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@ -23,13 +23,13 @@ class RandomErasing:
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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: 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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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-cannel random (normal) color
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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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max_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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@ -37,14 +37,15 @@ class RandomErasing:
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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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mode='const', max_count=1, device='cuda'):
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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', min_count=1, max_count=None, 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.min_count = 1
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self.max_count = max_count
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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.min_count = min_count
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self.max_count = max_count or min_count
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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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@ -64,9 +65,8 @@ class RandomErasing:
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random.randint(self.min_count, self.max_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.sl, self.sh) * area / count
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log_ratio = (math.log(self.min_aspect), math.log(1 / self.min_aspect))
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aspect_ratio = math.exp(random.uniform(*log_ratio))
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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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