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| author | Sang-Hoon Park <sang-hoon.park@arm.com> | 2020-01-15 14:44:04 +0000 |
|---|---|---|
| committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2020-01-28 16:05:27 +0000 |
| commit | 11fedda86532cf632b9a3ae4b0f57e85f2a7c4f4 (patch) | |
| tree | 6fd8003a38fe9baa262696754bdd5cb1d1595947 /examples/graph_lenet.cpp | |
| parent | 6c89ffac750010cb9335794defe8a366c04db937 (diff) | |
| download | ComputeLibrary-11fedda86532cf632b9a3ae4b0f57e85f2a7c4f4.tar.gz | |
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>
Diffstat (limited to 'examples/graph_lenet.cpp')
| -rw-r--r-- | examples/graph_lenet.cpp | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index 9936ea52ae..7b475c2c03 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2019 ARM Limited. + * Copyright (c) 2017-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -66,8 +66,9 @@ public: unsigned int batches = 4; /** Number of batches */ // Create input descriptor - const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, batches), 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(28U, 28U, 1U, batches), 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; @@ -82,14 +83,14 @@ public: get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("conv1") - << 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") << ConvolutionLayer( 5U, 5U, 50U, get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("conv2") - << 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") << FullyConnectedLayer( 500U, get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout), |