From afd38f0c617d6f89b2b4532c6c44f116617e2b6f Mon Sep 17 00:00:00 2001 From: Felix Thomasmathibalan Date: Wed, 27 Sep 2023 17:46:17 +0100 Subject: Apply clang-format on repository Code is formatted as per a revised clang format configuration file(not part of this delivery). Version 14.0.6 is used. Exclusion List: - files with .cl extension - files that are not strictly C/C++ (e.g. Android.bp, Sconscript ...) And the following directories - compute_kernel_writer/validation/ - tests/ - include/ - src/core/NEON/kernels/convolution/ - src/core/NEON/kernels/arm_gemm/ - src/core/NEON/kernels/arm_conv/ - data/ There will be a follow up for formatting of .cl files and the files under tests/ and compute_kernel_writer/validation/. Signed-off-by: Felix Thomasmathibalan Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391 Benchmark: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gunes Bayir --- examples/graph_resnext50.cpp | 119 +++++++++++++++++++++++-------------------- 1 file changed, 65 insertions(+), 54 deletions(-) (limited to 'examples/graph_resnext50.cpp') diff --git a/examples/graph_resnext50.cpp b/examples/graph_resnext50.cpp index 6378f6c741..12a1507c4c 100644 --- a/examples/graph_resnext50.cpp +++ b/examples/graph_resnext50.cpp @@ -22,6 +22,7 @@ * SOFTWARE. */ #include "arm_compute/graph.h" + #include "support/ToolchainSupport.h" #include "utils/CommonGraphOptions.h" #include "utils/GraphUtils.h" @@ -35,8 +36,7 @@ using namespace arm_compute::graph_utils; class GraphResNeXt50Example : public Example { public: - GraphResNeXt50Example() - : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50") + GraphResNeXt50Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50") { } bool do_setup(int argc, char **argv) override @@ -49,14 +49,15 @@ public: common_params = consume_common_graph_parameters(common_opts); // Return when help menu is requested - if(common_params.help) + if (common_params.help) { cmd_parser.print_help(argv[0]); return false; } // Checks - ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), + "QASYMM8 not supported for this graph"); // Print parameter values std::cout << common_params << std::endl; @@ -66,28 +67,33 @@ public: // Create input descriptor const auto operation_layout = common_params.data_layout; - const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); - TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); + const TensorShape tensor_shape = + permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); + TensorDescriptor input_descriptor = + TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); // Set weights trained layout const DataLayout weights_layout = DataLayout::NCHW; - graph << common_params.target - << common_params.fast_math_hint + graph << common_params.target << common_params.fast_math_hint << InputLayer(input_descriptor, get_input_accessor(common_params)) << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) - .set_name("bn_data/Scale") + .set_name("bn_data/Scale") << ConvolutionLayer( - 7U, 7U, 64U, - get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout), - get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), - PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) - .set_name("conv0/Convolution") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); - - add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); + 7U, 7U, 64U, + get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout), + get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), + PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) + .set_name("conv0/Convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name("conv0/Relu") + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))) + .set_name("pool0"); + + add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, + /*stride_conv_unit1*/ 1); add_residual_block(data_path, weights_layout, 512, 2, 4, 2); add_residual_block(data_path, weights_layout, 1024, 3, 6, 2); add_residual_block(data_path, weights_layout, 2048, 4, 3, 2); @@ -121,10 +127,14 @@ private: CommonGraphParams common_params; Stream graph; - void add_residual_block(const std::string &data_path, DataLayout weights_layout, - unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) + void add_residual_block(const std::string &data_path, + DataLayout weights_layout, + unsigned int base_depth, + unsigned int stage, + unsigned int num_units, + unsigned int stride_conv_unit1) { - for(unsigned int i = 0; i < num_units; ++i) + for (unsigned int i = 0; i < num_units; ++i) { std::stringstream unit_path_ss; unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_"; @@ -135,54 +145,55 @@ private: std::string unit_name = unit_name_ss.str(); PadStrideInfo pad_grouped_conv(1, 1, 1, 1); - if(i == 0) + if (i == 0) { - pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR); + pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) + : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, + DimensionRoundingType::FLOOR); } SubStream right(graph); - right << ConvolutionLayer( - 1U, 1U, base_depth / 2, - get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), - get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), - PadStrideInfo(1, 1, 0, 0)) - .set_name(unit_name + "conv1/convolution") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") - - << ConvolutionLayer( - 3U, 3U, base_depth / 2, - get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), - std::unique_ptr(nullptr), - pad_grouped_conv, 32) - .set_name(unit_name + "conv2/convolution") + right << ConvolutionLayer(1U, 1U, base_depth / 2, + get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "conv1/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name(unit_name + "conv1/Relu") + + << ConvolutionLayer(3U, 3U, base_depth / 2, + get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), + std::unique_ptr(nullptr), pad_grouped_conv, + 32) + .set_name(unit_name + "conv2/convolution") << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"), get_weights_accessor(data_path, unit_path + "bn2_add.npy")) - .set_name(unit_name + "conv1/Scale") - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu") + .set_name(unit_name + "conv1/Scale") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name(unit_name + "conv2/Relu") - << ConvolutionLayer( - 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), - get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), - PadStrideInfo(1, 1, 0, 0)) - .set_name(unit_name + "conv3/convolution"); + << ConvolutionLayer(1U, 1U, base_depth, + get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), + get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name(unit_name + "conv3/convolution"); SubStream left(graph); - if(i == 0) + if (i == 0) { - left << ConvolutionLayer( - 1U, 1U, base_depth, - get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout), - std::unique_ptr(nullptr), - PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) - .set_name(unit_name + "sc/convolution") + left << ConvolutionLayer(1U, 1U, base_depth, + get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) + .set_name(unit_name + "sc/convolution") << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"), get_weights_accessor(data_path, unit_path + "sc_bn_add.npy")) - .set_name(unit_name + "sc/scale"); + .set_name(unit_name + "sc/scale"); } graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); - graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); + graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + .set_name(unit_name + "Relu"); } } }; -- cgit v1.2.1