| /********************************************************* |
| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| * |
| ************************************************************/ |
| #ifndef SINGA_MODEL_OPERATION_POOLING_H_ |
| #define SINGA_MODEL_OPERATION_POOLING_H_ |
| |
| #include <string> |
| #include "singa/core/tensor.h" |
| |
| #ifdef USE_MKLDNN |
| #include <mkldnn.hpp> |
| #endif // USE_MKLDNN |
| |
| #ifdef USE_CUDNN |
| #include <cudnn.h> |
| #include "../layer/cudnn_utils.h" |
| #endif |
| |
| namespace singa { |
| |
| class PoolingHandle { |
| public: |
| PoolingHandle(const Tensor &input, const std::vector<int>& kernel_size, |
| const std::vector<int>& stride, const std::vector<int>& padding, |
| const bool is_max = true); |
| ~PoolingHandle(); |
| |
| int kernel_w; |
| int pad_w; |
| int stride_w; |
| int kernel_h; |
| int pad_h; |
| int stride_h; |
| |
| int batchsize; |
| int channels; |
| int height; |
| int width; |
| |
| int pooled_height; |
| int pooled_width; |
| |
| bool is_max_pooling; |
| |
| #ifdef USE_MKLDNN |
| mkldnn::memory::data_type dtype; |
| mkldnn::memory::dims x_dims; |
| mkldnn::memory::dims y_dims; |
| mkldnn::memory::dims s_dims; |
| mkldnn::memory::dims k_dims; |
| mkldnn::memory::dims p_dims; |
| mkldnn::algorithm pooling_algo; |
| const mkldnn::memory::desc *x_md = nullptr; |
| const mkldnn::memory::desc *y_md = nullptr; |
| const mkldnn::pooling_forward::desc *pool_fwd_d = nullptr; |
| const mkldnn::pooling_forward::primitive_desc *pool_fwd_pd = nullptr; |
| const mkldnn::memory::primitive_desc *pool_ws_d = nullptr; |
| const mkldnn::memory *ws_mem = nullptr; |
| #endif // USE_MKLDNN |
| }; |
| |
| #ifdef USE_MKLDNN |
| |
| Tensor CpuPoolingForward(const PoolingHandle &ph, const Tensor &x); |
| Tensor CpuPoolingBackward(const PoolingHandle &ph, const Tensor &dy, |
| const Tensor& x, const Tensor& y); |
| |
| #endif // USE_MKLDNN |
| |
| #ifdef USE_CUDNN |
| class CudnnPoolingHandle : public PoolingHandle { |
| public: |
| CudnnPoolingHandle(const Tensor &input, const std::vector<int>& kernel_size, |
| const std::vector<int>& stride, const std::vector<int>& padding, |
| const bool is_max = true); |
| ~CudnnPoolingHandle(); |
| |
| cudnnTensorDescriptor_t x_desc = nullptr; |
| cudnnTensorDescriptor_t y_desc = nullptr; |
| cudnnPoolingDescriptor_t pool_desc = nullptr; |
| cudnnNanPropagation_t nan_prop = CUDNN_PROPAGATE_NAN; |
| |
| }; |
| |
| Tensor GpuPoolingForward(const CudnnPoolingHandle &cph, const Tensor &x); |
| |
| Tensor GpuPoolingBackward(const CudnnPoolingHandle &cph, const Tensor &dy, |
| const Tensor& x, const Tensor& y); |
| |
| #endif //USE_CUDNN |
| |
| } // namespace singa |
| |
| #endif // SINGA_MODEL_OPERATION_POOLING_H_ |