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155 lines
7.5 KiB
155 lines
7.5 KiB
""" Random Erasing (Cutout)
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Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
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Copyright Zhun Zhong & Liang Zheng
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Hacked together by / Copyright 2019, Ross Wightman
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"""
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import random
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import math
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import torch
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def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
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# NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
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# paths, flip the order so normal is run on CPU if this becomes a problem
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# Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
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if per_pixel:
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return torch.empty(patch_size, dtype=dtype, device=device).normal_()
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elif rand_color:
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return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
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else:
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return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
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class RandomErasing:
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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 variant of RandomErasing is intended to be applied to either a batch
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or single image tensor after it has been normalized by dataset mean and std.
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Args:
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probability: Probability that the Random Erasing operation will be performed.
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min_area: Minimum percentage of erased area wrt input image area.
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max_area: Maximum percentage of erased area wrt input image area.
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min_aspect: Minimum aspect ratio of erased area.
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mode: pixel color mode, one of 'const', 'rand', or 'pixel'
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'const' - erase block is constant color of 0 for all channels
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'rand' - erase block is same per-channel random (normal) color
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'pixel' - erase block is per-pixel random (normal) color
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count: maximum number of erasing blocks per image, area per box is scaled by count.
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per-image count is randomly chosen between 1 and this value.
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"""
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def __init__(
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self,
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probability=0.5, min_area=0.02, max_area=1/3, min_aspect=0.3, max_aspect=None,
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mode='const', count=1, num_splits=0):
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self.probability = probability
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self.min_area = min_area
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self.max_area = max_area
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max_aspect = max_aspect or 1 / min_aspect
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self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
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self.count = count
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self.num_splits = num_splits
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mode = mode.lower()
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self.rand_color = False
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self.per_pixel = False
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if mode == 'rand':
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self.rand_color = True # per block random normal
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elif mode == 'pixel':
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self.per_pixel = True # per pixel random normal
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else:
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assert not mode or mode == 'const'
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def _erase(self, img, chan, img_h, img_w, dtype):
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device = img.device
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if random.random() > self.probability:
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return
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area = img_h * img_w
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count = random.randint(1, self.count) if self.count > 1 else self.count
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for _ in range(count):
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for attempt in range(10):
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target_area = random.uniform(self.min_area, self.max_area) * area / count
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aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
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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 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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img[:, top:top + h, left:left + w] = _get_pixels(
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self.per_pixel, self.rand_color, (chan, h, w), dtype=dtype, device=device)
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break
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def __call__(self, x):
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if len(x.size()) == 3:
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self._erase(x, *x.shape, x.dtype)
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else:
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batch_size, chan, img_h, img_w = x.shape
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# skip first slice of batch if num_splits is set (for clean portion of samples)
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batch_start = batch_size // self.num_splits if self.num_splits > 1 else 0
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for i in range(batch_start, batch_size):
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self._erase(x[i], chan, img_h, img_w, x.dtype)
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return x
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class RandomErasingMasked:
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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 variant of RandomErasing is intended to be applied to either a batch
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or single image tensor after it has been normalized by dataset mean and std.
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Args:
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probability: Probability that the Random Erasing operation will be performed for each box (count)
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min_area: Minimum percentage of erased area wrt input image area.
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max_area: Maximum percentage of erased area wrt input image area.
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min_aspect: Minimum aspect ratio of erased area.
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count: maximum number of erasing blocks per image, area per box is scaled by count.
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per-image count is between 0 and this value.
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"""
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def __init__(
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self,
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probability=0.5, min_area=0.02, max_area=1/3, min_aspect=0.3, max_aspect=None,
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mode='const', count=1, num_splits=0):
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self.probability = probability
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self.min_area = min_area
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self.max_area = max_area
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max_aspect = max_aspect or 1 / min_aspect
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self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
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self.mode = mode # FIXME currently ignored, add back options besides normal mean=0, std=1 noise?
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self.count = count
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self.num_splits = num_splits
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@torch.no_grad()
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def __call__(self, x: torch.Tensor) -> torch.Tensor:
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device = x.device
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batch_size, _, img_h, img_w = x.shape
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batch_start = batch_size // self.num_splits if self.num_splits > 1 else 0
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# NOTE simplified from v1 with with one count value and same prob applied for all
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enable = (torch.empty((batch_size, self.count), device=device).uniform_() < self.probability).float()
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enable = enable / enable.sum(dim=1, keepdim=True).clamp(min=1)
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target_area = torch.empty(
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(batch_size, self.count), device=device).uniform_(self.min_area, self.max_area) * enable
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aspect_ratio = torch.empty((batch_size, self.count), device=device).uniform_(*self.log_aspect_ratio).exp()
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h_coord = torch.arange(0, img_h, device=device).unsqueeze(-1).expand(-1, self.count).float()
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w_coord = torch.arange(0, img_w, device=device).unsqueeze(-1).expand(-1, self.count).float()
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h_mid = torch.rand((batch_size, self.count), device=device) * img_h
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w_mid = torch.rand((batch_size, self.count), device=device) * img_w
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noise = torch.empty_like(x[0]).normal_()
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for i in range(batch_start, batch_size):
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h_half = (img_h / 2) * torch.sqrt(target_area[i] * aspect_ratio[i]) # 1/2 box h
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h_mask = (h_coord > (h_mid[i] - h_half)) & (h_coord < (h_mid[i] + h_half))
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w_half = (img_w / 2) * torch.sqrt(target_area[i] / aspect_ratio[i]) # 1/2 box w
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w_mask = (w_coord > (w_mid[i] - w_half)) & (w_coord < (w_mid[i] + w_half))
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#mask = (h_mask.unsqueeze(1) & w_mask.unsqueeze(0)).any(dim=-1)
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#x[i].copy_(torch.where(mask, noise, x[i]))
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mask = ~(h_mask.unsqueeze(1) & w_mask.unsqueeze(0)).any(dim=-1)
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x[i] = x[i].where(mask, noise)
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#x[i].masked_scatter_(mask, noise)
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return x
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