| /* |
| * Copyright (c) 2017-2018 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 "arm_compute/core/CL/kernels/CLDepthwiseVectorToTensorKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/CLValidate.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::S32, DataType::F16, DataType::F32); |
| |
| if(output->total_size() != 0) |
| { |
| TensorShape output_shape = compute_vector_to_tensor_output_shape(input->tensor_shape(), conv_w, conv_h, output->data_layout()); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| } // namespace |
| |
| CLDepthwiseVectorToTensorKernel::CLDepthwiseVectorToTensorKernel() |
| : _input(nullptr), _output(nullptr) |
| { |
| } |
| |
| void CLDepthwiseVectorToTensorKernel::configure(const ICLTensor *input, ICLTensor *output, size_t conv_w, size_t conv_h) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Output auto inizialitation if not yet initialized |
| TensorShape output_shape = compute_vector_to_tensor_output_shape(input->info()->tensor_shape(), conv_w, conv_h, output->info()->data_layout()); |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), conv_w, conv_h)); |
| |
| _input = input; |
| _output = output; |
| |
| // Create kernel |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); |
| build_opts.add_option("-DCONV_WIDTH=" + support::cpp11::to_string(conv_w)); |
| build_opts.add_option("-DCONV_HEIGHT=" + support::cpp11::to_string(conv_h)); |
| build_opts.add_option("-D" + string_from_data_layout(output->info()->data_layout())); |
| |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("depthwise_vector_to_tensor", build_opts.options())); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*input->info(), Steps()); |
| // The CLDepthwisevectorToTensorKernel doesn't need padding so update_window_and_padding() can be skipped |
| output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| ICLKernel::configure_internal(win); |
| } |
| |
| Status CLDepthwiseVectorToTensorKernel::validate(const ITensorInfo *input, const ITensorInfo *output, size_t conv_w, size_t conv_h) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, conv_w, conv_h)); |
| return Status{}; |
| } |
| |
| void CLDepthwiseVectorToTensorKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window); |
| |
| Window slice = window.first_slice_window_1D(); |
| Window slice_out = window.first_slice_window_3D(); |
| |
| // Setup slice |
| slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), 1)); |
| |
| // Setup output slice |
| // The first three dimensions of the output are increased by the inner loops |
| slice_out.set(Window::DimX, Window::Dimension(0, 0, 0)); |
| slice_out.set(Window::DimY, Window::Dimension(0, 0, 0)); |
| slice_out.set(Window::DimZ, Window::Dimension(0, 0, 0)); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_1D_tensor_argument(idx, _input, slice); |
| add_3D_tensor_argument(idx, _output, slice_out); |
| enqueue(queue, *this, slice); |
| } |
| while(window.slide_window_slice_1D(slice) && window.slide_window_slice_3D(slice_out)); |
| } |