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| import math
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| import random
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| import numpy as np
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| class MaskingGenerator:
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| def __init__(
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| self,
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| input_size,
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| num_masking_patches=None,
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| min_num_patches=4,
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| max_num_patches=None,
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| min_aspect=0.3,
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| max_aspect=None,
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| ):
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| if not isinstance(input_size, tuple):
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| input_size = (input_size,) * 2
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| self.height, self.width = input_size
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| self.num_patches = self.height * self.width
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| self.num_masking_patches = num_masking_patches
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| self.min_num_patches = min_num_patches
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| self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches
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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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| def __repr__(self):
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| repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
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| self.height,
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| self.width,
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| self.min_num_patches,
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| self.max_num_patches,
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| self.num_masking_patches,
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| self.log_aspect_ratio[0],
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| self.log_aspect_ratio[1],
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| )
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| return repr_str
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| def get_shape(self):
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| return self.height, self.width
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| def _mask(self, mask, max_mask_patches):
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| delta = 0
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| for _ in range(10):
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| target_area = random.uniform(self.min_num_patches, max_mask_patches)
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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 < self.width and h < self.height:
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| top = random.randint(0, self.height - h)
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| left = random.randint(0, self.width - w)
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| num_masked = mask[top : top + h, left : left + w].sum()
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| if 0 < h * w - num_masked <= max_mask_patches:
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| for i in range(top, top + h):
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| for j in range(left, left + w):
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| if mask[i, j] == 0:
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| mask[i, j] = 1
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| delta += 1
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| if delta > 0:
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| break
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| return delta
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| def __call__(self, num_masking_patches=0):
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| mask = np.zeros(shape=self.get_shape(), dtype=bool)
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| mask_count = 0
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| while mask_count < num_masking_patches:
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| max_mask_patches = num_masking_patches - mask_count
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| max_mask_patches = min(max_mask_patches, self.max_num_patches)
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| delta = self._mask(mask, max_mask_patches)
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| if delta == 0:
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| break
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| else:
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| mask_count += delta
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| return self.complete_mask_randomly(mask, num_masking_patches)
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| def complete_mask_randomly(self, mask, num_masking_patches):
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| shape = mask.shape
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| m2 = mask.flatten()
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| to_add = np.random.choice(np.where(~m2)[0], size=num_masking_patches - m2.sum(), replace=False)
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| m2[to_add] = True
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| return m2.reshape(shape)
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