blob: 86a485288bd772b6c96e5f92117437193eba4793 [file] [log] [blame]
/*********************************************************
*
* 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_CUDNN
#include <cudnn.h>
#include "../layer/cudnn_utils.h"
#endif
#ifdef USE_DNNL
#include <singa/utils/dnnl_utils.h>
#endif // USE_DNNL
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_DNNL
dnnl::memory::desc x_md;
dnnl::memory::desc y_md;
dnnl::memory ws_mem;
dnnl::pooling_forward::primitive_desc pool_fwd_pd;
dnnl::pooling_backward::primitive_desc pool_bwd_pd;
#endif // USE_DNNL
};
#ifdef USE_DNNL
Tensor CpuPoolingForward(const PoolingHandle &ph, const Tensor &x);
Tensor CpuPoolingBackward(const PoolingHandle &ph, const Tensor &dy,
const Tensor &x, const Tensor &y);
#endif // USE_DNNL
#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_