diff options
| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-01-02 13:27:37 +0000 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:42:33 +0000 |
| commit | 1250a5a259962514d31bb5f8148f1d0f0a82b946 (patch) | |
| tree | a9c16daffa5228926715c805d73310b4b3c2e324 /examples/graph_mobilenet.cpp | |
| parent | 7c23ad01c028f73aef0b439fc5d5d14e92e5f4e2 (diff) | |
| download | ComputeLibrary-1250a5a259962514d31bb5f8148f1d0f0a82b946.tar.gz | |
COMPMID-767 : Propagate hints to subgraph.
-Propagates hints to subgraph.
-Fixes dispatching of apropriate optimized DepthwiseConvolution kernel
for OpenCL backend. NEON backend is altered to default to the generic
case until COMPMID-769 is addressed.
Change-Id: I544f05cd99a9ac253f1b19aa4e4bb222b8fdd087
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/114781
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'examples/graph_mobilenet.cpp')
| -rw-r--r-- | examples/graph_mobilenet.cpp | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp index 0c916c7ba0..553253383f 100644 --- a/examples/graph_mobilenet.cpp +++ b/examples/graph_mobilenet.cpp @@ -86,7 +86,8 @@ void main_graph_mobilenet(int argc, const char **argv) constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */ // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON - TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); + TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); + ConvolutionMethodHint convolution_hint = target_hint == TargetHint::NEON ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT; // Parse arguments if(argc < 2) @@ -125,6 +126,7 @@ void main_graph_mobilenet(int argc, const char **argv) graph << target_hint << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), get_input_accessor(image, mean_r, mean_g, mean_b)) + << convolution_hint << ConvolutionLayer( 3U, 3U, 32U, get_weights_accessor(data_path, "/cnn_data/mobilenet_v1_model/Conv2d_0_weights.npy"), |