COMPMID-797 Integrate Mobilenet QASYMM8 with new graph.

Change-Id: I4df63ec2f4eb27a8a6eec2bea27741bf8dec6910
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126966
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 43 insertions, 48 deletions

diff --git a/examples/graph_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
index cb49ffdfb4..ddf6175c4a 100644
--- a/examples/graph_mobilenet_qasymm8.cpp
+++ b/examples/graph_mobilenet_qasymm8.cpp
@@ -28,6 +28,7 @@
 
 #include <cstdlib>
 
+using namespace arm_compute;
 using namespace arm_compute::utils;
 using namespace arm_compute::graph::frontend;
 using namespace arm_compute::graph_utils;
@@ -40,8 +41,6 @@ using namespace arm_compute::graph_utils;
 class GraphMobileNetQASYMM8Example : public Example
 {
 public:
-    //FIXME: Missing quantization info to the tensor descriptor (Giorgio is working on it)
-#if 0
     void do_setup(int argc, char **argv) override
     {
         std::string data_path; /* Path to the trainable data */
@@ -93,8 +92,8 @@ public:
         };
 
         // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
-        const int  int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
-        TargetHint target_hint     = set_target_hint(int_target_hint);
+        const int target      = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
+        Target    target_hint = set_target_hint(target);
 
         // Parse arguments
         if(argc < 2)
@@ -123,54 +122,56 @@ public:
         }
 
         graph << target_hint
-              << arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::QASYMM8, in_quant_info),
-                                            get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
+              << DepthwiseConvolutionMethod::OPTIMIZED_3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method
+              << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::QASYMM8, in_quant_info),
+                            get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
               << ConvolutionLayer(
                   3U, 3U, 32U,
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_weights.npy"),
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Conv2d_0_bias.npy"),
                   PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
-                  1, WeightsInfo(),
-                  conv_weights_quant_info.at(0),
-                  mid_quant_info)
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
-              << get_dwsc_node(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0))
-              << get_dwsc_node(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1),
-                               point_weights_quant_info.at(1))
-              << get_dwsc_node(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2),
-                               point_weights_quant_info.at(2))
-              << get_dwsc_node(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3),
-                               point_weights_quant_info.at(3))
-              << get_dwsc_node(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4),
-                               point_weights_quant_info.at(4))
-              << get_dwsc_node(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5),
-                               point_weights_quant_info.at(5))
-              << get_dwsc_node(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6),
-                               point_weights_quant_info.at(6))
-              << get_dwsc_node(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7),
-                               point_weights_quant_info.at(7))
-              << get_dwsc_node(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8),
-                               point_weights_quant_info.at(8))
-              << get_dwsc_node(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9),
-                               point_weights_quant_info.at(9))
-              << get_dwsc_node(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10),
-                               point_weights_quant_info.at(10))
-              << get_dwsc_node(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11),
-                               point_weights_quant_info.at(11))
-              << get_dwsc_node(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),
+                  1, conv_weights_quant_info.at(0), mid_quant_info)
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
+        graph << get_dwsc_node(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0));
+        graph << get_dwsc_node(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1),
+                               point_weights_quant_info.at(1));
+        graph << get_dwsc_node(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2),
+                               point_weights_quant_info.at(2));
+        graph << get_dwsc_node(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3),
+                               point_weights_quant_info.at(3));
+        graph << get_dwsc_node(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4),
+                               point_weights_quant_info.at(4));
+        graph << get_dwsc_node(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5),
+                               point_weights_quant_info.at(5));
+        graph << get_dwsc_node(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6),
+                               point_weights_quant_info.at(6));
+        graph << get_dwsc_node(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7),
+                               point_weights_quant_info.at(7));
+        graph << get_dwsc_node(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8),
+                               point_weights_quant_info.at(8));
+        graph << get_dwsc_node(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9),
+                               point_weights_quant_info.at(9));
+        graph << get_dwsc_node(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10),
+                               point_weights_quant_info.at(10));
+        graph << get_dwsc_node(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11),
+                               point_weights_quant_info.at(11));
+        graph << get_dwsc_node(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),
                                point_weights_quant_info.at(12))
               << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << ConvolutionLayer(
                   1U, 1U, 1001U,
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_weights.npy"),
                   get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/Logits_Conv2d_1c_1x1_bias.npy"),
-                  PadStrideInfo(1U, 1U, 0U, 0U), 1, WeightsInfo(), conv_weights_quant_info.at(1))
+                  PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1))
               << ReshapeLayer(TensorShape(1001U))
               << SoftmaxLayer()
-              << arm_compute::graph::Tensor(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(label, 5));
 
-        // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
-        graph.graph_init(int_target_hint == 2);
+        // Finalize graph
+        GraphConfig config;
+        config.use_function_memory_manager = true;
+        config.use_tuner                   = (target == 2);
+        graph.finalize(target_hint, config);
     }
     void do_run() override
     {
@@ -179,7 +180,7 @@ public:
     }
 
 private:
-    Graph graph{};
+    Stream graph{ 0, "MobileNetV1_QASYMM8" };
 
     /** This function produces a depthwise separable convolution node (i.e. depthwise + pointwise layers) with ReLU6 activation after each layer.
      *
@@ -199,29 +200,23 @@ private:
                               QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info)
     {
         std::string total_path = "/cnn_data/mobilenet_qasymm8_model/" + param_path + "_";
-        SubGraph    sg;
+        SubStream   sg(graph);
 
         sg << DepthwiseConvolutionLayer(
                3U, 3U,
                get_weights_accessor(data_path, total_path + "depthwise_weights.npy"),
                get_weights_accessor(data_path, total_path + "depthwise_bias.npy"),
-               dwc_pad_stride_info,
-               true,
-               depth_weights_quant_info)
+               dwc_pad_stride_info, depth_weights_quant_info)
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
            << ConvolutionLayer(
                1U, 1U, conv_filt,
                get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
                get_weights_accessor(data_path, total_path + "pointwise_bias.npy"),
-               conv_pad_stride_info,
-               1, WeightsInfo(),
-               point_weights_quant_info)
+               conv_pad_stride_info, 1, point_weights_quant_info)
            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f));
 
         return BranchLayer(std::move(sg));
     }
-#endif /* if 0 */
-    Stream graph { 0, "MobileNetV1_QASYMM8" };
 };
 /** Main program for MobileNetQASYMM8
  *