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/*
* Copyright (c) 2017-2020 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.
*/
#include "arm_compute/graph.h"
#ifdef ARM_COMPUTE_CL
#include "arm_compute/runtime/CL/Utils.h"
#endif /* ARM_COMPUTE_CL */
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
using namespace arm_compute;
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement AlexNet's network using the Compute Library's graph API */
class GraphAlexnetExample : public Example
{
public:
GraphAlexnetExample()
: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "AlexNet")
{
}
bool do_setup(int argc, char **argv) override
{
// Parse arguments
cmd_parser.parse(argc, argv);
cmd_parser.validate();
// Consume common parameters
common_params = consume_common_graph_parameters(common_opts);
// Return when help menu is requested
if(common_params.help)
{
cmd_parser.print_help(argv[0]);
return false;
}
// Checks
ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
// Print parameter values
std::cout << common_params << std::endl;
// Get trainable parameters data path
std::string data_path = common_params.data_path;
// Create a preprocessor object
const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
// Create input descriptor
const auto operation_layout = common_params.data_layout;
const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 3U, 1U), DataLayout::NCHW, operation_layout);
TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
// Set weights trained layout
const DataLayout weights_layout = DataLayout::NCHW;
graph << common_params.target
<< common_params.fast_math_hint
<< InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
// Layer 1
<< ConvolutionLayer(
11U, 11U, 96U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
PadStrideInfo(4, 4, 0, 0))
.set_name("conv1")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1")
<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
// Layer 2
<< ConvolutionLayer(
5U, 5U, 256U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
PadStrideInfo(1, 1, 2, 2), 2)
.set_name("conv2")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2")
<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
// Layer 3
<< ConvolutionLayer(
3U, 3U, 384U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
.set_name("conv3")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu3")
// Layer 4
<< ConvolutionLayer(
3U, 3U, 384U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
PadStrideInfo(1, 1, 1, 1), 2)
.set_name("conv4")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu4")
// Layer 5
<< ConvolutionLayer(
3U, 3U, 256U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
PadStrideInfo(1, 1, 1, 1), 2)
.set_name("conv5")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
// Layer 6
<< FullyConnectedLayer(
4096U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
.set_name("fc6")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6")
// Layer 7
<< FullyConnectedLayer(
4096U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
.set_name("fc7")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7")
// Layer 8
<< FullyConnectedLayer(
1000U,
get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
.set_name("fc8")
// Softmax
<< SoftmaxLayer().set_name("prob")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
GraphConfig config;
config.num_threads = common_params.threads;
config.use_tuner = common_params.enable_tuner;
config.tuner_mode = common_params.tuner_mode;
config.tuner_file = common_params.tuner_file;
// Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed
// compilation won't be required.
if(common_params.enable_cl_cache)
{
#ifdef ARM_COMPUTE_CL
restore_program_cache_from_file();
#endif /* ARM_COMPUTE_CL */
}
graph.finalize(common_params.target, config);
// Save the opencl kernels to a file
if(common_opts.enable_cl_cache)
{
#ifdef ARM_COMPUTE_CL
save_program_cache_to_file();
#endif /* ARM_COMPUTE_CL */
}
return true;
}
void do_run() override
{
// Run graph
graph.run();
}
private:
CommandLineParser cmd_parser;
CommonGraphOptions common_opts;
CommonGraphParams common_params;
Stream graph;
};
/** Main program for AlexNet
*
* Model is based on:
* https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
* "ImageNet Classification with Deep Convolutional Neural Networks"
* Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E
*
* Provenance: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
*
* @note To list all the possible arguments execute the binary appended with the --help option
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments
*
* @return Return code
*/
int main(int argc, char **argv)
{
return arm_compute::utils::run_example<GraphAlexnetExample>(argc, argv);
}
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