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pytorch-image-models/data/random_erasing.py

132 lines
5.2 KiB

from __future__ import absolute_import
#from torchvision.transforms import *
from PIL import Image
import random
import math
import numpy as np
import torch
class RandomErasingNumpy:
""" 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 'Numpy' variant of RandomErasing is intended to be applied on a per
image basis after transforming the image to uint8 numpy array in
range 0-255 prior to tensor conversion and normalization
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.
r1: Minimum aspect ratio of erased area.
mean: Erasing value.
"""
def __init__(
self,
probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3,
per_pixel=False, rand_color=False,
pl=0, ph=255, mean=[255 * 0.485, 255 * 0.456, 255 * 0.406],
out_type=np.uint8):
self.probability = probability
if not per_pixel and not rand_color:
self.mean = np.array(mean).round().astype(out_type)
else:
self.mean = None
self.sl = sl
self.sh = sh
self.min_aspect = min_aspect
self.pl = pl
self.ph = ph
self.per_pixel = per_pixel # per pixel random, bounded by [pl, ph]
self.rand_color = rand_color # per block random, bounded by [pl, ph]
self.out_type = out_type
def __call__(self, img):
if random.random() > self.probability:
return img
chan, img_h, img_w = img.shape
area = img_h * img_w
for attempt in range(100):
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.min_aspect, 1 / self.min_aspect)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if self.rand_color:
c = np.random.randint(self.pl, self.ph + 1, (chan,), self.out_type)
elif not self.per_pixel:
c = self.mean[:chan]
if w < img_w and h < img_h:
top = random.randint(0, img_h - h)
left = random.randint(0, img_w - w)
if self.per_pixel:
img[:, top:top + h, left:left + w] = np.random.randint(
self.pl, self.ph + 1, (chan, h, w), self.out_type)
else:
img[:, top:top + h, left:left + w] = c
return img
return img
class RandomErasingTorch:
""" 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 'Torch' variant of RandomErasing is intended to be applied to a full batch
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.
r1: Minimum aspect ratio of erased area.
"""
def __init__(
self,
probability=0.5, sl=0.02, sh=1/3, min_aspect=0.3,
per_pixel=False, rand_color=False,
device='cuda'):
self.probability = probability
self.sl = sl
self.sh = sh
self.min_aspect = min_aspect
self.per_pixel = per_pixel # per pixel random, bounded by [pl, ph]
self.rand_color = rand_color # per block random, bounded by [pl, ph]
self.device = device
def __call__(self, batch):
batch_size, chan, img_h, img_w = batch.size()
area = img_h * img_w
for i in range(batch_size):
if random.random() > self.probability:
continue
img = batch[i]
for attempt in range(100):
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.min_aspect, 1 / self.min_aspect)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if self.rand_color:
c = torch.empty((chan, 1, 1), dtype=batch.dtype, device=self.device).normal_()
elif not self.per_pixel:
c = torch.zeros((chan, 1, 1), dtype=batch.dtype, device=self.device)
if w < img_w and h < img_h:
top = random.randint(0, img_h - h)
left = random.randint(0, img_w - w)
if self.per_pixel:
img[:, top:top + h, left:left + w] = torch.empty(
(chan, h, w), dtype=batch.dtype, device=self.device).normal_()
else:
img[:, top:top + h, left:left + w] = c
break
return batch