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| author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2018-03-21 17:45:31 +0000 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:16 +0000 |
| commit | ed99f411d52949720a4d64d91664cd71e46b79d5 (patch) | |
| tree | d903b523dea830aeb48d59a66b8da59e4dcf707a /examples/graph_alexnet.cpp | |
| parent | 6528aa20e768f2d801328aa164d672b7fdfe266f (diff) | |
| download | ComputeLibrary-ed99f411d52949720a4d64d91664cd71e46b79d5.tar.gz | |
COMPMID-1018 - Add Winograd support in VGG16 and Alexnet examples
Change-Id: I4a2deee9e4b2c54ea79d2895cfeca44190133b24
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125453
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'examples/graph_alexnet.cpp')
| -rw-r--r-- | examples/graph_alexnet.cpp | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp index a396c7686c..f887f97a12 100644 --- a/examples/graph_alexnet.cpp +++ b/examples/graph_alexnet.cpp @@ -57,8 +57,10 @@ public: const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; TargetHint target_hint = set_target_hint(int_target_hint); - const bool is_gemm_convolution5x5 = Graph::gpu_target() == arm_compute::GPUTarget::MIDGARD || target_hint == TargetHint::NEON; - ConvolutionMethodHint convolution_5x5_hint = is_gemm_convolution5x5 ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT; + const bool is_gemm_convolution5x5 = Graph::gpu_target() == arm_compute::GPUTarget::MIDGARD || target_hint == TargetHint::NEON; + const bool is_winograd_convolution3x3 = target_hint == TargetHint::OPENCL; + ConvolutionMethodHint convolution_5x5_hint = is_gemm_convolution5x5 ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT; + ConvolutionMethodHint convolution_3x3_hint = is_winograd_convolution3x3 ? ConvolutionMethodHint::WINOGRAD : ConvolutionMethodHint::GEMM; // Parse arguments if(argc < 2) @@ -114,7 +116,7 @@ public: << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))) - << ConvolutionMethodHint::GEMM + << convolution_3x3_hint // Layer 3 << ConvolutionLayer( 3U, 3U, 384U, |