COMPMID-782 Port examples to the new format
Change-Id: Ib178a97c080ff650094d02ee49e2a0aa22376dd0
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115717
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 3e4727f..2442cff 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -29,103 +29,88 @@
#include <cstdlib>
+using namespace arm_compute::utils;
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
- *
- * @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);
- }
-}
-} // 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] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches )
*/
-void main_graph_lenet(int argc, char **argv)
+class GraphLenetExample : public Example
{
- std::string data_path; /** Path to the trainable data */
- unsigned int batches = 4; /** Number of batches */
+public:
+ void do_setup(int argc, char **argv) override
+ {
+ 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);
+ // 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] << " [target] [path_to_data] [batches]\n\n";
- std::cout << "No data folder provided: using random values\n\n";
+ // Parse arguments
+ if(argc < 2)
+ {
+ // Print help
+ 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[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[2];
+ batches = std::strtol(argv[3], nullptr, 0);
+ }
+
+ //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
+ graph << target_hint
+ << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor())
+ << ConvolutionLayer(
+ 5U, 5U, 20U,
+ get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"),
+ get_weights_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_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"),
+ get_weights_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_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ << FullyConnectedLayer(
+ 10U,
+ get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
+ << SoftmaxLayer()
+ << Tensor(DummyAccessor());
}
- else if(argc == 2)
+ void do_run() override
{
- 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[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[2];
- batches = std::strtol(argv[3], nullptr, 0);
+ // Run graph
+ graph.run();
}
- Graph graph;
-
- //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
- graph << target_hint
- << 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();
-}
+private:
+ Graph graph{};
+};
/** Main program for LeNet
*
@@ -134,5 +119,5 @@
*/
int main(int argc, char **argv)
{
- return arm_compute::utils::run_example(argc, argv, main_graph_lenet);
+ return arm_compute::utils::run_example<GraphLenetExample>(argc, argv);
}