62 lines
2.0 KiB
62 lines
2.0 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "PoolLayer.h"
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namespace paddle {
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/**
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* @brief CudnnPoolLayer is subclass of PoolLayer, which is implemented by
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* cudnn api and only supports GPU.
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*
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* The config file api is img_pool_layer.
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*/
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class CudnnPoolLayer : public PoolLayer {
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protected:
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int windowHeight, windowWidth;
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int heightPadding, widthPadding, strideHeight, strideWidth;
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int imageH_, imageW_, outputH_, outputW_;
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/// mode_ is poolint type, inlcuding "cudnn-max-pool", "cudnn-avg-pool"
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/// "cudnn-avg-excl-pad-pool".
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hl_pooling_mode_t mode_;
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/// cudnn tensor descriptor for input.
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hl_tensor_descriptor inputDesc_;
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/// cudnn tensor descriptor for output.
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hl_tensor_descriptor outputDesc_;
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/// A description of a pooling operation.
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hl_pooling_descriptor poolingDesc_;
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public:
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static bool typeCheck(const std::string& poolType,
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hl_pooling_mode_t* mode = nullptr);
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explicit CudnnPoolLayer(const LayerConfig& config);
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~CudnnPoolLayer();
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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/**
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* Reshape input and output tensor descriptor.
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* The batch size maybe change during training in last batch of each pass.
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* So reshaping is needed.
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*/
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void reshape(int batchSize);
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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} // namespace paddle
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