# 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. # Modified from espnet(https://github.com/espnet/espnet) import paddle from paddle import nn from typeguard import check_argument_types def pad_list(xs, pad_value): """Perform padding for the list of tensors. Args: xs (List[Tensor]): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)]. pad_value (float): Value for padding. Returns: Tensor: Padded tensor (B, Tmax, `*`). Examples: >>> x = [paddle.ones([4]), paddle.ones([2]), paddle.ones([1])] >>> x [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])] >>> pad_list(x, 0) tensor([[1., 1., 1., 1.], [1., 1., 0., 0.], [1., 0., 0., 0.]]) """ n_batch = len(xs) max_len = max(x.shape[0] for x in xs) pad = paddle.full([n_batch, max_len, *xs[0].shape[1:]], pad_value, dtype=xs[0].dtype) for i in range(n_batch): pad[i, :xs[i].shape[0]] = xs[i] return pad def make_pad_mask(lengths, xs=None, length_dim=-1): """Make mask tensor containing indices of padded part. Args: lengths (Tensor(int64)): Batch of lengths (B,). xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor. length_dim (int, optional): Dimension indicator of the above tensor. See the example. Returns: Tensor(bool): Mask tensor containing indices of padded part bool. Examples: With only lengths. >>> lengths = [5, 3, 2] >>> make_non_pad_mask(lengths) masks = [[0, 0, 0, 0 ,0], [0, 0, 0, 1, 1], [0, 0, 1, 1, 1]] With the reference tensor. >>> xs = paddle.zeros((3, 2, 4)) >>> make_pad_mask(lengths, xs) tensor([[[0, 0, 0, 0], [0, 0, 0, 0]], [[0, 0, 0, 1], [0, 0, 0, 1]], [[0, 0, 1, 1], [0, 0, 1, 1]]]) >>> xs = paddle.zeros((3, 2, 6)) >>> make_pad_mask(lengths, xs) tensor([[[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]], [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], [[0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1]]]) With the reference tensor and dimension indicator. >>> xs = paddle.zeros((3, 6, 6)) >>> make_pad_mask(lengths, xs, 1) tensor([[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1]], [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]], [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1]]]) >>> make_pad_mask(lengths, xs, 2) tensor([[[0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 1]], [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]], [[0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1]]],) """ if length_dim == 0: raise ValueError("length_dim cannot be 0: {}".format(length_dim)) bs = paddle.shape(lengths)[0] if xs is None: maxlen = lengths.max() else: maxlen = paddle.shape(xs)[length_dim] seq_range = paddle.arange(0, maxlen, dtype=paddle.int64) seq_range_expand = seq_range.unsqueeze(0).expand([bs, maxlen]) seq_length_expand = lengths.unsqueeze(-1) mask = seq_range_expand >= seq_length_expand if xs is not None: assert paddle.shape(xs)[0] == bs, (paddle.shape(xs)[0], bs) if length_dim < 0: length_dim = len(paddle.shape(xs)) + length_dim # ind = (:, None, ..., None, :, , None, ..., None) ind = tuple( slice(None) if i in (0, length_dim) else None for i in range(len(paddle.shape(xs)))) mask = paddle.expand(mask[ind], paddle.shape(xs)) return mask def make_non_pad_mask(lengths, xs=None, length_dim=-1): """Make mask tensor containing indices of non-padded part. Args: lengths (Tensor(int64) or List): Batch of lengths (B,). xs (Tensor, optional): The reference tensor. If set, masks will be the same shape as this tensor. length_dim (int, optional): Dimension indicator of the above tensor. See the example. Returns: Tensor(bool): mask tensor containing indices of padded part bool. Examples: With only lengths. >>> lengths = [5, 3, 2] >>> make_non_pad_mask(lengths) masks = [[1, 1, 1, 1 ,1], [1, 1, 1, 0, 0], [1, 1, 0, 0, 0]] With the reference tensor. >>> xs = paddle.zeros((3, 2, 4)) >>> make_non_pad_mask(lengths, xs) tensor([[[1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 0], [1, 1, 1, 0]], [[1, 1, 0, 0], [1, 1, 0, 0]]]) >>> xs = paddle.zeros((3, 2, 6)) >>> make_non_pad_mask(lengths, xs) tensor([[[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]], [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0]], [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0]]]) With the reference tensor and dimension indicator. >>> xs = paddle.zeros((3, 6, 6)) >>> make_non_pad_mask(lengths, xs, 1) tensor([[[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], [[1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]]) >>> make_non_pad_mask(lengths, xs, 2) tensor([[[1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 0]], [[1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0]], [[1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0]]]) """ return paddle.logical_not(make_pad_mask(lengths, xs, length_dim)) def initialize(model: nn.Layer, init: str): """Initialize weights of a neural network module. Parameters are initialized using the given method or distribution. Custom initialization routines can be implemented into submodules Args: model (nn.Layer): Target. init (str): Method of initialization. """ assert check_argument_types() if init == "xavier_uniform": nn.initializer.set_global_initializer(nn.initializer.XavierUniform(), nn.initializer.Constant()) elif init == "xavier_normal": nn.initializer.set_global_initializer(nn.initializer.XavierNormal(), nn.initializer.Constant()) elif init == "kaiming_uniform": nn.initializer.set_global_initializer(nn.initializer.KaimingUniform(), nn.initializer.Constant()) elif init == "kaiming_normal": nn.initializer.set_global_initializer(nn.initializer.KaimingNormal(), nn.initializer.Constant()) else: raise ValueError("Unknown initialization: " + init)