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70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
# Copyright (c) 2020, NVIDIA CORPORATION. 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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from typing import Dict, Optional
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from torch import Tensor
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from nemo.collections.common.parts import MultiLayerPerceptron
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from nemo.collections.nlp.modules.common.classifier import Classifier
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from nemo.core.classes import typecheck
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from nemo.core.neural_types import NeuralType, RegressionValuesType
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__all__ = ['SequenceRegression']
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class SequenceRegression(Classifier):
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"""
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Args:
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hidden_size: the hidden size of the mlp head on the top of the encoder
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num_layers: number of the linear layers of the mlp head on the top of the encoder
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activation: type of activations between layers of the mlp head
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dropout: the dropout used for the mlp head
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use_transformer_init: initializes the weights with the same approach used in Transformer
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idx_conditioned_on: index of the token to use as the sequence representation for the classification task,
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default is the first token
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"""
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@property
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def output_types(self) -> Optional[Dict[str, NeuralType]]:
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return {"preds": NeuralType(tuple('B'), RegressionValuesType())}
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def __init__(
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self,
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hidden_size: int,
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num_layers: int = 2,
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activation: str = 'relu',
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dropout: float = 0.0,
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use_transformer_init: bool = True,
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idx_conditioned_on: int = 0,
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):
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""" Initializes the SequenceRegression module. """
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super().__init__(hidden_size=hidden_size, dropout=dropout)
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self._idx_conditioned_on = idx_conditioned_on
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self.mlp = MultiLayerPerceptron(
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hidden_size, num_classes=1, num_layers=num_layers, activation=activation, log_softmax=False,
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)
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self.post_init(use_transformer_init=use_transformer_init)
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@typecheck()
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def forward(self, hidden_states: Tensor) -> Tensor:
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""" Forward pass through the module.
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Args:
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hidden_states: hidden states for each token in a sequence, for example, BERT module output
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"""
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hidden_states = self.dropout(hidden_states)
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preds = self.mlp(hidden_states[:, self._idx_conditioned_on])
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return preds.view(-1)
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