| /* |
| * Copyright (c) 2018-2023 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/core/utils/quantization/AsymmHelpers.h" |
| #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 &¶m_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 &¶m_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 &¶m_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 &¶m_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 &¶m_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 &¶m_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, quantization::get_min_max_values_from_quantized_data_type(common_params.data_type).first); |
| 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); |
| } |