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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::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement ResNeXt50 network using the Compute Library's graph API */
class GraphResNeXt50Example : public Example
{
public:
GraphResNeXt50Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50")
{
}
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 input descriptor
const auto operation_layout = common_params.data_layout;
const TensorShape tensor_shape =
permute_shape(TensorShape(224U, 224U, 3U, common_params.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;
graph << common_params.target << common_params.fast_math_hint
<< InputLayer(input_descriptor, get_input_accessor(common_params))
<< ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"),
get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy"))
.set_name("bn_data/Scale")
<< ConvolutionLayer(
7U, 7U, 64U,
get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"),
PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR))
.set_name("conv0/Convolution")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name("conv0/Relu")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout,
PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR)))
.set_name("pool0");
add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3,
/*stride_conv_unit1*/ 1);
add_residual_block(data_path, weights_layout, 512, 2, 4, 2);
add_residual_block(data_path, weights_layout, 1024, 3, 6, 2);
add_residual_block(data_path, weights_layout, 2048, 4, 3, 2);
graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool1")
<< FlattenLayer().set_name("predictions/Reshape")
<< OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32));
// 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;
void add_residual_block(const std::string &data_path,
DataLayout weights_layout,
unsigned int base_depth,
unsigned int stage,
unsigned int num_units,
unsigned int stride_conv_unit1)
{
for (unsigned int i = 0; i < num_units; ++i)
{
std::stringstream unit_path_ss;
unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_";
std::string unit_path = unit_path_ss.str();
std::stringstream unit_name_ss;
unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/";
std::string unit_name = unit_name_ss.str();
PadStrideInfo pad_grouped_conv(1, 1, 1, 1);
if (i == 0)
{
pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1)
: PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1,
DimensionRoundingType::FLOOR);
}
SubStream right(graph);
right << ConvolutionLayer(1U, 1U, base_depth / 2,
get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
get_weights_accessor(data_path, unit_path + "conv1_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "conv1/convolution")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name(unit_name + "conv1/Relu")
<< ConvolutionLayer(3U, 3U, base_depth / 2,
get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), pad_grouped_conv,
32)
.set_name(unit_name + "conv2/convolution")
<< ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"),
get_weights_accessor(data_path, unit_path + "bn2_add.npy"))
.set_name(unit_name + "conv1/Scale")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name(unit_name + "conv2/Relu")
<< ConvolutionLayer(1U, 1U, base_depth,
get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
get_weights_accessor(data_path, unit_path + "conv3_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name(unit_name + "conv3/convolution");
SubStream left(graph);
if (i == 0)
{
left << ConvolutionLayer(1U, 1U, base_depth,
get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0))
.set_name(unit_name + "sc/convolution")
<< ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"),
get_weights_accessor(data_path, unit_path + "sc_bn_add.npy"))
.set_name(unit_name + "sc/scale");
}
graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add");
graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
.set_name(unit_name + "Relu");
}
}
};
/** Main program for ResNeXt50
*
* Model is based on:
* https://arxiv.org/abs/1611.05431
* "Aggregated Residual Transformations for Deep Neural Networks"
* Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He.
*
* @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<GraphResNeXt50Example>(argc, argv);
}
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