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android/platform/external/ComputeLibrary/refs/heads/android17-security-release/./examples/graph_ssd_mobilenet.cpp
blob: 9fe7f5b454a6138f8594b9f4ed10ff55bdf26791 [file] [edit]
/*
* Copyright (c) 2018-2022 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 MobileNetSSD's network using the Compute Library's graph API */
class GraphSSDMobilenetExample : public Example
{
public:
GraphSSDMobilenetExample()
: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetSSD")
{
// Add topk option
keep_topk_opt = cmd_parser.add_option<SimpleOption<int>>("topk", 100);
keep_topk_opt->set_help("Top k detections results per image. Used for data type F32.");
// Add output option
detection_boxes_opt = cmd_parser.add_option<SimpleOption<std::string>>("detection_boxes_opt", "");
detection_boxes_opt->set_help("Filename containing the reference values for the graph output detection_boxes. Used for data type QASYMM8.");
detection_classes_opt = cmd_parser.add_option<SimpleOption<std::string>>("detection_classes_opt", "");
detection_classes_opt->set_help("Filename containing the reference values for the output detection_classes. Used for data type QASYMM8.");
detection_scores_opt = cmd_parser.add_option<SimpleOption<std::string>>("detection_scores_opt", "");
detection_scores_opt->set_help("Filename containing the reference values for the output detection_scores. Used for data type QASYMM8.");
num_detections_opt = cmd_parser.add_option<SimpleOption<std::string>>("num_detections_opt", "");
num_detections_opt->set_help("Filename containing the reference values for the output num_detections. Used with datatype QASYMM8.");
}
GraphSSDMobilenetExample(const GraphSSDMobilenetExample &) = delete;
GraphSSDMobilenetExample &operator=(const GraphSSDMobilenetExample &) = delete;
~GraphSSDMobilenetExample() 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;
// Create input descriptor
const TensorShape tensor_shape = permute_shape(TensorShape(300, 300, 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);
}
else
{
create_graph_qasymm(input_descriptor);
}
// Finalize graph
GraphConfig config;
config.num_threads = common_params.threads;
config.use_tuner = common_params.enable_tuner;
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;
SimpleOption<int> *keep_topk_opt{ nullptr };
CommonGraphParams common_params;
Stream graph;
SimpleOption<std::string> *detection_boxes_opt{ nullptr };
SimpleOption<std::string> *detection_classes_opt{ nullptr };
SimpleOption<std::string> *detection_scores_opt{ nullptr };
SimpleOption<std::string> *num_detections_opt{ nullptr };
ConcatLayer get_node_A_float(IStream &main_graph, const std::string &data_path, std::string &&param_path,
unsigned int conv_filt,
PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
{
const std::string total_path = param_path + "_";
SubStream sg(main_graph);
sg << DepthwiseConvolutionLayer(
3U, 3U,
get_weights_accessor(data_path, total_path + "dw_w.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
dwc_pad_stride_info)
.set_name(param_path + "/dw")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "dw_bn_mean.npy"),
get_weights_accessor(data_path, total_path + "dw_bn_var.npy"),
get_weights_accessor(data_path, total_path + "dw_scale_w.npy"),
get_weights_accessor(data_path, total_path + "dw_scale_b.npy"), 0.00001f)
.set_name(param_path + "/dw/bn")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "dw/relu")
<< ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "w.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
conv_pad_stride_info)
.set_name(param_path + "/pw")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "bn_mean.npy"),
get_weights_accessor(data_path, total_path + "bn_var.npy"),
get_weights_accessor(data_path, total_path + "scale_w.npy"),
get_weights_accessor(data_path, total_path + "scale_b.npy"), 0.00001f)
.set_name(param_path + "/pw/bn")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "pw/relu");
return ConcatLayer(std::move(sg));
}
ConcatLayer get_node_B_float(IStream &main_graph, const std::string &data_path, std::string &&param_path,
unsigned int conv_filt,
PadStrideInfo conv_pad_stride_info_1, PadStrideInfo conv_pad_stride_info_2)
{
const std::string total_path = param_path + "_";
SubStream sg(main_graph);
sg << ConvolutionLayer(
1, 1, conv_filt / 2,
get_weights_accessor(data_path, total_path + "1_w.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
conv_pad_stride_info_1)
.set_name(total_path + "1/conv")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "1_bn_mean.npy"),
get_weights_accessor(data_path, total_path + "1_bn_var.npy"),
get_weights_accessor(data_path, total_path + "1_scale_w.npy"),
get_weights_accessor(data_path, total_path + "1_scale_b.npy"), 0.00001f)
.set_name(total_path + "1/bn")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "1/relu");
sg << ConvolutionLayer(
3, 3, conv_filt,
get_weights_accessor(data_path, total_path + "2_w.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
conv_pad_stride_info_2)
.set_name(total_path + "2/conv")
<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "2_bn_mean.npy"),
get_weights_accessor(data_path, total_path + "2_bn_var.npy"),
get_weights_accessor(data_path, total_path + "2_scale_w.npy"),
get_weights_accessor(data_path, total_path + "2_scale_b.npy"), 0.00001f)
.set_name(total_path + "2/bn")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "2/relu");
return ConcatLayer(std::move(sg));
}
ConcatLayer get_node_C_float(IStream &main_graph, const std::string &data_path, std::string &&param_path,
unsigned int conv_filt, PadStrideInfo conv_pad_stride_info)
{
const std::string total_path = param_path + "_";
SubStream sg(main_graph);
sg << ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "w.npy"),
get_weights_accessor(data_path, total_path + "b.npy"),
conv_pad_stride_info)
.set_name(param_path + "/conv");
if(common_params.data_layout == DataLayout::NCHW)
{
sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC).set_name(param_path + "/perm");
}
sg << FlattenLayer().set_name(param_path + "/flat");
return ConcatLayer(std::move(sg));
}
void create_graph_float(TensorDescriptor &input_descriptor)
{
// Create a preprocessor object
const std::array<float, 3> mean_rgb{ { 127.5f, 127.5f, 127.5f } };
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb, true, 0.007843f);
// 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/ssd_mobilenet_model/";
}
graph << InputLayer(input_descriptor,
get_input_accessor(common_params, std::move(preprocessor)));
SubStream conv_11(graph);
conv_11 << ConvolutionLayer(
3U, 3U, 32U,
get_weights_accessor(data_path, "conv0_w.npy"),
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
PadStrideInfo(2, 2, 1, 1))
.set_name("conv0");
conv_11 << BatchNormalizationLayer(get_weights_accessor(data_path, "conv0_bn_mean.npy"),
get_weights_accessor(data_path, "conv0_bn_var.npy"),
get_weights_accessor(data_path, "conv0_scale_w.npy"),
get_weights_accessor(data_path, "conv0_scale_b.npy"), 0.00001f)
.set_name("conv0/bn")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/relu");
conv_11 << get_node_A_float(conv_11, data_path, "conv1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv2", 128, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv3", 128, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv4", 256, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv5", 256, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv6", 512, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv7", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv8", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv9", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv10", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_11 << get_node_A_float(conv_11, data_path, "conv11", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
SubStream conv_13(conv_11);
conv_13 << get_node_A_float(conv_11, data_path, "conv12", 1024, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0));
conv_13 << get_node_A_float(conv_13, data_path, "conv13", 1024, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
SubStream conv_14(conv_13);
conv_14 << get_node_B_float(conv_13, data_path, "conv14", 512, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
SubStream conv_15(conv_14);
conv_15 << get_node_B_float(conv_14, data_path, "conv15", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
SubStream conv_16(conv_15);
conv_16 << get_node_B_float(conv_15, data_path, "conv16", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
SubStream conv_17(conv_16);
conv_17 << get_node_B_float(conv_16, data_path, "conv17", 128, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1));
//mbox_loc
SubStream conv_11_mbox_loc(conv_11);
conv_11_mbox_loc << get_node_C_float(conv_11, data_path, "conv11_mbox_loc", 12, PadStrideInfo(1, 1, 0, 0));
SubStream conv_13_mbox_loc(conv_13);
conv_13_mbox_loc << get_node_C_float(conv_13, data_path, "conv13_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
SubStream conv_14_2_mbox_loc(conv_14);
conv_14_2_mbox_loc << get_node_C_float(conv_14, data_path, "conv14_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
SubStream conv_15_2_mbox_loc(conv_15);
conv_15_2_mbox_loc << get_node_C_float(conv_15, data_path, "conv15_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
SubStream conv_16_2_mbox_loc(conv_16);
conv_16_2_mbox_loc << get_node_C_float(conv_16, data_path, "conv16_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
SubStream conv_17_2_mbox_loc(conv_17);
conv_17_2_mbox_loc << get_node_C_float(conv_17, data_path, "conv17_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0));
SubStream mbox_loc(graph);
mbox_loc << ConcatLayer(std::move(conv_11_mbox_loc), std::move(conv_13_mbox_loc), conv_14_2_mbox_loc, std::move(conv_15_2_mbox_loc),
std::move(conv_16_2_mbox_loc), std::move(conv_17_2_mbox_loc));
//mbox_conf
SubStream conv_11_mbox_conf(conv_11);
conv_11_mbox_conf << get_node_C_float(conv_11, data_path, "conv11_mbox_conf", 63, PadStrideInfo(1, 1, 0, 0));
SubStream conv_13_mbox_conf(conv_13);
conv_13_mbox_conf << get_node_C_float(conv_13, data_path, "conv13_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
SubStream conv_14_2_mbox_conf(conv_14);
conv_14_2_mbox_conf << get_node_C_float(conv_14, data_path, "conv14_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
SubStream conv_15_2_mbox_conf(conv_15);
conv_15_2_mbox_conf << get_node_C_float(conv_15, data_path, "conv15_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
SubStream conv_16_2_mbox_conf(conv_16);
conv_16_2_mbox_conf << get_node_C_float(conv_16, data_path, "conv16_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
SubStream conv_17_2_mbox_conf(conv_17);
conv_17_2_mbox_conf << get_node_C_float(conv_17, data_path, "conv17_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0));
SubStream mbox_conf(graph);
mbox_conf << ConcatLayer(std::move(conv_11_mbox_conf), std::move(conv_13_mbox_conf), std::move(conv_14_2_mbox_conf),
std::move(conv_15_2_mbox_conf), std::move(conv_16_2_mbox_conf), std::move(conv_17_2_mbox_conf));
mbox_conf << ReshapeLayer(TensorShape(21U, 1917U)).set_name("mbox_conf/reshape");
mbox_conf << SoftmaxLayer().set_name("mbox_conf/softmax");
mbox_conf << FlattenLayer().set_name("mbox_conf/flat");
const std::vector<float> priorbox_variances = { 0.1f, 0.1f, 0.2f, 0.2f };
const float priorbox_offset = 0.5f;
const std::vector<float> priorbox_aspect_ratios = { 2.f, 3.f };
//mbox_priorbox branch
SubStream conv_11_mbox_priorbox(conv_11);
conv_11_mbox_priorbox << PriorBoxLayer(SubStream(graph),
PriorBoxLayerInfo({ 60.f }, priorbox_variances, priorbox_offset, true, false, {}, { 2.f }))
.set_name("conv11/priorbox");
SubStream conv_13_mbox_priorbox(conv_13);
conv_13_mbox_priorbox << PriorBoxLayer(SubStream(graph),
PriorBoxLayerInfo({ 105.f }, priorbox_variances, priorbox_offset, true, false, { 150.f }, priorbox_aspect_ratios))
.set_name("conv13/priorbox");
SubStream conv_14_2_mbox_priorbox(conv_14);
conv_14_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
PriorBoxLayerInfo({ 150.f }, priorbox_variances, priorbox_offset, true, false, { 195.f }, priorbox_aspect_ratios))
.set_name("conv14/priorbox");
SubStream conv_15_2_mbox_priorbox(conv_15);
conv_15_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
PriorBoxLayerInfo({ 195.f }, priorbox_variances, priorbox_offset, true, false, { 240.f }, priorbox_aspect_ratios))
.set_name("conv15/priorbox");
SubStream conv_16_2_mbox_priorbox(conv_16);
conv_16_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
PriorBoxLayerInfo({ 240.f }, priorbox_variances, priorbox_offset, true, false, { 285.f }, priorbox_aspect_ratios))
.set_name("conv16/priorbox");
SubStream conv_17_2_mbox_priorbox(conv_17);
conv_17_2_mbox_priorbox << PriorBoxLayer(SubStream(graph),
PriorBoxLayerInfo({ 285.f }, priorbox_variances, priorbox_offset, true, false, { 300.f }, priorbox_aspect_ratios))
.set_name("conv17/priorbox");
SubStream mbox_priorbox(graph);
mbox_priorbox << ConcatLayer(
(common_params.data_layout == DataLayout::NCHW) ? arm_compute::graph::descriptors::ConcatLayerDescriptor(DataLayoutDimension::WIDTH) : arm_compute::graph::descriptors::ConcatLayerDescriptor(
DataLayoutDimension::CHANNEL),
std::move(conv_11_mbox_priorbox), std::move(conv_13_mbox_priorbox), std::move(conv_14_2_mbox_priorbox),
std::move(conv_15_2_mbox_priorbox), std::move(conv_16_2_mbox_priorbox), std::move(conv_17_2_mbox_priorbox));
const int num_classes = 21;
const bool share_location = true;
const DetectionOutputLayerCodeType detection_type = DetectionOutputLayerCodeType::CENTER_SIZE;
const int keep_top_k = keep_topk_opt->value();
const float nms_threshold = 0.45f;
const int label_id_background = 0;
const float conf_thrs = 0.25f;
const int top_k = 100;
SubStream detection_ouput(mbox_loc);
detection_ouput << DetectionOutputLayer(std::move(mbox_conf), std::move(mbox_priorbox),
DetectionOutputLayerInfo(num_classes, share_location, detection_type, keep_top_k, nms_threshold, top_k, label_id_background, conf_thrs));
detection_ouput << OutputLayer(get_detection_output_accessor(common_params, { input_descriptor.shape }));
}
ConcatLayer get_node_A_qasymm(IStream &main_graph, const std::string &data_path, std::string &&param_path,
unsigned int conv_filt,
PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info,
std::pair<QuantizationInfo, QuantizationInfo> depth_quant_info, std::pair<QuantizationInfo, QuantizationInfo> point_quant_info)
{
const std::string total_path = param_path + "_";
SubStream sg(main_graph);
sg << DepthwiseConvolutionLayer(
3U, 3U,
get_weights_accessor(data_path, total_path + "dw_w.npy"),
get_weights_accessor(data_path, total_path + "dw_b.npy"),
dwc_pad_stride_info, 1, depth_quant_info.first, depth_quant_info.second)
.set_name(param_path + "/dw")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(param_path + "/dw/relu6");
sg << ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "w.npy"),
get_weights_accessor(data_path, total_path + "b.npy"),
conv_pad_stride_info, 1, point_quant_info.first, point_quant_info.second)
.set_name(param_path + "/pw")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(param_path + "/pw/relu6");
return ConcatLayer(std::move(sg));
}
ConcatLayer get_node_B_qasymm(IStream &main_graph, const std::string &data_path, std::string &&param_path,
unsigned int conv_filt,
PadStrideInfo conv_pad_stride_info_1x1, PadStrideInfo conv_pad_stride_info_3x3,
const std::pair<QuantizationInfo, QuantizationInfo> quant_info_1x1, const std::pair<QuantizationInfo, QuantizationInfo> quant_info_3x3)
{
const std::string total_path = param_path + "_";
SubStream sg(main_graph);
sg << ConvolutionLayer(
1, 1, conv_filt / 2,
get_weights_accessor(data_path, total_path + "1x1_w.npy"),
get_weights_accessor(data_path, total_path + "1x1_b.npy"),
conv_pad_stride_info_1x1, 1, quant_info_1x1.first, quant_info_1x1.second)
.set_name(total_path + "1x1/conv")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "1x1/conv/relu6");
sg << ConvolutionLayer(
3, 3, conv_filt,
get_weights_accessor(data_path, total_path + "3x3_w.npy"),
get_weights_accessor(data_path, total_path + "3x3_b.npy"),
conv_pad_stride_info_3x3, 1, quant_info_3x3.first, quant_info_3x3.second)
.set_name(total_path + "3x3/conv")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "3x3/conv/relu6");
return ConcatLayer(std::move(sg));
}
ConcatLayer get_node_C_qasymm(IStream &main_graph, const std::string &data_path, std::string &&param_path,
unsigned int conv_filt, PadStrideInfo conv_pad_stride_info,
const std::pair<QuantizationInfo, QuantizationInfo> quant_info, TensorShape reshape_shape)
{
const std::string total_path = param_path + "_";
SubStream sg(main_graph);
sg << ConvolutionLayer(
1U, 1U, conv_filt,
get_weights_accessor(data_path, total_path + "w.npy"),
get_weights_accessor(data_path, total_path + "b.npy"),
conv_pad_stride_info, 1, quant_info.first, quant_info.second)
.set_name(param_path + "/conv");
if(common_params.data_layout == DataLayout::NCHW)
{
sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC);
}
sg << ReshapeLayer(reshape_shape).set_name(param_path + "/reshape");
return ConcatLayer(std::move(sg));
}
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/ssd_mobilenet_qasymm8_model/";
}
// Quantization info are saved as pair for each (pointwise/depthwise) convolution layer: <weight_quant_info, output_quant_info>
const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> conv_quant_info =
{
{ QuantizationInfo(0.03624850884079933f, 163), QuantizationInfo(0.22219789028167725f, 113) }, // conv0
{ QuantizationInfo(0.0028752065263688564f, 113), QuantizationInfo(0.05433657020330429f, 128) }, // conv13_2_1_1
{ QuantizationInfo(0.0014862528769299388f, 125), QuantizationInfo(0.05037643015384674f, 131) }, // conv13_2_3_3
{ QuantizationInfo(0.00233650766313076f, 113), QuantizationInfo(0.04468846693634987f, 126) }, // conv13_3_1_1
{ QuantizationInfo(0.002501056529581547f, 120), QuantizationInfo(0.06026708707213402f, 111) }, // conv13_3_3_3
{ QuantizationInfo(0.002896666992455721f, 121), QuantizationInfo(0.037775348871946335f, 117) }, // conv13_4_1_1
{ QuantizationInfo(0.0023875406477600336f, 122), QuantizationInfo(0.03881589323282242f, 108) }, // conv13_4_3_3
{ QuantizationInfo(0.0022081052884459496f, 77), QuantizationInfo(0.025450613349676132f, 125) }, // conv13_5_1_1
{ QuantizationInfo(0.00604657270014286f, 121), QuantizationInfo(0.033533502370119095f, 109) } // conv13_5_3_3
};
const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> depth_quant_info =
{
{ QuantizationInfo(0.03408717364072f, 131), QuantizationInfo(0.29286590218544006f, 108) }, // dwsc1
{ QuantizationInfo(0.027518004179000854f, 107), QuantizationInfo(0.20796941220760345, 117) }, // dwsc2
{ QuantizationInfo(0.052489638328552246f, 85), QuantizationInfo(0.4303881824016571f, 142) }, // dwsc3
{ QuantizationInfo(0.016570359468460083f, 79), QuantizationInfo(0.10512150079011917f, 116) }, // dwsc4
{ QuantizationInfo(0.060739465057849884f, 65), QuantizationInfo(0.15331414341926575f, 94) }, // dwsc5
{ QuantizationInfo(0.01324534136801958f, 124), QuantizationInfo(0.13010895252227783f, 153) }, // dwsc6
{ QuantizationInfo(0.032326459884643555f, 124), QuantizationInfo(0.11565316468477249, 156) }, // dwsc7
{ QuantizationInfo(0.029948478564620018f, 155), QuantizationInfo(0.11413891613483429f, 146) }, // dwsc8
{ QuantizationInfo(0.028054025024175644f, 129), QuantizationInfo(0.1142905130982399f, 140) }, // dwsc9
{ QuantizationInfo(0.025204822421073914f, 129), QuantizationInfo(0.14668069779872894f, 149) }, // dwsc10
{ QuantizationInfo(0.019332280382514f, 110), QuantizationInfo(0.1480235457420349f, 91) }, // dwsc11
{ QuantizationInfo(0.0319712869822979f, 88), QuantizationInfo(0.10424695909023285f, 117) }, // dwsc12
{ QuantizationInfo(0.04378943517804146f, 164), QuantizationInfo(0.23176774382591248f, 138) } // dwsc13
};
const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> point_quant_info =
{
{ QuantizationInfo(0.028777318075299263f, 144), QuantizationInfo(0.2663874328136444f, 121) }, // pw1
{ QuantizationInfo(0.015796702355146408f, 127), QuantizationInfo(0.1739964485168457f, 111) }, // pw2
{ QuantizationInfo(0.009349990636110306f, 127), QuantizationInfo(0.1805974692106247f, 104) }, // pw3
{ QuantizationInfo(0.012920888140797615f, 106), QuantizationInfo(0.1205204650759697f, 100) }, // pw4
{ QuantizationInfo(0.008119508624076843f, 145), QuantizationInfo(0.12272439152002335f, 97) }, // pw5
{ QuantizationInfo(0.0070041813887655735f, 115), QuantizationInfo(0.0947074219584465f, 101) }, // pw6
{ QuantizationInfo(0.004827278666198254f, 115), QuantizationInfo(0.0842885747551918f, 110) }, // pw7
{ QuantizationInfo(0.004755120258778334f, 128), QuantizationInfo(0.08283159881830215f, 116) }, // pw8
{ QuantizationInfo(0.007527193054556847f, 142), QuantizationInfo(0.12555131316184998f, 137) }, // pw9
{ QuantizationInfo(0.006050156895071268f, 109), QuantizationInfo(0.10871313512325287f, 124) }, // pw10
{ QuantizationInfo(0.00490700313821435f, 127), QuantizationInfo(0.10364262014627457f, 140) }, // pw11
{ QuantizationInfo(0.006063731852918863, 124), QuantizationInfo(0.11241862177848816f, 125) }, // pw12
{ QuantizationInfo(0.007901716977357864f, 139), QuantizationInfo(0.49889302253723145f, 141) } // pw13
};
// Quantization info taken from the TfLite SSD MobileNet example
const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
// Create core graph
graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
get_weights_accessor(data_path, common_params.image, DataLayout::NHWC));
graph << ConvolutionLayer(
3U, 3U, 32U,
get_weights_accessor(data_path, "conv0_w.npy"),
get_weights_accessor(data_path, "conv0_b.npy"),
PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), 1, conv_quant_info.at(0).first, conv_quant_info.at(0).second)
.set_name("conv0");
graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("conv0/relu");
graph << get_node_A_qasymm(graph, data_path, "conv1", 64U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(0),
point_quant_info.at(0));
graph << get_node_A_qasymm(graph, data_path, "conv2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(1),
point_quant_info.at(1));
graph << get_node_A_qasymm(graph, data_path, "conv3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(2),
point_quant_info.at(2));
graph << get_node_A_qasymm(graph, data_path, "conv4", 256U, PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(3),
point_quant_info.at(3));
graph << get_node_A_qasymm(graph, data_path, "conv5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(4),
point_quant_info.at(4));
graph << get_node_A_qasymm(graph, data_path, "conv6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(5),
point_quant_info.at(5));
graph << get_node_A_qasymm(graph, data_path, "conv7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(6),
point_quant_info.at(6));
graph << get_node_A_qasymm(graph, data_path, "conv8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(7),
point_quant_info.at(7));
graph << get_node_A_qasymm(graph, data_path, "conv9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(8),
point_quant_info.at(8));
graph << get_node_A_qasymm(graph, data_path, "conv10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(9),
point_quant_info.at(9));
graph << get_node_A_qasymm(graph, data_path, "conv11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(10),
point_quant_info.at(10));
SubStream conv_13(graph);
conv_13 << get_node_A_qasymm(graph, data_path, "conv12", 1024U, PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(11),
point_quant_info.at(11));
conv_13 << get_node_A_qasymm(conv_13, data_path, "conv13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(12),
point_quant_info.at(12));
SubStream conv_14(conv_13);
conv_14 << get_node_B_qasymm(conv_13, data_path, "conv13_2", 512U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(1),
conv_quant_info.at(2));
SubStream conv_15(conv_14);
conv_15 << get_node_B_qasymm(conv_14, data_path, "conv13_3", 256U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(3),
conv_quant_info.at(4));
SubStream conv_16(conv_15);
conv_16 << get_node_B_qasymm(conv_15, data_path, "conv13_4", 256U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(5),
conv_quant_info.at(6));
SubStream conv_17(conv_16);
conv_17 << get_node_B_qasymm(conv_16, data_path, "conv13_5", 128U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(7),
conv_quant_info.at(8));
// box_predictor
const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> box_enc_pred_quant_info =
{
{ QuantizationInfo(0.005202020984143019f, 136), QuantizationInfo(0.08655580133199692f, 183) }, // boxpredictor0_bep
{ QuantizationInfo(0.003121797926723957f, 132), QuantizationInfo(0.03218776360154152f, 140) }, // boxpredictor1_bep
{ QuantizationInfo(0.002995674265548587f, 130), QuantizationInfo(0.029072262346744537f, 125) }, // boxpredictor2_bep
{ QuantizationInfo(0.0023131705820560455f, 130), QuantizationInfo(0.026488754898309708f, 127) }, // boxpredictor3_bep
{ QuantizationInfo(0.0013905081432312727f, 132), QuantizationInfo(0.0199890099465847f, 137) }, // boxpredictor4_bep
{ QuantizationInfo(0.00216794665902853f, 121), QuantizationInfo(0.019798893481492996f, 151) } // boxpredictor5_bep
};
const std::vector<TensorShape> box_reshape = // NHWC
{
TensorShape(4U, 1U, 1083U), // boxpredictor0_bep_reshape
TensorShape(4U, 1U, 600U), // boxpredictor1_bep_reshape
TensorShape(4U, 1U, 150U), // boxpredictor2_bep_reshape
TensorShape(4U, 1U, 54U), // boxpredictor3_bep_reshape
TensorShape(4U, 1U, 24U), // boxpredictor4_bep_reshape
TensorShape(4U, 1U, 6U) // boxpredictor5_bep_reshape
};
SubStream conv_11_box_enc_pre(graph);
conv_11_box_enc_pre << get_node_C_qasymm(graph, data_path, "BoxPredictor_0_BEP", 12U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(0), box_reshape.at(0));
SubStream conv_13_box_enc_pre(conv_13);
conv_13_box_enc_pre << get_node_C_qasymm(conv_13, data_path, "BoxPredictor_1_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(1), box_reshape.at(1));
SubStream conv_14_2_box_enc_pre(conv_14);
conv_14_2_box_enc_pre << get_node_C_qasymm(conv_14, data_path, "BoxPredictor_2_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(2), box_reshape.at(2));
SubStream conv_15_2_box_enc_pre(conv_15);
conv_15_2_box_enc_pre << get_node_C_qasymm(conv_15, data_path, "BoxPredictor_3_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(3), box_reshape.at(3));
SubStream conv_16_2_box_enc_pre(conv_16);
conv_16_2_box_enc_pre << get_node_C_qasymm(conv_16, data_path, "BoxPredictor_4_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(4), box_reshape.at(4));
SubStream conv_17_2_box_enc_pre(conv_17);
conv_17_2_box_enc_pre << get_node_C_qasymm(conv_17, data_path, "BoxPredictor_5_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(5), box_reshape.at(5));
SubStream box_enc_pre(graph);
const QuantizationInfo bep_concate_qinfo = QuantizationInfo(0.08655580133199692f, 183);
box_enc_pre << ConcatLayer(arm_compute::graph::descriptors::ConcatLayerDescriptor(DataLayoutDimension::HEIGHT, bep_concate_qinfo),
std::move(conv_11_box_enc_pre), std::move(conv_13_box_enc_pre), conv_14_2_box_enc_pre, std::move(conv_15_2_box_enc_pre),
std::move(conv_16_2_box_enc_pre), std::move(conv_17_2_box_enc_pre))
.set_name("BoxPredictor/concat");
box_enc_pre << ReshapeLayer(TensorShape(4U, 1917U)).set_name("BoxPredictor/reshape");
// class_predictor
const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> class_pred_quant_info =
{
{ QuantizationInfo(0.002744135679677129f, 125), QuantizationInfo(0.05746262148022652f, 234) }, // boxpredictor0_cp
{ QuantizationInfo(0.0024326108396053314f, 80), QuantizationInfo(0.03764628246426582f, 217) }, // boxpredictor1_cp
{ QuantizationInfo(0.0013898586621508002f, 141), QuantizationInfo(0.034081317484378815f, 214) }, // boxpredictor2_cp
{ QuantizationInfo(0.0014176908880472183f, 133), QuantizationInfo(0.033889178186655045f, 215) }, // boxpredictor3_cp
{ QuantizationInfo(0.001090311910957098f, 125), QuantizationInfo(0.02646234817802906f, 230) }, // boxpredictor4_cp
{ QuantizationInfo(0.001134163816459477f, 115), QuantizationInfo(0.026926767081022263f, 218) } // boxpredictor5_cp
};
const std::vector<TensorShape> class_reshape =
{
TensorShape(91U, 1083U), // boxpredictor0_cp_reshape
TensorShape(91U, 600U), // boxpredictor1_cp_reshape
TensorShape(91U, 150U), // boxpredictor2_cp_reshape
TensorShape(91U, 54U), // boxpredictor3_cp_reshape
TensorShape(91U, 24U), // boxpredictor4_cp_reshape
TensorShape(91U, 6U) // boxpredictor5_cp_reshape
};
SubStream conv_11_class_pre(graph);
conv_11_class_pre << get_node_C_qasymm(graph, data_path, "BoxPredictor_0_CP", 273U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(0), class_reshape.at(0));
SubStream conv_13_class_pre(conv_13);
conv_13_class_pre << get_node_C_qasymm(conv_13, data_path, "BoxPredictor_1_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(1), class_reshape.at(1));
SubStream conv_14_2_class_pre(conv_14);
conv_14_2_class_pre << get_node_C_qasymm(conv_14, data_path, "BoxPredictor_2_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(2), class_reshape.at(2));
SubStream conv_15_2_class_pre(conv_15);
conv_15_2_class_pre << get_node_C_qasymm(conv_15, data_path, "BoxPredictor_3_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(3), class_reshape.at(3));
SubStream conv_16_2_class_pre(conv_16);
conv_16_2_class_pre << get_node_C_qasymm(conv_16, data_path, "BoxPredictor_4_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(4), class_reshape.at(4));
SubStream conv_17_2_class_pre(conv_17);
conv_17_2_class_pre << get_node_C_qasymm(conv_17, data_path, "BoxPredictor_5_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(5), class_reshape.at(5));
const QuantizationInfo cp_concate_qinfo = QuantizationInfo(0.0584389753639698f, 230);
SubStream class_pred(graph);
class_pred << ConcatLayer(
arm_compute::graph::descriptors::ConcatLayerDescriptor(DataLayoutDimension::WIDTH, cp_concate_qinfo),
std::move(conv_11_class_pre), std::move(conv_13_class_pre), std::move(conv_14_2_class_pre),
std::move(conv_15_2_class_pre), std::move(conv_16_2_class_pre), std::move(conv_17_2_class_pre))
.set_name("ClassPrediction/concat");
const QuantizationInfo logistic_out_qinfo = QuantizationInfo(0.00390625f, 0);
class_pred << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC), logistic_out_qinfo).set_name("ClassPrediction/logistic");
const int max_detections = 10;
const int max_classes_per_detection = 1;
const float nms_score_threshold = 0.30000001192092896f;
const float nms_iou_threshold = 0.6000000238418579f;
const int num_classes = 90;
const float x_scale = 10.f;
const float y_scale = 10.f;
const float h_scale = 5.f;
const float w_scale = 5.f;
std::array<float, 4> scales = { y_scale, x_scale, w_scale, h_scale };
const QuantizationInfo anchors_qinfo = QuantizationInfo(0.006453060545027256f, 0);
SubStream detection_ouput(box_enc_pre);
detection_ouput << DetectionPostProcessLayer(std::move(class_pred),
DetectionPostProcessLayerInfo(max_detections, max_classes_per_detection, nms_score_threshold, nms_iou_threshold, num_classes, scales),
get_weights_accessor(data_path, "anchors.npy"), anchors_qinfo)
.set_name("DetectionPostProcess");
SubStream ouput_0(detection_ouput);
ouput_0 << OutputLayer(get_npy_output_accessor(detection_boxes_opt->value(), TensorShape(4U, 10U), DataType::F32), 0);
SubStream ouput_1(detection_ouput);
ouput_1 << OutputLayer(get_npy_output_accessor(detection_classes_opt->value(), TensorShape(10U), DataType::F32), 1);
SubStream ouput_2(detection_ouput);
ouput_2 << OutputLayer(get_npy_output_accessor(detection_scores_opt->value(), TensorShape(10U), DataType::F32), 2);
SubStream ouput_3(detection_ouput);
ouput_3 << OutputLayer(get_npy_output_accessor(num_detections_opt->value(), TensorShape(1U), DataType::F32), 3);
}
};
/** Main program for MobileNetSSD
*
* Model is based on:
* http://arxiv.org/abs/1512.02325
* SSD: Single Shot MultiBox Detector
* Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg
*
* Provenance: https://github.com/chuanqi305/MobileNet-SSD
*
* @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<GraphSSDMobilenetExample>(argc, argv);
}