# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Union
import paddle
[docs]def is_broadcastable(shp1, shp2):
for a, b in zip(shp1[::-1], shp2[::-1]):
if a == 1 or b == 1 or a == b:
pass
else:
return False
return True
# assume that len(shp1) == len(shp2)
[docs]def broadcast_shape(shp1, shp2):
result = []
for a, b in zip(shp1[::-1], shp2[::-1]):
result.append(max(a, b))
return result[::-1]
[docs]def masked_fill(xs: paddle.Tensor,
mask: paddle.Tensor,
value: Union[float, int]):
# comment following line for converting dygraph to static graph.
# assert is_broadcastable(xs.shape, mask.shape) is True
bshape = broadcast_shape(xs.shape, mask.shape)
mask.stop_gradient = True
mask = mask.broadcast_to(bshape)
trues = paddle.ones_like(xs) * value
mask = mask.cast(dtype=paddle.bool)
xs = paddle.where(mask, trues, xs)
return xs