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>
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 1d04199..b2a5be6 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -42,72 +42,6 @@
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 @@
// 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 @@
<< 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 @@
// 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));