diff options
Diffstat (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp')
| -rw-r--r-- | src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp | 357 |
1 files changed, 60 insertions, 297 deletions
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp index 6071153639..ec672e0fc4 100644 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp +++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp @@ -40,11 +40,8 @@ #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/ToolchainSupport.h" -using namespace arm_compute; -using namespace arm_compute::detail; -using namespace arm_compute::misc::shape_calculator; -using namespace depthwise; - +namespace arm_compute +{ namespace { template <typename T1, typename T2, unsigned int stridex> @@ -65,7 +62,7 @@ public: const int kernel_stride_z = weights->info()->strides_in_bytes().z(); const int output_w = output->info()->dimension(0); const int output_h = output->info()->dimension(1); - const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); + const int delta_input = detail::get_input_num_elems_processed<stridex>(num_elems_written_per_iteration); const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); const unsigned int conv_pad_x = conv_info.pad_left(); const unsigned int conv_pad_y = conv_info.pad_top(); @@ -100,9 +97,9 @@ public: const auto ptr_weights_r0 = reinterpret_cast<const T1 *>(ptr_weights_base); const auto ptr_weights_r1 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y); const auto ptr_weights_r2 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y * 2); - const auto vw_r0 = load_matrix_row(ptr_weights_r0, weights_offset); - const auto vw_r1 = load_matrix_row(ptr_weights_r1, weights_offset); - const auto vw_r2 = load_matrix_row(ptr_weights_r2, weights_offset); + const auto vw_r0 = detail::load_matrix_row(ptr_weights_r0, weights_offset); + const auto vw_r1 = detail::load_matrix_row(ptr_weights_r1, weights_offset); + const auto vw_r2 = detail::load_matrix_row(ptr_weights_r2, weights_offset); for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) { @@ -115,8 +112,8 @@ public: in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) { - auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset); - store_results<stridex>(p_out, vres); + auto vres = detail::convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset); + detail::store_results<stridex>(p_out, vres); } } }, @@ -145,7 +142,7 @@ inline void convolve_3x3(const Window &window, unsigned int num_elems_written_pe } } -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, bool is_optimized) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); @@ -156,15 +153,11 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != 3 || weights->dimension(height_idx) != 3); - - if(!is_optimized) - { - ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); - } + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); if(output->total_size() != 0) { - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); if(is_data_type_quantized_asymmetric(input->data_type())) @@ -180,87 +173,53 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, bool is_optimized, - IDepthwiseConvolution *convolver = nullptr) +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { Window win; bool window_changed = false; - if(is_optimized) - { - if(convolver != nullptr) - { - auto win_last = convolver->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); + // Get convolved dimensions + const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); - // Auto-configure output - bool same_padding = conv_info.has_padding(); - TensorShape output_shape{ input->tensor_shape() }; + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt)); - output_shape.set(1, convolver->output_size(output_shape.y(), same_padding)); // Set width - output_shape.set(2, convolver->output_size(output_shape.z(), same_padding)); // Set height + // Configure kernel window (generic) + const unsigned int conv_stride_x = conv_info.stride().first; + const unsigned int conv_stride_y = conv_info.stride().second; + const unsigned int conv_pad_top = conv_info.pad_top(); + const unsigned int conv_pad_left = conv_info.pad_left(); - const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); + unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; + unsigned int num_elems_read_per_iteration = 0; - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt)); - - // Configure window (optimised) - // Set padding in channels - const int num_channels = weights->dimension(0); - if((num_channels >= 128) && (num_channels % 16 == 0)) - { - input->extend_padding(PaddingSize(0, 4, 0, 0)); - weights->extend_padding(PaddingSize(0, 4, 0, 0)); - output->extend_padding(PaddingSize(0, 4, 0, 0)); - } - } - } - else + switch(input->data_type()) { - // Get convolved dimensions - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); - const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt)); - - // Configure kernel window (generic) - const unsigned int conv_stride_x = conv_info.stride().first; - const unsigned int conv_stride_y = conv_info.stride().second; - const unsigned int conv_pad_top = conv_info.pad_top(); - const unsigned int conv_pad_left = conv_info.pad_left(); - - unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; - unsigned int num_elems_read_per_iteration = 0; - - switch(input->data_type()) - { - case DataType::QASYMM8: - num_elems_read_per_iteration = 16; - break; + case DataType::QASYMM8: + num_elems_read_per_iteration = 16; + break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - num_elems_read_per_iteration = 24; - break; + case DataType::F16: + num_elems_read_per_iteration = 24; + break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F32: - num_elems_read_per_iteration = 12; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported."); - } + case DataType::F32: + num_elems_read_per_iteration = 12; + break; + default: + ARM_COMPUTE_ERROR("Data type not supported."); + } - // Configure kernel window - win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); + // Configure kernel window + win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); - AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3, conv_stride_x, conv_stride_y); - AccessWindowStatic weights_access(weights, 0, 0, 3, 3); - AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); + AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3, conv_stride_x, conv_stride_y); + AccessWindowStatic weights_access(weights, 0, 0, 3, 3); + AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); - window_changed = update_window_and_padding(win, input_access, weights_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - } + window_changed = update_window_and_padding(win, input_access, weights_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, win); @@ -268,7 +227,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen } // namespace NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() - : _border_size(0), _input(), _output(), _weights(), _conv_info(), _convolver(nullptr), _num_elems_written_per_iteration(0), _run_optimized(false), _depth_multiplier(1) + : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1) { } @@ -277,34 +236,28 @@ BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const return _border_size; } -void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - DataLayout data_layout) +void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info, depth_multiplier)); - _input = input; - _output = output; - _weights = weights; - _conv_info = conv_info; - _depth_multiplier = depth_multiplier; - _convolver = nullptr; - - _run_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->info()->tensor_shape(), - conv_info, - input->info()->data_type(), depth_multiplier, - data_layout); + _input = input; + _output = output; + _weights = weights; + _conv_info = conv_info; + _depth_multiplier = depth_multiplier; + _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; + _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); - (_run_optimized) ? configure_optimized() : configure_generic(); + auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + INEKernel::configure(win_config.second); } Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - - bool is_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->tensor_shape(), conv_info, input->data_type(), depth_multiplier, input->data_layout()); - - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, is_optimized)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, is_optimized).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier).first); return Status{}; } @@ -313,80 +266,6 @@ void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const Threa ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_UNUSED(info); - (_run_optimized) ? run_optimized(window, info) : run_generic(window, info); -} - -bool NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(TensorShape input_shape, PadStrideInfo conv_info, DataType dt, unsigned int depth_multiplier, DataLayout data_layout) -{ - // Reshape input shape if in NHWC format - TensorShape in_shape{ input_shape }; - if(data_layout == DataLayout::NHWC) - { - in_shape.set(Window::DimX, input_shape.y()); - in_shape.set(Window::DimY, input_shape.z()); - in_shape.set(Window::DimZ, input_shape.x()); - } - - // Check supported data type - bool supported_datatype = is_data_type_float(dt) || is_data_type_quantized(dt); - - // Check for supported strides - const auto &strides = conv_info.stride(); - bool supported_strides = (strides.first == strides.second) && ((strides.first == 1) || (strides.first == 2)); - - // Check for supported padding - const auto pad_top = conv_info.pad_top(); - const auto pad_right = conv_info.pad_right(); - const auto pad_bottom = conv_info.pad_bottom(); - const auto pad_left = conv_info.pad_left(); - PadStrideInfo same_pad = calculate_same_pad(in_shape, TensorShape(3U, 3U), conv_info); - bool is_same_padding = (pad_top == same_pad.pad_top()) && (pad_right == same_pad.pad_right()) && (pad_bottom == same_pad.pad_bottom()) && (pad_left == same_pad.pad_left()); - bool is_valid_padding = (pad_top == 0) && (pad_right == 0) && (pad_bottom == 0) && (pad_left == 0); - bool supported_padding = is_same_padding || is_valid_padding; - - return supported_datatype && supported_strides && supported_padding && (depth_multiplier == 1); -} - -void NEDepthwiseConvolutionLayer3x3Kernel::generate_convolver() -{ - ARM_COMPUTE_ERROR_ON_CPU_F16_UNSUPPORTED(_input); - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(_input, _weights); - ARM_COMPUTE_ERROR_ON(_weights->info()->dimension(1) != 3 || _weights->info()->dimension(2) != 3); - - _convolver = create_convolver_object(_conv_info, _weights, _input, _output, true); - if(_convolver) - { - _convolver->set_offsets(-_input->info()->quantization_info().offset, -_weights->info()->quantization_info().offset); - } -} - -void NEDepthwiseConvolutionLayer3x3Kernel::configure_generic() -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, _run_optimized)); - - _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; - _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); - - auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, false); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -void NEDepthwiseConvolutionLayer3x3Kernel::configure_optimized() -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, _run_optimized)); - - _border_size = BorderSize(0, 0); - _convolver = create_convolver_object(_conv_info, _weights, _input, _output); - - auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, true, _convolver.get()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - INEKernel::configure(win_config.second); -} - -void NEDepthwiseConvolutionLayer3x3Kernel::run_generic(const Window &window, const ThreadInfo &info) -{ ARM_COMPUTE_UNUSED(info); switch(_input->info()->data_type()) @@ -406,120 +285,4 @@ void NEDepthwiseConvolutionLayer3x3Kernel::run_generic(const Window &window, con ARM_COMPUTE_ERROR("Not implemented"); } } - -void NEDepthwiseConvolutionLayer3x3Kernel::run_optimized(const Window &window, const ThreadInfo &info) -{ - ARM_COMPUTE_UNUSED(info); - ARM_COMPUTE_ERROR_ON(!_convolver); - - const size_t start = window.x().start(); - const size_t end = window.x().end(); - _convolver->run(start, end); -} - -std::unique_ptr<depthwise::IDepthwiseConvolution> NEDepthwiseConvolutionLayer3x3Kernel::create_convolver_object(PadStrideInfo conv_info, - const ITensor *w, - const ITensor *in, - ITensor *out, - bool setup_strides) -{ - const DataType dt = in->info()->data_type(); - const TensorShape shape = in->info()->tensor_shape(); - const int in_rows = shape.z(); - const int in_cols = shape.y(); - const int n_batches = shape[3]; - const int n_channels = shape.x(); - const bool padding_same = conv_info.has_padding(); - const int weight_col_stride = (setup_strides) ? w->info()->strides_in_bytes().y() / w->info()->element_size() : 0; - const int weight_row_stride = (setup_strides) ? w->info()->strides_in_bytes().z() / w->info()->element_size() : 0; - const int input_col_stride = (setup_strides) ? in->info()->strides_in_bytes().y() / in->info()->element_size() : 0; - const int input_row_stride = (setup_strides) ? in->info()->strides_in_bytes().z() / in->info()->element_size() : 0; - const int input_batch_stride = (setup_strides) ? in->info()->strides_in_bytes()[3] / in->info()->element_size() : 0; - const int output_col_stride = (setup_strides) ? out->info()->strides_in_bytes().y() / out->info()->element_size() : 0; - const int output_row_stride = (setup_strides) ? out->info()->strides_in_bytes().z() / out->info()->element_size() : 0; - const int output_batch_stride = (setup_strides) ? out->info()->strides_in_bytes()[3] / out->info()->element_size() : 0; - - const auto stride_x = conv_info.stride().first; - switch(dt) - { - case DataType::QASYMM8: - { - switch(stride_x) - { - case 1: - return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 1, 1, uint8_t, int32_t>>( - n_batches, in_rows, in_cols, n_channels, padding_same, - reinterpret_cast<const uint8_t *>(w->ptr_to_element(Coordinates())), - in->ptr_to_element(Coordinates()), - reinterpret_cast<int32_t *>(out->ptr_to_element(Coordinates())), weight_col_stride, - weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, - output_col_stride, output_row_stride, output_batch_stride); - case 2: - return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 2, 2, uint8_t, int32_t>>( - n_batches, in_rows, in_cols, n_channels, padding_same, - reinterpret_cast<const uint8_t *>(w->ptr_to_element(Coordinates())), - in->ptr_to_element(Coordinates()), - reinterpret_cast<int32_t *>(out->ptr_to_element(Coordinates())), weight_col_stride, - weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, - output_col_stride, output_row_stride, output_batch_stride); - default: - return nullptr; - } - break; - } -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F16: - { - switch(stride_x) - { - case 1: - return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 1, 1, float16_t, float16_t>>( - n_batches, in_rows, in_cols, n_channels, padding_same, - reinterpret_cast<const float16_t *>(w->ptr_to_element(Coordinates())), - reinterpret_cast<float16_t *>(in->ptr_to_element(Coordinates())), - reinterpret_cast<float16_t *>(out->ptr_to_element(Coordinates())), weight_col_stride, - weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, - output_col_stride, output_row_stride, output_batch_stride); - case 2: - return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 2, 2, float16_t, float16_t>>( - n_batches, in_rows, in_cols, n_channels, padding_same, - reinterpret_cast<const float16_t *>(w->ptr_to_element(Coordinates())), - reinterpret_cast<float16_t *>(in->ptr_to_element(Coordinates())), - reinterpret_cast<float16_t *>(out->ptr_to_element(Coordinates())), weight_col_stride, - weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, - output_col_stride, output_row_stride, output_batch_stride); - default: - return nullptr; - } - break; - } -#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - case DataType::F32: - { - switch(stride_x) - { - case 1: - return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<4, 4, 3, 3, 1, 1, float, float>>( - n_batches, in_rows, in_cols, n_channels, padding_same, - reinterpret_cast<const float *>(w->ptr_to_element(Coordinates())), - reinterpret_cast<float *>(in->ptr_to_element(Coordinates())), - reinterpret_cast<float *>(out->ptr_to_element(Coordinates())), weight_col_stride, - weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, - output_col_stride, output_row_stride, output_batch_stride); - case 2: - return arm_compute::support::cpp14::make_unique<DepthwiseConvolution<3, 3, 3, 3, 2, 2, float, float>>( - n_batches, in_rows, in_cols, n_channels, padding_same, - reinterpret_cast<const float *>(w->ptr_to_element(Coordinates())), - reinterpret_cast<float *>(in->ptr_to_element(Coordinates())), - reinterpret_cast<float *>(out->ptr_to_element(Coordinates())), weight_col_stride, - weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, - output_col_stride, output_row_stride, output_batch_stride); - default: - return nullptr; - } - break; - } - default: - return nullptr; - } -}
\ No newline at end of file +} // namespace arm_compute |