diff options
Diffstat (limited to 'examples/graph_ssd_mobilenet.cpp')
| -rw-r--r-- | examples/graph_ssd_mobilenet.cpp | 772 |
1 files changed, 438 insertions, 334 deletions
diff --git a/examples/graph_ssd_mobilenet.cpp b/examples/graph_ssd_mobilenet.cpp index c0859227ab..6218d47dd6 100644 --- a/examples/graph_ssd_mobilenet.cpp +++ b/examples/graph_ssd_mobilenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2021 Arm Limited. + * Copyright (c) 2018-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -21,7 +21,9 @@ * 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" @@ -36,23 +38,26 @@ using namespace arm_compute::graph_utils; class GraphSSDMobilenetExample : public Example { public: - GraphSSDMobilenetExample() - : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetSSD") + 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_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_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."); + 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."); + num_detections_opt->set_help( + "Filename containing the reference values for the output num_detections. Used with datatype QASYMM8."); } - GraphSSDMobilenetExample(const GraphSSDMobilenetExample &) = delete; + GraphSSDMobilenetExample(const GraphSSDMobilenetExample &) = delete; GraphSSDMobilenetExample &operator=(const GraphSSDMobilenetExample &) = delete; ~GraphSSDMobilenetExample() override = default; bool do_setup(int argc, char **argv) override @@ -65,7 +70,7 @@ public: common_params = consume_common_graph_parameters(common_opts); // Return when help menu is requested - if(common_params.help) + if (common_params.help) { cmd_parser.print_help(argv[0]); return false; @@ -75,15 +80,16 @@ public: 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); + 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; + graph << common_params.target << common_params.fast_math_hint; // Create core graph - if(arm_compute::is_data_type_float(common_params.data_type)) + if (arm_compute::is_data_type_float(common_params.data_type)) { create_graph_float(input_descriptor); } @@ -112,99 +118,98 @@ public: private: CommandLineParser cmd_parser; CommonGraphOptions common_opts; - SimpleOption<int> *keep_topk_opt{ nullptr }; + 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 &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) + 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(master_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") + 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") + .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"); + .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) + 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(master_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") + 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") + .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"); + .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) + 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(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) + 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"); } @@ -216,62 +221,77 @@ private: void create_graph_float(TensorDescriptor &input_descriptor) { // Create a preprocessor object - const std::array<float, 3> mean_rgb{ { 127.5f, 127.5f, 127.5f } }; + 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()) + if (!data_path.empty()) { data_path += "/cnn_data/ssd_mobilenet_model/"; } - graph << InputLayer(input_descriptor, - get_input_accessor(common_params, std::move(preprocessor))); + 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 << 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)); + .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)); + 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)); + 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)); + 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)); + 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)); + 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); @@ -293,8 +313,9 @@ private: 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_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); @@ -304,67 +325,79 @@ private: 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)); + 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)); + 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)); + 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)); + 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 << 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 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 }; + 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"); + 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"); + 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"); + 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"); + 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"); + 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"); + 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)); + (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; @@ -377,77 +410,85 @@ private: 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 })); + 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 &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<QuantizationInfo, QuantizationInfo> depth_quant_info, std::pair<QuantizationInfo, QuantizationInfo> point_quant_info) + 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(master_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"); + 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 &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<QuantizationInfo, QuantizationInfo> quant_info_1x1, const std::pair<QuantizationInfo, QuantizationInfo> quant_info_3x3) + 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(master_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"); + 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 &master_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) + 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(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, 1, quant_info.first, quant_info.second) - .set_name(param_path + "/conv"); - if(common_params.data_layout == DataLayout::NCHW) + 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); } @@ -462,57 +503,59 @@ private: std::string data_path = common_params.data_path; // Add model path to data path - if(!data_path.empty()) + 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>> 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>> 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 + 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 @@ -520,114 +563,154 @@ private: // 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)); + 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)); + 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)); + 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)); + 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)); + 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)); + 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<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 - }; + { + 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)); + 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)); + 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)); + 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)); + 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)); + 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)); + 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), + 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"); + .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<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 = - { + const std::vector<TensorShape> class_reshape = { TensorShape(91U, 1083U), // boxpredictor0_cp_reshape TensorShape(91U, 600U), // boxpredictor1_cp_reshape TensorShape(91U, 150U), // boxpredictor2_cp_reshape @@ -637,60 +720,81 @@ private: }; 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)); + 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)); + 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)); + 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)); + 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)); + 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)); + 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); + 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), + 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"); + .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); + 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); + 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); + 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); 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