From e22aa1301a30dc97341aa7dfce933d71b0d226ea Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Thu, 13 Sep 2018 10:35:33 +0100 Subject: COMPMID-1460 Create Yolo v3 graph example Change-Id: I1778a76f6c225da7e2c07b39fdf6c3894624701b Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/149933 Reviewed-by: Michele DiGiorgio Tested-by: bsgcomp --- examples/graph_yolov3.cpp | 377 ++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 349 insertions(+), 28 deletions(-) (limited to 'examples') diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp index 098855e280..aa136e1d6c 100644 --- a/examples/graph_yolov3.cpp +++ b/examples/graph_yolov3.cpp @@ -39,6 +39,7 @@ public: : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3") { } + bool do_setup(int argc, char **argv) override { // Parse arguments @@ -75,9 +76,349 @@ public: graph << common_params.target << common_params.fast_math_hint - << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) - // Layer 1 + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)); + std::pair intermediate_layers = darknet53(data_path, weights_layout); + graph << ConvolutionLayer( + 1U, 1U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_53") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"), + 0.000001f) + .set_name("conv2d_53/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_53/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_54") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"), + 0.000001f) + .set_name("conv2d_54/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_54/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_55") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"), + 0.000001f) + .set_name("conv2d_55/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_55/LeakyRelu") << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_56") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"), + 0.000001f) + .set_name("conv2d_56/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_56/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_57") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"), + 0.000001f) + .set_name("conv2d_57/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_57/LeakyRelu"); + SubStream route_1(graph); + graph << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_58") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"), + 0.000001f) + .set_name("conv2d_58/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_58/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 255U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_59") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_59/Linear") + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80) + << OutputLayer(get_output_accessor(common_params, 5)); + route_1 << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_60") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"), + 0.000001f) + .set_name("conv2d_59/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_60/LeakyRelu") + << UpsampleLayer(Size2D(2, 2), InterpolationPolicy::NEAREST_NEIGHBOR).set_name("Upsample_60"); + SubStream concat_1(route_1); + concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)) + << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_61") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"), + 0.000001f) + .set_name("conv2d_60/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_61/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_62") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"), + 0.000001f) + .set_name("conv2d_61/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_62/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_63") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"), + 0.000001f) + .set_name("conv2d_62/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_63/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_64") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"), + 0.000001f) + .set_name("conv2d_63/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_64/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_65") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"), + 0.000001f) + .set_name("conv2d_65/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_65/LeakyRelu"); + SubStream route_2(concat_1); + concat_1 << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_66") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"), + 0.000001f) + .set_name("conv2d_65/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_66/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 255U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_67") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_67/Linear") + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80) + << OutputLayer(get_output_accessor(common_params, 5)); + route_2 << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_68") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"), + 0.000001f) + .set_name("conv2d_66/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_68/LeakyRelu") + << UpsampleLayer(Size2D(2, 2), InterpolationPolicy::NEAREST_NEIGHBOR).set_name("Upsample_68"); + SubStream concat_2(route_2); + concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)) + << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_69") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"), + 0.000001f) + .set_name("conv2d_67/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_69/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_70") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"), + 0.000001f) + .set_name("conv2d_68/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_70/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_71") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"), + 0.000001f) + .set_name("conv2d_69/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_71/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_72") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"), + 0.000001f) + .set_name("conv2d_70/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_72/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_73") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"), + 0.000001f) + .set_name("conv2d_71/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_73/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_74") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"), + 0.000001f) + .set_name("conv2d_72/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_74/LeakyRelu") + << ConvolutionLayer( + 1U, 1U, 255U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout), + PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_75") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_75/Linear") + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80) + << OutputLayer(get_output_accessor(common_params, 5)); + + // 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; + CommonGraphParams common_params; + Stream graph; + + std::pair darknet53(const std::string &data_path, DataLayout weights_layout) + { + graph << ConvolutionLayer( 3U, 3U, 32U, get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout), std::unique_ptr(nullptr), @@ -91,8 +432,6 @@ public: 0.000001f) .set_name("conv2d_1/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_1/LeakyRelu") - - // Layer 2 << ConvolutionLayer( 3U, 3U, 64U, get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout), @@ -146,6 +485,7 @@ public: darknet53_block(data_path, "21", weights_layout, 128U); darknet53_block(data_path, "23", weights_layout, 128U); darknet53_block(data_path, "25", weights_layout, 128U); + SubStream layer_36(graph); graph << ConvolutionLayer( 3U, 3U, 512U, get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout), @@ -168,6 +508,7 @@ public: darknet53_block(data_path, "38", weights_layout, 256U); darknet53_block(data_path, "40", weights_layout, 256U); darknet53_block(data_path, "42", weights_layout, 256U); + SubStream layer_61(graph); graph << ConvolutionLayer( 3U, 3U, 1024U, get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout), @@ -186,29 +527,9 @@ public: darknet53_block(data_path, "47", weights_layout, 512U); darknet53_block(data_path, "49", weights_layout, 512U); darknet53_block(data_path, "51", weights_layout, 512U); - graph << OutputLayer(get_output_accessor(common_params, 5)); - - // 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; + return std::pair(layer_36, layer_61); } - void do_run() override - { - // Run graph - graph.run(); - } - -private: - CommandLineParser cmd_parser; - CommonGraphOptions common_opts; - CommonGraphParams common_params; - Stream graph; void darknet53_block(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, unsigned int filter_size) @@ -228,7 +549,7 @@ private: get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"), get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"), 0.000001f) - .set_name("conv2d" + param_path + "/BatchNorm") + .set_name("conv2d_" + param_path + "/BatchNorm") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path + "/LeakyRelu") << ConvolutionLayer( 3U, 3U, filter_size * 2, @@ -241,8 +562,8 @@ private: get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"), get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"), 0.000001f) - .set_name("conv2d" + param_path2 + "/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path2 + "/LeakyRelu"); + .set_name("conv2d_" + param_path2 + "/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path2 + "/LeakyRelu"); graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add); } -- cgit v1.2.1