| /* |
| * Copyright (c) 2017 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/CLDepthwiseConvolutionLayer3x3Kernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLKernel.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| /** Calculates expected output shape dimension |
| * |
| * @param[in] Input shape |
| * |
| * @return Expected output shape |
| */ |
| TensorShape get_output_shape(TensorShape input_shape, TensorShape weights_shape, PadStrideInfo conv_info) |
| { |
| unsigned int output_width = 0; |
| unsigned int output_height = 0; |
| |
| std::tie(output_width, output_height) = scaled_dimensions(input_shape.x(), input_shape.y(), weights_shape.x(), weights_shape.y(), conv_info); |
| |
| TensorShape output_shape = input_shape; |
| output_shape.set(0, output_width); |
| output_shape.set(1, output_height); |
| |
| return output_shape; |
| } |
| } // namespace |
| |
| CLDepthwiseConvolutionLayer3x3Kernel::CLDepthwiseConvolutionLayer3x3Kernel() |
| : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0) |
| { |
| } |
| |
| BorderSize CLDepthwiseConvolutionLayer3x3Kernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3); |
| |
| if(biases != nullptr) |
| { |
| if(is_data_type_quantized_asymmetric(weights->info()->data_type())) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| } |
| ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2)); |
| ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); |
| } |
| |
| // Get convolved dimensions |
| TensorShape output_shape = get_output_shape(input->info()->tensor_shape(), weights->info()->tensor_shape(), conv_info); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), |
| output_shape, |
| 1, |
| input->info()->data_type(), |
| input->info()->fixed_point_position(), |
| input->info()->quantization_info()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| |
| _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(); |
| _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left); |
| |
| // Set build options |
| ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3); |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); |
| build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); |
| |
| // Create kernel |
| std::string kernel_name = is_data_type_quantized_asymmetric(_input->info()->data_type()) ? "depthwise_convolution_3x3_quantized" : "depthwise_convolution_3x3"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Set static arguments |
| if(is_data_type_quantized_asymmetric(_input->info()->data_type())) |
| { |
| float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale; |
| int output_multiplier = 0; |
| int output_shift = 0; |
| quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| |
| unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0); |
| |
| _kernel.setArg(idx++, -_input->info()->quantization_info().offset); |
| _kernel.setArg(idx++, -_weights->info()->quantization_info().offset); |
| _kernel.setArg(idx++, _output->info()->quantization_info().offset); |
| _kernel.setArg(idx++, output_multiplier); |
| _kernel.setArg(idx++, output_shift); |
| } |
| |
| // Configure the local work size for Bifrost with a value obtained |
| // via exhaustive autotuning for the MobileNets tensor shapes. |
| const GPUTarget gpu_target = get_arch_from_target(get_target()); |
| if(gpu_target == GPUTarget::BIFROST) |
| { |
| const size_t width = input->info()->dimension(0); |
| if(width >= 56) // 56 or 112 |
| { |
| _lws_hint = cl::NDRange(8, 5, 2); |
| } |
| else if(width >= 14) // 14 or 28 |
| { |
| _lws_hint = cl::NDRange(1, 5, 2); |
| } |
| else // 7 |
| { |
| _lws_hint = cl::NDRange(1, 1, 2); |
| } |
| } |
| |
| // Configure kernel window |
| const unsigned int num_elems_processed_per_iteration = 2; |
| const unsigned int num_elems_written_per_iteration = 2; |
| const unsigned int num_elems_read_per_iteration = 3 + _conv_stride_x; |
| const unsigned int num_rows_read_per_iteration = 3; |
| |
| Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowRectangle input_access(input->info(), -border_size().left, -border_size().top, num_elems_read_per_iteration, num_rows_read_per_iteration, _conv_stride_x, _conv_stride_y); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1)); |
| |
| update_window_and_padding(win, input_access, weights_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| ICLKernel::configure(win); |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Create input window and adjust |
| Window win_in = window; |
| win_in.adjust(Window::DimX, -_conv_pad_left, true); |
| win_in.adjust(Window::DimY, -_conv_pad_top, true); |
| win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x); |
| win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y); |
| |
| Window slice_in = win_in.first_slice_window_3D(); |
| Window slice_out = window.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); |
| |
| // Set biases |
| if(_biases != nullptr) |
| { |
| unsigned int idx = 3 * num_arguments_per_3D_tensor(); |
| Window slice_biases; |
| slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); |
| add_1D_tensor_argument(idx, _biases, slice_biases); |
| } |
| |
| do |
| { |
| unsigned int 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(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in)); |
| } |