arm_compute v17.12
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index bb5905e..0d5531f 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -21,16 +21,8 @@
* 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/core/Logger.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/Nodes.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/CPP/CPPScheduler.h"
-#include "arm_compute/runtime/Scheduler.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
@@ -42,76 +34,10 @@
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[in] path Path to the data files
- * @param[in] 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);
- }
-}
-
-/** Generates appropriate input accessor according to the specified ppm_path
- *
- * @note If ppm_path is empty will generate a DummyAccessor else will generate a PPMAccessor
- *
- * @param[in] ppm_path Path to PPM file
- * @param[in] mean_r Red mean value to be subtracted from red channel
- * @param[in] mean_g Green mean value to be subtracted from green channel
- * @param[in] mean_b Blue mean value to be subtracted from blue channel
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_input_accessor(const std::string &ppm_path, float mean_r, float mean_g, float mean_b)
-{
- if(ppm_path.empty())
- {
- return arm_compute::support::cpp14::make_unique<DummyAccessor>();
- }
- else
- {
- return arm_compute::support::cpp14::make_unique<PPMAccessor>(ppm_path, true, mean_r, mean_g, mean_b);
- }
-}
-
-/** Generates appropriate output accessor according to the specified labels_path
- *
- * @note If labels_path is empty will generate a DummyAccessor else will generate a TopNPredictionsAccessor
- *
- * @param[in] labels_path Path to labels text file
- * @param[in] top_n (Optional) Number of output classes to print
- * @param[out] output_stream (Optional) Output stream
- *
- * @return An appropriate tensor accessor
- */
-std::unique_ptr<ITensorAccessor> get_output_accessor(const std::string &labels_path, size_t top_n = 5, std::ostream &output_stream = std::cout)
-{
- if(labels_path.empty())
- {
- return arm_compute::support::cpp14::make_unique<DummyAccessor>();
- }
- else
- {
- return arm_compute::support::cpp14::make_unique<TopNPredictionsAccessor>(labels_path, top_n, output_stream);
- }
-}
-
/** 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)
{
@@ -123,62 +49,62 @@
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";
+ 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 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 << "No text file with labels provided: skipping output accessor\n\n";
}
else
{
- data_path = argv[1];
- image = argv[2];
- label = argv[3];
- }
-
- // Check if OpenCL is available and initialize the scheduler
- TargetHint hint = TargetHint::NEON;
- if(arm_compute::opencl_is_available())
- {
- arm_compute::CLScheduler::get().default_init();
- hint = TargetHint::OPENCL;
+ data_path = argv[2];
+ image = argv[3];
+ label = argv[4];
}
Graph graph;
- arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO);
- 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
<< ConvolutionLayer(
11U, 11U, 96U,
- get_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
PadStrideInfo(4, 4, 0, 0))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< 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_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
PadStrideInfo(1, 1, 2, 2), 2)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
@@ -186,42 +112,42 @@
// Layer 3
<< ConvolutionLayer(
3U, 3U, 384U,
- get_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
PadStrideInfo(1, 1, 1, 1))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 4
<< ConvolutionLayer(
3U, 3U, 384U,
- get_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
PadStrideInfo(1, 1, 1, 1), 2)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 5
<< ConvolutionLayer(
3U, 3U, 256U,
- get_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
PadStrideInfo(1, 1, 1, 1), 2)
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
// Layer 6
<< FullyConnectedLayer(
4096U,
- get_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 7
<< FullyConnectedLayer(
4096U,
- get_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
// Layer 8
<< FullyConnectedLayer(
1000U,
- get_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
- get_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
// Softmax
<< SoftmaxLayer()
<< Tensor(get_output_accessor(label, 5));
@@ -233,7 +159,7 @@
/** 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)
{