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149 lines
3.7 KiB
149 lines
3.7 KiB
# Copyright (c) 2016 PaddlePaddle Authors. 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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"""
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__all__ = [
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"BasePoolingType", "MaxPooling", "AvgPooling", "MaxWithMaskPooling",
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"CudnnMaxPooling", "CudnnAvgPooling", "CudnnAvgInclPadPooling",
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"SumPooling", "SquareRootNPooling"
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]
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class BasePoolingType(object):
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"""
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Base Pooling Type.
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Note these pooling types are used for sequence input, not for images.
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Each PoolingType contains one parameter:
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:param name: pooling layer type name used by paddle.
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:type name: basestring
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"""
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def __init__(self, name):
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self.name = name
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class MaxPooling(BasePoolingType):
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"""
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Max pooling.
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Return the very large values for each dimension in sequence or time steps.
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.. math::
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max(samples\\_of\\_a\\_sequence)
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:param output_max_index: True if output sequence max index instead of max
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value. None means use default value in proto.
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:type output_max_index: bool|None
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"""
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def __init__(self, output_max_index=None):
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BasePoolingType.__init__(self, "max")
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self.output_max_index = output_max_index
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class MaxWithMaskPooling(BasePoolingType):
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"""
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MaxWithMask pooling.
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Not only return the very large values for each dimension in sequence or time steps,
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but also the location indices of found maxinum values.
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"""
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def __init__(self):
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BasePoolingType.__init__(self, "max-pool-with-mask")
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class CudnnMaxPooling(BasePoolingType):
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"""
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Cudnn max pooling only support GPU. Return the maxinum value in the
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pooling window.
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"""
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def __init__(self):
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BasePoolingType.__init__(self, "cudnn-max-pool")
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class CudnnAvgPooling(BasePoolingType):
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"""
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Cudnn average pooling only support GPU. Return the average value in the
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pooling window.
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"""
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def __init__(self):
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BasePoolingType.__init__(self, "cudnn-avg-pool")
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class CudnnAvgInclPadPooling(BasePoolingType):
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"""
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Cudnn average pooling only support GPU. Return the average value in the
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pooling window taking into account the padding cells.
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"""
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def __init__(self):
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BasePoolingType.__init__(self, "cudnn-avg-incl-pad-pool")
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class AvgPooling(BasePoolingType):
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"""
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Average pooling.
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Return the average values for each dimension in sequence or time steps.
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.. math::
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sum(samples\\_of\\_a\\_sequence)/sample\\_num
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"""
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STRATEGY_AVG = "average"
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STRATEGY_SUM = "sum"
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STRATEGY_SQROOTN = "squarerootn"
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def __init__(self, strategy=STRATEGY_AVG):
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BasePoolingType.__init__(self, "average")
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self.strategy = strategy
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class SumPooling(AvgPooling):
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"""
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Sum pooling.
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Return the sum values of each dimension in sequence or time steps.
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.. math::
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sum(samples\\_of\\_a\\_sequence)
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"""
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def __init__(self):
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AvgPooling.__init__(self, AvgPooling.STRATEGY_SUM)
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class SquareRootNPooling(AvgPooling):
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"""
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Square Root Pooling.
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Return the square root values of each dimension in sequence or time steps.
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.. math::
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sum(samples\\_of\\_a\\_sequence)/sqrt(sample\\_num)
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"""
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def __init__(self):
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AvgPooling.__init__(self, AvgPooling.STRATEGY_SQROOTN)
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