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/*
* Copyright (c) 2017 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
#error "This example needs to be built with -DARM_COMPUTE_CL"
#endif /* ARM_COMPUTE_CL */
#include "arm_compute/core/Logger.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/Nodes.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "arm_compute/runtime/CPP/CPPScheduler.h"
#include "arm_compute/runtime/Scheduler.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include <cstdlib>
#include <iostream>
#include <memory>
using namespace arm_compute::graph;
using namespace arm_compute::graph_utils;
/** Generates appropriate accessor according to the specified path
*
* @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
*
* @param[in] path Path to the data files
* @param[in] data_file Relative path to the data files from path
*
* @return An appropriate tensor accessor
*/
std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
{
if(path.empty())
{
return arm_compute::support::cpp14::make_unique<DummyAccessor>();
}
else
{
return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
}
}
/** Example demonstrating how to implement AlexNet's network using the Compute Library's graph API
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
*/
void main_graph_alexnet(int argc, const char **argv)
{
std::string data_path; /** Path to the trainable data */
unsigned int batches = 4; /** Number of batches */
// Parse arguments
if(argc < 2)
{
// Print help
std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 2)
{
//Do something with argv[1]
data_path = argv[1];
std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
}
else
{
//Do something with argv[1] and argv[2]
data_path = argv[1];
batches = std::strtol(argv[2], nullptr, 0);
}
// Check if OpenCL is available and initialize the scheduler
TargetHint hint = TargetHint::NEON;
if(arm_compute::opencl_is_available())
{
arm_compute::CLScheduler::get().default_init();
hint = TargetHint::OPENCL;
}
Graph graph;
arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO);
graph << hint
<< Tensor(TensorInfo(TensorShape(227U, 227U, 3U, batches), 1, DataType::F32), DummyAccessor())
// Layer 1
<< ConvolutionLayer(
11U, 11U, 96U,
get_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
PadStrideInfo(4, 4, 0, 0))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
// Layer 2
<< ConvolutionMethodHint::DIRECT
<< ConvolutionLayer(
5U, 5U, 256U,
get_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
PadStrideInfo(1, 1, 2, 2), 2)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
// Layer 3
<< ConvolutionLayer(
3U, 3U, 384U,
get_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 4
<< ConvolutionLayer(
3U, 3U, 384U,
get_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
PadStrideInfo(1, 1, 1, 1), 2)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 5
<< ConvolutionLayer(
3U, 3U, 256U,
get_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
PadStrideInfo(1, 1, 1, 1), 2)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
// Layer 6
<< FullyConnectedLayer(
4096U,
get_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 7
<< FullyConnectedLayer(
4096U,
get_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 8
<< FullyConnectedLayer(
1000U,
get_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
get_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
// Softmax
<< SoftmaxLayer()
<< Tensor(DummyAccessor());
// Run graph
graph.run();
}
/** Main program for AlexNet
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
*/
int main(int argc, const char **argv)
{
return arm_compute::utils::run_example(argc, argv, main_graph_alexnet);
}
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