COMPMID-657 - Add PPMAccessor and TopNPredictionsAccessor to GoogleNet

Change-Id: Ib6f2f9e73043d2c59b2698c243fb1a9f51c526e9
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94363
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

1 files changed, 16 insertions, 82 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 1d041997e3..b2a5be647f 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -42,72 +42,6 @@ using namespace arm_compute::graph;
 using namespace arm_compute::graph_utils;
 using namespace arm_compute::logging;
 
-/** 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
@@ -166,8 +100,8 @@ void main_graph_alexnet(int argc, const char **argv)
           // 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))
@@ -176,8 +110,8 @@ void main_graph_alexnet(int argc, const char **argv)
           << ConvolutionMethodHint::DIRECT
           << 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))
@@ -185,42 +119,42 @@ void main_graph_alexnet(int argc, const char **argv)
           // 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));