| /* |
| * 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/ClDirectConv2dKernel.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/ITensor.h" |
| #include "arm_compute/core/PixelValue.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/CLUtils.h" |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "src/gpu/cl/kernels/gemm/ClGemmHelpers.h" |
| #include "support/Cast.h" |
| #include "support/StringSupport.h" |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, |
| const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); |
| |
| const DataLayout data_layout = src->data_layout(); |
| const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != src->dimension(channel_idx), "Weights feature map dimension should match the respective src's one"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional"); |
| |
| if(data_layout == DataLayout::NCHW) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 3 || weights->dimension(width_idx) == 5 || weights->dimension(width_idx) == 9) && std::get<0>(conv_info.stride()) > 2, |
| "Strides larger than 2 not supported for 3x3, 5x5, 9x9 convolution."); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_data_type_float(src->data_type()) && act_info.enabled(), "Activation supported only for floating point and NHWC."); |
| |
| if(is_data_type_quantized(src->data_type())) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9, |
| "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported with quantized data types"); |
| } |
| else |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5, |
| "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported with float data types"); |
| } |
| } |
| |
| if(biases != nullptr) |
| { |
| if(is_data_type_quantized_asymmetric(src->data_type())) |
| { |
| 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_MSG(biases->dimension(0) != weights->dimension(3), |
| "Biases size and number of dst feature maps should match"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(biases->num_dimensions() > 1, |
| "Biases should be one dimensional"); |
| } |
| |
| // Checks performed when dst is configured |
| if(dst->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), |
| misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst); |
| } |
| |
| const auto data_type = src->data_type(); |
| if(is_data_type_quantized(data_type)) |
| { |
| const UniformQuantizationInfo iqinfo = src->quantization_info().uniform(); |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); |
| |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); |
| } |
| return Status{}; |
| } |
| |
| bool export_to_cl_image_support(ITensorInfo *tensor, GPUTarget gpu_target, DataLayout data_layout) |
| { |
| if(tensor->tensor_shape()[0] % 4 || (data_layout != DataLayout::NHWC)) |
| { |
| return false; |
| } |
| |
| // If not floating point |
| if(!is_data_type_float(tensor->data_type())) |
| { |
| return false; |
| } |
| |
| if(gpu_target == GPUTarget::G71 || get_arch_from_target(gpu_target) == GPUTarget::MIDGARD) |
| { |
| return false; |
| } |
| |
| // Check if the cl_khr_image2d_from_buffer extension is supported on the target platform |
| if(!image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) |
| { |
| return false; |
| } |
| |
| // Check cl image pitch alignment |
| if(get_cl_image_pitch_alignment(CLKernelLibrary::get().get_device()) == 0) |
| { |
| return false; |
| } |
| |
| const size_t image_w = tensor->tensor_shape()[0] / 4; |
| const size_t image_h = tensor->tensor_shape()[1] * tensor->tensor_shape()[2] * tensor->tensor_shape()[3]; |
| const size_t max_image_w = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); |
| const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); |
| |
| if(image_w > max_image_w || image_h > max_image_h) |
| { |
| return false; |
| } |
| |
| return true; |
| } |
| |
| } // namespace |
| |
| ClDirectConv2dKernel::ClDirectConv2dKernel() |
| { |
| _type = CLKernelType::DIRECT; |
| } |
| |
| void ClDirectConv2dKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, |
| const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); |
| |
| // Perform validation |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info, act_info)); |
| |
| const int conv_stride_x = std::get<0>(conv_info.stride()); |
| const int conv_stride_y = std::get<1>(conv_info.stride()); |
| |
| _data_layout = src->data_layout(); |
| _conv_info = conv_info; |
| |
| 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 kernel_size = weights->dimension(width_idx); |
| const DataType data_type = src->data_type(); |
| |
| const GPUTarget gpu_target = get_target(); |
| unsigned int _num_elems_processed_per_iteration = 0; |
| |
| // Get dst shape |
| TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*src, *weights, conv_info); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*dst, output_shape, |
| 1, |
| src->data_type(), |
| src->quantization_info()); |
| |
| // Configure kernel window |
| Window win; |
| if(_data_layout == DataLayout::NHWC) |
| { |
| const unsigned int vec_size = std::min(static_cast<unsigned int>(dst->tensor_shape()[0]), 4u); |
| unsigned int num_rows = 1U; |
| if(dst->tensor_shape()[0] > 16) |
| { |
| num_rows = src->data_type() == DataType::F32 ? 2U : 4U; |
| } |
| |
| // Create window and update padding |
| win = calculate_max_window(output_shape, Steps(vec_size, num_rows)); |
| } |
| else if(_data_layout == DataLayout::NCHW) |
| { |
| _num_elems_processed_per_iteration = 1u; |
| win = calculate_max_window(*dst, Steps(_num_elems_processed_per_iteration)); |
| } |
| |
| ICLKernel::configure_internal(win); |
| |
| std::stringstream kernel_name; |
| CLBuildOptions build_options; |
| |
| if(_data_layout == DataLayout::NHWC) |
| { |
| kernel_name << "direct_convolution_nhwc"; |
| |
| const unsigned int n0 = win.x().step(); |
| const unsigned int m0 = win.y().step(); |
| const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, src->dimension(channel_idx)); |
| const unsigned int partial_store_n0 = dst->dimension(channel_idx) % n0; |
| const unsigned int pad_left = conv_info.pad_left(); |
| const unsigned int pad_top = conv_info.pad_top(); |
| const bool export_to_cl_image = export_to_cl_image_support(weights, gpu_target, _data_layout); |
| |
| // Update the padding for the weights tensor if we can export to cl_image |
| if(export_to_cl_image) |
| { |
| gemm::update_padding_for_cl_image(weights); |
| } |
| |
| if(biases != nullptr) |
| { |
| build_options.add_option(std::string("-DHAS_BIAS")); |
| build_options.add_option(std::string("-DBIA_DATA_TYPE=" + get_cl_type_from_data_type(biases->data_type()))); |
| } |
| |
| build_options.add_option("-cl-fast-relaxed-math"); |
| build_options.add_option("-DSRC_TENSOR_TYPE=BUFFER"); |
| build_options.add_option("-DSRC_DATA_TYPE=" + get_cl_type_from_data_type(src->data_type())); |
| build_options.add_option("-DDST_TENSOR_TYPE=BUFFER"); |
| build_options.add_option("-DDST_DATA_TYPE=" + get_cl_type_from_data_type(dst->data_type())); |
| build_options.add_option_if_else(export_to_cl_image, "-DWEI_TENSOR_TYPE=IMAGE", "-DWEI_TENSOR_TYPE=BUFFER"); |
| build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx))); |
| build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx))); |
| build_options.add_option("-DWEI_DATA_TYPE=" + get_cl_type_from_data_type(weights->data_type())); |
| build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)); |
| build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_stride_y)); |
| build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(pad_left)); |
| build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(pad_top)); |
| build_options.add_option("-DN0=" + support::cpp11::to_string(n0)); |
| build_options.add_option("-DM0=" + support::cpp11::to_string(m0)); |
| build_options.add_option("-DK0=" + support::cpp11::to_string(k0)); |
| build_options.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); |
| build_options.add_option_if((src->dimension(channel_idx) % k0) != 0, "-DLEFTOVER_LOOP"); |
| build_options.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); |
| |
| if(is_data_type_quantized(data_type)) |
| { |
| const UniformQuantizationInfo iqinfo = src->quantization_info().uniform(); |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); |
| |
| PixelValue zero_value = PixelValue(0, src->data_type(), src->quantization_info()); |
| int zero_value_s32; |
| zero_value.get(zero_value_s32); |
| |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); |
| build_options.add_option("-DIS_QUANTIZED"); |
| build_options.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); |
| build_options.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift)); |
| build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); |
| build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); |
| build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); |
| build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(zero_value_s32)); |
| build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(DataType::S32)); |
| } |
| else |
| { |
| build_options.add_option("-DACC_DATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| build_options.add_option("-DZERO_VALUE=" + support::cpp11::to_string(0)); |
| build_options.add_option("-DSRC_OFFSET=" + support::cpp11::to_string(0)); |
| build_options.add_option("-DWEI_OFFSET=" + support::cpp11::to_string(0)); |
| build_options.add_option("-DDST_OFFSET=" + support::cpp11::to_string(0)); |
| build_options.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); |
| build_options.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); |
| } |
| } |
| else |
| { |
| kernel_name << "direct_convolution_nchw"; |
| build_options.add_option_if(biases != nullptr, std::string("-DHAS_BIAS")); |
| build_options.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(src->dimension(width_idx))); |
| build_options.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(src->dimension(height_idx))); |
| build_options.add_option("-DSRC_CHANNELS=" + support::cpp11::to_string(src->dimension(channel_idx))); |
| build_options.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); |
| build_options.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); |
| build_options.add_option("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x)); |
| build_options.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(conv_stride_y)); |
| build_options.add_option("-DWEI_WIDTH=" + support::cpp11::to_string(weights->dimension(width_idx))); |
| build_options.add_option("-DWEI_HEIGHT=" + support::cpp11::to_string(weights->dimension(height_idx))); |
| build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type))); |
| build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type))); |
| build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(weights->dimension(channel_idx)))); |
| build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(conv_stride_x))); |
| build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type))); |
| build_options.add_option(std::string("-DVEC_SIZE=" + support::cpp11::to_string(_num_elems_processed_per_iteration))); |
| build_options.add_option(std::string("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(src->dimension(0) % _num_elems_processed_per_iteration))); |
| |
| if(is_data_type_quantized(data_type)) |
| { |
| const UniformQuantizationInfo iqinfo = src->quantization_info().uniform(); |
| const UniformQuantizationInfo wqinfo = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform(); |
| |
| float multiplier = iqinfo.scale * wqinfo.scale / oqinfo.scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); |
| build_options.add_option("-DIS_QUANTIZED"); |
| build_options.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); |
| build_options.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); |
| build_options.add_option("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size)); |
| build_options.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-iqinfo.offset)); |
| build_options.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wqinfo.offset)); |
| build_options.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oqinfo.offset)); |
| } |
| } |
| |
| _kernel = create_kernel(compile_context, kernel_name.str(), build_options.options()); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = kernel_name.str(); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_type(data_type)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(kernel_size); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().left); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().top); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().right); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(border_size().bottom); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(conv_stride_x); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(conv_stride_y); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(dst->dimension(width_idx)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(dst->dimension(height_idx)); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_layout(_data_layout)); |
| } |
| |
| Status ClDirectConv2dKernel::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, |
| const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info)); |
| return Status{}; |
| } |
| |
| void ClDirectConv2dKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Get initial windows |
| Window slice = window.first_slice_window_3D(); |
| |
| const auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); |
| const auto weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); |
| const auto biases = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| |
| if(_data_layout == DataLayout::NHWC) |
| { |
| cl::Image2D weights_cl_image; |
| |
| const size_t dim_y_collapsed = ceil_to_multiple(dst->info()->dimension(1) * dst->info()->dimension(2), slice.y().step()); |
| const bool export_to_cl_image = export_to_cl_image_support(weights->info(), get_target(), _data_layout); |
| |
| slice.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, slice.y().step())); |
| slice.set(Window::DimZ, Window::Dimension(0, dst->info()->dimension(3), 1)); |
| |
| if(export_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 TensorShape shape2d(image_w, image_h); |
| const size_t image_row_pitch = weights->info()->strides_in_bytes()[1]; |
| |
| // Export cl_buffer to cl_image |
| weights_cl_image = create_image2d_from_buffer(CLKernelLibrary::get().context(), weights->cl_buffer(), shape2d, weights->info()->data_type(), image_row_pitch); |
| } |
| |
| unsigned int idx = 0; |
| add_4d_tensor_nhwc_argument(idx, src); |
| add_4d_tensor_nhwc_argument(idx, dst); |
| if(export_to_cl_image) |
| { |
| _kernel.setArg(idx++, weights_cl_image); |
| } |
| add_4d_tensor_nhwc_argument(idx, weights); |
| if(biases != nullptr) |
| { |
| add_1D_tensor_argument(idx, biases, slice); |
| } |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| else |
| { |
| unsigned int idx1 = 2 * num_arguments_per_3D_tensor(); |
| add_3D_tensor_argument(idx1, weights, slice); |
| |
| if(biases != nullptr) |
| { |
| Window slice_biases; |
| slice_biases.use_tensor_dimensions(biases->info()->tensor_shape()); |
| add_1D_tensor_argument(idx1, biases, slice_biases); |
| } |
| |
| _kernel.setArg(idx1++, static_cast<unsigned int>(weights->info()->strides_in_bytes()[3])); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, src, slice); |
| add_3D_tensor_argument(idx, dst, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window.slide_window_slice_3D(slice)); |
| } |
| } |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |
