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_yolov3.cpp b/examples/graph_yolov3.cpp
index 3c8ddbf..5c8d342 100644
--- a/examples/graph_yolov3.cpp
+++ b/examples/graph_yolov3.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 @@
class GraphYOLOv3Example : public Example
{
public:
- GraphYOLOv3Example()
- : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3")
+ GraphYOLOv3Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3")
{
}
@@ -50,14 +50,15 @@
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;
@@ -69,331 +70,322 @@
std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f);
// Create input descriptor
- const TensorShape tensor_shape = permute_shape(TensorShape(608U, 608U, 3U, 1U), 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(608U, 608U, 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;
- 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, std::move(preprocessor), false));
std::pair<SubStream, SubStream> intermediate_layers = darknet53(data_path, weights_layout);
- graph << ConvolutionLayer(
- 1U, 1U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_53")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"),
- 0.000001f)
- .set_name("conv2d_53/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_53/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 1024U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_54")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"),
- 0.000001f)
- .set_name("conv2d_54/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_54/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_55")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"),
- 0.000001f)
- .set_name("conv2d_55/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_55/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 1024U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_56")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"),
- 0.000001f)
- .set_name("conv2d_56/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_56/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_57")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"),
- 0.000001f)
- .set_name("conv2d_57/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_57/LeakyRelu");
+ graph
+ << ConvolutionLayer(
+ 1U, 1U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_53")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"), 0.000001f)
+ .set_name("conv2d_53/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_53/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 1024U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_54")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"), 0.000001f)
+ .set_name("conv2d_54/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_54/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_55")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"), 0.000001f)
+ .set_name("conv2d_55/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_55/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 1024U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_56")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"), 0.000001f)
+ .set_name("conv2d_56/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_56/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_57")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"), 0.000001f)
+ .set_name("conv2d_57/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_57/LeakyRelu");
SubStream route_1(graph);
- graph << ConvolutionLayer(
- 3U, 3U, 1024U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_58")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"),
- 0.000001f)
- .set_name("conv2d_58/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_58/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 255U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_59")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_59/Linear")
- << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo1")
- << OutputLayer(get_output_accessor(common_params, 5));
+ graph
+ << ConvolutionLayer(
+ 3U, 3U, 1024U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_58")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"), 0.000001f)
+ .set_name("conv2d_58/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_58/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 255U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_59")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f))
+ .set_name("conv2d_59/Linear")
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo1")
+ << OutputLayer(get_output_accessor(common_params, 5));
route_1 << ConvolutionLayer(
- 1U, 1U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_60")
+ 1U, 1U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_60")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"),
- 0.000001f)
- .set_name("conv2d_59/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_60/LeakyRelu")
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_59/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_60/LeakyRelu")
<< ResizeLayer(InterpolationPolicy::NEAREST_NEIGHBOR, 2, 2).set_name("Upsample_60");
SubStream concat_1(route_1);
- concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1")
- << ConvolutionLayer(
- 1U, 1U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_61")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"),
- 0.000001f)
- .set_name("conv2d_60/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_61/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_62")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"),
- 0.000001f)
- .set_name("conv2d_61/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_62/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_63")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"),
- 0.000001f)
- .set_name("conv2d_62/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_63/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_64")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"),
- 0.000001f)
- .set_name("conv2d_63/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_64/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_65")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"),
- 0.000001f)
- .set_name("conv2d_65/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_65/LeakyRelu");
+ concat_1
+ << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1")
+ << ConvolutionLayer(
+ 1U, 1U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_61")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"), 0.000001f)
+ .set_name("conv2d_60/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_61/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_62")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"), 0.000001f)
+ .set_name("conv2d_61/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_62/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_63")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"), 0.000001f)
+ .set_name("conv2d_62/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_63/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_64")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"), 0.000001f)
+ .set_name("conv2d_63/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_64/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_65")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"), 0.000001f)
+ .set_name("conv2d_65/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_65/LeakyRelu");
SubStream route_2(concat_1);
- concat_1 << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_66")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"),
- 0.000001f)
- .set_name("conv2d_65/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_66/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 255U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_67")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_67/Linear")
- << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo2")
- << OutputLayer(get_output_accessor(common_params, 5));
+ concat_1
+ << ConvolutionLayer(
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_66")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"), 0.000001f)
+ .set_name("conv2d_65/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_66/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 255U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_67")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f))
+ .set_name("conv2d_67/Linear")
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo2")
+ << OutputLayer(get_output_accessor(common_params, 5));
route_2 << ConvolutionLayer(
- 1U, 1U, 128U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_68")
+ 1U, 1U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_68")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"),
- 0.000001f)
- .set_name("conv2d_66/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_68/LeakyRelu")
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_66/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_68/LeakyRelu")
<< ResizeLayer(InterpolationPolicy::NEAREST_NEIGHBOR, 2, 2).set_name("Upsample_68");
SubStream concat_2(route_2);
- concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2")
- << ConvolutionLayer(
- 1U, 1U, 128U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_69")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"),
- 0.000001f)
- .set_name("conv2d_67/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_69/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_70")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"),
- 0.000001f)
- .set_name("conv2d_68/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_70/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 128U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_71")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"),
- 0.000001f)
- .set_name("conv2d_69/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_71/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_72")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"),
- 0.000001f)
- .set_name("conv2d_70/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_72/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 128U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_73")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"),
- 0.000001f)
- .set_name("conv2d_71/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_73/LeakyRelu")
- << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_74")
- << BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"),
- 0.000001f)
- .set_name("conv2d_72/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_74/LeakyRelu")
- << ConvolutionLayer(
- 1U, 1U, 255U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_75")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_75/Linear")
- << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo3")
- << OutputLayer(get_output_accessor(common_params, 5));
+ concat_2
+ << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2")
+ << ConvolutionLayer(
+ 1U, 1U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_69")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"), 0.000001f)
+ .set_name("conv2d_67/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_69/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_70")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"), 0.000001f)
+ .set_name("conv2d_68/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_70/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_71")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"), 0.000001f)
+ .set_name("conv2d_69/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_71/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_72")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"), 0.000001f)
+ .set_name("conv2d_70/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_72/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_73")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"), 0.000001f)
+ .set_name("conv2d_71/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_73/LeakyRelu")
+ << ConvolutionLayer(
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_74")
+ << BatchNormalizationLayer(
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"), 0.000001f)
+ .set_name("conv2d_72/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_74/LeakyRelu")
+ << ConvolutionLayer(
+ 1U, 1U, 255U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout),
+ PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_75")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f))
+ .set_name("conv2d_75/Linear")
+ << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo3")
+ << OutputLayer(get_output_accessor(common_params, 5));
// Finalize graph
GraphConfig config;
@@ -422,64 +414,64 @@
std::pair<SubStream, SubStream> darknet53(const std::string &data_path, DataLayout weights_layout)
{
graph << ConvolutionLayer(
- 3U, 3U, 32U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_1/Conv2D")
+ 3U, 3U, 32U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_1/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"),
- 0.000001f)
- .set_name("conv2d_1/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_1/LeakyRelu")
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_1/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_1/LeakyRelu")
<< ConvolutionLayer(
- 3U, 3U, 64U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_2/Conv2D")
+ 3U, 3U, 64U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+ .set_name("conv2d_2/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"),
- 0.000001f)
- .set_name("conv2d_2/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_2/LeakyRelu");
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_2/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_2/LeakyRelu");
darknet53_block(data_path, "3", weights_layout, 32U);
graph << ConvolutionLayer(
- 3U, 3U, 128U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_5/Conv2D")
+ 3U, 3U, 128U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+ .set_name("conv2d_5/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"),
- 0.000001f)
- .set_name("conv2d_5/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_5/LeakyRelu");
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_5/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_5/LeakyRelu");
darknet53_block(data_path, "6", weights_layout, 64U);
darknet53_block(data_path, "8", weights_layout, 64U);
graph << ConvolutionLayer(
- 3U, 3U, 256U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_10/Conv2D")
+ 3U, 3U, 256U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+ .set_name("conv2d_10/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"),
- 0.000001f)
- .set_name("conv2d_10/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_10/LeakyRelu");
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_10/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_10/LeakyRelu");
darknet53_block(data_path, "11", weights_layout, 128U);
darknet53_block(data_path, "13", weights_layout, 128U);
darknet53_block(data_path, "15", weights_layout, 128U);
@@ -490,19 +482,19 @@
darknet53_block(data_path, "25", weights_layout, 128U);
SubStream layer_36(graph);
graph << ConvolutionLayer(
- 3U, 3U, 512U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_27/Conv2D")
+ 3U, 3U, 512U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+ .set_name("conv2d_27/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"),
- 0.000001f)
- .set_name("conv2d_27/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_27/LeakyRelu");
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_27/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_27/LeakyRelu");
darknet53_block(data_path, "28", weights_layout, 256U);
darknet53_block(data_path, "30", weights_layout, 256U);
darknet53_block(data_path, "32", weights_layout, 256U);
@@ -513,19 +505,19 @@
darknet53_block(data_path, "42", weights_layout, 256U);
SubStream layer_61(graph);
graph << ConvolutionLayer(
- 3U, 3U, 1024U,
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(2, 2, 1, 1))
- .set_name("conv2d_44/Conv2D")
+ 3U, 3U, 1024U,
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+ .set_name("conv2d_44/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"),
- get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"),
- 0.000001f)
- .set_name("conv2d_44/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_44/LeakyRelu");
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"),
+ get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_44/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_44/LeakyRelu");
darknet53_block(data_path, "45", weights_layout, 512U);
darknet53_block(data_path, "47", weights_layout, 512U);
darknet53_block(data_path, "49", weights_layout, 512U);
@@ -534,43 +526,48 @@
return std::pair<SubStream, SubStream>(layer_36, layer_61);
}
- void darknet53_block(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
- unsigned int filter_size)
+ void darknet53_block(const std::string &data_path,
+ std::string &¶m_path,
+ DataLayout weights_layout,
+ unsigned int filter_size)
{
- std::string total_path = "/cnn_data/yolov3_model/";
- std::string param_path2 = arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1);
- SubStream i_a(graph);
- SubStream i_b(graph);
+ std::string total_path = "/cnn_data/yolov3_model/";
+ std::string param_path2 =
+ arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1);
+ SubStream i_a(graph);
+ SubStream i_b(graph);
i_a << ConvolutionLayer(
- 1U, 1U, filter_size,
- get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 0, 0))
- .set_name("conv2d_" + param_path + "/Conv2D")
+ 1U, 1U, filter_size,
+ get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+ .set_name("conv2d_" + param_path + "/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"),
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"),
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"),
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"),
- 0.000001f)
- .set_name("conv2d_" + param_path + "/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path + "/LeakyRelu")
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"),
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"),
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"),
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_" + param_path + "/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_" + param_path + "/LeakyRelu")
<< ConvolutionLayer(
- 3U, 3U, filter_size * 2,
- get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout),
- std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
- PadStrideInfo(1, 1, 1, 1))
- .set_name("conv2d_" + param_path2 + "/Conv2D")
+ 3U, 3U, filter_size * 2,
+ get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout),
+ std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+ .set_name("conv2d_" + param_path2 + "/Conv2D")
<< BatchNormalizationLayer(
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"),
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"),
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"),
- get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"),
- 0.000001f)
- .set_name("conv2d_" + param_path2 + "/BatchNorm")
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path2 + "/LeakyRelu");
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"),
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"),
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"),
+ get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"),
+ 0.000001f)
+ .set_name("conv2d_" + param_path2 + "/BatchNorm")
+ << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+ .set_name("conv2d_" + param_path2 + "/LeakyRelu");
- graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add).set_name("").set_name("add_" + param_path + "_" + param_path2);
+ graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add)
+ .set_name("")
+ .set_name("add_" + param_path + "_" + param_path2);
}
};