COMPMID-657 - Add PPMAccessor and TopNPredictionsAccessor to SqueezeNet

Change-Id: I8baaee68b82e200a4829c3fc6c60dd211c06e14f
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/94534
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 50 insertions, 57 deletions

diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index a1a7b8619e..195fe0addb 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -43,43 +43,22 @@ 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 path       Path to the data files
- * @param 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);
-    }
-}
-
 BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
 {
     std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_";
     SubGraph    i_a;
     i_a << ConvolutionLayer(
             1U, 1U, expand1_filt,
-            get_accessor(data_path, total_path + "expand1x1_w.npy"),
-            get_accessor(data_path, total_path + "expand1x1_b.npy"),
+            get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
+            get_weights_accessor(data_path, total_path + "expand1x1_b.npy"),
             PadStrideInfo(1, 1, 0, 0))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
     SubGraph i_b;
     i_b << ConvolutionLayer(
             3U, 3U, expand3_filt,
-            get_accessor(data_path, total_path + "expand3x3_w.npy"),
-            get_accessor(data_path, total_path + "expand3x3_b.npy"),
+            get_weights_accessor(data_path, total_path + "expand3x3_w.npy"),
+            get_weights_accessor(data_path, total_path + "expand3x3_b.npy"),
             PadStrideInfo(1, 1, 1, 1))
         << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
@@ -89,32 +68,44 @@ BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&par
 /** Example demonstrating how to implement Squeezenet'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_squeezenet(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";
+    }
+    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";
     }
     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
@@ -126,81 +117,83 @@ void main_graph_squeezenet(int argc, const char **argv)
     Graph graph;
 
     graph << TargetHint::OPENCL
-          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, batches), 1, DataType::F32), DummyAccessor())
+          << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
+                    get_input_accessor(image, mean_r, mean_g, mean_b))
           << ConvolutionLayer(
               7U, 7U, 96U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"),
               PadStrideInfo(2, 2, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
           << ConvolutionLayer(
               1U, 1U, 16U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire2", 64U, 64U)
           << ConvolutionLayer(
               1U, 1U, 16U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire3", 64U, 64U)
           << ConvolutionLayer(
               1U, 1U, 32U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire4", 128U, 128U)
           << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
           << ConvolutionLayer(
               1U, 1U, 32U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire5", 128U, 128U)
           << ConvolutionLayer(
               1U, 1U, 48U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire6", 192U, 192U)
           << ConvolutionLayer(
               1U, 1U, 48U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire7", 192U, 192U)
           << ConvolutionLayer(
               1U, 1U, 64U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire8", 256U, 256U)
           << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL)))
           << ConvolutionLayer(
               1U, 1U, 64U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << get_expand_fire_node(data_path, "fire9", 256U, 256U)
           << ConvolutionLayer(
               1U, 1U, 1000U,
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
-              get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"),
+              get_weights_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"),
               PadStrideInfo(1, 1, 0, 0))
           << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
           << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 13, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)))
+          << FlattenLayer()
           << SoftmaxLayer()
-          << Tensor(DummyAccessor());
+          << Tensor(get_output_accessor(label, 5));
 
     graph.run();
 }
@@ -208,9 +201,9 @@ void main_graph_squeezenet(int argc, const char **argv)
 /** Main program for Squeezenet v1.0
  *
  * @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)
 {
     return arm_compute::utils::run_example(argc, argv, main_graph_squeezenet);
-}
+}
\ No newline at end of file