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| author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2018-05-09 14:11:34 +0100 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:51:37 +0000 |
| commit | c13021e335b3e395c9d1a3a9935baedb42aebf08 (patch) | |
| tree | 5ee995961cea6b76fe4c1aee2e60920ee845a83e /examples/graph_squeezenet_v1_1.cpp | |
| parent | e29e0d4292a2e569ecad340438942632ae6a92e4 (diff) | |
| download | ComputeLibrary-c13021e335b3e395c9d1a3a9935baedb42aebf08.tar.gz | |
COMPMID-1109 - Enabling Winograd in the graph when possible
Change-Id: I524abd28188995ae9c7a43b189b1eb2d7546be93
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/130576
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Diffstat (limited to 'examples/graph_squeezenet_v1_1.cpp')
| -rw-r--r-- | examples/graph_squeezenet_v1_1.cpp | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp index 1696b7df43..e1a1f661fb 100644 --- a/examples/graph_squeezenet_v1_1.cpp +++ b/examples/graph_squeezenet_v1_1.cpp @@ -56,10 +56,9 @@ public: std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb); // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON - const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; - Target target_hint = set_target_hint(target); - - ConvolutionMethod convolution_hint = (target_hint == Target::CL) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; + const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + Target target_hint = set_target_hint(target); + ConvolutionMethod convolution_hint = target_hint == Target::NEON ? ConvolutionMethod::GEMM : ConvolutionMethod::DEFAULT; // Parse arguments if(argc < 2) @@ -102,9 +101,10 @@ public: get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy"), get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_b.npy"), PadStrideInfo(2, 2, 0, 0)) + << convolution_hint << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << convolution_hint + << ConvolutionMethod::DEFAULT << ConvolutionLayer( 1U, 1U, 16U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy"), |