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maxpool_op.h
/*
COPYRIGHT
All contributions by Taiga Nomi
Copyright (c) 2013, Taiga Nomi
All rights reserved.
All other contributions:
Copyright (c) 2013-2016, the respective contributors.
All rights reserved.
Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.
LICENSE
The BSD 3-Clause License
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 tiny-dnn 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/framework/op_kernel.h"
#include "tiny_dnn/core/kernels/maxpool_op_internal.h"
#include "tiny_dnn/core/kernels/maxpool_op_nnpack.h"
#include "tiny_dnn/core/kernels/maxpool_op_avx.h"
namespace tiny_dnn {
class MaxPoolOp : public core::OpKernel {
public:
explicit MaxPoolOp(const core::OpKernelConstruction& context)
: core::OpKernel(context) {}
void compute(const core::OpKernelContext& context) override {
auto& params = OpKernel::params_->maxpool();
// incomimg/outcoming data
const tensor_t& in_data = context.input(0);
tensor_t& out_data = context.output(1);
// initialize outputs
fill_tensor(out_data, float_t(0));
// call convolution algorithm depending
// on the selected engine type
const core::backend_t engine = context.engine();
if (engine == core::backend_t::internal) {
kernels::maxpool_op_internal(
in_data,
out_data,
params.out2inmax,
params.out2in,
context.parallelize());
} else if (engine == core::backend_t::nnpack) {
// NNPACK supports stride != 2 or pool_size !=2
// there's optimization over stride=2 and pool_size=2
/*
if (params.stride_x != 2 || params.stride_y != 2) {
throw nn_error("NNPACK Max-Pool requires a stride == 2.");
}
if (params.pool_size_x != 2 || params.pool_size_y != 2) {
throw nn_error("NNPACK Max-Pool requires a pool size == 2.");
}
*/
kernels::maxpool_op_nnpack(
in_data,
out_data,
params);
} else if (engine == core::backend_t::avx) {
kernels::maxpool_op_avx(
in_data,
out_data,
params.out2inmax,
params.out2in,
context.parallelize());
} else {
throw nn_error("Not supported engine: " + to_string(engine));
}
}
};
} // namespace tiny_dnn
tiny_dnn::MaxPoolOp
Definition maxpool_op.h:55
tiny_dnn::core::OpKernelConstruction
Definition op_kernel.h:55
tiny_dnn::core::OpKernelContext
Definition op_kernel.h:72
tiny_dnn::core::OpKernel
Definition op_kernel.h:175
tiny_dnn::image
Simple image utility class.
Definition image.h:94
tiny_dnn::nn_error
error exception class for tiny-dnn
Definition nn_error.h:37