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 <felixjohnny.thomasmathibalan@arm.com>
Change-Id: Ib7eb1fcf4e7537b9feaefcfc15098a804a3fde0a
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10391
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 53a4547..be0b8a7 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -39,8 +39,7 @@
class GraphAlexnetExample : public Example
{
public:
- GraphAlexnetExample()
- : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "AlexNet")
+ GraphAlexnetExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "AlexNet")
{
}
bool do_setup(int argc, char **argv) override
@@ -53,14 +52,15 @@
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;
@@ -69,88 +69,80 @@
std::string data_path = common_params.data_path;
// Create a preprocessor object
- const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
+ const std::array<float, 3> mean_rgb{{122.68f, 116.67f, 104.01f}};
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb);
// Create input descriptor
const auto operation_layout = common_params.data_layout;
- const TensorShape tensor_shape = permute_shape(TensorShape(227U, 227U, 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(227U, 227U, 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
- << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
- // Layer 1
- << ConvolutionLayer(
- 11U, 11U, 96U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
- PadStrideInfo(4, 4, 0, 0))
- .set_name("conv1")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1")
- << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
- // Layer 2
- << ConvolutionLayer(
- 5U, 5U, 256U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
- PadStrideInfo(1, 1, 2, 2), 2)
- .set_name("conv2")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2")
- << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
- // Layer 3
- << ConvolutionLayer(
- 3U, 3U, 384U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv3")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu3")
- // Layer 4
- << ConvolutionLayer(
- 3U, 3U, 384U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
- PadStrideInfo(1, 1, 1, 1), 2)
- .set_name("conv4")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu4")
- // Layer 5
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
- PadStrideInfo(1, 1, 1, 1), 2)
- .set_name("conv5")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5")
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
- // Layer 6
- << FullyConnectedLayer(
- 4096U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
- .set_name("fc6")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6")
- // Layer 7
- << FullyConnectedLayer(
- 4096U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
- .set_name("fc7")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7")
- // Layer 8
- << FullyConnectedLayer(
- 1000U,
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
- .set_name("fc8")
- // Softmax
- << SoftmaxLayer().set_name("prob")
- << OutputLayer(get_output_accessor(common_params, 5));
+ graph
+ << common_params.target << common_params.fast_math_hint
+ << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
+ // Layer 1
+ << ConvolutionLayer(11U, 11U, 96U,
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
+ PadStrideInfo(4, 4, 0, 0))
+ .set_name("conv1")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1")
+ << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool1")
+ // Layer 2
+ << ConvolutionLayer(
+ 5U, 5U, 256U, get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"), PadStrideInfo(1, 1, 2, 2), 2)
+ .set_name("conv2")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2")
+ << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool2")
+ // Layer 3
+ << ConvolutionLayer(
+ 3U, 3U, 384U, get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv3")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu3")
+ // Layer 4
+ << ConvolutionLayer(
+ 3U, 3U, 384U, get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"), PadStrideInfo(1, 1, 1, 1), 2)
+ .set_name("conv4")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu4")
+ // Layer 5
+ << ConvolutionLayer(
+ 3U, 3U, 256U, get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"), PadStrideInfo(1, 1, 1, 1), 2)
+ .set_name("conv5")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5")
+ << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0)))
+ .set_name("pool5")
+ // Layer 6
+ << FullyConnectedLayer(4096U,
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
+ .set_name("fc6")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6")
+ // Layer 7
+ << FullyConnectedLayer(4096U,
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
+ .set_name("fc7")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7")
+ // Layer 8
+ << FullyConnectedLayer(1000U,
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+ .set_name("fc8")
+ // Softmax
+ << SoftmaxLayer().set_name("prob") << OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
GraphConfig config;
@@ -163,7 +155,7 @@
// Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed
// compilation won't be required.
- if(common_params.enable_cl_cache)
+ if (common_params.enable_cl_cache)
{
#ifdef ARM_COMPUTE_CL
restore_program_cache_from_file();
@@ -173,7 +165,7 @@
graph.finalize(common_params.target, config);
// Save the opencl kernels to a file
- if(common_opts.enable_cl_cache)
+ if (common_opts.enable_cl_cache)
{
#ifdef ARM_COMPUTE_CL
save_program_cache_to_file();