From c755f78e366443ffbe168bf5096c96b8121304b9 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Mon, 22 Jul 2019 17:40:27 +0100 Subject: COMPMID-1850: Port TFLite mobilenetSSD as a graph example Change-Id: Ic0ad6e61239db61def262a78ddd0b9b68742fe6d Signed-off-by: Isabella Gottardi Reviewed-on: https://review.mlplatform.org/c/1698 Tested-by: Arm Jenkins Reviewed-by: Manuel Bottini Reviewed-by: Michele Di Giorgio --- examples/graph_ssd_mobilenet.cpp | 550 +++++++++++++++++++++++++++++++-------- 1 file changed, 445 insertions(+), 105 deletions(-) diff --git a/examples/graph_ssd_mobilenet.cpp b/examples/graph_ssd_mobilenet.cpp index 55c9d75b7f..e84a00e00a 100644 --- a/examples/graph_ssd_mobilenet.cpp +++ b/examples/graph_ssd_mobilenet.cpp @@ -41,7 +41,16 @@ public: { // Add topk option keep_topk_opt = cmd_parser.add_option>("topk", 100); - keep_topk_opt->set_help("Top k detections results per image."); + 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>("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>("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>("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>("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; @@ -76,8 +85,137 @@ public: << common_params.fast_math_hint; // Create core graph - std::string model_path = "/cnn_data/ssd_mobilenet_model/"; + 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; + + graph.finalize(common_params.target, config); + + return true; + } + void do_run() override + { + // Run graph + graph.run(); + } + +private: + CommandLineParser cmd_parser; + CommonGraphOptions common_opts; + SimpleOption *keep_topk_opt{ nullptr }; + CommonGraphParams common_params; + Stream graph; + + SimpleOption *detection_boxes_opt{ nullptr }; + SimpleOption *detection_classes_opt{ nullptr }; + SimpleOption *detection_scores_opt{ nullptr }; + SimpleOption *num_detections_opt{ nullptr }; + + ConcatLayer get_node_A_float(IStream &master_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(master_graph); + + sg << DepthwiseConvolutionLayer( + 3U, 3U, + get_weights_accessor(data_path, total_path + "dw_w.npy"), + std::unique_ptr(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(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 &master_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(master_graph); + + sg << ConvolutionLayer( + 1, 1, conv_filt / 2, + get_weights_accessor(data_path, total_path + "1_w.npy"), + std::unique_ptr(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(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 &master_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(master_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 mean_rgb{ { 127.5f, 127.5f, 127.5f } }; std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb, true, 0.007843f); @@ -88,7 +226,7 @@ public: // Add model path to data path if(!data_path.empty()) { - data_path += model_path; + data_path += "/cnn_data/ssd_mobilenet_model/"; } graph << InputLayer(input_descriptor, @@ -108,52 +246,52 @@ public: .set_name("conv0/bn") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/relu"); - conv_11 << get_node_A(conv_11, data_path, "conv1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv2", 128, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv3", 128, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv4", 256, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv5", 256, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv6", 512, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv7", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv8", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv9", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv10", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_11 << get_node_A(conv_11, data_path, "conv11", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); + 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(conv_11, data_path, "conv12", 1024, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); - conv_13 << get_node_A(conv_13, data_path, "conv13", 1024, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); + 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(conv_13, data_path, "conv14", 512, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); + 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(conv_14, data_path, "conv15", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); + 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(conv_15, data_path, "conv16", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); + 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(conv_16, data_path, "conv17", 128, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); + 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(conv_11, data_path, "conv11_mbox_loc", 12, PadStrideInfo(1, 1, 0, 0)); + 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(conv_13, data_path, "conv13_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); + 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(conv_14, data_path, "conv14_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); + 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(conv_15, data_path, "conv15_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); + 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(conv_16, data_path, "conv16_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); + 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(conv_17, data_path, "conv17_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); + 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), @@ -161,22 +299,22 @@ public: //mbox_conf SubStream conv_11_mbox_conf(conv_11); - conv_11_mbox_conf << get_node_C(conv_11, data_path, "conv11_mbox_conf", 63, PadStrideInfo(1, 1, 0, 0)); + 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(conv_13, data_path, "conv13_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); + 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(conv_14, data_path, "conv14_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); + 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(conv_15, data_path, "conv15_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); + 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(conv_16, data_path, "conv16_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); + 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(conv_17, data_path, "conv17_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); + 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), @@ -224,7 +362,8 @@ public: SubStream mbox_priorbox(graph); mbox_priorbox << ConcatLayer( - (common_params.data_layout == DataLayout::NCHW) ? DataLayoutDimension::WIDTH : DataLayoutDimension::CHANNEL, + (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)); @@ -240,35 +379,13 @@ public: 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, { tensor_shape })); - - // 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(); + detection_ouput << OutputLayer(get_detection_output_accessor(common_params, { input_descriptor.shape })); } -private: - CommandLineParser cmd_parser; - CommonGraphOptions common_opts; - SimpleOption *keep_topk_opt{ nullptr }; - CommonGraphParams common_params; - Stream graph; - - ConcatLayer get_node_A(IStream &master_graph, const std::string &data_path, std::string &¶m_path, - unsigned int conv_filt, - PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) + ConcatLayer get_node_A_qasymm(IStream &master_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 depth_quant_info, std::pair point_quant_info) { const std::string total_path = param_path + "_"; SubStream sg(master_graph); @@ -276,70 +393,52 @@ private: sg << DepthwiseConvolutionLayer( 3U, 3U, get_weights_accessor(data_path, total_path + "dw_w.npy"), - std::unique_ptr(nullptr), - dwc_pad_stride_info) + 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") - << 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") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(param_path + "/dw/relu6"); - << ConvolutionLayer( + sg << ConvolutionLayer( 1U, 1U, conv_filt, get_weights_accessor(data_path, total_path + "w.npy"), - std::unique_ptr(nullptr), - conv_pad_stride_info) + 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") - << 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"); + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(param_path + "/pw/relu6"); return ConcatLayer(std::move(sg)); } - ConcatLayer get_node_B(IStream &master_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) + ConcatLayer get_node_B_qasymm(IStream &master_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 quant_info_1x1, const std::pair quant_info_3x3) { const std::string total_path = param_path + "_"; SubStream sg(master_graph); sg << ConvolutionLayer( 1, 1, conv_filt / 2, - get_weights_accessor(data_path, total_path + "1_w.npy"), - std::unique_ptr(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"); + 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 + "2_w.npy"), - std::unique_ptr(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"); + 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(IStream &master_graph, const std::string &data_path, std::string &¶m_path, - unsigned int conv_filt, PadStrideInfo conv_pad_stride_info) + ConcatLayer get_node_C_qasymm(IStream &master_graph, const std::string &data_path, std::string &¶m_path, + unsigned int conv_filt, PadStrideInfo conv_pad_stride_info, + const std::pair quant_info, TensorShape reshape_shape) { const std::string total_path = param_path + "_"; SubStream sg(master_graph); @@ -347,16 +446,257 @@ private: 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) + 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).set_name(param_path + "/perm"); + sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC); } - sg << FlattenLayer().set_name(param_path + "/flat"); + 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: + const std::vector> 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> 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> 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> 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 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> 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 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 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 -- cgit v1.2.1