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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_vgg16.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_vgg16.cpp')
| -rw-r--r-- | examples/graph_vgg16.cpp | 9 |
1 files changed, 6 insertions, 3 deletions
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index faaf579047..516b7b18f0 100644 --- a/examples/graph_vgg16.cpp +++ b/examples/graph_vgg16.cpp @@ -71,8 +71,10 @@ public: bool enable_memory_management = true; // Check if we can use GEMM-based convolutions evaluating if the platform has at least 1.8 GB of available memory - const size_t memory_required = 1932735283L; - ConvolutionMethod convolution_hint = convolution_hint_vgg16(memory_required); + const size_t memory_required = 1932735283L; + const bool is_opencl = target_hint == Target::CL; + ConvolutionMethod first_convolution3x3_hint = is_opencl ? ConvolutionMethod::DIRECT : ConvolutionMethod::GEMM; + ConvolutionMethod convolution3x3_hint = is_opencl ? ConvolutionMethod::WINOGRAD : convolution_hint_vgg16(memory_required); // Parse arguments if(argc < 2) @@ -107,7 +109,7 @@ public: } graph << target_hint - << convolution_hint + << first_convolution3x3_hint << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), DataType::F32), get_input_accessor(image, std::move(preprocessor))) // Layer 1 @@ -117,6 +119,7 @@ public: get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << convolution3x3_hint // Layer 2 << ConvolutionLayer( 3U, 3U, 64U, |