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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import random
import math
import numpy as np
class MaskingGenerator:
def __init__(
self,
input_size,
num_masking_patches=None,
min_num_patches=4,
max_num_patches=None,
min_aspect=0.3,
max_aspect=None,
):
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_masking_patches = num_masking_patches
self.min_num_patches = min_num_patches
self.max_num_patches = num_masking_patches if max_num_patches is None else max_num_patches
max_aspect = max_aspect or 1 / min_aspect
self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
def __repr__(self):
repr_str = "Generator(%d, %d -> [%d ~ %d], max = %d, %.3f ~ %.3f)" % (
self.height,
self.width,
self.min_num_patches,
self.max_num_patches,
self.num_masking_patches,
self.log_aspect_ratio[0],
self.log_aspect_ratio[1],
)
return repr_str
def get_shape(self):
return self.height, self.width
def _mask(self, mask, max_mask_patches):
delta = 0
for _ in range(10):
target_area = random.uniform(self.min_num_patches, max_mask_patches)
aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < self.width and h < self.height:
top = random.randint(0, self.height - h)
left = random.randint(0, self.width - w)
num_masked = mask[top : top + h, left : left + w].sum()
# Overlap
if 0 < h * w - num_masked <= max_mask_patches:
for i in range(top, top + h):
for j in range(left, left + w):
if mask[i, j] == 0:
mask[i, j] = 1
delta += 1
if delta > 0:
break
return delta
def __call__(self, num_masking_patches=0):
mask = np.zeros(shape=self.get_shape(), dtype=bool)
mask_count = 0
while mask_count < num_masking_patches:
max_mask_patches = num_masking_patches - mask_count
max_mask_patches = min(max_mask_patches, self.max_num_patches)
delta = self._mask(mask, max_mask_patches)
if delta == 0:
break
else:
mask_count += delta
return mask
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