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| author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-07-17 12:28:42 +0100 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:54:54 +0000 |
| commit | 7d66a8e3f603f2cd363f04a750847e3f9eabdfd4 (patch) | |
| tree | 0d7e1ad5bf0ecd32cd919074f756d27c351d7638 /examples/graph_lenet.cpp | |
| parent | ae54e026c86aec7d6819ee3ef76372c1a3c92467 (diff) | |
| download | ComputeLibrary-7d66a8e3f603f2cd363f04a750847e3f9eabdfd4.tar.gz | |
COMPMID-1386: Add support for converting weights for CL.
Change-Id: I62e3ead903366baeeb1488f233a9b8b0c388c9de
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/140403
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'examples/graph_lenet.cpp')
| -rw-r--r-- | examples/graph_lenet.cpp | 19 |
1 files changed, 13 insertions, 6 deletions
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index 0d8a943737..f3aa266c50 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -60,7 +60,7 @@ public: // Checks ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "Unsupported data type!"); - ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC, "Unsupported data layout!"); + ARM_COMPUTE_EXIT_ON_MSG(common_params.data_layout == DataLayout::NHWC && common_params.target != Target::CL, "Unsupported data layout!"); // Print parameter values std::cout << common_params << std::endl; @@ -69,33 +69,40 @@ public: std::string data_path = common_params.data_path; unsigned int batches = 4; /** Number of batches */ + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U, batches), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + + // Set weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), common_params.data_type), get_input_accessor(common_params)) + << InputLayer(input_descriptor, get_input_accessor(common_params)) << ConvolutionLayer( 5U, 5U, 20U, - get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("conv1") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") << ConvolutionLayer( 5U, 5U, 50U, - get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name("conv2") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") << FullyConnectedLayer( 500U, - get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy")) .set_name("ip1") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu") << FullyConnectedLayer( 10U, - get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy")) .set_name("ip2") << SoftmaxLayer().set_name("prob") |