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/*
* Copyright (c) 2017-2019 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 MobileNet's network using the Compute Library's graph API */
class GraphMobilenetExample : public Example
{
public:
GraphMobilenetExample()
: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1")
{
// Add model id option
model_id_opt = cmd_parser.add_option<SimpleOption<int>>("model-id", 0);
model_id_opt->set_help("Mobilenet model id (0: 1.0_224, else: 0.75_160");
}
GraphMobilenetExample(const GraphMobilenetExample &) = delete;
GraphMobilenetExample &operator=(const GraphMobilenetExample &) = delete;
GraphMobilenetExample(GraphMobilenetExample &&) = default; // NOLINT
GraphMobilenetExample &operator=(GraphMobilenetExample &&) = default; // NOLINT
~GraphMobilenetExample() 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;
}
// Print parameter values
std::cout << common_params << std::endl;
// Get model parameters
int model_id = model_id_opt->value();
// Create input descriptor
unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160;
// Create input descriptor
const TensorShape tensor_shape = permute_shape(TensorShape(spatial_size, spatial_size, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
// Set graph hints
graph << common_params.target
<< common_params.fast_math_hint;
// Create core graph
if(arm_compute::is_data_type_float(common_params.data_type))
{
create_graph_float(input_descriptor, model_id);
}
else
{
create_graph_qasymm(input_descriptor);
}
// Create common tail
graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape")
<< SoftmaxLayer().set_name("Softmax")
<< 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;
graph.finalize(common_params.target, config);
return true;
}
void do_run() override
{
// Run graph
graph.run();
}
private:
CommandLineParser cmd_parser;
CommonGraphOptions common_opts;
SimpleOption<int> *model_id_opt{ nullptr };
CommonGraphParams common_params;
Stream graph;
void create_graph_float(TensorDescriptor &input_descriptor, int model_id)
{
float depth_scale = (model_id == 0) ? 1.f : 0.75;
std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
// Create a preprocessor object
std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
// Get trainable parameters data path
std::string data_path = common_params.data_path;
// Add model path to data path
if(!data_path.empty())
{
data_path += model_path;
}
graph << InputLayer(input_descriptor,
get_input_accessor(common_params, std::move(preprocessor), false))
<< ConvolutionLayer(
3U, 3U, 32U * depth_scale,
get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
.set_name("Conv2d_0")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"),
get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"),
get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"),
0.001f)
.set_name("Conv2d_0/BatchNorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
<< ConvolutionLayer(
1U, 1U, 1001U,
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name("Logits/Conv2d_1c_1x1");
}
void create_graph_qasymm(TensorDescriptor &input_descriptor)
{
// Get trainable parameters data path
std::string data_path = common_params.data_path;
// Add model path to data path
if(!data_path.empty())
{
data_path += "/cnn_data/mobilenet_qasymm8_model/";
}
// Quantization info taken from the AndroidNN QASYMM8 MobileNet example
const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
const std::vector<QuantizationInfo> conv_weights_quant_info =
{
QuantizationInfo(0.02182667888700962f, 151), // conv0
QuantizationInfo(0.004986600950360298f, 74) // conv14
};
const std::vector<QuantizationInfo> conv_out_quant_info =
{
QuantizationInfo(0.023528477177023888f, 0), // conv0
QuantizationInfo(0.16609922051429749f, 66) // conv14
};
const std::vector<QuantizationInfo> depth_weights_quant_info =
{
QuantizationInfo(0.29219913482666016f, 110), // dwsc1
QuantizationInfo(0.40277284383773804f, 130), // dwsc2
QuantizationInfo(0.06053730100393295f, 160), // dwsc3
QuantizationInfo(0.01675807684659958f, 123), // dwsc4
QuantizationInfo(0.04105526953935623f, 129), // dwsc5
QuantizationInfo(0.013460792601108551f, 122), // dwsc6
QuantizationInfo(0.036934755742549896f, 132), // dwsc7
QuantizationInfo(0.042609862983226776f, 94), // dwsc8
QuantizationInfo(0.028358859941363335f, 127), // dwsc9
QuantizationInfo(0.024329448118805885f, 134), // dwsc10
QuantizationInfo(0.019366811960935593f, 106), // dwsc11
QuantizationInfo(0.007835594937205315f, 126), // dwsc12
QuantizationInfo(0.12616927921772003f, 211) // dwsc13
};
const std::vector<QuantizationInfo> point_weights_quant_info =
{
QuantizationInfo(0.030420949682593346f, 121), // dwsc1
QuantizationInfo(0.015148180536925793f, 104), // dwsc2
QuantizationInfo(0.013755458407104015f, 94), // dwsc3
QuantizationInfo(0.007601846940815449f, 151), // dwsc4
QuantizationInfo(0.006431614048779011f, 122), // dwsc5
QuantizationInfo(0.00917122047394514f, 109), // dwsc6
QuantizationInfo(0.005300046876072884f, 140), // dwsc7
QuantizationInfo(0.0049632852897048f, 127), // dwsc8
QuantizationInfo(0.007770895957946777f, 89), // dwsc9
QuantizationInfo(0.009658650495111942f, 99), // dwsc10
QuantizationInfo(0.005446993745863438f, 153), // dwsc11
QuantizationInfo(0.00817922968417406f, 130), // dwsc12
QuantizationInfo(0.018048152327537537f, 95) // dwsc13
};
graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
get_weights_accessor(data_path, common_params.image))
<< ConvolutionLayer(
3U, 3U, 32U,
get_weights_accessor(data_path, "Conv2d_0_weights.npy"),
get_weights_accessor(data_path, "Conv2d_0_bias.npy"),
PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
1, conv_weights_quant_info.at(0), conv_out_quant_info.at(0))
.set_name("Conv2d_0")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1),
point_weights_quant_info.at(1));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2),
point_weights_quant_info.at(2));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3),
point_weights_quant_info.at(3));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4),
point_weights_quant_info.at(4));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5),
point_weights_quant_info.at(5));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6),
point_weights_quant_info.at(6));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7),
point_weights_quant_info.at(7));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8),
point_weights_quant_info.at(8));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9),
point_weights_quant_info.at(9));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10),
point_weights_quant_info.at(10));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11),
point_weights_quant_info.at(11));
graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),
point_weights_quant_info.at(12))
<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool_1a")
<< ConvolutionLayer(
1U, 1U, 1001U,
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"),
PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1), conv_out_quant_info.at(1))
.set_name("Logits/Conv2d_1c_1x1");
}
ConcatLayer get_dwsc_node_float(const std::string &data_path, std::string &¶m_path,
unsigned int conv_filt,
PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
{
std::string total_path = param_path + "_";
SubStream sg(graph);
sg << DepthwiseConvolutionLayer(
3U, 3U,
get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
dwc_pad_stride_info)
.set_name(total_path + "depthwise/depthwise")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
0.001f)
.set_name(total_path + "depthwise/BatchNorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6")
<< ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
conv_pad_stride_info)
.set_name(total_path + "pointwise/Conv2D")
<< BatchNormalizationLayer(
get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
0.001f)
.set_name(total_path + "pointwise/BatchNorm")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6");
return ConcatLayer(std::move(sg));
}
ConcatLayer get_dwsc_node_qasymm(const std::string &data_path, std::string &¶m_path,
const unsigned int conv_filt,
PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info,
QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info)
{
std::string total_path = param_path + "_";
SubStream sg(graph);
sg << DepthwiseConvolutionLayer(
3U, 3U,
get_weights_accessor(data_path, total_path + "depthwise_weights.npy"),
get_weights_accessor(data_path, total_path + "depthwise_bias.npy"),
dwc_pad_stride_info, 1, std::move(depth_weights_quant_info))
.set_name(total_path + "depthwise/depthwise")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6")
<< ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
get_weights_accessor(data_path, total_path + "pointwise_bias.npy"),
conv_pad_stride_info, 1, std::move(point_weights_quant_info))
.set_name(total_path + "pointwise/Conv2D")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6");
return ConcatLayer(std::move(sg));
}
};
/** Main program for MobileNetV1
*
* Model is based on:
* https://arxiv.org/abs/1704.04861
* "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
* Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam
*
* Provenance: download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz
* download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160.tgz
*
* @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<GraphMobilenetExample>(argc, argv);
}
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