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/*
* Copyright (c) 2017-2018 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/Graph.h"
#include "arm_compute/graph/Nodes.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
using namespace arm_compute;
using namespace arm_compute::graph;
using namespace arm_compute::graph_utils;
/** Example demonstrating how to implement QASYMM8 MobileNet's network using the Compute Library's graph API
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
*/
class GraphMobileNetQASYMM8Example : public utils::Example
{
public:
void do_setup(int argc, char **argv) override
{
std::string data_path; /* Path to the trainable data */
std::string input; /* Image data */
std::string label; /* Label data */
// Quantization info taken from the AndroidNN QASYMM8 MobileNet example
const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
const QuantizationInfo mid_quant_info = QuantizationInfo(0.0784313753247f, 128);
const std::vector<QuantizationInfo> conv_weights_quant_info =
{
QuantizationInfo(0.031778190285f, 156), // conv0
QuantizationInfo(0.00604454148561f, 66) // conv14
};
const std::vector<QuantizationInfo> depth_weights_quant_info =
{
QuantizationInfo(0.254282623529f, 129), // dwsc1
QuantizationInfo(0.12828284502f, 172), // dwsc2
QuantizationInfo(0.265911251307f, 83), // dwsc3
QuantizationInfo(0.0985597148538f, 30), // dwsc4
QuantizationInfo(0.0631204470992f, 54), // dwsc5
QuantizationInfo(0.0137207424268f, 141), // dwsc6
QuantizationInfo(0.0817828401923f, 125), // dwsc7
QuantizationInfo(0.0393880493939f, 164), // dwsc8
QuantizationInfo(0.211694166064f, 129), // dwsc9
QuantizationInfo(0.158015936613f, 103), // dwsc10
QuantizationInfo(0.0182712618262f, 137), // dwsc11
QuantizationInfo(0.0127998134121f, 134), // dwsc12
QuantizationInfo(0.299285322428f, 161) // dwsc13
};
const std::vector<QuantizationInfo> point_weights_quant_info =
{
QuantizationInfo(0.0425766184926f, 129), // dwsc1
QuantizationInfo(0.0250773020089f, 94), // dwsc2
QuantizationInfo(0.015851572156f, 93), // dwsc3
QuantizationInfo(0.0167811904103f, 98), // dwsc4
QuantizationInfo(0.00951790809631f, 135), // dwsc5
QuantizationInfo(0.00999817531556f, 128), // dwsc6
QuantizationInfo(0.00590536883101f, 126), // dwsc7
QuantizationInfo(0.00576109671965f, 133), // dwsc8
QuantizationInfo(0.00830461271107f, 142), // dwsc9
QuantizationInfo(0.0152327232063f, 72), // dwsc10
QuantizationInfo(0.00741417845711f, 125), // dwsc11
QuantizationInfo(0.0135628981516f, 142), // dwsc12
QuantizationInfo(0.0338749065995f, 140) // dwsc13
};
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
TargetHint target_hint = set_target_hint(int_target_hint);
// Parse arguments
if(argc < 2)
{
// Print help
std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels]\n\n";
std::cout << "No data folder provided: using random values\n\n";
}
else if(argc == 2)
{
std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels]\n\n";
std::cout << "No input provided: using random values\n\n";
}
else if(argc == 4)
{
data_path = argv[2];
input = argv[3];
std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
std::cout << "No text file with labels provided: skipping output accessor\n\n";
}
else
{
data_path = argv[2];
input = argv[3];
label = argv[4];
}
graph << target_hint
<< arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::QASYMM8, in_quant_info),
get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
<< ConvolutionLayer(
3U, 3U, 32U,
get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"),
get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"),
PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
1, WeightsInfo(),
conv_weights_quant_info.at(0),
mid_quant_info)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< get_dwsc_node(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))
<< ConvolutionLayer(
1U, 1U, 1001U,
get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"),
get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"),
PadStrideInfo(1U, 1U, 0U, 0U), 1, WeightsInfo(), conv_weights_quant_info.at(1))
<< ReshapeLayer(TensorShape(1001U))
<< SoftmaxLayer()
<< arm_compute::graph::Tensor(get_output_accessor(label, 5));
// In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
graph.graph_init(int_target_hint == 2);
}
void do_run() override
{
// Run graph
graph.run();
}
private:
Graph graph{};
/** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer.
*
* @param[in] data_path Path to trainable data folder
* @param[in] param_path Prefix of specific set of weights/biases data
* @param[in] conv_filt Filters depths for pointwise convolution
* @param[in] dwc_pad_stride_info PadStrideInfo for depthwise convolution
* @param[in] conv_pad_stride_info PadStrideInfo for pointwise convolution
* @param[in] depth_weights_quant_info QuantizationInfo for depthwise convolution's weights
* @param[in] point_weights_quant_info QuantizationInfo for pointwise convolution's weights
*
* @return The complete dwsc node
*/
BranchLayer get_dwsc_node(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 = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_";
SubGraph sg;
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,
true,
depth_weights_quant_info)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
<< 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, WeightsInfo(),
point_weights_quant_info)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
return BranchLayer(std::move(sg));
}
};
/** Main program for MobileNetQASYMM8
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
*/
int main(int argc, char **argv)
{
return utils::run_example<GraphMobileNetQASYMM8Example>(argc, argv);
}
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