diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/SConscript | 24 | ||||
| -rw-r--r-- | examples/cl_convolution.cpp | 5 | ||||
| -rw-r--r-- | examples/cl_events.cpp | 5 | ||||
| -rw-r--r-- | examples/graph_lenet.cpp | 142 | ||||
| -rw-r--r-- | examples/neon_cnn.cpp | 8 | ||||
| -rw-r--r-- | examples/neon_copy_objects.cpp | 4 | ||||
| -rw-r--r-- | examples/neoncl_scale_median_gaussian.cpp | 5 |
7 files changed, 179 insertions, 14 deletions
diff --git a/examples/SConscript b/examples/SConscript index 748f771ec..853a1bb51 100644 --- a/examples/SConscript +++ b/examples/SConscript @@ -23,8 +23,6 @@ import SCons import os.path Import('env') -Import('arm_compute_a') -Import('arm_compute_so') if env['opencl']: Import('opencl') @@ -38,17 +36,33 @@ examples_env.Append(LIBPATH = ["#build/%s/opencl-1.2-stubs" % env['build_dir']]) # Build examples utils = examples_env.Object("../utils/Utils.cpp") -if env['os'] in ['android', 'bare_metal']: +if env['os'] in ['android', 'bare_metal'] or env['standalone']: + Import('arm_compute_a') arm_compute_lib = arm_compute_a arm_compute_dependency = arm_compute_a else: + Import('arm_compute_so') arm_compute_lib = "arm_compute" arm_compute_dependency = arm_compute_so if env['opencl'] and env['neon']: for file in Glob("./neoncl_*.cpp"): example = os.path.basename(os.path.splitext(str(file))[0]) - prog = examples_env.Program(example, ["{}.cpp".format(example), utils], LIBS = [arm_compute_lib, "OpenCL"]) + prog = examples_env.Program(example, ["{}.cpp".format(example), utils], CPPDEFINES=['ARM_COMPUTE_CL'], LIBS = [arm_compute_lib, "OpenCL"]) + Depends(prog, [arm_compute_dependency, opencl]) + alias = examples_env.Alias(example, prog) + Default(alias) + Import('arm_compute_graph_a') + Import('arm_compute_graph_so') + if env['os'] == 'android': + arm_compute_graph_lib = arm_compute_graph_a + else: + arm_compute_graph_lib = "arm_compute_graph" + + graph_utils = examples_env.Object("../utils/GraphUtils.cpp") + for file in Glob("./graph_*.cpp"): + example = os.path.basename(os.path.splitext(str(file))[0]) + prog = examples_env.Program(example, ["{}.cpp".format(example), utils, graph_utils], CPPDEFINES=['ARM_COMPUTE_CL'], LIBS = [arm_compute_graph_lib, "OpenCL"]) Depends(prog, [arm_compute_dependency, opencl]) alias = examples_env.Alias(example, prog) Default(alias) @@ -56,7 +70,7 @@ if env['opencl'] and env['neon']: if env['opencl']: for file in Glob("./cl_*.cpp"): example = os.path.basename(os.path.splitext(str(file))[0]) - prog = examples_env.Program(example, ["{}.cpp".format(example), utils], LIBS = [arm_compute_lib, "OpenCL"]) + prog = examples_env.Program(example, ["{}.cpp".format(example), utils], CPPDEFINES=['ARM_COMPUTE_CL'], LIBS = [arm_compute_lib, "OpenCL"]) Depends(prog, [arm_compute_dependency, opencl]) alias = examples_env.Alias(example, prog) Default(alias) diff --git a/examples/cl_convolution.cpp b/examples/cl_convolution.cpp index 06f6f144e..b780193f1 100644 --- a/examples/cl_convolution.cpp +++ b/examples/cl_convolution.cpp @@ -21,7 +21,10 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#define ARM_COMPUTE_CL /* So that OpenCL exceptions get caught too */ +#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */ +#error "This example needs to be built with -DARM_COMPUTE_CL" +#endif /* ARM_COMPUTE_CL */ + #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/CLFunctions.h" #include "arm_compute/runtime/CL/CLScheduler.h" diff --git a/examples/cl_events.cpp b/examples/cl_events.cpp index 768f62062..213f4a19d 100644 --- a/examples/cl_events.cpp +++ b/examples/cl_events.cpp @@ -21,7 +21,10 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#define ARM_COMPUTE_CL /* So that OpenCL exceptions get caught too */ +#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */ +#error "This example needs to be built with -DARM_COMPUTE_CL" +#endif /* ARM_COMPUTE_CL */ + #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/CLFunctions.h" #include "arm_compute/runtime/CL/CLScheduler.h" diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp new file mode 100644 index 000000000..676fdb9ce --- /dev/null +++ b/examples/graph_lenet.cpp @@ -0,0 +1,142 @@ +/* + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */ +#error "This example needs to be built with -DARM_COMPUTE_CL" +#endif /* ARM_COMPUTE_CL */ + +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/runtime/Scheduler.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include <cstdlib> +#include <iostream> +#include <memory> + +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<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file) +{ + if(path.empty()) + { + return arm_compute::support::cpp14::make_unique<DummyAccessor>(); + } + else + { + return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file); + } +} + +/** 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 ) + */ +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 */ + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\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"; + } + 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 + if(arm_compute::opencl_is_available()) + { + arm_compute::CLScheduler::get().default_init(); + } + + Graph graph; + graph.set_info_enablement(true); + + //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx + graph << Hint::OPENCL + << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor()) + << ConvolutionLayer( + 5U, 5U, 20U, + get_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"), + get_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + << ConvolutionLayer( + 5U, 5U, 50U, + get_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"), + get_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + << FullyConnectedLayer( + 500U, + get_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"), + get_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy")) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << FullyConnectedLayer( + 10U, + get_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"), + get_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy")) + << SoftmaxLayer() + << Tensor(DummyAccessor()); + + graph.run(); +} + +/** Main program for LeNet + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches ) + */ +int main(int argc, const char **argv) +{ + return arm_compute::utils::run_example(argc, argv, main_graph_lenet); +} diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp index 952ae4d48..238f0572d 100644 --- a/examples/neon_cnn.cpp +++ b/examples/neon_cnn.cpp @@ -143,22 +143,22 @@ void main_cnn(int argc, const char **argv) /* [Configure functions] */ // in:32x32x1: 5x5 convolution, 8 output features maps (OFM) - conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo()); + conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */)); // in:32x32x8, out:32x32x8, Activation function: relu act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max - pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2)); + pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); // in:16x16x8: 3x3 convolution, 16 output features maps (OFM) - conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo()); + conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */)); // in:16x16x16, out:16x16x16, Activation function: relu act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average - pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2)); + pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); // in:8x8x16, out:128 fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0); diff --git a/examples/neon_copy_objects.cpp b/examples/neon_copy_objects.cpp index 191f45555..04024530d 100644 --- a/examples/neon_copy_objects.cpp +++ b/examples/neon_copy_objects.cpp @@ -75,7 +75,7 @@ void main_neon_copy_objects(int argc, const char **argv) // Fill the input tensor: // Simplest way: create an iterator to iterate through each element of the input tensor: Window input_window; - input_window.use_tensor_dimensions(input.info()); + input_window.use_tensor_dimensions(input.info()->tensor_shape()); std::cout << " Dimensions of the input's iterator:\n"; std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n"; std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n"; @@ -109,7 +109,7 @@ void main_neon_copy_objects(int argc, const char **argv) // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor: Window output_window; - output_window.use_tensor_dimensions(output.info(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element) + output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element) std::cout << " Dimensions of the output's iterator:\n"; std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n"; std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n"; diff --git a/examples/neoncl_scale_median_gaussian.cpp b/examples/neoncl_scale_median_gaussian.cpp index a32ba6daf..e53a48e07 100644 --- a/examples/neoncl_scale_median_gaussian.cpp +++ b/examples/neoncl_scale_median_gaussian.cpp @@ -21,7 +21,10 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#define ARM_COMPUTE_CL /* So that OpenCL exceptions get caught too */ +#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */ +#error "This example needs to be built with -DARM_COMPUTE_CL" +#endif /* ARM_COMPUTE_CL */ + #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/CLFunctions.h" #include "arm_compute/runtime/CL/CLScheduler.h" |