From a4c6188262d6d9f75f019e437f8190bdd56e604d Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Fri, 3 Nov 2017 12:11:55 +0000 Subject: COMPMID-657 - Add PPMAccessor and TopNPredictionsAccessor to GoogleNet Change-Id: Ib6f2f9e73043d2c59b2698c243fb1a9f51c526e9 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94363 Tested-by: Kaizen Reviewed-by: Gian Marco Iodice --- examples/graph_googlenet.cpp | 96 ++++++++++++++++++++------------------------ 1 file changed, 44 insertions(+), 52 deletions(-) (limited to 'examples/graph_googlenet.cpp') diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp index 0e82c1e85d..354a2a39e4 100644 --- a/examples/graph_googlenet.cpp +++ b/examples/graph_googlenet.cpp @@ -42,27 +42,6 @@ using namespace arm_compute::graph; using namespace arm_compute::graph_utils; -/** Generates appropriate accessor according to the specified path - * - * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader - * - * @param path Path to the data files - * @param data_file Relative path to the data files from path - * - * @return An appropriate tensor accessor - */ -std::unique_ptr get_accessor(const std::string &path, const std::string &data_file) -{ - if(path.empty()) - { - return arm_compute::support::cpp14::make_unique(); - } - else - { - return arm_compute::support::cpp14::make_unique(path + data_file); - } -} - BranchLayer get_inception_node(const std::string &data_path, std::string &¶m_path, unsigned int a_filt, std::tuple b_filters, @@ -73,36 +52,36 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &¶m SubGraph i_a; i_a << ConvolutionLayer( 1U, 1U, a_filt, - get_accessor(data_path, total_path + "1x1_w.npy"), - get_accessor(data_path, total_path + "1x1_b.npy"), + get_weights_accessor(data_path, total_path + "1x1_w.npy"), + get_weights_accessor(data_path, total_path + "1x1_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); SubGraph i_b; i_b << ConvolutionLayer( 1U, 1U, std::get<0>(b_filters), - get_accessor(data_path, total_path + "3x3_reduce_w.npy"), - get_accessor(data_path, total_path + "3x3_reduce_b.npy"), + get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy"), + get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 3U, 3U, std::get<1>(b_filters), - get_accessor(data_path, total_path + "3x3_w.npy"), - get_accessor(data_path, total_path + "3x3_b.npy"), + get_weights_accessor(data_path, total_path + "3x3_w.npy"), + get_weights_accessor(data_path, total_path + "3x3_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); SubGraph i_c; i_c << ConvolutionLayer( 1U, 1U, std::get<0>(c_filters), - get_accessor(data_path, total_path + "5x5_reduce_w.npy"), - get_accessor(data_path, total_path + "5x5_reduce_b.npy"), + get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy"), + get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 5U, 5U, std::get<1>(c_filters), - get_accessor(data_path, total_path + "5x5_w.npy"), - get_accessor(data_path, total_path + "5x5_b.npy"), + get_weights_accessor(data_path, total_path + "5x5_w.npy"), + get_weights_accessor(data_path, total_path + "5x5_b.npy"), PadStrideInfo(1, 1, 2, 2)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -110,8 +89,8 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &¶m i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))) << ConvolutionLayer( 1U, 1U, d_filt, - get_accessor(data_path, total_path + "pool_proj_w.npy"), - get_accessor(data_path, total_path + "pool_proj_b.npy"), + get_weights_accessor(data_path, total_path + "pool_proj_w.npy"), + get_weights_accessor(data_path, total_path + "pool_proj_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); @@ -121,32 +100,44 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &¶m /** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches ) + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels ) */ void main_graph_googlenet(int argc, const char **argv) { - std::string data_path; /** Path to the trainable data */ - unsigned int batches = 4; /** Number of batches */ + 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 */ // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; + std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; std::cout << "No data folder provided: using random values\n\n"; } else if(argc == 2) { //Do something with argv[1] data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; - std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n"; + } + else if(argc == 3) + { + data_path = argv[1]; + image = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n"; } else { - //Do something with argv[1] and argv[2] data_path = argv[1]; - batches = std::strtol(argv[2], nullptr, 0); + image = argv[2]; + label = argv[3]; } // Check if OpenCL is available and initialize the scheduler @@ -158,25 +149,26 @@ void main_graph_googlenet(int argc, const char **argv) Graph graph; graph << TargetHint::OPENCL - << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, batches), 1, DataType::F32), DummyAccessor()) + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, mean_r, mean_g, mean_b)) << ConvolutionLayer( 7U, 7U, 64U, - get_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"), - get_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"), PadStrideInfo(2, 2, 3, 3)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) << ConvolutionLayer( 1U, 1U, 64U, - get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"), - get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"), PadStrideInfo(1, 1, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << ConvolutionLayer( 3U, 3U, 192U, - get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"), - get_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) @@ -195,10 +187,10 @@ void main_graph_googlenet(int argc, const char **argv) << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))) << FullyConnectedLayer( 1000U, - get_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"), - get_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy")) + get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy"), + get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy")) << SoftmaxLayer() - << Tensor(DummyAccessor()); + << Tensor(get_output_accessor(label, 5)); graph.run(); } @@ -206,7 +198,7 @@ void main_graph_googlenet(int argc, const char **argv) /** Main program for Googlenet * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches ) + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { -- cgit v1.2.1