diff options
Diffstat (limited to 'examples')
-rw-r--r-- | examples/graph_alexnet.cpp | 44 | ||||
-rw-r--r-- | examples/graph_googlenet.cpp | 47 | ||||
-rw-r--r-- | examples/graph_lenet.cpp | 32 | ||||
-rw-r--r-- | examples/graph_mobilenet.cpp | 46 | ||||
-rw-r--r-- | examples/graph_squeezenet.cpp | 47 | ||||
-rw-r--r-- | examples/graph_vgg16.cpp | 43 | ||||
-rw-r--r-- | examples/graph_vgg19.cpp | 43 |
7 files changed, 165 insertions, 137 deletions
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp index 534ee45bcd..0d5531f282 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] 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 ) */ void main_graph_alexnet(int argc, const char **argv) { @@ -49,43 +49,45 @@ void main_graph_alexnet(int argc, const char **argv) 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; + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + 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) { - data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n\n"; + 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[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\n"; + 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 + else if(argc == 4) { - data_path = argv[1]; - image = argv[2]; - label = argv[3]; + 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"; } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) + else { - hint = TargetHint::OPENCL; + data_path = argv[2]; + image = argv[3]; + label = argv[4]; } Graph graph; - graph << hint + graph << target_hint << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) // Layer 1 @@ -98,7 +100,7 @@ void main_graph_alexnet(int argc, const char **argv) << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))) // Layer 2 - << ConvolutionMethodHint::DIRECT + << convolution_hint << ConvolutionLayer( 5U, 5U, 256U, get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"), @@ -157,7 +159,7 @@ void main_graph_alexnet(int argc, const char **argv) /** Main program for AlexNet * * @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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp index b7ff4e4bf0..d08382ab8e 100644 --- a/examples/graph_googlenet.cpp +++ b/examples/graph_googlenet.cpp @@ -34,6 +34,8 @@ using namespace arm_compute::graph; using namespace arm_compute::graph_utils; +namespace +{ BranchLayer get_inception_node(const std::string &data_path, std::string &¶m_path, unsigned int a_filt, std::tuple<unsigned int, unsigned int> b_filters, @@ -88,11 +90,12 @@ BranchLayer get_inception_node(const std::string &data_path, std::string &¶m return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)); } +} // namespace /** 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] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ void main_graph_googlenet(int argc, const char **argv) { @@ -104,44 +107,45 @@ void main_graph_googlenet(int argc, const char **argv) 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; + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + 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) { - //Do something with argv[1] - data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n"; + 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[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"; + 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 + else if(argc == 4) { - data_path = argv[1]; - image = argv[2]; - label = argv[3]; + 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"; } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) + else { - hint = TargetHint::OPENCL; + data_path = argv[2]; + image = argv[3]; + label = argv[4]; } Graph graph; - graph << hint + graph << target_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) << ConvolutionLayer( @@ -152,6 +156,7 @@ void main_graph_googlenet(int argc, const char **argv) << 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)) + << convolution_hint << ConvolutionLayer( 1U, 1U, 64U, get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy"), @@ -191,7 +196,7 @@ 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] image, [optional] labels ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index ad4a4e02c7..d4a44382b4 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -32,6 +32,8 @@ using namespace arm_compute::graph; using namespace arm_compute::graph_utils; +namespace +{ /** Generates appropriate accessor according to the specified path * * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader @@ -52,49 +54,51 @@ std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file); } } +} // namespace /** 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] 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 ) */ void main_graph_lenet(int argc, const char **argv) { std::string data_path; /** Path to the trainable data */ unsigned int batches = 4; /** Number of batches */ + // 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); + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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 << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { //Do something with argv[1] - data_path = argv[1]; + data_path = argv[2]; 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"; } else { //Do something with argv[1] and argv[2] - data_path = argv[1]; - batches = std::strtol(argv[2], nullptr, 0); - } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) - { - hint = TargetHint::OPENCL; + data_path = argv[2]; + batches = std::strtol(argv[3], nullptr, 0); } Graph graph; //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx - graph << hint + graph << target_hint << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor()) << ConvolutionLayer( 5U, 5U, 20U, @@ -126,7 +130,7 @@ void main_graph_lenet(int argc, const char **argv) /** Main program for LeNet * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [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 ) */ int main(int argc, const char **argv) { diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index 081fae67e2..0c916c7ba0 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -32,6 +32,8 @@ using namespace arm_compute::graph; using namespace arm_compute::graph_utils; +namespace +{ BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path, unsigned int conv_filt, PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) @@ -66,11 +68,12 @@ BranchLayer get_dwsc_node(const std::string &data_path, std::string &¶m_path return BranchLayer(std::move(sg)); } +} // namespace /** 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] 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 ) */ void main_graph_mobilenet(int argc, const char **argv) { @@ -82,42 +85,44 @@ void main_graph_mobilenet(int argc, const char **argv) 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); + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + 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) { - data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n\n"; + 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[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\n"; + 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 + else if(argc == 4) { - data_path = argv[1]; - image = argv[2]; - label = argv[3]; + 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"; } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) + else { - hint = TargetHint::OPENCL; + data_path = argv[2]; + image = argv[3]; + label = argv[4]; } Graph graph; - graph << hint + + graph << target_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) << ConvolutionLayer( @@ -131,6 +136,7 @@ void main_graph_mobilenet(int argc, const char **argv) get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_beta.npy"), get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_BatchNorm_gamma.npy"), 0.001f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) << get_dwsc_node(data_path, "Conv2d_1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)) << get_dwsc_node(data_path, "Conv2d_2", 128, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0)) @@ -161,7 +167,7 @@ void main_graph_mobilenet(int argc, const char **argv) /** Main program for MobileNetV1 * * @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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp index 1743509256..c8c411aa8b 100644 --- a/examples/graph_squeezenet.cpp +++ b/examples/graph_squeezenet.cpp @@ -35,6 +35,8 @@ 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 + "_"; @@ -56,11 +58,12 @@ BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&par 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 * * @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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ void main_graph_squeezenet(int argc, const char **argv) { @@ -72,44 +75,45 @@ void main_graph_squeezenet(int argc, const char **argv) 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; + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + 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) { - //Do something with argv[1] - data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n"; + 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[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"; + 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 + else if(argc == 4) { - data_path = argv[1]; - image = argv[2]; - label = argv[3]; + 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"; } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) + else { - hint = TargetHint::OPENCL; + data_path = argv[2]; + image = argv[3]; + label = argv[4]; } Graph graph; - graph << hint + graph << target_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) << ConvolutionLayer( @@ -117,6 +121,7 @@ void main_graph_squeezenet(int argc, const char **argv) 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( @@ -194,7 +199,7 @@ void main_graph_squeezenet(int argc, const char **argv) /** Main program for Squeezenet v1.0 * * @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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index 44dd1f63e4..cac38d30a7 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] 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 ) */ void main_graph_vgg16(int argc, const char **argv) { @@ -47,43 +47,46 @@ void main_graph_vgg16(int argc, const char **argv) constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */ constexpr float mean_b = 103.939f; /* 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 = ConvolutionMethodHint::DIRECT; + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + 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) { - data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n\n"; + 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[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\n"; + 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 + else if(argc == 4) { - data_path = argv[1]; - image = argv[2]; - label = argv[3]; + 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"; } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) + else { - hint = TargetHint::OPENCL; + data_path = argv[2]; + image = argv[3]; + label = argv[4]; } Graph graph; - graph << hint + graph << target_hint + << convolution_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) << ConvolutionMethodHint::DIRECT @@ -211,7 +214,7 @@ void main_graph_vgg16(int argc, const char **argv) /** Main program for VGG16 * * @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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp index a39e255ad0..49ae0fe51c 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] 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 ) */ void main_graph_vgg19(int argc, const char **argv) { @@ -47,43 +47,46 @@ void main_graph_vgg19(int argc, const char **argv) constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */ constexpr float mean_b = 103.939f; /* 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 = ConvolutionMethodHint::DIRECT; + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [image] [labels]\n\n"; + 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) { - data_path = argv[1]; - std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n"; - std::cout << "No image provided: using random values\n\n"; + 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[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\n"; + 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 + else if(argc == 4) { - data_path = argv[1]; - image = argv[2]; - label = argv[3]; + 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"; } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) + else { - hint = TargetHint::OPENCL; + data_path = argv[2]; + image = argv[3]; + label = argv[4]; } Graph graph; - graph << hint + graph << target_hint + << convolution_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) // Layer 1 @@ -220,7 +223,7 @@ void main_graph_vgg19(int argc, const char **argv) /** Main program for VGG19 * * @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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) */ int main(int argc, const char **argv) { |