Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,616 Bytes
2d9a728 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
"""
This implementation is based on
https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/random_erasing.py
pulished under an Apache License 2.0.
"""
import math
import random
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
if per_pixel:
return torch.empty(patch_size, dtype=dtype, device=device).normal_()
elif rand_color:
return torch.empty(
(patch_size[0], 1, 1), dtype=dtype, device=device
).normal_()
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: Probability that the Random Erasing operation will be performed.
min_area: Minimum percentage of erased area wrt input image area.
max_area: Maximum percentage of erased area wrt input image area.
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-channel 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,
min_area=0.02,
max_area=1 / 3,
min_aspect=0.3,
max_aspect=None,
mode="const",
min_count=1,
max_count=None,
num_splits=0,
device="cuda",
cube=True,
):
self.probability = probability
self.min_area = min_area
self.max_area = max_area
max_aspect = max_aspect or 1 / min_aspect
self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
self.min_count = min_count
self.max_count = max_count or min_count
self.num_splits = num_splits
mode = mode.lower()
self.rand_color = False
self.per_pixel = False
self.cube = cube
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 _ in range(10):
target_area = (
random.uniform(self.min_area, self.max_area) * area / count
)
aspect_ratio = math.exp(random.uniform(*self.log_aspect_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 _erase_cube(
self,
img,
batch_start,
batch_size,
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 _ in range(100):
target_area = (
random.uniform(self.min_area, self.max_area) * area / count
)
aspect_ratio = math.exp(random.uniform(*self.log_aspect_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)
for i in range(batch_start, batch_size):
img_instance = img[i]
img_instance[
:, 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()
# skip first slice of batch if num_splits is set (for clean portion of samples)
batch_start = (
batch_size // self.num_splits if self.num_splits > 1 else 0
)
if self.cube:
self._erase_cube(
input,
batch_start,
batch_size,
chan,
img_h,
img_w,
input.dtype,
)
else:
for i in range(batch_start, batch_size):
self._erase(input[i], chan, img_h, img_w, input.dtype)
return input
|