| /* |
| * Copyright (c) 2017-2021 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/gpu/cl/kernels/ClIm2ColKernel.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/CL/OpenCL.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/AccessWindowStatic.h" |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/Cast.h" |
| #include "support/StringSupport.h" |
| |
| #include <cmath> |
| #include <tuple> |
| #include <utility> |
| |
| namespace arm_compute |
| { |
| using namespace misc::shape_calculator; |
| namespace opencl |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| struct Im2ColConfiguration |
| { |
| std::string kernel_name{}; |
| std::set<std::string> build_options{}; |
| unsigned int num_elems_processed_per_iteration{}; |
| bool is_padding_required_nchw{}; |
| }; |
| |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, |
| unsigned int num_groups) |
| { |
| const unsigned int channel_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized(src->data_type()) && has_bias); |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(dst); |
| ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); |
| ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::UNKNOWN); |
| ARM_COMPUTE_RETURN_ERROR_ON(num_groups == 0); |
| ARM_COMPUTE_RETURN_ERROR_ON(src->data_layout() == DataLayout::NHWC && num_groups > 1); |
| ARM_COMPUTE_RETURN_ERROR_ON((src->dimension(channel_idx) % num_groups) != 0); |
| |
| // Since there's no implicit padding added, check the total input spatial dimensions (with conv paddings) are big enough for the kernel dimensions |
| const unsigned int width_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); |
| const unsigned total_width = src->dimension(width_idx) + conv_info.pad_left() + conv_info.pad_right(); |
| const unsigned total_height = src->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom(); |
| ARM_COMPUTE_RETURN_ERROR_ON((total_width < kernel_dims.width) || (total_height < kernel_dims.height)); |
| |
| if(dst->total_size() > 0) |
| { |
| const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, |
| unsigned int num_elems_processed_per_iteration, bool is_padding_required_nchw, unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| |
| // Output tensor auto initialization if not yet initialized |
| TensorShape expected_output_shape = compute_im2col_conv_shape(src, kernel_dims, conv_info, has_bias, dilation, num_groups == 1, num_groups); |
| |
| auto_init_if_empty(*dst, src->clone()->set_tensor_shape(expected_output_shape)); |
| |
| const DataLayout data_layout = src->data_layout(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int input_width = src->dimension(width_idx); |
| const unsigned int input_height = src->dimension(height_idx); |
| |
| // Configure the execute window based on the selected optimal OpenCL kernel |
| bool window_changed = false; |
| Window win; |
| |
| if(data_layout == DataLayout::NHWC) |
| { |
| win = calculate_max_window(*src, Steps(num_elems_processed_per_iteration)); |
| } |
| else |
| { |
| if(is_padding_required_nchw) |
| { |
| const BorderSize border(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()); |
| win = calculate_max_window(*src, |
| Steps(num_elems_processed_per_iteration * conv_info.stride().first, conv_info.stride().second)); |
| AccessWindowStatic input_access(src, |
| -border.left, |
| -border.top, |
| ceil_to_multiple(input_width + border.right, kernel_dims.width * num_elems_processed_per_iteration), |
| input_height + border.bottom); |
| window_changed = window_changed || update_window_and_padding(win, input_access); |
| } |
| else |
| { |
| // For the generic case, CLIm2ColKernel doesn't need padding (we do not read out-of-bounds elements) so |
| // update_window_and_padding() can be skipped |
| win = calculate_max_window(*src, Steps()); |
| } |
| } |
| |
| // set the Z dimension's step same size as the whole dimension so that one can't split across the Z dimension |
| win.set_dimension_step(Window::DimZ, win[Window::DimZ].end() - win[Window::DimZ].start()); |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| |
| Im2ColConfiguration configure_opencl_kernel(const ITensorInfo *src, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, unsigned int num_groups) |
| { |
| const DataLayout data_layout = src->data_layout(); |
| const DataType data_type = src->data_type(); |
| const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| const unsigned int input_width = src->dimension(width_idx); |
| const unsigned int input_height = src->dimension(height_idx); |
| const unsigned int input_channel = src->dimension(channel_idx); |
| |
| const std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation); |
| |
| // Im2Col configuration |
| std::string kernel_name = "im2col_generic_"; |
| CLBuildOptions build_opts; |
| unsigned int num_elems_processed_per_iteration = 1; |
| bool is_padding_required_nchw = false; |
| const UniformQuantizationInfo qinfo = src->quantization_info().uniform(); |
| |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(src->element_size())); |
| build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width)); |
| build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height)); |
| build_opts.add_option("-DCONVOLVED_WIDTH=" + support::cpp11::to_string(convolved_dims.first)); |
| build_opts.add_option("-DCONVOLVED_HEIGHT=" + support::cpp11::to_string(convolved_dims.second)); |
| build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_info.stride().first)); |
| build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_info.stride().second)); |
| build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); |
| build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); |
| build_opts.add_option("-DPAD_RIGHT=" + support::cpp11::to_string(conv_info.pad_right())); |
| build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom())); |
| build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input_width)); |
| build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input_height)); |
| build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(input_channel)); |
| 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(num_groups > 1, "-DNUM_GROUPS=" + support::cpp11::to_string(num_groups)); |
| build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(qinfo.offset), "-DPAD_VALUE=0"); |
| build_opts.add_option_if(has_bias, "-DHAS_BIAS"); |
| |
| if(data_layout == DataLayout::NHWC) |
| { |
| num_elems_processed_per_iteration = std::min(2U, input_channel); |
| is_padding_required_nchw = false; |
| |
| // Only the 3x3 and 9x9 cases are optimized for NHWC |
| if(kernel_dims == Size2D(3U, 3U)) |
| { |
| kernel_name = "im2col3x3_"; |
| build_opts.add_option("-DIM2COL_3X3"); |
| } |
| else if(kernel_dims == Size2D(9U, 9U)) |
| { |
| kernel_name = "im2col9x9_"; |
| build_opts.add_option("-DIM2COL_9X9"); |
| } |
| else |
| { |
| build_opts.add_option("-DIM2COL_GENERIC"); |
| } |
| |
| // Get boundary vector (the first/last vector with potentially a partial vector size) size |
| // If input_channel is a multiple of num_elems_processed_per_iteration, the boundary vec size is the (full) vector size |
| // otherwise, the boundary vec size is the (partial) remainder vector size |
| const unsigned int vec_size = num_elems_processed_per_iteration; |
| const unsigned int partial_vec_size = input_channel % vec_size; |
| const unsigned int boundary_vec_size = vec_size - ((vec_size - partial_vec_size) % vec_size); |
| build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vec_size)); |
| build_opts.add_option("-DBOUNDARY_VECTOR_SIZE=" + support::cpp11::to_string(boundary_vec_size)); |
| } |
| else |
| { |
| if(dilation == Size2D(1U, 1U)) |
| { |
| const bool squared_im2col = kernel_dims.width == kernel_dims.height; |
| if(squared_im2col) |
| { |
| // Check if we can run an optimized im2col for NCHW |
| switch(kernel_dims.width) |
| { |
| case 1: |
| // Optimized im2col1x1 if stride_x = 1 and conv_info.has_padding() = false |
| if(conv_info.stride().first == 1 && !conv_info.has_padding()) |
| { |
| kernel_name = "im2col1x1_stridex1_"; |
| num_elems_processed_per_iteration = 4; |
| is_padding_required_nchw = true; |
| } |
| break; |
| case 3: |
| kernel_name = "im2col3x3_"; |
| num_elems_processed_per_iteration = 1; |
| is_padding_required_nchw = true; |
| break; |
| case 5: |
| kernel_name = "im2col5x5_"; |
| num_elems_processed_per_iteration = 1; |
| is_padding_required_nchw = true; |
| break; |
| case 11: |
| // Optimized im2col11x11 if pad_x = pad_y = 0 |
| if(!conv_info.has_padding()) |
| { |
| kernel_name = "im2col11x11_padx0_pady0_"; |
| num_elems_processed_per_iteration = 1; |
| is_padding_required_nchw = true; |
| } |
| break; |
| default: |
| kernel_name = "im2col_generic_"; |
| num_elems_processed_per_iteration = 1; |
| is_padding_required_nchw = false; |
| break; |
| } |
| } |
| else if(kernel_dims.width > 1 && !conv_info.has_padding()) |
| { |
| kernel_name = "im2col_generic_padx0_pady0_"; |
| num_elems_processed_per_iteration = 1; |
| is_padding_required_nchw = false; |
| |
| // Optimized im2col is performed using one or more vector operations with the specified vector size |
| // and a remainder. For example, for 5x5 convolutions, im2col is performed using vectors of size 4 |
| // and scalars; for 7x7 convolutions, using vectors of size 4 and vectors of size 3. |
| // Using the vector size of 4 is always safe since OpenCL supports vectors of size 2 and 3. |
| // Using the vector size of 8, however, may be faster. |
| // For 2x2 convolutions, use vectors of size 2. (For 3x3 convolutions, im2col_kernel3x3_padx0_pady0 |
| // is used instead.) |
| const size_t vector_size = std::min(static_cast<size_t>(4), kernel_dims.width); |
| const size_t width_mod_vector_size = kernel_dims.width % vector_size; |
| build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size)); |
| build_opts.add_option("-DWIDTH_MOD_VECTOR_SIZE=" + support::cpp11::to_string(width_mod_vector_size)); |
| } |
| } |
| } |
| |
| // Append the data layout to the kernel_name |
| kernel_name += lower_string(string_from_data_layout(data_layout)); |
| |
| Im2ColConfiguration im2col_config; |
| im2col_config.kernel_name = kernel_name; |
| im2col_config.build_options = build_opts.options(); |
| im2col_config.num_elems_processed_per_iteration = num_elems_processed_per_iteration; |
| im2col_config.is_padding_required_nchw = is_padding_required_nchw; |
| |
| return im2col_config; |
| } |
| } // namespace |
| |
| ClIm2ColKernel::ClIm2ColKernel() |
| : _data_layout(DataLayout::UNKNOWN), _convolved_dims(), _num_elems_processed_per_iteration(1), _kernel_dims(), _conv_info(), _num_groups() |
| { |
| _type = CLKernelType::ELEMENTWISE; |
| } |
| |
| void ClIm2ColKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, |
| const Size2D &dilation, |
| unsigned int num_groups) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups)); |
| |
| auto padding_info = get_padding_info({ src, dst }); |
| _data_layout = src->data_layout(); |
| |
| const unsigned int width_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const unsigned int height_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| const unsigned int input_width = src->dimension(width_idx); |
| const unsigned int input_height = src->dimension(height_idx); |
| |
| // Select and configure the optimal OpenCL kernel to run. |
| // This function returns the OpenCL kernel's name, the arguments to pass at compile time, the number of elements processed per iteration |
| // and the padding requirement flag |
| Im2ColConfiguration im2col_config = configure_opencl_kernel(src, kernel_dims, conv_info, has_bias, dilation, num_groups); |
| |
| // Create kernel |
| _kernel = create_kernel(compile_context, im2col_config.kernel_name, im2col_config.build_options); |
| |
| _convolved_dims = scaled_dimensions(input_width, input_height, kernel_dims.width, kernel_dims.height, conv_info, dilation); |
| _num_elems_processed_per_iteration = im2col_config.num_elems_processed_per_iteration; |
| _kernel_dims = kernel_dims; // Only needed by the Tuner |
| _conv_info = conv_info; // Only needed by the Tuner |
| _num_groups = num_groups; |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(src, dst, kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration, |
| im2col_config.is_padding_required_nchw, num_groups); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| IClKernel::configure_internal(win_config.second); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = im2col_config.kernel_name; |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_type(src->data_type())); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(num_groups); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(dst->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(dst->dimension(1)); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_layout(_data_layout)); |
| |
| ARM_COMPUTE_ERROR_ON(src->data_layout() == DataLayout::NHWC && has_padding_changed(padding_info)); |
| } |
| |
| Status ClIm2ColKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation, |
| unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, kernel_dims, conv_info, has_bias, dilation, num_groups)); |
| Im2ColConfiguration im2col_config = configure_opencl_kernel(src, kernel_dims, conv_info, has_bias, dilation, num_groups); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), kernel_dims, conv_info, has_bias, dilation, im2col_config.num_elems_processed_per_iteration, |
| im2col_config.is_padding_required_nchw, num_groups) |
| .first); |
| return Status{}; |
| } |
| |
| void ClIm2ColKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(IClKernel::window(), window); |
| ARM_COMPUTE_ERROR_ON(tensors.empty()); |
| |
| // Get initial windows |
| // Collapse in order to have (SRC_DEPTH * BATCH_SIZE) on the 3rd dimension |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| window_collapsed.set_dimension_step(Window::DimZ, 1); |
| |
| auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| |
| Window window_output; |
| window_output.use_tensor_dimensions(dst->info()->tensor_shape()); |
| |
| const Window first_slice_3d = window_collapsed.first_slice_window_3D(); |
| |
| Window slice = first_slice_3d; |
| Window slice_in = first_slice_3d; |
| Window slice_out = window_output.first_slice_window_2D(); |
| |
| if(_data_layout == DataLayout::NHWC) |
| { |
| const Window tmp_win = window.collapse_if_possible(ICLKernel::window(), 3); |
| const int num_batches = tmp_win[3].end(); |
| |
| slice.set(1, Window::Dimension(0, static_cast<int>(dst->info()->tensor_shape()[1]), 1)); |
| slice.set(2, Window::Dimension(0, static_cast<int>(num_batches), 1)); |
| } |
| else |
| { |
| slice.set(0, Window::Dimension(0, static_cast<int>(ceil_to_multiple(_convolved_dims.first, _num_elems_processed_per_iteration)), _num_elems_processed_per_iteration)); |
| slice.set(1, Window::Dimension(0, static_cast<int>(_convolved_dims.second), 1)); |
| // Note: In case of NCHW the 3rd dimension is already set collapsing the input window |
| } |
| |
| // Setup input slice |
| // The dimensions of the input are increased within the OpenCL kernel |
| slice_in.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| slice_in.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| slice_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| // Setup output slice |
| // The dimensions of the output are increased within the OpenCL kernel |
| slice_out.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| slice_out.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| |
| unsigned int idx = num_arguments_per_3D_tensor() + (_num_groups == 1 ? num_arguments_per_2D_tensor() : num_arguments_per_3D_tensor()); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[((_num_groups == 1) ? 2 : 3)])); |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, src, slice_in); |
| if(_num_groups == 1) |
| { |
| add_2D_tensor_argument(idx, dst, slice_out); |
| } |
| else |
| { |
| add_3D_tensor_argument(idx, dst, slice_out); |
| } |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice) && window_output.slide_window_slice_2D(slice_out) && window_collapsed.slide_window_slice_3D(slice_in)); |
| } |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |
