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