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| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-02-16 11:42:38 +0000 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:47:18 +0000 |
| commit | 140fdc76e99c92b2f71865b679de0659a70b713f (patch) | |
| tree | a8218bfd303772ed68f6c4d57e17d5ddd7b61ce0 /examples/graph_vgg16.cpp | |
| parent | 72f39be2f372b9a810cb27320dba5d0722407549 (diff) | |
| download | ComputeLibrary-140fdc76e99c92b2f71865b679de0659a70b713f.tar.gz | |
COMPMID-913: Fix preprocessing step for TF models.
Change-Id: If0fbb6bbe5384038124d3dc189274b8266f796ca
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120771
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
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 | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index ccb9dbb19d..eb8bd42e1a 100644 --- a/examples/graph_vgg16.cpp +++ b/examples/graph_vgg16.cpp @@ -61,9 +61,9 @@ public: std::string image; /* Image data */ std::string label; /* Label data */ - constexpr float mean_r = 123.68f; /* Mean value to subtract from red channel */ - constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */ - constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */ + // Create a preprocessor object + const std::array<float, 3> mean_rgb{ { 123.68f, 116.779f, 103.939f } }; + 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 int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; @@ -111,7 +111,7 @@ public: graph << target_hint << convolution_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), - get_input_accessor(image, mean_r, mean_g, mean_b)) + get_input_accessor(image, std::move(preprocessor))) // Layer 1 << ConvolutionLayer( 3U, 3U, 64U, |