112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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# 按照这个 batch 里面最长的补零
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def pad_list(xs, pad_value):
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"""Perform padding for the list of tensors.
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Args:
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xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
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pad_value (float): Value for padding.
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Returns:
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Tensor: Padded tensor (B, Tmax, `*`).
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Examples:
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>>> x = [paddle.ones([4]), paddle.ones([2]), paddle.ones([1])]
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>>> x
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[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
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>>> pad_list(x, 0)
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tensor([[1., 1., 1., 1.],
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[1., 1., 0., 0.],
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[1., 0., 0., 0.]])
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"""
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n_batch = len(xs)
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max_len = max(x.shape[0] for x in xs)
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# pad = xs[0].new(n_batch, max_len, *xs[0].shape[1:]).fill_(pad_value)
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pad = paddle.full([n_batch, max_len, *xs[0].shape[1:]], pad_value)
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for i in range(n_batch):
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pad[i, :xs[i].shape[0]] = xs[i]
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return pad
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def make_pad_mask(lengths, length_dim=-1):
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"""Make mask tensor containing indices of padded part.
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Args:
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lengths (LongTensor or List): Batch of lengths (B,).
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Returns:
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Tensor: Mask tensor containing indices of padded part bool.
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Examples:
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With only lengths.
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>>> lengths = [5, 3, 2]
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>>> make_non_pad_mask(lengths)
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masks = [[0, 0, 0, 0 ,0],
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[0, 0, 0, 1, 1],
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[0, 0, 1, 1, 1]]
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"""
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if length_dim == 0:
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raise ValueError("length_dim cannot be 0: {}".format(length_dim))
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if not isinstance(lengths, list):
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lengths = lengths.tolist()
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bs = int(len(lengths))
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maxlen = int(max(lengths))
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seq_range = paddle.arange(0, maxlen, dtype=paddle.int64)
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seq_range_expand = seq_range.unsqueeze(0).expand([bs, maxlen])
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seq_length_expand = paddle.to_tensor(
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lengths, dtype=seq_range_expand.dtype).unsqueeze(-1)
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mask = seq_range_expand >= seq_length_expand
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return mask
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def make_non_pad_mask(lengths, length_dim=-1):
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"""Make mask tensor containing indices of non-padded part.
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Args:
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lengths (LongTensor or List): Batch of lengths (B,).
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xs (Tensor, optional): The reference tensor.
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If set, masks will be the same shape as this tensor.
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length_dim (int, optional): Dimension indicator of the above tensor.
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See the example.
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Returns:
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ByteTensor: mask tensor containing indices of padded part bool.
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Examples:
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With only lengths.
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>>> lengths = [5, 3, 2]
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>>> make_non_pad_mask(lengths)
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masks = [[1, 1, 1, 1 ,1],
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[1, 1, 1, 0, 0],
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[1, 1, 0, 0, 0]]
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"""
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return paddle.logical_not(make_pad_mask(lengths, length_dim))
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