From c63b722591ff23c8c6fe5fb8ef8c8516d40f03aa Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Wed, 30 Jun 2021 08:39:44 +0000 Subject: Revert "Rework OpenCL Depthwise Convolution" This reverts commit 561c176598cd14245e2e7918fdf136d1c888d1da. Reason for revert: Change-Id: I6f2d61c27520439bb538e9265736532104b24cf8 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5127 Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas Comments-Addressed: Arm Jenkins --- .../CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp | 432 +++++++++++++++++++++ 1 file changed, 432 insertions(+) create mode 100644 src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp (limited to 'src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp') diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp new file mode 100644 index 0000000000..dda70d2231 --- /dev/null +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp @@ -0,0 +1,432 @@ +/* + * Copyright (c) 2018-2021 Arm Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.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.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "src/core/AccessWindowStatic.h" +#include "src/core/CL/CLValidate.h" +#include "src/core/CL/ICLKernel.h" +#include "src/core/helpers/AutoConfiguration.h" +#include "src/core/helpers/WindowHelpers.h" +#include "support/StringSupport.h" + +namespace arm_compute +{ +using namespace arm_compute::misc::shape_calculator; + +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D dilation, + const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +{ + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); + 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_MSG((act_info.enabled()) && (input->data_type() == DataType::QASYMM8 || input->data_type() == DataType::QASYMM8_SIGNED) + && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) + && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) + && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU) + && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC), + "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); + + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); + + if(biases != nullptr) + { + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); + } + ARM_COMPUTE_RETURN_ERROR_ON((biases->dimension(0) != weights->dimension(2)) && (weights->dimension(2) != 1 || biases->dimension(0) != weights->dimension(3))); + ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); + } + + if(is_qasymm) + { + 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); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); + 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())) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QSYMM8_PER_CHANNEL); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_multipliers->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(2) != output_shifts->dimension(0)); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_RETURN_ERROR_ON(1 != output_multipliers->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(1 != output_shifts->dimension(0)); + } + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + } + + if(output->total_size() != 0) + { + const ConvolutionInfo info{ conv_info, depth_multiplier, ActivationLayerInfo(), dilation }; + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + } + + return Status{}; +} + +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, + unsigned int depth_multiplier, std::string &kernel_name, const Size2D dilation) +{ + // Output auto inizialitation if not yet initialized + const ConvolutionInfo info + { + conv_info, depth_multiplier, ActivationLayerInfo(), dilation + }; + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, info); + auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); + + const unsigned int conv_stride_x = conv_info.stride().first; + const unsigned int conv_stride_y = conv_info.stride().second; + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); + + // Configure kernel window + unsigned int num_elems_read_per_iteration_x = 0; + unsigned int num_elems_read_per_iteration_y = 0; + unsigned int num_elems_written_per_iteration_x = 0; + unsigned int num_elems_written_per_iteration_y = 0; + + if(input->data_type() == DataType::F16) + { + kernel_name = "depthwise_convolution_3x3_f16"; + num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); + num_elems_written_per_iteration_y = 1; + num_elems_read_per_iteration_y = 3; + switch(conv_stride_x) + { + case 1: + num_elems_read_per_iteration_x = 8; + break; + case 2: + num_elems_read_per_iteration_x = 9; + break; + case 3: + num_elems_read_per_iteration_x = 16; + break; + default: + num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; + break; + } + if(conv_stride_x == 1 && conv_stride_y == 1) + { + kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_f16"; + num_elems_read_per_iteration_x = 8; + num_elems_written_per_iteration_x = 4; + num_elems_read_per_iteration_y = 6; + num_elems_written_per_iteration_y = 4; + } + else if(conv_stride_x == 2 && conv_stride_y == 2) + { + kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_f16"; + num_elems_read_per_iteration_x = 10; + num_elems_written_per_iteration_x = 4; + num_elems_read_per_iteration_y = 5; + num_elems_written_per_iteration_y = 2; + } + } + else if(input->data_type() == DataType::F32) + { + if(conv_stride_x == 1 && conv_stride_y == 1) + { + kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_f32"; + num_elems_read_per_iteration_x = 4; + num_elems_read_per_iteration_y = 6; + num_elems_written_per_iteration_x = 2; + num_elems_written_per_iteration_y = 4; + } + else if(conv_stride_x == 2 && conv_stride_y == 2) + { + kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_f32"; + num_elems_read_per_iteration_x = 6; + num_elems_read_per_iteration_y = 5; + num_elems_written_per_iteration_x = 2; + num_elems_written_per_iteration_y = 2; + } + else + { + kernel_name = "depthwise_convolution_3x3"; + num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); + num_elems_written_per_iteration_y = 1; + num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; + num_elems_read_per_iteration_y = 3; + } + } + else + { + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_data_type_quantized_per_channel(weights->data_type()); + + kernel_name = is_qasymm ? "dwc_3x3_native_quantized8" : "depthwise_convolution_3x3"; + kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : ""); + kernel_name += (is_qasymm ? "_nchw" : ""); + + num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); + num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1 && dilation.y() == 1) ? 2 : 1; + num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x + (conv_stride_x > 1 ? 1 : 0); + num_elems_read_per_iteration_y = num_elems_written_per_iteration_y + 2; + } + // The OpenCL routine convolution1x3 does loadn(addr), loadn(addr + dilation_x) and loadn(addr + 2 * dilation_x) on the input. + // Each of the three convolution1x3 gets called by passing addr, (addr + dilation_y) and (addr + 2 * dilation_y) + // Hence we must add 2 * dilation.x/y() to the number of elements read in those axes per thread + num_elems_read_per_iteration_x += 2 * dilation.x(); + num_elems_read_per_iteration_y += 2 * dilation.y(); + + // Create window and update padding + Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); + + AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), + num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, + conv_stride_x, conv_stride_y); + AccessWindowStatic weights_access(weights, 0, 0, 3, 3); + AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); + + bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel() + : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_y(1), _output_multipliers(), _output_shifts(), _is_quantized(false), _conv_stride_x(0), _conv_pad_top(0), _conv_pad_left(0) +{ +} + +BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const +{ + return _border_size; +} + +void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, + const ICLTensor *output_multipliers, const ICLTensor *output_shifts) +{ + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts); +} + +void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation, + const ICLTensor *output_multipliers, const ICLTensor *output_shifts) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), + conv_info, depth_multiplier, act_info, dilation, + (output_multipliers != nullptr) ? output_multipliers->info() : nullptr, + (output_shifts != nullptr) ? output_shifts->info() : nullptr)); + + _input = input; + _output = output; + _weights = weights; + _biases = biases; + _conv_stride_x = conv_info.stride().first; + _conv_stride_y = conv_info.stride().second; + _conv_pad_left = conv_info.pad_left(); + _conv_pad_top = conv_info.pad_top(); + _output_multipliers = output_multipliers; + _output_shifts = output_shifts; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + + // Configure kernel window + std::string kernel_name; + + auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, kernel_name, dilation); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + ICLKernel::configure_internal(win_config.second); + + _border_size = BorderSize(input->info()->padding()); + + // Set build options + CLBuildOptions build_opts; + build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); + build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z())); + build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); + build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); + build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); + + if(_is_quantized) + { + const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform(); + + const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->info()->data_type()); + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()) && !is_quantized_per_channel; + build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); + build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iq_info.offset)); + build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); + build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); + build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_info.offset)); + build_opts.add_option_if(is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); + build_opts.add_option_if(is_dot8_supported, "-DIS_DOT8"); + + // Compute non-per-channel multiplier and shift anyway to make OpenCL kernel simpler + float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; + int output_multiplier = 0; + int output_shift = 0; + quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); + build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); + + if(act_info.enabled()) + { + int a_val{}; + int b_val{}; + std::tie(b_val, a_val) = get_quantized_activation_min_max(act_info, input->info()->data_type(), oq_info); + + const int o1 = oq_info.offset; + + build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); + build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); + build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); + + const float s1 = iq_info.scale; + build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); + build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); + } + + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DWEIGHTS_TYPE=" + get_cl_type_from_data_type(weights->info()->data_type())); + build_opts.add_option("-DWEIGHTS_PROMOTED_TYPE=" + get_cl_promoted_type_from_data_type(weights->info()->data_type())); + } + else + { + build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); + build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); + build_opts.add_option_if(act_info.enabled(), "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(win_config.second.x().step())); + } + + build_opts.add_option_if(input->info()->data_type() == DataType::F16, "-DIS_F16"); + build_opts.add_option_if(input->info()->data_type() == DataType::F32, "-DIS_F32"); + + _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); + + // Set config_id for enabling LWS tuning + _config_id = kernel_name; + _config_id += "_"; + _config_id += lower_string(string_from_data_type(input->info()->data_type())); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(input->info()->dimension(2)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->info()->dimension(1)); +} + +Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, + const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, + const Size2D &dilation, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) +{ + std::string kernel_name; + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation, output_multipliers, output_shifts)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), + conv_info, depth_multiplier, kernel_name, dilation) + .first); + + return Status{}; +} + +void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); + + // Create input window and adjust + Window collapsed_in = collapsed; + collapsed_in.adjust(Window::DimX, -_conv_pad_left, true); + collapsed_in.adjust(Window::DimY, -_conv_pad_top, true); + collapsed_in.set_dimension_step(Window::DimX, collapsed_in.x().step() * _conv_stride_x); + collapsed_in.set_dimension_step(Window::DimY, collapsed_in.y().step() * _conv_stride_y); + + Window slice_in = collapsed_in.first_slice_window_3D(); + Window slice_out = collapsed.first_slice_window_3D(); + Window slice_weights = window.first_slice_window_3D(); + slice_weights.set_dimension_step(Window::DimX, 0); + slice_weights.set_dimension_step(Window::DimY, 0); + + unsigned int idx = 3 * num_arguments_per_3D_tensor(); + + // Set output multipliers in case of quantized data type + if(_is_quantized) + { + Window slice; + slice.use_tensor_dimensions(_output_multipliers->info()->tensor_shape()); + add_1D_tensor_argument(idx, _output_multipliers, slice); + add_1D_tensor_argument(idx, _output_shifts, slice); + } + + // Set biases + if(_biases != nullptr) + { + Window slice_biases; + slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); + add_1D_tensor_argument(idx, _biases, slice_biases); + } + + do + { + idx = 0; + add_3D_tensor_argument(idx, _input, slice_in); + add_3D_tensor_argument(idx, _output, slice_out); + add_3D_tensor_argument(idx, _weights, slice_weights); + + enqueue(queue, *this, slice_out, lws_hint()); + } + while(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in)); +} +} // namespace arm_compute -- cgit v1.2.1