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/*
Copyright (c) 2016, Taiga Nomi, Edgar Riba
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the <organization> nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY
EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#pragma once
#include "tiny_dnn/core/params/conv_params.h"
#ifdef CNN_USE_NNPACK
#include "nnpack.h"
inline nnp_convolution_algorithm nnp_algorithm() {
return nnp_convolution_algorithm_auto;
}
inline nnp_convolution_transform_strategy nnp_kts() {
return nnp_convolution_transform_strategy_tuple_based;//some algorithm accept tuple based only
}
#endif
namespace tiny_dnn {
namespace kernels {
inline void
conv2d_op_nnpack(const tensor_t& in_data,
const vec_t& W,
const vec_t& bias,
tensor_t& out_data,
const core::conv_params& params) {
#ifdef CNN_USE_NNPACK
nnp_status init_status = nnp_initialize();
if (init_status != nnp_status_success) {
throw nn_error("Cannot initialize NNPACK.");
}
// TOOD: use input config
const auto algorithm = nnp_algorithm();
const auto kernel_transform_strategy = nnp_kts();
const serial_size_t input_channels = params.in.depth_;
const serial_size_t output_channels = params.out.depth_;
//input data passed by convolution layer has been padded already
//set input_size to padded size
const nnp_size input_size = {
static_cast<size_t>(params.in_padded.width_),
static_cast<size_t>(params.in_padded.height_)
};
const nnp_size kernel_size = {
static_cast<size_t>(params.weight.width_),
static_cast<size_t>(params.weight.height_)
};
// input padded ,so no need to do padding
const float_t dx =0;// params.in_padded.width_ - params.in.width_;
const float_t dy =0;// params.in_padded.height_ - params.in.height_;
// we'll assume that padding is symmetric
const nnp_padding padding = {
static_cast<size_t>(dy/2), // top
static_cast<size_t>(dx/2), // right
static_cast<size_t>(dy/2), // bottom
static_cast<size_t>(dx/2) // left
};
const float* input_ptr = reinterpret_cast<const float*>(in_data[0].data());
const float* kernel_ptr = reinterpret_cast<const float*>(W.data());
const float* bias_ptr = reinterpret_cast<const float*>(bias.data());
const nnp_size stride= {
static_cast<size_t>(params.w_stride),
static_cast<size_t>(params.h_stride)
};
float* output_ptr = out_data[0].data();
// TODO: embed it into a class
const size_t num_mkl_threads = 1;
pthreadpool_t threadpool = pthreadpool_create(num_mkl_threads);
nnp_profile* profile = nullptr;
nnp_status status =
nnp_convolution_inference(
algorithm,
kernel_transform_strategy,
input_channels,
output_channels,
input_size,
padding,
kernel_size,
stride,
input_ptr,
kernel_ptr,
bias_ptr,
output_ptr,
threadpool,
profile);
if (status != nnp_status_success) {
throw nn_error("Could not succeed with nnp_convolution_inference");
}
// TODO: embed it into a class
pthreadpool_destroy(threadpool);
#else
throw nn_error("TinyDNN has not been compiled with NNPACK support.");
#endif
}
} // namespace kernels
} // namespace tiny_dnn
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