arm_compute v18.08
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 32c7582..6b9f302 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -22,13 +22,11 @@
* SOFTWARE.
*/
#include "arm_compute/graph.h"
-
#include "support/ToolchainSupport.h"
+#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
-#include <cstdlib>
-
using namespace arm_compute::utils;
using namespace arm_compute::graph::frontend;
using namespace arm_compute::graph_utils;
@@ -41,87 +39,83 @@
class GraphLenetExample : public Example
{
public:
- void do_setup(int argc, char **argv) override
+ GraphLenetExample()
+ : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet")
{
- std::string data_path; /** Path to the trainable data */
- unsigned int batches = 4; /** Number of batches */
-
- // 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);
-
- FastMathHint fast_math_hint = FastMathHint::DISABLED;
-
+ }
+ bool do_setup(int argc, char **argv) override
+ {
// Parse arguments
- if(argc < 2)
+ cmd_parser.parse(argc, argv);
+
+ // Consume common parameters
+ common_params = consume_common_graph_parameters(common_opts);
+
+ // Return when help menu is requested
+ if(common_params.help)
{
- // Print help
- 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";
+ cmd_parser.print_help(argv[0]);
+ return false;
}
- else if(argc == 2)
- {
- 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] [fast_math_hint]\n\n";
- std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
- }
- else if(argc == 4)
- {
- 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;
- }
+
+ // Checks
+ ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+
+ // Print parameter values
+ std::cout << common_params << std::endl;
+
+ // Get trainable parameters data path
+ std::string data_path = common_params.data_path;
+ unsigned int batches = 4; /** Number of batches */
+
+ // Create input descriptor
+ const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, common_params.data_layout);
+ TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+
+ // Set weights trained layout
+ const DataLayout weights_layout = DataLayout::NCHW;
//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(""))
+ graph << common_params.target
+ << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params))
<< 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_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name("conv1")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
<< 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_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"),
PadStrideInfo(1, 1, 0, 0))
.set_name("conv2")
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
<< FullyConnectedLayer(
500U,
- get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy"))
.set_name("ip1")
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("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_w.npy", weights_layout),
get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy"))
.set_name("ip2")
<< SoftmaxLayer().set_name("prob")
- << OutputLayer(get_output_accessor(""));
+ << OutputLayer(get_output_accessor(common_params));
// Finalize graph
GraphConfig config;
- config.use_tuner = (target == 2);
- graph.finalize(target_hint, config);
+ config.num_threads = common_params.threads;
+ config.use_tuner = common_params.enable_tuner;
+ config.tuner_file = common_params.tuner_file;
+
+ graph.finalize(common_params.target, config);
+
+ return true;
}
void do_run() override
{
@@ -130,13 +124,18 @@
}
private:
- Stream graph{ 0, "LeNet" };
+ CommandLineParser cmd_parser;
+ CommonGraphOptions common_opts;
+ CommonGraphParams common_params;
+ Stream graph;
};
/** Main program for LeNet
*
+ * @note To list all the possible arguments execute the binary appended with the --help option
+ *
* @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] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) )
+ * @param[in] argv Arguments
*/
int main(int argc, char **argv)
{