From 2b5f0f2574551f59970bb9d710bafad2bc4bbd4a Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 10 Jan 2018 14:08:50 +0000 Subject: COMPMID-782 Port examples to the new format Change-Id: Ib178a97c080ff650094d02ee49e2a0aa22376dd0 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115717 Reviewed-by: Anthony Barbier Tested-by: Jenkins --- examples/graph_squeezenet.cpp | 294 ++++++++++++++++++++++-------------------- 1 file changed, 152 insertions(+), 142 deletions(-) (limited to 'examples/graph_squeezenet.cpp') diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp index 11a80a7bd8..b21f2fe5c4 100644 --- a/examples/graph_squeezenet.cpp +++ b/examples/graph_squeezenet.cpp @@ -31,33 +31,13 @@ #include #include +using namespace arm_compute::utils; using namespace arm_compute::graph; using namespace arm_compute::graph_utils; using namespace arm_compute::logging; namespace { -BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, unsigned int expand1_filt, unsigned int expand3_filt) -{ - std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_"; - SubGraph i_a; - i_a << ConvolutionLayer( - 1U, 1U, expand1_filt, - get_weights_accessor(data_path, total_path + "expand1x1_w.npy"), - get_weights_accessor(data_path, total_path + "expand1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - SubGraph i_b; - i_b << ConvolutionLayer( - 3U, 3U, expand3_filt, - get_weights_accessor(data_path, total_path + "expand3x3_w.npy"), - get_weights_accessor(data_path, total_path + "expand3x3_b.npy"), - PadStrideInfo(1, 1, 1, 1)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - - return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); -} } // namespace /** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API @@ -65,136 +45,166 @@ BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&par * @param[in] argc Number of arguments * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ -void main_graph_squeezenet(int argc, char **argv) +class GraphSqueezenetExample : public Example { - std::string data_path; /* Path to the trainable data */ - std::string image; /* Image data */ - std::string label; /* Label data */ +public: + void do_setup(int argc, char **argv) override + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ - constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */ - constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */ - constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */ + constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */ + constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */ + constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */ - // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON - TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); - ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT; + // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON + TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); + ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT; - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 2) - { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n"; - std::cout << "No data folder provided: using random values\n\n"; - } - else if(argc == 3) - { - data_path = argv[2]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n\n"; - } - else if(argc == 4) - { - data_path = argv[2]; - image = argv[3]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; - std::cout << "No text file with labels provided: skipping output accessor\n\n"; + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + } + + graph << target_hint + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, mean_r, mean_g, mean_b)) + << ConvolutionLayer( + 7U, 7U, 96U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"), + PadStrideInfo(2, 2, 0, 0)) + << convolution_hint + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 16U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire2", 64U, 64U) + << ConvolutionLayer( + 1U, 1U, 16U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire3", 64U, 64U) + << ConvolutionLayer( + 1U, 1U, 32U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire4", 128U, 128U) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 32U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire5", 128U, 128U) + << ConvolutionLayer( + 1U, 1U, 48U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire6", 192U, 192U) + << ConvolutionLayer( + 1U, 1U, 48U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire7", 192U, 192U) + << ConvolutionLayer( + 1U, 1U, 64U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire8", 256U, 256U) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 64U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire9", 256U, 256U) + << ConvolutionLayer( + 1U, 1U, 1000U, + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"), + get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + << FlattenLayer() + << SoftmaxLayer() + << Tensor(get_output_accessor(label, 5)); } - else + void do_run() override { - data_path = argv[2]; - image = argv[3]; - label = argv[4]; + // Run graph + graph.run(); } - Graph graph; +private: + Graph graph{}; - graph << target_hint - << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), - get_input_accessor(image, mean_r, mean_g, mean_b)) - << ConvolutionLayer( - 7U, 7U, 96U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"), - PadStrideInfo(2, 2, 0, 0)) - << convolution_hint - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << ConvolutionLayer( - 1U, 1U, 16U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire2", 64U, 64U) - << ConvolutionLayer( - 1U, 1U, 16U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire3", 64U, 64U) - << ConvolutionLayer( - 1U, 1U, 32U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire4", 128U, 128U) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << ConvolutionLayer( - 1U, 1U, 32U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire5", 128U, 128U) - << ConvolutionLayer( - 1U, 1U, 48U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire6", 192U, 192U) - << ConvolutionLayer( - 1U, 1U, 48U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire7", 192U, 192U) - << ConvolutionLayer( - 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire8", 256U, 256U) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << ConvolutionLayer( - 1U, 1U, 64U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << get_expand_fire_node(data_path, "fire9", 256U, 256U) - << ConvolutionLayer( - 1U, 1U, 1000U, - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"), - get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) - << FlattenLayer() - << SoftmaxLayer() - << Tensor(get_output_accessor(label, 5)); + BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, unsigned int expand1_filt, unsigned int expand3_filt) + { + std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, expand1_filt, + get_weights_accessor(data_path, total_path + "expand1x1_w.npy"), + get_weights_accessor(data_path, total_path + "expand1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); - graph.run(); -} + SubGraph i_b; + i_b << ConvolutionLayer( + 3U, 3U, expand3_filt, + get_weights_accessor(data_path, total_path + "expand3x3_w.npy"), + get_weights_accessor(data_path, total_path + "expand3x3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); + } +}; /** Main program for Squeezenet v1.0 * @@ -203,5 +213,5 @@ void main_graph_squeezenet(int argc, char **argv) */ int main(int argc, char **argv) { - return arm_compute::utils::run_example(argc, argv, main_graph_squeezenet); + return arm_compute::utils::run_example(argc, argv); } -- cgit v1.2.1