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73 lines
2.9 KiB
Python
73 lines
2.9 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 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 LogitsType, LogprobsType, NeuralType
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__all__ = ['SequenceClassifier']
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class SequenceClassifier(Classifier):
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@property
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def output_types(self) -> Optional[Dict[str, NeuralType]]:
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if not self.log_softmax:
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return {"logits": NeuralType(('B', 'D'), LogitsType())}
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else:
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return {"log_probs": NeuralType(('B', 'D'), LogprobsType())}
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def __init__(
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self,
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hidden_size: int,
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num_classes: int,
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num_layers: int = 2,
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activation: str = 'relu',
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log_softmax: bool = True,
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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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"""
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Initializes the SequenceClassifier module.
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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_classes: number of the classes to predict
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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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log_softmax: applies the log softmax on the output
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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, default is the first token
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"""
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super().__init__(hidden_size=hidden_size, dropout=dropout)
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self.log_softmax = log_softmax
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self._idx_conditioned_on = idx_conditioned_on
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self.mlp = MultiLayerPerceptron(
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hidden_size=hidden_size,
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num_classes=num_classes,
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num_layers=num_layers,
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activation=activation,
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log_softmax=log_softmax,
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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):
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hidden_states = self.dropout(hidden_states)
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logits = self.mlp(hidden_states[:, self._idx_conditioned_on])
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return logits
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