From 59631a174e1b5ef23bd3a0102f60b57c99502766 Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Wed, 2 May 2018 13:59:04 +0100 Subject: COMPMID-1104 Add fast math hint in the graph API Change-Id: I83db135fa94c6884e080f0229a9b6430d908c029 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129823 Tested-by: Jenkins Reviewed-by: Gian Marco Iodice Reviewed-by: Anthony Barbier --- examples/graph_alexnet.cpp | 25 ++++++++++++++++++------- examples/graph_googlenet.cpp | 25 ++++++++++++++++++------- examples/graph_inception_v3.cpp | 30 +++++++++++++++++++++--------- examples/graph_inception_v4.cpp | 30 +++++++++++++++++++++--------- examples/graph_lenet.cpp | 25 ++++++++++++++++++------- examples/graph_mobilenet.cpp | 31 ++++++++++++++++++++++--------- examples/graph_mobilenet_qasymm8.cpp | 27 +++++++++++++++++++-------- examples/graph_resnet50.cpp | 25 ++++++++++++++++++------- examples/graph_squeezenet.cpp | 25 ++++++++++++++++++------- examples/graph_squeezenet_v1_1.cpp | 25 ++++++++++++++++++------- examples/graph_vgg16.cpp | 25 ++++++++++++++++++------- examples/graph_vgg19.cpp | 31 +++++++++++++++++++++---------- 12 files changed, 230 insertions(+), 94 deletions(-) (limited to 'examples') diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp index 45c2b56cc2..b97ca54c78 100644 --- a/examples/graph_alexnet.cpp +++ b/examples/graph_alexnet.cpp @@ -37,7 +37,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement AlexNet's network using the Compute Library's graph API * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphAlexnetExample : public Example { @@ -60,40 +60,51 @@ public: const bool is_neon = (target_hint == Target::NEON); ConvolutionMethod convolution_5x5_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DIRECT; ConvolutionMethod convolution_3x3_hint = is_neon ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) // Layer 1 @@ -185,7 +196,7 @@ private: /** Main program for AlexNet * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp index deafe5a822..bac3c7c6d5 100644 --- a/examples/graph_googlenet.cpp +++ b/examples/graph_googlenet.cpp @@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils; /** 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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphGooglenetExample : public Example { @@ -55,40 +55,51 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) << ConvolutionLayer( @@ -206,7 +217,7 @@ private: /** Main program for Googlenet * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp index 7fa0fc74fe..8e30fd97f2 100644 --- a/examples/graph_inception_v3.cpp +++ b/examples/graph_inception_v3.cpp @@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement InceptionV3'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] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class InceptionV3Example : public Example { @@ -54,41 +54,53 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } - graph << target_hint << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), - get_input_accessor(image, std::move(preprocessor), false)) + graph << target_hint + << fast_math_hint + << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), + get_input_accessor(image, std::move(preprocessor), false)) << ConvolutionLayer(3U, 3U, 32U, get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy"), std::unique_ptr(nullptr), PadStrideInfo(2, 2, 0, 0)) @@ -759,7 +771,7 @@ private: /** Main program for Inception V3 * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_inception_v4.cpp b/examples/graph_inception_v4.cpp index 4217c78554..827370ec5e 100644 --- a/examples/graph_inception_v4.cpp +++ b/examples/graph_inception_v4.cpp @@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement InceptionV4's network using the Compute Library's graph API * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class InceptionV4Example final : public Example { @@ -57,41 +57,53 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } - graph << target_hint << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), - get_input_accessor(image, std::move(preprocessor), false)) + graph << target_hint + << fast_math_hint + << InputLayer(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), DataType::F32), + get_input_accessor(image, std::move(preprocessor), false)) // Conv2d_1a_3x3 << ConvolutionLayer(3U, 3U, 32U, get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy"), @@ -737,7 +749,7 @@ private: /** Main program for Inception V4 * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index ea0916b317..92be2d48c1 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement LeNet's network using the Compute Library's graph API * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphLenetExample : public Example { @@ -50,34 +50,45 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); + FastMathHint fast_math_hint = FastMathHint::DISABLED; + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches] [fast_math_hint]\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] [batches]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [batches] [fast_math_hint]\n\n"; std::cout << "No data folder provided: using random values\n\n"; } else if(argc == 3) { //Do something with argv[1] data_path = argv[2]; - std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; + std::cout << "Usage: " << argv[0] << " [path_to_data] [batches] [fast_math_hint]\n\n"; std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n"; } - else + else if(argc == 4) { - //Do something with argv[1] and argv[2] data_path = argv[2]; batches = std::strtol(argv[3], nullptr, 0); + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + //Do something with argv[1] and argv[2] + data_path = argv[2]; + batches = std::strtol(argv[3], nullptr, 0); + fast_math_hint = (std::strtol(argv[4], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx graph << target_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor("")) << ConvolutionLayer( 5U, 5U, 20U, @@ -125,7 +136,7 @@ private: /** Main program for LeNet * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index 813c0bfe1d..7bfc6808fa 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement MobileNet's network using the Compute Library's graph API * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] data layout, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphMobilenetExample : public Example { @@ -54,6 +54,7 @@ public: Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = ConvolutionMethod::GEMM; DepthwiseConvolutionMethod depthwise_convolution_hint = DepthwiseConvolutionMethod::OPTIMIZED_3x3; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160) int model_id = (argc > 2) ? std::strtol(argv[2], nullptr, 10) : 0; @@ -72,33 +73,33 @@ public: if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; std::cout << "No data layout provided: using NCHW\n\n"; std::cout << "No data folder provided: using random values\n\n"; } else if(argc == 2) { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [model] [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; std::cout << "No model ID provided: using MobileNetV1_1.0_224\n\n"; std::cout << "No data layout provided: using NCHW\n\n"; std::cout << "No data folder provided: using random values\n\n"; } else if(argc == 3) { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [layout] [path_to_data] [image] [labels] [fast_math_hint]\n\n"; std::cout << "No data layout provided: using NCHW\n\n"; std::cout << "No data folder provided: using random values\n\n"; } else if(argc == 4) { - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [path_to_data] [image] [labels] [fast_math_hint]\n\n"; std::cout << "No data folder provided: using random values\n\n"; } else if(argc == 5) { data_path = argv[4]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [image] [labels] [fast_math_hint]\n\n"; std::cout << "No image provided: using random values\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } @@ -106,14 +107,23 @@ public: { data_path = argv[4]; image = argv[5]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 7) { data_path = argv[4]; image = argv[5]; label = argv[6]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[4]; + image = argv[5]; + label = argv[6]; + fast_math_hint = (std::strtol(argv[7], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } // Add model path to data path @@ -125,6 +135,7 @@ public: graph << target_hint << convolution_hint << depthwise_convolution_hint + << fast_math_hint << InputLayer(input_descriptor, get_input_accessor(image, std::move(preprocessor), false)) << ConvolutionLayer( @@ -225,7 +236,9 @@ private: * [optional] Model ID (0 = MobileNetV1_1.0_224, 1 = MobileNetV1_0.75_160), * [optional] Path to the weights folder, * [optional] image, - * [optional] labels ) + * [optional] labels, + * [optional] data layout, + * [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp index 7edd1822ae..2801209985 100644 --- a/examples/graph_mobilenet_qasymm8.cpp +++ b/examples/graph_mobilenet_qasymm8.cpp @@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement QASYMM8 MobileNet's network using the Compute Library's graph API * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphMobileNetQASYMM8Example : public Example { @@ -92,37 +92,48 @@ public: }; // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels] [fast_math_hint]\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] [npy_input] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels] [fast_math_hint]\n\n"; std::cout << "No input provided: using random values\n\n"; } else if(argc == 4) { data_path = argv[2]; input = argv[3]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; input = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + input = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info), get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input)) << ConvolutionLayer( @@ -220,7 +231,7 @@ private: /** Main program for MobileNetQASYMM8 * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels ) + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] npy_input, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp index 18a028d48c..6fac372550 100644 --- a/examples/graph_resnet50.cpp +++ b/examples/graph_resnet50.cpp @@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphResNet50Example : public Example { @@ -55,40 +55,51 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */)) << ConvolutionLayer( @@ -243,7 +254,7 @@ private: /** Main program for ResNet50 * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp index 8ed43f707d..2627c96774 100644 --- a/examples/graph_squeezenet.cpp +++ b/examples/graph_squeezenet.cpp @@ -37,7 +37,7 @@ using namespace arm_compute::logging; /** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphSqueezenetExample : public Example { @@ -56,40 +56,51 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) << ConvolutionLayer( @@ -209,7 +220,7 @@ private: /** Main program for Squeezenet v1.0 * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp index 529f4fe80a..c4a5433352 100644 --- a/examples/graph_squeezenet_v1_1.cpp +++ b/examples/graph_squeezenet_v1_1.cpp @@ -40,7 +40,7 @@ namespace /** Example demonstrating how to implement Squeezenet's v1.1 network using the Compute Library's graph API * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphSqueezenet_v1_1Example : public Example { @@ -59,40 +59,51 @@ public: const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; Target target_hint = set_target_hint(target); ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) << ConvolutionMethod::DIRECT @@ -214,7 +225,7 @@ private: /** Main program for Squeezenet v1.1 * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index 44b4c4c3f2..6db4e386de 100644 --- a/examples/graph_vgg16.cpp +++ b/examples/graph_vgg16.cpp @@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement VGG16's network using the Compute Library's graph API * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphVGG16Example : public Example { @@ -57,40 +57,51 @@ public: ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM; ConvolutionMethod convolution3x3_hint = ConvolutionMethod::DEFAULT; + FastMathHint fast_math_hint = FastMathHint::DISABLED; // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint + << fast_math_hint << first_convolution3x3_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) @@ -246,7 +257,7 @@ private: /** Main program for VGG16 * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp index 229112bb6f..5a281ea86a 100644 --- a/examples/graph_vgg19.cpp +++ b/examples/graph_vgg19.cpp @@ -35,7 +35,7 @@ using namespace arm_compute::graph_utils; /** Example demonstrating how to implement VGG19's network using the Compute Library's graph API * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ class GraphVGG19Example : public Example { @@ -51,9 +51,10 @@ public: std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(mean_rgb); // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - const bool is_opencl = target_hint == Target::CL; + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + FastMathHint fast_math_hint = FastMathHint::DISABLED; + const bool is_opencl = target_hint == Target::CL; ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM; ConvolutionMethod convolution3x3_hint = ConvolutionMethod::DEFAULT; @@ -62,36 +63,46 @@ public: if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels] [fast_math_hint]\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 << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels] [fast_math_hint]\n\n"; std::cout << "No text file with labels provided: skipping output accessor\n\n"; } - else + else if(argc == 5) { data_path = argv[2]; image = argv[3]; label = argv[4]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " " << argv[4] << " [fast_math_hint]\n\n"; + std::cout << "No fast math info provided: disabling fast math\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + fast_math_hint = (std::strtol(argv[5], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; } graph << target_hint << first_convolution3x3_hint + << fast_math_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) // Layer 1 @@ -259,7 +270,7 @@ private: /** Main program for VGG19 * * @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 ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { -- cgit v1.2.1