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_mobilenet.cpp b/examples/graph_mobilenet.cpp
index 4630dc9..e3a6ef1 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -22,6 +22,7 @@
* SOFTWARE.
*/
#include "arm_compute/graph.h"
+
#include "support/ToolchainSupport.h"
#include "utils/CommonGraphOptions.h"
#include "utils/GraphUtils.h"
@@ -36,14 +37,13 @@
class GraphMobilenetExample : public Example
{
public:
- GraphMobilenetExample()
- : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1")
+ GraphMobilenetExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV1")
{
// Add model id option
model_id_opt = cmd_parser.add_option<SimpleOption<int>>("model-id", 0);
model_id_opt->set_help("Mobilenet model id (0: 1.0_224, else: 0.75_160");
}
- GraphMobilenetExample(const GraphMobilenetExample &) = delete;
+ GraphMobilenetExample(const GraphMobilenetExample &) = delete;
GraphMobilenetExample &operator=(const GraphMobilenetExample &) = delete;
~GraphMobilenetExample() override = default;
bool do_setup(int argc, char **argv) override
@@ -56,7 +56,7 @@
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;
@@ -72,15 +72,17 @@
unsigned int spatial_size = (model_id == 0 || common_params.data_type == DataType::QASYMM8) ? 224 : 160;
// Create input descriptor
- const TensorShape tensor_shape = permute_shape(TensorShape(spatial_size, spatial_size, 3U, common_params.batches), DataLayout::NCHW, common_params.data_layout);
- TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+ const TensorShape tensor_shape =
+ permute_shape(TensorShape(spatial_size, spatial_size, 3U, common_params.batches), DataLayout::NCHW,
+ common_params.data_layout);
+ TensorDescriptor input_descriptor =
+ TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
// Set graph hints
- graph << common_params.target
- << common_params.fast_math_hint;
+ graph << common_params.target << common_params.fast_math_hint;
// Create core graph
- if(arm_compute::is_data_type_float(common_params.data_type))
+ if (arm_compute::is_data_type_float(common_params.data_type))
{
create_graph_float(input_descriptor, model_id);
}
@@ -90,8 +92,7 @@
}
// Create common tail
- graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape")
- << SoftmaxLayer().set_name("Softmax")
+ graph << ReshapeLayer(TensorShape(1001U)).set_name("Reshape") << SoftmaxLayer().set_name("Softmax")
<< OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
@@ -115,14 +116,15 @@
private:
CommandLineParser cmd_parser;
CommonGraphOptions common_opts;
- SimpleOption<int> *model_id_opt{ nullptr };
+ SimpleOption<int> *model_id_opt{nullptr};
CommonGraphParams common_params;
Stream graph;
void create_graph_float(TensorDescriptor &input_descriptor, int model_id)
{
float depth_scale = (model_id == 0) ? 1.f : 0.75;
- std::string model_path = (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
+ std::string model_path =
+ (model_id == 0) ? "/cnn_data/mobilenet_v1_1_224_model/" : "/cnn_data/mobilenet_v1_075_160_model/";
// Create a preprocessor object
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
@@ -131,47 +133,68 @@
std::string data_path = common_params.data_path;
// Add model path to data path
- if(!data_path.empty())
+ if (!data_path.empty())
{
data_path += model_path;
}
- graph << InputLayer(input_descriptor,
- get_input_accessor(common_params, std::move(preprocessor), false))
- << ConvolutionLayer(
- 3U, 3U, 32U * depth_scale,
- get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
- .set_name("Conv2d_0")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"),
- 0.001f)
- .set_name("Conv2d_0/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
- graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL), PadStrideInfo(1, 1, 0, 0));
- graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a")
- << ConvolutionLayer(
- 1U, 1U, 1001U,
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("Logits/Conv2d_1c_1x1");
+ graph << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
+ << ConvolutionLayer(3U, 3U, 32U * depth_scale,
+ get_weights_accessor(data_path, "Conv2d_0_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))
+ .set_name("Conv2d_0")
+ << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, "Conv2d_0_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, "Conv2d_0_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, "Conv2d_0_BatchNorm_beta.npy"), 0.001f)
+ .set_name("Conv2d_0/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name("Conv2d_0/Relu6");
+ graph << get_dwsc_node_float(data_path, "Conv2d_1", 64 * depth_scale, PadStrideInfo(1, 1, 1, 1),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_2", 128 * depth_scale,
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_3", 128 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_4", 256 * depth_scale,
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_5", 256 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_6", 512 * depth_scale,
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_7", 512 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_8", 512 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_9", 512 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_10", 512 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_11", 512 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_12", 1024 * depth_scale,
+ PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph << get_dwsc_node_float(data_path, "Conv2d_13", 1024 * depth_scale,
+ PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::CEIL),
+ PadStrideInfo(1, 1, 0, 0));
+ graph
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a")
+ << ConvolutionLayer(
+ 1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
+ get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), PadStrideInfo(1, 1, 0, 0))
+ .set_name("Logits/Conv2d_1c_1x1");
}
void create_graph_qasymm(TensorDescriptor &input_descriptor)
@@ -180,7 +203,7 @@
std::string data_path = common_params.data_path;
// Add model path to data path
- if(!data_path.empty())
+ if (!data_path.empty())
{
data_path += "/cnn_data/mobilenet_qasymm8_model/";
}
@@ -188,19 +211,16 @@
// Quantization info taken from the AndroidNN QASYMM8 MobileNet example
const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
- const std::vector<QuantizationInfo> conv_weights_quant_info =
- {
+ const std::vector<QuantizationInfo> conv_weights_quant_info = {
QuantizationInfo(0.02182667888700962f, 151), // conv0
QuantizationInfo(0.004986600950360298f, 74) // conv14
};
- const std::vector<QuantizationInfo> conv_out_quant_info =
- {
+ const std::vector<QuantizationInfo> conv_out_quant_info = {
QuantizationInfo(0.023528477177023888f, 0), // conv0
QuantizationInfo(0.16609922051429749f, 66) // conv14
};
- const std::vector<QuantizationInfo> depth_weights_quant_info =
- {
+ const std::vector<QuantizationInfo> depth_weights_quant_info = {
QuantizationInfo(0.29219913482666016f, 110), // dwsc1
QuantizationInfo(0.40277284383773804f, 130), // dwsc2
QuantizationInfo(0.06053730100393295f, 160), // dwsc3
@@ -216,8 +236,7 @@
QuantizationInfo(0.12616927921772003f, 211) // dwsc13
};
- const std::vector<QuantizationInfo> point_weights_quant_info =
- {
+ const std::vector<QuantizationInfo> point_weights_quant_info = {
QuantizationInfo(0.030420949682593346f, 121), // dwsc1
QuantizationInfo(0.015148180536925793f, 104), // dwsc2
QuantizationInfo(0.013755458407104015f, 94), // dwsc3
@@ -235,108 +254,121 @@
graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
get_input_accessor(common_params, nullptr, false))
- << ConvolutionLayer(
- 3U, 3U, 32U,
- get_weights_accessor(data_path, "Conv2d_0_weights.npy"),
- get_weights_accessor(data_path, "Conv2d_0_bias.npy"),
- PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
- 1, conv_weights_quant_info.at(0), conv_out_quant_info.at(0))
- .set_name("Conv2d_0")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv2d_0/Relu6");
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0), point_weights_quant_info.at(0));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1),
- point_weights_quant_info.at(1));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2),
- point_weights_quant_info.at(2));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3),
- point_weights_quant_info.at(3));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4),
- point_weights_quant_info.at(4));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5),
- point_weights_quant_info.at(5));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6),
- point_weights_quant_info.at(6));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7),
- point_weights_quant_info.at(7));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8),
- point_weights_quant_info.at(8));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9),
- point_weights_quant_info.at(9));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10),
- point_weights_quant_info.at(10));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11),
- point_weights_quant_info.at(11));
- graph << get_dwsc_node_qasymm(data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR), PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12),
- point_weights_quant_info.at(12))
- << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a")
- << ConvolutionLayer(
- 1U, 1U, 1001U,
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
- get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"),
- PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1), conv_out_quant_info.at(1))
- .set_name("Logits/Conv2d_1c_1x1");
+ << ConvolutionLayer(3U, 3U, 32U, get_weights_accessor(data_path, "Conv2d_0_weights.npy"),
+ get_weights_accessor(data_path, "Conv2d_0_bias.npy"),
+ PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), 1,
+ conv_weights_quant_info.at(0), conv_out_quant_info.at(0))
+ .set_name("Conv2d_0")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
+ .set_name("Conv2d_0/Relu6");
+ graph << get_dwsc_node_qasymm(data_path, "Conv2d_1", 64U, PadStrideInfo(1U, 1U, 1U, 1U),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(0),
+ point_weights_quant_info.at(0));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(1), point_weights_quant_info.at(1));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(2), point_weights_quant_info.at(2));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_4", 256U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(3), point_weights_quant_info.at(3));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(4), point_weights_quant_info.at(4));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(5), point_weights_quant_info.at(5));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(6), point_weights_quant_info.at(6));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(7), point_weights_quant_info.at(7));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(8), point_weights_quant_info.at(8));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(9), point_weights_quant_info.at(9));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(10), point_weights_quant_info.at(10));
+ graph << get_dwsc_node_qasymm(
+ data_path, "Conv2d_12", 1024U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(11), point_weights_quant_info.at(11));
+ graph
+ << get_dwsc_node_qasymm(
+ data_path, "Conv2d_13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::FLOOR),
+ PadStrideInfo(1U, 1U, 0U, 0U), depth_weights_quant_info.at(12), point_weights_quant_info.at(12))
+ << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool_1a")
+ << ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
+ get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_bias.npy"),
+ PadStrideInfo(1U, 1U, 0U, 0U), 1, conv_weights_quant_info.at(1),
+ conv_out_quant_info.at(1))
+ .set_name("Logits/Conv2d_1c_1x1");
}
- ConcatLayer get_dwsc_node_float(const std::string &data_path, std::string &¶m_path,
- unsigned int conv_filt,
- PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info)
+ ConcatLayer get_dwsc_node_float(const std::string &data_path,
+ std::string &¶m_path,
+ unsigned int conv_filt,
+ PadStrideInfo dwc_pad_stride_info,
+ PadStrideInfo conv_pad_stride_info)
{
std::string total_path = param_path + "_";
SubStream sg(graph);
sg << DepthwiseConvolutionLayer(
- 3U, 3U,
- get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- dwc_pad_stride_info)
- .set_name(total_path + "depthwise/depthwise")
+ 3U, 3U,
+ get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), dwc_pad_stride_info)
+ .set_name(total_path + "depthwise/depthwise")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
- 0.001f)
- .set_name(total_path + "depthwise/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6")
- << ConvolutionLayer(
- 1U, 1U, conv_filt,
- get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- conv_pad_stride_info)
- .set_name(total_path + "pointwise/Conv2D")
+ get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), 0.001f)
+ .set_name(total_path + "depthwise/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name(total_path + "depthwise/Relu6")
+ << ConvolutionLayer(1U, 1U, conv_filt,
+ get_weights_accessor(data_path, total_path + "pointwise_weights.npy", DataLayout::NCHW),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), conv_pad_stride_info)
+ .set_name(total_path + "pointwise/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"),
- 0.001f)
- .set_name(total_path + "pointwise/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6");
+ get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_mean.npy"),
+ get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_moving_variance.npy"),
+ get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_gamma.npy"),
+ get_weights_accessor(data_path, total_path + "pointwise_BatchNorm_beta.npy"), 0.001f)
+ .set_name(total_path + "pointwise/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
+ .set_name(total_path + "pointwise/Relu6");
return ConcatLayer(std::move(sg));
}
- ConcatLayer get_dwsc_node_qasymm(const std::string &data_path, std::string &¶m_path,
+ ConcatLayer get_dwsc_node_qasymm(const std::string &data_path,
+ std::string &¶m_path,
const unsigned int conv_filt,
- PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info,
- QuantizationInfo depth_weights_quant_info, QuantizationInfo point_weights_quant_info)
+ PadStrideInfo dwc_pad_stride_info,
+ PadStrideInfo conv_pad_stride_info,
+ QuantizationInfo depth_weights_quant_info,
+ QuantizationInfo point_weights_quant_info)
{
std::string total_path = param_path + "_";
SubStream sg(graph);
- sg << DepthwiseConvolutionLayer(
- 3U, 3U,
- get_weights_accessor(data_path, total_path + "depthwise_weights.npy"),
- get_weights_accessor(data_path, total_path + "depthwise_bias.npy"),
- dwc_pad_stride_info, 1, std::move(depth_weights_quant_info))
- .set_name(total_path + "depthwise/depthwise")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "depthwise/Relu6")
- << ConvolutionLayer(
- 1U, 1U, conv_filt,
- get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
- get_weights_accessor(data_path, total_path + "pointwise_bias.npy"),
- conv_pad_stride_info, 1, std::move(point_weights_quant_info))
- .set_name(total_path + "pointwise/Conv2D")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(total_path + "pointwise/Relu6");
+ sg << DepthwiseConvolutionLayer(3U, 3U, get_weights_accessor(data_path, total_path + "depthwise_weights.npy"),
+ get_weights_accessor(data_path, total_path + "depthwise_bias.npy"),
+ dwc_pad_stride_info, 1, std::move(depth_weights_quant_info))
+ .set_name(total_path + "depthwise/depthwise")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
+ .set_name(total_path + "depthwise/Relu6")
+ << ConvolutionLayer(1U, 1U, conv_filt, get_weights_accessor(data_path, total_path + "pointwise_weights.npy"),
+ get_weights_accessor(data_path, total_path + "pointwise_bias.npy"), conv_pad_stride_info,
+ 1, std::move(point_weights_quant_info))
+ .set_name(total_path + "pointwise/Conv2D")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f))
+ .set_name(total_path + "pointwise/Relu6");
return ConcatLayer(std::move(sg));
}