examples/graph_squeezenet.cpp - platform/external/ComputeLibrary - Git at Google


            
            /*

            
             * 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 Squeezenet's network using the Compute Library's graph API */

            
            class GraphSqueezenetExample : public Example

            
            {

            
            public:

            
                GraphSqueezenetExample()

            
                    : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1")

            
                {

            
                }

            
                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;

            
                    }


                    // 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 = std::make_unique<CaffePreproccessor>(mean_rgb);


                    // 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, std::move(preprocessor)))

            
                          << ConvolutionLayer(

            
                              7U, 7U, 96U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),

            
                              PadStrideInfo(2, 2, 0, 0))

            
                          .set_name("conv1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")

            
                          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")

            
                          << ConvolutionLayer(

            
                              1U, 1U, 16U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire2/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire2/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U).set_name("fire2/concat");

            
                    graph << ConvolutionLayer(

            
                              1U, 1U, 16U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire3/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat");

            
                    graph << ConvolutionLayer(

            
                              1U, 1U, 32U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire4/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat");

            
                    graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4")

            
                          << ConvolutionLayer(

            
                              1U, 1U, 32U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire5/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat");

            
                    graph << ConvolutionLayer(

            
                              1U, 1U, 48U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire6/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire6/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U).set_name("fire6/concat");

            
                    graph << ConvolutionLayer(

            
                              1U, 1U, 48U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire7/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire7/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U).set_name("fire7/concat");

            
                    graph << ConvolutionLayer(

            
                              1U, 1U, 64U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire8/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat");

            
                    graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8")

            
                          << ConvolutionLayer(

            
                              1U, 1U, 64U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("fire9/squeeze1x1")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire9/relu_squeeze1x1");

            
                    graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U).set_name("fire9/concat");

            
                    graph << ConvolutionLayer(

            
                              1U, 1U, 1000U,

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy", weights_layout),

            
                              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),

            
                              PadStrideInfo(1, 1, 0, 0))

            
                          .set_name("conv10")

            
                          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")

            
                          << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10")

            
                          << FlattenLayer().set_name("flatten")

            
                          << 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;

            
                    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;

            
                CommonGraphParams  common_params;

            
                Stream             graph;


                ConcatLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,

            
                                                 unsigned int expand1_filt, unsigned int expand3_filt)

            
                {

            
                    std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";

            
                    SubStream   i_a(graph);

            
                    i_a << ConvolutionLayer(

            
                            1U, 1U, expand1_filt,

            
                            get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout),

            
                            get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),

            
                            PadStrideInfo(1, 1, 0, 0))

            
                        .set_name(param_path + "/expand1x1")

            
                        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand1x1");


                    SubStream i_b(graph);

            
                    i_b << ConvolutionLayer(

            
                            3U, 3U, expand3_filt,

            
                            get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout),

            
                            get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),

            
                            PadStrideInfo(1, 1, 1, 1))

            
                        .set_name(param_path + "/expand3x3")

            
                        << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand3x3");


                    return ConcatLayer(std::move(i_a), std::move(i_b));

            
                }

            
            };


            /** Main program for Squeezenet v1.0

            
             *

            
             * Model is based on:

            
             *      https://arxiv.org/abs/1602.07360

            
             *      "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"

            
             *      Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer

            
             *

            
             * Provenance: https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel

            
             *

            
             * @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<GraphSqueezenetExample>(argc, argv);

            
            }
	/*
	* 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 Squeezenet's network using the Compute Library's graph API */
	class GraphSqueezenetExample : public Example
	{
	public:
	GraphSqueezenetExample()
	: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "SqueezeNetV1")
	{
	}
	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;
	}

	// 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 = std::make_unique<CaffePreproccessor>(mean_rgb);

	// 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, std::move(preprocessor)))
	<< ConvolutionLayer(
	7U, 7U, 96U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
	PadStrideInfo(2, 2, 0, 0))
	.set_name("conv1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv1")
	<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1")
	<< ConvolutionLayer(
	1U, 1U, 16U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire2/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire2/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire2", weights_layout, 64U, 64U).set_name("fire2/concat");
	graph << ConvolutionLayer(
	1U, 1U, 16U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire3/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire3/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire3", weights_layout, 64U, 64U).set_name("fire3/concat");
	graph << ConvolutionLayer(
	1U, 1U, 32U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire4/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire4/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire4", weights_layout, 128U, 128U).set_name("fire4/concat");
	graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4")
	<< ConvolutionLayer(
	1U, 1U, 32U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire5/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire5/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire5", weights_layout, 128U, 128U).set_name("fire5/concat");
	graph << ConvolutionLayer(
	1U, 1U, 48U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire6/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire6/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire6", weights_layout, 192U, 192U).set_name("fire6/concat");
	graph << ConvolutionLayer(
	1U, 1U, 48U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire7/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire7/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire7", weights_layout, 192U, 192U).set_name("fire7/concat");
	graph << ConvolutionLayer(
	1U, 1U, 64U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire8/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire8/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire8", weights_layout, 256U, 256U).set_name("fire8/concat");
	graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool8")
	<< ConvolutionLayer(
	1U, 1U, 64U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("fire9/squeeze1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("fire9/relu_squeeze1x1");
	graph << get_expand_fire_node(data_path, "fire9", weights_layout, 256U, 256U).set_name("fire9/concat");
	graph << ConvolutionLayer(
	1U, 1U, 1000U,
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy", weights_layout),
	get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name("conv10")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu_conv10")
	<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool10")
	<< FlattenLayer().set_name("flatten")
	<< 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;
	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;
	CommonGraphParams common_params;
	Stream graph;

	ConcatLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,
	unsigned int expand1_filt, unsigned int expand3_filt)
	{
	std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
	SubStream i_a(graph);
	i_a << ConvolutionLayer(
	1U, 1U, expand1_filt,
	get_weights_accessor(data_path, total_path + "expand1x1_w.npy", weights_layout),
	get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
	PadStrideInfo(1, 1, 0, 0))
	.set_name(param_path + "/expand1x1")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand1x1");

	SubStream i_b(graph);
	i_b << ConvolutionLayer(
	3U, 3U, expand3_filt,
	get_weights_accessor(data_path, total_path + "expand3x3_w.npy", weights_layout),
	get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
	PadStrideInfo(1, 1, 1, 1))
	.set_name(param_path + "/expand3x3")
	<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_expand3x3");

	return ConcatLayer(std::move(i_a), std::move(i_b));
	}
	};

	/** Main program for Squeezenet v1.0
	*
	* Model is based on:
	* https://arxiv.org/abs/1602.07360
	* "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
	* Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
	*
	* Provenance: https://github.com/DeepScale/SqueezeNet/blob/master/SqueezeNet_v1.0/squeezenet_v1.0.caffemodel
	*
	* @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<GraphSqueezenetExample>(argc, argv);
	}