"""OneFlow implementation of Random Erasing(Cutout)
Modified from https://github.com/rwightman/pytorch-image-models/blob/master/timm/data/random_erasing.py
"""
import random
import math
import oneflow as flow
def _get_pixels(per_pixel, rand_color, patch_size, dtype=flow.float32, device="cuda"):
if per_pixel:
return flow.empty(patch_size, dtype=dtype, device=device).normal_()
elif rand_color:
return flow.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
else:
return flow.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
[docs]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",
):
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
self.mode = mode.lower()
self.rand_color = False
self.per_pixel = False
if self.mode == "rand":
self.rand_color = True # per block random normal
elif self.mode == "pixel":
self.per_pixel = True # per pixel random normal
else:
assert not self.mode or self.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.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 __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
for i in range(batch_start, batch_size):
self._erase(input[i], chan, img_h, img_w, input.dtype)
return input
def __repr__(self):
# NOTE simplified state for repr
fs = self.__class__.__name__ + f"(p={self.probability}, mode={self.mode}"
fs += f", count=({self.min_count}, {self.max_count}))"
return fs