From 62c3639b086d768661edc04b9b7e01a54edf486b Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Thu, 31 Jan 2019 12:53:10 +0000 Subject: COMPMID-1913: Add names to all graph examples Change-Id: I90e7bb61a31403fc002cb451752d8260dad0d35e Signed-off-by: Georgios Pinitas Reviewed-on: https://review.mlplatform.org/620 Tested-by: Arm Jenkins Reviewed-by: Isabella Gottardi --- examples/graph_yolov3.cpp | 30 ++++++++++++++++-------------- 1 file changed, 16 insertions(+), 14 deletions(-) (limited to 'examples/graph_yolov3.cpp') diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp index 11d564c778..6d0f67e1f5 100644 --- a/examples/graph_yolov3.cpp +++ b/examples/graph_yolov3.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -170,7 +170,7 @@ public: 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) + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo1") << OutputLayer(get_output_accessor(common_params, 5)); route_1 << ConvolutionLayer( 1U, 1U, 256U, @@ -188,7 +188,7 @@ public: << 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)) + concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1") << ConvolutionLayer( 1U, 1U, 256U, get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout), @@ -281,7 +281,7 @@ public: 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) + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo2") << OutputLayer(get_output_accessor(common_params, 5)); route_2 << ConvolutionLayer( 1U, 1U, 128U, @@ -299,7 +299,7 @@ public: << 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)) + concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2") << ConvolutionLayer( 1U, 1U, 128U, get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout), @@ -391,7 +391,7 @@ public: 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) + << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f), 80).set_name("Yolo3") << OutputLayer(get_output_accessor(common_params, 5)); // Finalize graph @@ -423,7 +423,7 @@ private: get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) - .set_name("conv2d_1") + .set_name("conv2d_1/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"), get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"), @@ -437,7 +437,7 @@ private: get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_2") + .set_name("conv2d_2/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"), get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"), @@ -452,7 +452,7 @@ private: get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_5") + .set_name("conv2d_5/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"), get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"), @@ -468,7 +468,7 @@ private: get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_10") + .set_name("conv2d_10/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"), get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"), @@ -491,7 +491,7 @@ private: get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_27") + .set_name("conv2d_27/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"), get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"), @@ -514,7 +514,7 @@ private: get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 1, 1)) - .set_name("conv2d_44") + .set_name("conv2d_44/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"), get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"), @@ -543,6 +543,7 @@ private: get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name("conv2d_" + param_path + "/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"), get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"), @@ -550,12 +551,13 @@ private: get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"), 0.000001f) .set_name("conv2d_" + param_path + "/BatchNorm") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path + "/LeakyRelu") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path + "/LeakyRelu") << ConvolutionLayer( 3U, 3U, filter_size * 2, get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_" + param_path2 + "/Conv2D") << BatchNormalizationLayer( get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"), get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"), @@ -565,7 +567,7 @@ private: .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); + graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add).set_name("").set_name("add_" + param_path + "_" + param_path2); } }; -- cgit v1.2.1