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conv2d_op_opencl.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-cnn 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
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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*/
#pragma once
#include "tiny_dnn/core/framework/op_kernel.h"
namespace tiny_dnn {
class Conv2dOpenCLForwardOp : public core::OpKernel {
public:
explicit Conv2dOpenCLForwardOp(const core::OpKernelConstruction& context)
: core::OpKernel(context) {}
void compute(const core::OpKernelContext& context) override {
#if defined(USE_OPENCL) || defined(USE_CUDA)
auto params = OpKernel::params_->conv();
// incoming/outcoming data
const tensor_t& in_data = context.input(0);
const tensor_t& W = context.input(1);
const tensor_t& bias = context.input(2);
tensor_t& out_data = context.output(1);
// initialize outputs
fill_tensor(out_data, float_t(0));
// retrieve program from register
CLCudaAPI::Program program = ProgramManager::getInstance()
.program(Program(context.device(), context.Layer()));
nn_warn("Got Program");
// Creates the kernel from the compiled program and sets the three arguments.
// Note that the indices of the arguments have to be set according to their
// order in the kernel.
auto kernel = CLCudaAPI::Kernel(program, "CFMulti");
nn_warn("Got Kernel");
tiny_dnn::Device* device = context.device();
CLCudaAPI::Context ctx = context.device()->context();
CLCudaAPI::Queue queue = context.device()->queue();
// TODO(edgar): check if we really need that
for (serial_size_t i = 0; i < in_data.size(); ++i) {
// Creates device buffers and copies the host data to these
// device buffers.
auto dev_in = CLCudaAPI::Buffer<float_t>(ctx, queue,
in_data[i].begin(), in_data[i].end());
auto dev_W = CLCudaAPI::Buffer<float_t>(ctx, queue,
W[0].begin(), W[0].end());
auto dev_bias = CLCudaAPI::Buffer<float_t>(ctx, queue,
bias[0].begin(), bias[0].end());
auto dev_out = CLCudaAPI::Buffer<float_t>(ctx, queue,
out_data[i].begin(), out_data[i].end());
kernel.SetArgument(0, dev_in); // image_data
kernel.SetArgument(1, 0); // image_offset
kernel.SetArgument(2, dev_W); // kernel_data
kernel.SetArgument(3, 0); // kernel_offset
kernel.SetArgument(4, dev_bias); // bias
kernel.SetArgument(5, 0); // bias_offset
kernel.SetArgument(6, dev_out); // convolved_image
kernel.SetArgument(7, 0); // convolved_image_offset
kernel.SetArgument(8, static_cast<cl_ushort>(params.in.width_)); // WIDTH
kernel.SetArgument(9, static_cast<cl_ushort>(params.in.height_)); // HEIGHT
kernel.SetArgument(10, static_cast<cl_ushort>(params.out.width_)); // OUTPUT_W
kernel.SetArgument(11, static_cast<cl_ushort>(params.out.height_)); // OUTPUT_H
// We make sure that work group size is multiple of 16
serial_size_t res = device->device().MaxWorkGroupSize() % 16;
serial_size_t size = device->device().MaxWorkGroupSize() - res;
auto global = std::vector<size_t>{size};
auto local = std::vector<size_t>{16};
// Creates a new CLCudaAPI event to be able to time kernels
auto event = CLCudaAPI::Event();
// Enqueues the kernel and waits for the result.
// Note that launching the kernel is always a-synchronous and thus
// requires finishing the queue in order to complete the operation.
nn_info("## Running the kernel ...");
kernel.Launch(queue, global, local, event.pointer());
queue.Finish(event);
nn_info(" > Took " + to_string(event.GetElapsedTime()) + " ms");
// Upload data GPU -> CPU
std::vector<float_t> out(out_data[i].size(), 0);
dev_out.Read(queue, out_data[i].size(), out);
// FOR DEBUG ONLY
nn_warn("output kernel");
for (serial_size_t j = 0; j < out.size(); ++j) {
std::cout << out[j] << " ";
}
std::cout << std::endl;
// copy back
std::copy(std::begin(out), std::end(out), std::back_inserter(out_data[i]));
}
#else
throw nn_error("Not compiled with OpenCL");
#endif
}
};
class Conv2dOpenCLBackwardOp : public core::OpKernel {
public:
explicit Conv2dOpenCLBackwardOp(const core::OpKernelConstruction& context)
: core::OpKernel(context) {}
void compute(const core::OpKernelContext& context) override {
nn_error("Not implemented yet.");
}
};
} // namespace tiny_dnn
tiny_dnn::Conv2dOpenCLBackwardOp
Definition conv2d_op_opencl.h:156
tiny_dnn::Conv2dOpenCLForwardOp
Definition conv2d_op_opencl.h:51
tiny_dnn::Device
Definition device.fwd.h:73
tiny_dnn::Program
Definition program.h:63
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
tiny_dnn::nn_info
info class for tiny-dnn (for debug)
Definition nn_error.h:69
tiny_dnn::nn_warn
warning class for tiny-dnn (for debug)
Definition nn_error.h:52