| /* |
| * Copyright (c) 2017-2020 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/GLES_COMPUTE/kernels/GCDepthwiseConvolutionLayer3x3Kernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" |
| #include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h" |
| #include "arm_compute/core/GLES_COMPUTE/IGCKernel.h" |
| #include "arm_compute/core/GLES_COMPUTE/IGCTensor.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/misc/ShapeCalculator.h" |
| #include "support/StringSupport.h" |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| GCDepthwiseConvolutionLayer3x3Kernel::GCDepthwiseConvolutionLayer3x3Kernel() |
| : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0), _lws(gles::NDRange(1U, 1U, 1U)) |
| { |
| } |
| |
| BorderSize GCDepthwiseConvolutionLayer3x3Kernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void GCDepthwiseConvolutionLayer3x3Kernel::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16); |
| 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) |
| { |
| 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 |
| const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), |
| output_shape, |
| 1, |
| input->info()->data_type()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| ARM_COMPUTE_ERROR_ON(output->info()->dimension(2) != weights->info()->dimension(2)); |
| |
| _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); |
| std::set<std::string> options; |
| |
| options.emplace("#define DEPTH_MULTIPLIER " + support::cpp11::to_string(depth_multiplier)); |
| options.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(_lws[0])); |
| options.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(_lws[1])); |
| options.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(_lws[2])); |
| options.emplace("#define STRIDE_X " + support::cpp11::to_string(_conv_stride_x)); |
| options.emplace("#define STRIDE_Y " + support::cpp11::to_string(_conv_stride_y)); |
| |
| std::string dt_name = (input->info()->data_type() == DataType::F32) ? "DATA_TYPE_FP32" : "DATA_TYPE_FP16"; |
| options.emplace(("#define " + dt_name)); |
| |
| unsigned int num_elems_read_per_iteration_x = 8; |
| unsigned int num_elems_read_per_iteration_y = 1; |
| unsigned int num_elems_written_per_iteration_x = 4; |
| unsigned int num_elems_written_per_iteration_y = 1; |
| unsigned int num_elems_written_per_iteration_z = 1; |
| |
| if((_conv_stride_x == 1) && (_conv_stride_y == 1)) |
| { |
| switch(input->info()->data_type()) |
| { |
| #define PROCESS_4X_3Y_1Z |
| |
| case DataType::F16: |
| #if defined(PROCESS_4X_3Y_1Z) |
| options.emplace("#define PROCESS_4X_3Y_1Z"); |
| num_elems_read_per_iteration_y = 5; |
| num_elems_written_per_iteration_y = 3; |
| #endif /* PROCESS_4X_3Y_1Z */ |
| #undef PROCESS_4X_3Y_1Z |
| break; |
| |
| default: |
| ARM_COMPUTE_ERROR("Current data type is not supported"); |
| break; |
| } |
| } |
| else |
| { |
| switch(input->info()->data_type()) |
| { |
| case DataType::F16: |
| options.emplace("#define PROCESS_4X_1Y_1Z"); |
| break; |
| |
| default: |
| ARM_COMPUTE_ERROR("Current data type is not supported"); |
| break; |
| } |
| } |
| |
| if(_biases != nullptr) |
| { |
| options.emplace("#define BIAS"); |
| } |
| |
| // Create kernel |
| std::string kernel_name = "depthwise_convolution_3x3"; |
| _kernel = static_cast<GCKernel>(GCKernelLibrary::get().create_kernel(kernel_name, options)); |
| |
| // Calculate output right and bottom border |
| const int output_width = output->info()->dimension(0); |
| const int output_height = output->info()->dimension(1); |
| const int output_padding_right = ceil_to_multiple(output_width, num_elems_written_per_iteration_x * _lws[0]) - output_width; |
| const int output_padding_bottom = ceil_to_multiple(output_height, num_elems_written_per_iteration_y * _lws[1]) - output_height; |
| |
| // Calculate input right and bottom border |
| const int input_width = input->info()->dimension(0); |
| const int input_height = input->info()->dimension(1); |
| |
| const int input_total_width = std::max(int(input->info()->padding().left), int(_conv_pad_left)) + input_width + std::max(int(input->info()->padding().right), int(_conv_pad_left)); |
| const int input_total_height = std::max(int(input->info()->padding().top), int(_conv_pad_top)) + input_height + std::max(int(input->info()->padding().bottom), int(_conv_pad_top)); |
| |
| const int input_padding_right = ceil_to_multiple(input_total_width, num_elems_read_per_iteration_x * _lws[0]) - input_width - _conv_pad_left; |
| const int input_padding_bottom = ceil_to_multiple(input_total_height, num_elems_read_per_iteration_y * _lws[1]) - input_height - _conv_pad_top; |
| |
| BorderSize border = BorderSize(0, output_padding_right, output_padding_bottom, 0); |
| |
| Window win = calculate_max_enlarged_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y, num_elems_written_per_iteration_z), border); |
| |
| AccessWindowStatic input_access(input->info(), -_conv_pad_left, -_conv_pad_top, input_width + input_padding_right, input_height + input_padding_bottom); |
| AccessWindowStatic weights_access = AccessWindowStatic(nullptr, 0, 0, 0, 0); |
| AccessWindowStatic bias_access = AccessWindowStatic(nullptr, 0, 0, 0, 1); |
| |
| switch(weights->info()->data_type()) |
| { |
| case DataType::F16: |
| weights_access = AccessWindowStatic(weights->info(), 0, 0, 4, 3); |
| if(_biases != nullptr) |
| { |
| bias_access = AccessWindowStatic(_biases->info(), 0, 0, _biases->info()->dimension(0) + 1, 1); |
| } |
| break; |
| |
| default: |
| ARM_COMPUTE_ERROR("Current data type is not supported"); |
| break; |
| } |
| |
| AccessWindowStatic output_access(output->info(), 0, 0, output_width + output_padding_right, output_height + output_padding_bottom); |
| |
| if(_biases != nullptr) |
| { |
| update_window_and_padding(win, input_access, weights_access, bias_access, output_access); |
| } |
| else |
| { |
| update_window_and_padding(win, input_access, weights_access, output_access); |
| } |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| IGCKernel::configure(win); |
| } |
| |
| void GCDepthwiseConvolutionLayer3x3Kernel::run(const Window &window) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| _kernel.use(); |
| |
| _output->set_needs_shifting(true); |
| |
| // 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, 4, slice_biases); |
| } |
| |
| slice_out.shift(Window::DimX, -(_output->info()->padding()).left); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, 1, slice_in); |
| add_3D_tensor_argument(idx, _output, 2, slice_out); |
| add_3D_tensor_argument(idx, _weights, 3, slice_weights); |
| |
| _kernel.update_shader_params(); |
| enqueue(*this, slice_out, _lws); |
| } |
| while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in)); |
| } |