mirror of https://github.com/alibaba/EasyCV.git
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
# Copyright 2019 Alibaba Inc. 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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# ==============================================================================
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"""Activation modules."""
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from __future__ import absolute_import, division, print_function
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import torch
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from easycv.core.sailfish.util import ModelParallel
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class LogSoftmax(torch.nn.Module):
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r"""Applies the :math:`\log(\text{Softmax}(x))` function to an n-dimensional
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input Tensor rescaling them so that the elements of the
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n-dimensional output Tensor lie in the range (0, 1].
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Shape:
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- Input: :math:`(*)` where `*` means, any number of additional
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dimensions
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- Output: :math:`(*)`, same shape as the input
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Returns:
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a Tensor of the same dimension and shape as the input with
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values in the range (-inf,0].
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Examples::
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>>> m = LogSoftmax()
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>>> input = torch.randn(2, 3)
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>>> output = m(input)
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"""
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def __init__(self, epsilon=0, parallel=None):
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super(LogSoftmax, self).__init__()
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self.epsilon = epsilon
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self.parallel = parallel
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def forward(self, logits): # pylint: disable=arguments-differ
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if isinstance(self.parallel, ModelParallel):
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return self.parallel.log_softmax(logits, epsilon=self.epsilon)
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return torch.nn.functional.log_softmax(logits, _stacklevel=5)
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