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/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp
index e34b692..b95abe7 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.cpp
@@ -23,16 +23,17 @@
*/
#include "src/core/CL/kernels/CLDepthwiseConvolutionLayerNativeKernel.h"
-#include "arm_compute/core/utils/ActivationFunctionUtils.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/utils/ActivationFunctionUtils.h"
#include "arm_compute/core/utils/helpers/AdjustVecSize.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/core/utils/StringUtils.h"
+
#include "src/core/CL/CLUtils.h"
#include "src/core/CL/CLValidate.h"
#include "src/core/CL/ICLKernel.h"
@@ -45,12 +46,18 @@
{
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const DWCComputeKernelInfo &dwc_info,
- const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+Status validate_arguments(const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *output,
+ const DWCComputeKernelInfo &dwc_info,
+ const ConvolutionInfo &conv_info,
+ const ITensorInfo *output_multipliers,
+ const ITensorInfo *output_shifts)
{
ARM_COMPUTE_UNUSED(dwc_info);
bool in_place = false;
- if(output == nullptr || output == input)
+ if (output == nullptr || output == input)
{
in_place = true;
output = input;
@@ -58,11 +65,14 @@
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(input, DataLayout::NHWC);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+ DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first > 1 && dwc_info.m0 != 1);
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation.x() > 1 && dwc_info.m0 != 1);
ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_input_to_cl_image == true));
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) && (export_to_cl_image(weights) == false), "Weights cannot be exported to cl_image!");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((dwc_info.export_weights_to_cl_image == true) &&
+ (export_to_cl_image(weights) == false),
+ "Weights cannot be exported to cl_image!");
ARM_COMPUTE_RETURN_ERROR_ON((dwc_info.export_weights_to_cl_image == true) && ((dwc_info.n0 % 4) != 0));
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().first < 1);
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride().second < 1);
@@ -72,33 +82,40 @@
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_c) != (input->dimension(idx_c) * conv_info.depth_multiplier));
// In place restrictions
- if(in_place)
+ if (in_place)
{
- const int weights_width_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH);
- const int weights_height_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT);
- ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U || weights->tensor_shape()[weights_height_idx] != 1U);
+ const int weights_width_idx =
+ get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH);
+ const int weights_height_idx =
+ get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->tensor_shape()[weights_width_idx] != 1U ||
+ weights->tensor_shape()[weights_height_idx] != 1U);
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.depth_multiplier != 1U);
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.stride() != std::make_pair(1U, 1U));
ARM_COMPUTE_RETURN_ERROR_ON(conv_info.dilation != Size2D(1U, 1U));
- ARM_COMPUTE_RETURN_ERROR_ON(conv_info.pad_stride_info.has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it
+ ARM_COMPUTE_RETURN_ERROR_ON(
+ conv_info.pad_stride_info
+ .has_padding()); // Note that in princple padding can be supported with in_place but we choose not to support it
}
- const ConvolutionInfo info{ conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(), conv_info.dilation };
- const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info);
+ const ConvolutionInfo info{conv_info.pad_stride_info, conv_info.depth_multiplier, ActivationLayerInfo(),
+ conv_info.dilation};
+ const TensorShape output_shape =
+ arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info);
- if(conv_info.depth_multiplier > 1 && dwc_info.n0 > 1)
+ if (conv_info.depth_multiplier > 1 && dwc_info.n0 > 1)
{
ARM_COMPUTE_RETURN_ERROR_ON((conv_info.depth_multiplier % dwc_info.n0) != 0);
}
const bool is_quantized = is_data_type_quantized(input->data_type());
- if(biases != nullptr)
+ if (biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != output_shape[idx_c]);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
- if(is_quantized)
+ if (is_quantized)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
}
@@ -108,7 +125,7 @@
}
}
- if(is_quantized)
+ if (is_quantized)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output_multipliers, output_shifts);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
@@ -116,7 +133,7 @@
ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
- if(is_data_type_quantized_per_channel(weights->data_type()))
+ if (is_data_type_quantized_per_channel(weights->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON(output_shape[idx_c] != output_multipliers->dimension(0));
@@ -134,22 +151,24 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
}
- if(output->total_size() != 0)
+ if (output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
- if(is_data_type_quantized(input->data_type()))
+ if (is_data_type_quantized(input->data_type()))
{
const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info;
+ const UniformQuantizationInfo oq_info =
+ (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info;
float multiplier = iq_info.scale * wq_info.scale / oq_info.scale;
int output_multiplier = 0;
int output_shift = 0;
- ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift));
}
return Status{};
@@ -171,30 +190,48 @@
_type = CLKernelType::DEPTHWISE;
}
-void CLDepthwiseConvolutionLayerNativeKernel::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
- const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info,
- const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
+void CLDepthwiseConvolutionLayerNativeKernel::configure(ICLTensor *input,
+ const ICLTensor *weights,
+ const ICLTensor *biases,
+ ICLTensor *output,
+ const DWCComputeKernelInfo &dwc_info,
+ const ConvolutionInfo &conv_info,
+ const ICLTensor *output_multipliers,
+ const ICLTensor *output_shifts)
{
- configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts);
+ configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, dwc_info, conv_info,
+ output_multipliers, output_shifts);
}
-void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output,
- const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info,
- const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
+void CLDepthwiseConvolutionLayerNativeKernel::configure(const CLCompileContext &compile_context,
+ ICLTensor *input,
+ const ICLTensor *weights,
+ const ICLTensor *biases,
+ ICLTensor *output,
+ const DWCComputeKernelInfo &dwc_info,
+ const ConvolutionInfo &conv_info,
+ const ICLTensor *output_multipliers,
+ const ICLTensor *output_shifts)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
- if(output == nullptr)
+ if (output == nullptr)
{
// In-place
output = input;
}
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(),
- dwc_info, conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, (output_shifts != nullptr) ? output_shifts->info() : nullptr));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(
+ input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), dwc_info,
+ conv_info, (output_multipliers != nullptr) ? output_multipliers->info() : nullptr,
+ (output_shifts != nullptr) ? output_shifts->info() : nullptr));
- auto padding_info = get_padding_info({ input, output });
+ auto padding_info = get_padding_info({input, output});
- const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*(input->info()), *(weights->info()), conv_info);
- auto_init_if_empty(*(output->info()), input->info()->clone()->set_tensor_shape(output_shape).set_quantization_info(output->info()->quantization_info()));
+ const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(
+ *(input->info()), *(weights->info()), conv_info);
+ auto_init_if_empty(*(output->info()), input->info()
+ ->clone()
+ ->set_tensor_shape(output_shape)
+ .set_quantization_info(output->info()->quantization_info()));
_input = input;
_output = output;
@@ -214,12 +251,12 @@
CLBuildOptions build_opts;
// Update the padding for the input/weights tensor if we can export to cl_image
- if(_export_input_to_cl_image)
+ if (_export_input_to_cl_image)
{
arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(input->info());
}
- if(_export_weights_to_cl_image)
+ if (_export_weights_to_cl_image)
{
arm_compute::opencl::kernels::gemm::update_padding_for_cl_image(weights->info());
}
@@ -229,9 +266,10 @@
const auto act_function = conv_info.act_info.activation();
const auto dst_data_type = _output->info()->data_type();
- if((gpu_target != GPUTarget::G71 && (gpu_target & GPUTarget::GPU_ARCH_MASK) == GPUTarget::BIFROST)
- && (act_function == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU || act_function == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
- && (dst_data_type == DataType::F32 || dst_data_type == DataType::F16))
+ if ((gpu_target != GPUTarget::G71 && (gpu_target & GPUTarget::GPU_ARCH_MASK) == GPUTarget::BIFROST) &&
+ (act_function == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU ||
+ act_function == ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) &&
+ (dst_data_type == DataType::F32 || dst_data_type == DataType::F16))
{
// -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations
// to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations
@@ -268,23 +306,24 @@
build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
build_opts.add_option("-DM0_A=" + support::cpp11::to_string(_weights->info()->dimension(1) + m0 - 1));
- build_opts.add_option_if_else(conv_info.depth_multiplier > 1, "-DN0_A=1", "-DN0_A=" + support::cpp11::to_string(n0));
+ build_opts.add_option_if_else(conv_info.depth_multiplier > 1, "-DN0_A=1",
+ "-DN0_A=" + support::cpp11::to_string(n0));
build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(_output->info()->dimension(0) % n0));
build_opts.add_option_if(_input->info()->num_dimensions() > 3, "-DBATCHED_EXECUTION");
// Force unroll with pragma when any of the following values exceed the maximum number of manual unroll
- set_unroll_with_pragma(build_opts, { static_cast<int>(_weights->info()->dimension(1) + m0 - 1),
- static_cast<int>(_weights->info()->dimension(1)),
- static_cast<int>(_weights->info()->dimension(2))
- });
+ set_unroll_with_pragma(build_opts, {static_cast<int>(_weights->info()->dimension(1) + m0 - 1),
+ static_cast<int>(_weights->info()->dimension(1)),
+ static_cast<int>(_weights->info()->dimension(2))});
- if(biases != nullptr)
+ if (biases != nullptr)
{
build_opts.add_option(std::string("-DHAS_BIAS"));
- build_opts.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type())));
+ build_opts.add_option(
+ std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->info()->data_type())));
}
- if(_is_quantized)
+ if (_is_quantized)
{
kernel_name = "dwc_native_quantized_nhwc";
const UniformQuantizationInfo iqinfo = input->info()->quantization_info().uniform();
@@ -306,13 +345,17 @@
build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset));
build_opts.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32));
build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32));
- build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" + get_cl_type_from_data_type(_output_multipliers->info()->data_type()));
- build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" + get_cl_type_from_data_type(_output_shifts->info()->data_type()));
- build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL, "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR");
+ build_opts.add_option("-DDST_MULTIPLIERS_DATA_TYPE=" +
+ get_cl_type_from_data_type(_output_multipliers->info()->data_type()));
+ build_opts.add_option("-DDST_SHIFTS_DATA_TYPE=" +
+ get_cl_type_from_data_type(_output_shifts->info()->data_type()));
+ build_opts.add_option_if_else(weights->info()->data_type() == DataType::QSYMM8_PER_CHANNEL,
+ "-DQUANTIZATION_TYPE=PER_CHANNEL", "-DQUANTIZATION_TYPE=PER_TENSOR");
// Note: We expect the input and output tensors to always adopt a per-tensor quantization approach
int a_val{};
int b_val{};
- std::tie(b_val, a_val) = get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo);
+ std::tie(b_val, a_val) =
+ get_quantized_activation_min_max(conv_info.act_info, input->info()->data_type(), oqinfo);
build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + support::cpp11::to_string(a_val));
build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + support::cpp11::to_string(b_val));
@@ -321,8 +364,10 @@
{
kernel_name = "dwc_native_fp_nhwc";
build_opts.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
- build_opts.add_option_if(conv_info.act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a()));
- build_opts.add_option_if(conv_info.act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b()));
+ build_opts.add_option_if(conv_info.act_info.enabled(),
+ "-DA_VAL=" + float_to_string_with_full_precision(conv_info.act_info.a()));
+ build_opts.add_option_if(conv_info.act_info.enabled(),
+ "-DB_VAL=" + float_to_string_with_full_precision(conv_info.act_info.b()));
}
Window win = calculate_max_window(*(output->info()), Steps(n0, m0));
@@ -350,10 +395,17 @@
_config_id += string_from_data_type(input->info()->data_type());
}
-Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
- const DWCComputeKernelInfo &dwc_info, const ConvolutionInfo &conv_info, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
+Status CLDepthwiseConvolutionLayerNativeKernel::validate(const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *biases,
+ const ITensorInfo *output,
+ const DWCComputeKernelInfo &dwc_info,
+ const ConvolutionInfo &conv_info,
+ const ITensorInfo *output_multipliers,
+ const ITensorInfo *output_shifts)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts));
+ ARM_COMPUTE_RETURN_ON_ERROR(
+ validate_arguments(input, weights, biases, output, dwc_info, conv_info, output_multipliers, output_shifts));
return Status{};
}
@@ -370,47 +422,52 @@
cl::Image2D input_cl_image;
cl::Image2D weights_cl_image;
- if(_export_input_to_cl_image || _export_weights_to_cl_image)
+ if (_export_input_to_cl_image || _export_weights_to_cl_image)
{
// Export cl_buffer to cl_image
- if(_export_input_to_cl_image)
+ if (_export_input_to_cl_image)
{
- const size_t image_w = _input->info()->dimension(0) / 4;
- const size_t image_h = _input->info()->dimension(1) * _input->info()->dimension(2) * _input->info()->dimension(3);
+ const size_t image_w = _input->info()->dimension(0) / 4;
+ const size_t image_h =
+ _input->info()->dimension(1) * _input->info()->dimension(2) * _input->info()->dimension(3);
const TensorShape shape2d(image_w, image_h);
const size_t image_row_pitch = _input->info()->strides_in_bytes()[1];
- input_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), _input->cl_buffer(), shape2d, _input->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
+ input_cl_image =
+ create_image2d_from_buffer(CLKernelLibrary::get().context(), _input->cl_buffer(), shape2d,
+ _input->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
}
- if(_export_weights_to_cl_image)
+ if (_export_weights_to_cl_image)
{
- const size_t image_w = _weights->info()->dimension(0) / 4;
- const size_t image_h = _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3);
+ const size_t image_w = _weights->info()->dimension(0) / 4;
+ const size_t image_h =
+ _weights->info()->dimension(1) * _weights->info()->dimension(2) * _weights->info()->dimension(3);
const TensorShape shape2d(image_w, image_h);
const size_t image_row_pitch = _weights->info()->strides_in_bytes()[1];
- weights_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d, _weights->info()->data_type(), image_row_pitch,
- CLImage2DType::ReadOnly);
+ weights_cl_image =
+ create_image2d_from_buffer(CLKernelLibrary::get().context(), _weights->cl_buffer(), shape2d,
+ _weights->info()->data_type(), image_row_pitch, CLImage2DType::ReadOnly);
}
}
unsigned int idx = 0;
- if(_export_input_to_cl_image)
+ if (_export_input_to_cl_image)
{
_kernel.setArg(idx++, input_cl_image);
}
add_4d_tensor_nhwc_argument(idx, _input);
add_4d_tensor_nhwc_argument(idx, _output);
- if(_export_weights_to_cl_image)
+ if (_export_weights_to_cl_image)
{
_kernel.setArg(idx++, weights_cl_image);
}
add_4d_tensor_nhwc_argument(idx, _weights);
- if(_is_quantized)
+ if (_is_quantized)
{
add_1D_tensor_argument(idx, _output_multipliers, slice);
add_1D_tensor_argument(idx, _output_shifts, slice);
}
- if(_biases != nullptr)
+ if (_biases != nullptr)
{
add_1D_tensor_argument(idx, _biases, slice);
}