| /* |
| * 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/CLPoolingLayerKernel.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/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/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| |
| #include <set> |
| #include <string> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| CLPoolingLayerKernel::CLPoolingLayerKernel() |
| : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1) |
| { |
| } |
| |
| BorderSize CLPoolingLayerKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLPoolingLayerKernel::configure(const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info) |
| { |
| int pool_pad_x = 0; |
| int pool_pad_y = 0; |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| unsigned int pooled_w = 0; |
| unsigned int pooled_h = 0; |
| const PoolingType pool_type = pool_info.pool_type(); |
| const int pool_size = pool_info.pool_size(); |
| const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); |
| bool exclude_padding = pool_info.exclude_padding(); |
| std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad(); |
| std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); |
| |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(output); |
| ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size); |
| |
| // Check output dimensions |
| std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0), |
| input->info()->dimension(1), |
| pool_size, |
| pool_size, |
| pool_info.pad_stride_info()); |
| |
| // Output auto initialization if not yet initialized |
| { |
| TensorShape output_shape{ input->info()->tensor_shape() }; |
| output_shape.set(0, pooled_w); |
| output_shape.set(1, pooled_h); |
| |
| auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); |
| } |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output); |
| ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h)); |
| |
| const int input_width = input->info()->dimension(0); |
| const int input_height = input->info()->dimension(1); |
| |
| // Set instance variables |
| _input = input; |
| _output = output; |
| _pool_info = pool_info; |
| _border_size = BorderSize(pool_pad_y, pool_pad_x); |
| |
| // Set build options |
| std::set<std::string> build_opts; |
| build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); |
| build_opts.emplace(("-DPOOL_" + string_from_pooling_type(pool_type))); |
| if(is_data_type_fixed_point(input->info()->data_type())) |
| { |
| build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| } |
| |
| build_opts.emplace(("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x))); |
| if(pool_type != PoolingType::MAX) |
| { |
| if(exclude_padding) |
| { |
| build_opts.emplace("-DEXCLUDE_PADDING"); |
| } |
| build_opts.emplace(("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_x)))); |
| build_opts.emplace(("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_y)))); |
| build_opts.emplace(("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y))); |
| build_opts.emplace(("-DPAD_X=" + support::cpp11::to_string(pool_pad_x))); |
| build_opts.emplace(("-DPAD_Y=" + support::cpp11::to_string(pool_pad_y))); |
| } |
| |
| // Create kernel |
| if((pool_size == 2) || (pool_size == 3) || (pool_size == 7)) |
| { |
| // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where |
| // each thread computes 4 output elements |
| const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type()); |
| |
| int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size; |
| if(is_pool3x3_stride_le3) |
| { |
| // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3 |
| _num_elems_processed_per_iteration = 4; |
| num_elements_read_per_iteration = pool_size * (pool_stride_x + 1); |
| } |
| |
| const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width; |
| const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; |
| |
| _border_size.right = std::max(upper_bound_w, pool_pad_x); |
| _border_size.bottom = std::max(upper_bound_h, pool_pad_y); |
| |
| std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size); |
| if(is_pool3x3_stride_le3) |
| { |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts)); |
| } |
| else |
| { |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts)); |
| } |
| } |
| else // Run general case |
| { |
| _num_elems_processed_per_iteration = 1; |
| |
| const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width; |
| const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height; |
| |
| _border_size.right = std::max(upper_bound_w, pool_pad_x); |
| _border_size.bottom = std::max(upper_bound_h, pool_pad_y); |
| |
| build_opts.emplace(("-DPOOL_SIZE=" + support::cpp11::to_string(pool_size))); |
| if(input->info()->data_type() == DataType::F16) |
| { |
| build_opts.emplace("-DFP16"); |
| } |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("pooling_layer_N", build_opts)); |
| } |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*output->info(), Steps(_num_elems_processed_per_iteration)); |
| AccessWindowStatic input_access(input->info(), -pool_pad_x, -pool_pad_y, input_width + _border_size.right, input_height + _border_size.bottom); |
| AccessWindowHorizontal output_access(output->info(), 0, _num_elems_processed_per_iteration); |
| update_window_and_padding(win, input_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| ICLKernel::configure(win); |
| } |
| |
| void CLPoolingLayerKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| unsigned int pool_pad_x, pool_pad_y, pool_stride_x, pool_stride_y = 0; |
| std::tie(pool_pad_x, pool_pad_y) = _pool_info.pad_stride_info().pad(); |
| std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride(); |
| |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| Window slice = window_collapsed.first_slice_window_3D(); |
| |
| do |
| { |
| // Upsample input by pool size |
| Window in_slice(slice); |
| in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start() - pool_pad_x, in_slice.x().end() * pool_stride_x, pool_stride_x * _num_elems_processed_per_iteration)); |
| in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - pool_pad_y, in_slice.y().end() * pool_stride_y, pool_stride_y)); |
| |
| // Set inputs |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, _input, in_slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |