# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Sequence-to-vector basic model"""
# pylint: disable=abstract-method
# pylint: disable=arguments-differ
from mindspore import nn
from mindspore import ops
from mindnlp.abc.backbones.base import BaseModel
[文档]class Seq2vecModel(BaseModel):
r"""
Basic class for seq2vec models
Args:
encoder (EncoderBase): The encoder.
head (nn.Cell): The module to process encoder output.
dropout (float): The drop out rate, greater than 0 and less equal than 1.
If None, not dropping out input units. Drfault: None.
"""
def __init__(self, encoder, head, dropout: float = None):
super().__init__()
self.encoder = encoder
self.head = head
if dropout is None:
self.dropout = None
else:
self.dropout = nn.Dropout(1 - dropout)
def construct(self, src_tokens, mask=None):
"""
Construct method.
Args:
src_tokens (Tensor): Tokens of source sentences with shape [batch, src_len].
mask (Tensor): Its elements identify whether the corresponding input token is padding or not.
If True, not padding token. If False, padding token. Defaults to None.
Returns:
Tensor, the result vector of seq2vec model with shape [batch, label_num].
"""
if mask is None:
mask = self._gen_mask(src_tokens)
context = self.get_context(src_tokens, mask)
if self.dropout is not None:
context = self.dropout(context)
result = self.head(context)
# TODO: Whether to add reduction
return result
def get_context(self, src_tokens, mask=None):
"""
Get Context from encoder.
Args:
src_tokens (Tensor): Tokens of source sentences with shape [batch, src_len].
mask (Tensor): Its elements identify whether the corresponding input token is padding or not.
If True, not padding token. If False, padding token. Defaults to None.
Returns:
Union[Tensor, tuple], the output of encoder.
"""
if mask is None:
mask = self._gen_mask(src_tokens)
return self.encoder(src_tokens, mask=mask)
def _gen_mask(self, inputs):
"""Generate mask tensor"""
return ops.ones_like(inputs)