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# --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --------------------------------------------------------
import numpy as np
class RandomMaskingGenerator:
def __init__(self, input_size, mask_ratio):
if not isinstance(input_size, tuple):
input_size = (input_size,) * 2
self.height, self.width = input_size
self.num_patches = self.height * self.width
self.num_mask = int(mask_ratio * self.num_patches)
def __repr__(self):
repr_str = "Maks: total patches {}, mask patches {}".format(
self.num_patches, self.num_mask
)
return repr_str
def __call__(self):
mask = np.hstack([
np.zeros(self.num_patches - self.num_mask),
np.ones(self.num_mask),
])
np.random.shuffle(mask)
return mask # [196]
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