commit 11fedda86532cf632b9a3ae4b0f57e85f2a7c4f4
author Sang-Hoon Park <sang-hoon.park@arm.com> Wed Jan 15 14:44:04 2020 +0000
committer Georgios Pinitas <georgios.pinitas@arm.com> Tue Jan 28 16:05:27 2020 +0000
tree 6fd8003a38fe9baa262696754bdd5cb1d1595947
parent 6c89ffac750010cb9335794defe8a366c04db937

COMPMID-2985 add data_layout to PoolingLayerInfo

- use data layout from PoolingLayerInfo if it's available
- deprecate constructors without data_layout
- (3RDPARTY_UPDATE) modify examples and test suites to give data layout

Change-Id: Ie9ae8cc4837c339ff69a16a816110be704863c2d
Signed-off-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2603
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

examples/graph_vgg19.cpp

diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index e3733d8..f9f5c21 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -75,8 +75,9 @@
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const auto        operation_layout = common_params.data_layout;
+        const TensorShape tensor_shape     = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
+        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
@@ -99,7 +100,7 @@
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv1_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1_2/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
               // Layer 2
               << ConvolutionLayer(
                   3U, 3U, 128U,
@@ -115,7 +116,7 @@
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv2_2")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2_2/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
               // Layer 3
               << ConvolutionLayer(
                   3U, 3U, 256U,
@@ -145,7 +146,7 @@
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv3_4")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3_4/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool3")
               // Layer 4
               << ConvolutionLayer(
                   3U, 3U, 512U,
@@ -175,7 +176,7 @@
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv4_4")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv4_4/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool4")
               // Layer 5
               << ConvolutionLayer(
                   3U, 3U, 512U,
@@ -205,7 +206,7 @@
                   PadStrideInfo(1, 1, 1, 1))
               .set_name("conv5_4")
               << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv5_4/Relu")
-              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
+              << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
               // Layer 6
               << FullyConnectedLayer(
                   4096U,