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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-07-17 12:28:42 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:54:54 +0000 |
commit | 7d66a8e3f603f2cd363f04a750847e3f9eabdfd4 (patch) | |
tree | 0d7e1ad5bf0ecd32cd919074f756d27c351d7638 /examples/graph_vgg16.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_vgg16.cpp')
-rw-r--r-- | examples/graph_vgg16.cpp | 45 |
1 files changed, 26 insertions, 19 deletions
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp index e677650d04..e23ea65dd7 100644 --- a/examples/graph_vgg16.cpp +++ b/examples/graph_vgg16.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; @@ -72,14 +72,21 @@ public: 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); + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), 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; + + // Create graph graph << common_params.target << common_params.fast_math_hint - << InputLayer(TensorDescriptor(TensorShape(224U, 224U, 3U, 1U), common_params.data_type), - get_input_accessor(common_params, std::move(preprocessor))) + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) // Layer 1 << ConvolutionLayer( 3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv1_1") @@ -87,7 +94,7 @@ public: // Layer 2 << ConvolutionLayer( 3U, 3U, 64U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv1_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv1_2") @@ -96,7 +103,7 @@ public: // Layer 3 << ConvolutionLayer( 3U, 3U, 128U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv2_1") @@ -104,7 +111,7 @@ public: // Layer 4 << ConvolutionLayer( 3U, 3U, 128U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv2_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv2_2") @@ -113,7 +120,7 @@ public: // Layer 5 << ConvolutionLayer( 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv3_1") @@ -121,7 +128,7 @@ public: // Layer 6 << ConvolutionLayer( 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv3_2") @@ -129,7 +136,7 @@ public: // Layer 7 << ConvolutionLayer( 3U, 3U, 256U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv3_3_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv3_3") @@ -138,7 +145,7 @@ public: // Layer 8 << ConvolutionLayer( 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv4_1") @@ -146,7 +153,7 @@ public: // Layer 9 << ConvolutionLayer( 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv4_2") @@ -154,7 +161,7 @@ public: // Layer 10 << ConvolutionLayer( 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv4_3_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv4_3") @@ -163,7 +170,7 @@ public: // Layer 11 << ConvolutionLayer( 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_1_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv5_1") @@ -171,7 +178,7 @@ public: // Layer 12 << ConvolutionLayer( 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_2_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv5_2") @@ -179,7 +186,7 @@ public: // Layer 13 << ConvolutionLayer( 3U, 3U, 512U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/conv5_3_b.npy"), PadStrideInfo(1, 1, 1, 1)) .set_name("conv5_3") @@ -188,21 +195,21 @@ public: // Layer 14 << FullyConnectedLayer( 4096U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc6_b.npy")) .set_name("fc6") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu") // Layer 15 << FullyConnectedLayer( 4096U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc7_b.npy")) .set_name("fc7") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Relu_1") // Layer 16 << FullyConnectedLayer( 1000U, - get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy"), + get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_w.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/vgg16_model/fc8_b.npy")) .set_name("fc8") // Softmax |