62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
# Copyright (c) 2021 Jingyong Hou
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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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import torch
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import torch.nn as nn
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class GlobalClassifier(nn.Module):
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"""Add a global average pooling before the classifier"""
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def __init__(self, classifier: nn.Module):
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super(GlobalClassifier, self).__init__()
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self.classifier = classifier
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def forward(self, x: torch.Tensor):
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x = torch.mean(x, dim=1)
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return self.classifier(x)
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class LastClassifier(nn.Module):
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"""Select last frame to do the classification"""
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def __init__(self, classifier: nn.Module):
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super(LastClassifier, self).__init__()
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self.classifier = classifier
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def forward(self, x: torch.Tensor):
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x = x[:, -1, :]
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return self.classifier(x)
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class ElementClassifier(nn.Module):
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"""Classify all the frames in an utterance"""
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def __init__(self, classifier: nn.Module):
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super(ElementClassifier, self).__init__()
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self.classifier = classifier
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def forward(self, x: torch.Tensor):
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return self.classifier(x)
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class LinearClassifier(nn.Module):
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""" Wrapper of Linear """
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def __init__(self, input_dim, output_dim):
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super().__init__()
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self.linear = torch.nn.Linear(input_dim, output_dim)
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self.quant = torch.quantization.QuantStub()
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self.dequant = torch.quantization.DeQuantStub()
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def forward(self, x: torch.Tensor):
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x = self.quant(x)
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x = self.linear(x)
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x = self.dequant(x)
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return x
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