_w = input.size()
for i in range(batch_size):
self._erase(input[i], chan, img_h, img_w, input.dtype)
return input