205 lines
9.3 KiB
C++
205 lines
9.3 KiB
C++
/*
|
|
* Copyright (c) 2018-2020 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"
|
|
#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, 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))
|
|
<< 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;
|
|
|
|
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);
|
|
}
|