From 11fedda86532cf632b9a3ae4b0f57e85f2a7c4f4 Mon Sep 17 00:00:00 2001 From: Sang-Hoon Park Date: Wed, 15 Jan 2020 14:44:04 +0000 Subject: 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 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2603 Reviewed-by: Michele Di Giorgio Reviewed-by: Georgios Pinitas Reviewed-by: Gian Marco Iodice Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins --- examples/graph_resnext50.cpp | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) (limited to 'examples/graph_resnext50.cpp') diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp index 4e505a05e5..2c50594b0c 100644 --- a/examples/graph_resnext50.cpp +++ b/examples/graph_resnext50.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2019 ARM Limited. + * Copyright (c) 2018-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -65,8 +65,9 @@ public: std::string data_path = common_params.data_path; // 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; @@ -84,14 +85,14 @@ public: PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) .set_name("conv0/Convolution") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); add_residual_block(data_path, weights_layout, 512, 2, 4, 2); add_residual_block(data_path, weights_layout, 1024, 3, 6, 2); add_residual_block(data_path, weights_layout, 2048, 4, 3, 2); - graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("pool1") + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool1") << FlattenLayer().set_name("predictions/Reshape") << OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32)); -- cgit v1.2.1