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/*
* Copyright (c) 2018-2021 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;
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement VGG based VDSR network using the Compute Library's graph API */
class GraphVDSRExample : public Example
{
public:
GraphVDSRExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VDSR")
{
model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 192);
model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 192);
// Add model id option
model_input_width->set_help("Input image width.");
model_input_height->set_help("Input image height.");
}
GraphVDSRExample(const GraphVDSRExample &) = delete;
GraphVDSRExample &operator=(const GraphVDSRExample &) = delete;
~GraphVDSRExample() override = default;
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;
}
// Get input image width and height
const unsigned int image_width = model_input_width->value();
const unsigned int image_height = model_input_height->value();
// Print parameter values
std::cout << common_params << std::endl;
std::cout << "Image width: " << image_width << std::endl;
std::cout << "Image height: " << image_height << std::endl;
// Get trainable parameters data path
const std::string data_path = common_params.data_path;
const std::string model_path = "/cnn_data/vdsr_model/";
// Create a preprocessor object
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
// Create input descriptor
const TensorShape tensor_shape =
permute_shape(TensorShape(image_width, image_height, 1U, common_params.batches), DataLayout::NCHW,
common_params.data_layout);
TensorDescriptor input_descriptor =
TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
// Set weights trained layout
const DataLayout weights_layout = DataLayout::NCHW;
// Note: Quantization info are random and used only for benchmarking purposes
graph << common_params.target << common_params.fast_math_hint
<< InputLayer(input_descriptor.set_quantization_info(QuantizationInfo(0.0078125f, 128)),
get_input_accessor(common_params, std::move(preprocessor), false));
SubStream left(graph);
SubStream right(graph);
// Layer 1
right << ConvolutionLayer(3U, 3U, 64U, get_weights_accessor(data_path, "conv0_w.npy", weights_layout),
get_weights_accessor(data_path, "conv0_b.npy"), PadStrideInfo(1, 1, 1, 1), 1,
QuantizationInfo(0.031778190285f, 156), QuantizationInfo(0.0784313753247f, 128))
.set_name("conv0")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name("conv0/Relu");
// Rest 17 layers
for (unsigned int i = 1; i < 19; ++i)
{
const std::string conv_w_path = "conv" + arm_compute::support::cpp11::to_string(i) + "_w.npy";
const std::string conv_b_path = "conv" + arm_compute::support::cpp11::to_string(i) + "_b.npy";
const std::string conv_name = "conv" + arm_compute::support::cpp11::to_string(i);
right << ConvolutionLayer(3U, 3U, 64U, get_weights_accessor(data_path, conv_w_path, weights_layout),
get_weights_accessor(data_path, conv_b_path), PadStrideInfo(1, 1, 1, 1), 1,
QuantizationInfo(0.015851572156f, 93))
.set_name(conv_name)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name(conv_name + "/Relu");
}
// Final layer
right << ConvolutionLayer(3U, 3U, 1U, get_weights_accessor(data_path, "conv20_w.npy", weights_layout),
get_weights_accessor(data_path, "conv20_b.npy"), PadStrideInfo(1, 1, 1, 1), 1,
QuantizationInfo(0.015851572156f, 93))
.set_name("conv20")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name("conv20/Relu");
// Add residual to input
graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name("add")
<< OutputLayer(std::make_unique<DummyAccessor>(0));
// 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;
config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type);
config.synthetic_type = common_params.data_type;
graph.finalize(common_params.target, config);
return true;
}
void do_run() override
{
// Run graph
graph.run();
}
private:
CommandLineParser cmd_parser;
CommonGraphOptions common_opts;
SimpleOption<unsigned int> *model_input_width{nullptr};
SimpleOption<unsigned int> *model_input_height{nullptr};
CommonGraphParams common_params;
Stream graph;
};
/** Main program for VGG-based VDSR
*
* Model is based on:
* https://arxiv.org/pdf/1511.04587.pdf
* "Accurate Image Super-Resolution Using Very Deep Convolutional Networks"
* Jiwon Kim, Jung Kwon Lee and Kyoung Mu Lee
*
* @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<GraphVDSRExample>(argc, argv);
}
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