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/*
* Copyright (c) 2017-2021 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"
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement LeNet's network using the Compute Library's graph API */
class GraphLenetExample : public Example
{
public:
GraphLenetExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet")
{
}
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;
unsigned int batches = 4; /** Number of batches */
// Create input descriptor
const auto operation_layout = common_params.data_layout;
const TensorShape tensor_shape =
permute_shape(TensorShape(28U, 28U, 1U, batches), 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;
//conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
graph << common_params.target << common_params.fast_math_hint
<< InputLayer(input_descriptor, get_input_accessor(common_params))
<< ConvolutionLayer(
5U, 5U, 20U, get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), PadStrideInfo(1, 1, 0, 0))
.set_name("conv1")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
.set_name("pool1")
<< ConvolutionLayer(
5U, 5U, 50U, get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), PadStrideInfo(1, 1, 0, 0))
.set_name("conv2")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0)))
.set_name("pool2")
<< FullyConnectedLayer(500U,
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
.set_name("ip1")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu")
<< FullyConnectedLayer(10U,
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
.set_name("ip2")
<< SoftmaxLayer().set_name("prob") << OutputLayer(get_output_accessor(common_params));
// 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;
config.mlgo_file = common_params.mlgo_file;
graph.finalize(common_params.target, config);
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 LeNet
*
* Model is based on:
* http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf
* "Gradient-Based Learning Applied to Document Recognition"
* Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner
*
* The original model uses tanh instead of relu activations. However the use of relu activations in lenet has been
* widely adopted to improve accuracy.*
*
* @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
*/
int main(int argc, char **argv)
{
return arm_compute::utils::run_example<GraphLenetExample>(argc, argv);
}
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