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# Copyright (c) 2020, NVIDIA CORPORATION. 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.
from typing import Dict, Optional
from nemo.collections.common.parts import MultiLayerPerceptron
from nemo.collections.nlp.modules.common.classifier import Classifier
from nemo.core.classes import typecheck
from nemo.core.neural_types import LogitsType, LogprobsType, NeuralType
__all__ = ['SequenceClassifier']
class SequenceClassifier(Classifier):
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
if not self.log_softmax:
return {"logits": NeuralType(('B', 'D'), LogitsType())}
else:
return {"log_probs": NeuralType(('B', 'D'), LogprobsType())}
def __init__(
self,
hidden_size: int,
num_classes: int,
num_layers: int = 2,
activation: str = 'relu',
log_softmax: bool = True,
dropout: float = 0.0,
use_transformer_init: bool = True,
idx_conditioned_on: int = 0,
):
"""
Initializes the SequenceClassifier module.
Args:
hidden_size: the hidden size of the mlp head on the top of the encoder
num_classes: number of the classes to predict
num_layers: number of the linear layers of the mlp head on the top of the encoder
activation: type of activations between layers of the mlp head
log_softmax: applies the log softmax on the output
dropout: the dropout used for the mlp head
use_transformer_init: initializes the weights with the same approach used in Transformer
idx_conditioned_on: index of the token to use as the sequence representation for the classification task, default is the first token
"""
super().__init__(hidden_size=hidden_size, dropout=dropout)
self.log_softmax = log_softmax
self._idx_conditioned_on = idx_conditioned_on
self.mlp = MultiLayerPerceptron(
hidden_size=hidden_size,
num_classes=num_classes,
num_layers=num_layers,
activation=activation,
log_softmax=log_softmax,
)
self.post_init(use_transformer_init=use_transformer_init)
@typecheck()
def forward(self, hidden_states):
hidden_states = self.dropout(hidden_states)
logits = self.mlp(hidden_states[:, self._idx_conditioned_on])
return logits
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