Update AlexNet example with accessors
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index dce7132..bb5905e 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -63,35 +63,91 @@
}
}
+/** 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] batches )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
*/
void main_graph_alexnet(int argc, const char **argv)
{
- std::string data_path; /** Path to the trainable data */
- unsigned int batches = 4; /** Number of batches */
+ std::string data_path; /* Path to the trainable data */
+ std::string image; /* Image data */
+ std::string label; /* Label data */
+
+ constexpr float mean_r = 122.68f; /* Mean value to subtract from red channel */
+ 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 */
// Parse arguments
if(argc < 2)
{
// Print help
- std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+ std::cout << "Usage: " << argv[0] << " [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] << " [path_to_data] [batches]\n\n";
- std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+ std::cout << "Usage: " << argv[0] << " " << argv[1] << " [image] [labels]\n\n";
+ std::cout << "No image 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";
}
else
{
- //Do something with argv[1] and argv[2]
data_path = argv[1];
- batches = std::strtol(argv[2], nullptr, 0);
+ image = argv[2];
+ label = argv[3];
}
// Check if OpenCL is available and initialize the scheduler
@@ -106,7 +162,8 @@
arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO);
graph << hint
- << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, batches), 1, DataType::F32), DummyAccessor())
+ << 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,
@@ -167,7 +224,7 @@
get_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
// Softmax
<< SoftmaxLayer()
- << Tensor(DummyAccessor());
+ << Tensor(get_output_accessor(label, 5));
// Run graph
graph.run();
@@ -176,7 +233,7 @@
/** Main program for AlexNet
*
* @param[in] argc Number of arguments
- * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] image, [optional] labels )
*/
int main(int argc, const char **argv)
{