| /* |
| * 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/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/CLValidate.h" |
| #include "arm_compute/core/CL/ICLKernel.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/Window.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "support/StringSupport.h" |
| |
| #include <set> |
| #include <string> |
| #include <tuple> |
| |
| namespace arm_compute |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| |
| namespace |
| { |
| // Internal window config info |
| using CLPoolingConfig = std::pair<unsigned int, BorderSize>; //num_elems_processed_per_iteration, border_size |
| |
| void auto_init(const ITensorInfo *input, ITensorInfo *output, PoolingLayerInfo pool_info) |
| { |
| TensorShape out_shape = compute_pool_shape(*input, pool_info); |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(out_shape)); |
| } |
| |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(indices, "Indices not supported in the CL backend."); |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((is_data_type_quantized_asymmetric(input->data_type()) && pool_info.pool_type == PoolingType::L2), |
| "Unsupported combination of parameters!"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_quantized(input->data_type()) && !pool_info.exclude_padding && (pool_info.pool_type == PoolingType::AVG) && pool_info.pad_stride_info.has_padding() |
| && (input->data_layout() == DataLayout::NHWC), |
| "exclude_padding equal false is not supported for AVG Pooling with padding on quantized types"); |
| |
| // Checks performed when output is configured |
| if(output->total_size() != 0) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); |
| TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type())); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info); |
| } |
| |
| return Status{}; |
| } |
| |
| std::tuple<Status, Window, CLPoolingConfig> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const PoolingLayerInfo &pool_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Get data layout |
| const DataLayout data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->data_layout() : pool_info.data_layout; |
| const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| unsigned int pooled_w = 0; |
| unsigned int pooled_h = 0; |
| int pool_size_x = pool_info.is_global_pooling ? input->dimension(idx_width) : pool_info.pool_size.width; |
| int pool_size_y = pool_info.is_global_pooling ? input->dimension(idx_height) : pool_info.pool_size.height; |
| const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; |
| std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); |
| const int pool_pad_right = pad_stride_info.pad_right(); |
| const int pool_pad_top = pad_stride_info.pad_top(); |
| const int pool_pad_left = pad_stride_info.pad_left(); |
| const int pool_pad_bottom = pad_stride_info.pad_bottom(); |
| BorderSize border_size = BorderSize(pool_pad_top, pool_pad_right, pool_pad_bottom, pool_pad_left); |
| |
| auto_init(input, output, pool_info); |
| pooled_w = output->tensor_shape()[idx_width]; |
| pooled_h = output->tensor_shape()[idx_height]; |
| |
| const DataType data_type = input->data_type(); |
| |
| const int input_width = input->dimension(idx_width); |
| const int input_height = input->dimension(idx_height); |
| |
| unsigned int num_elems_processed_per_iteration = 0; |
| bool window_changed = false; |
| Window win{}; |
| switch(data_layout) |
| { |
| case DataLayout::NCHW: |
| { |
| // Change the number of elements processed per iteration |
| // for pooling 3x3 with stride less equal than 3 |
| const bool can_optimize = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_quantized(data_type); |
| num_elems_processed_per_iteration = can_optimize ? 4 : 1; |
| const unsigned int num_elems_read_per_iteration = (num_elems_processed_per_iteration - 1) * pool_stride_x + pool_size_x; |
| |
| // Number of iterations in X dimension |
| const int num_iterations_x = (pooled_w + num_elems_processed_per_iteration - 1) / num_elems_processed_per_iteration; |
| |
| // Upper limit for the number of right/bottom border elements that are accessed |
| const int upper_bound_w = ((num_iterations_x - 1) * num_elems_processed_per_iteration * pool_stride_x - pool_pad_left + num_elems_read_per_iteration) - input_width; |
| const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_top + pool_size_y) - input_height; |
| |
| border_size.right = std::max(upper_bound_w, pool_pad_right); |
| border_size.bottom = std::max(upper_bound_h, pool_pad_bottom); |
| |
| win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowRectangle input_access(input, -pool_pad_left, -pool_pad_top, num_elems_read_per_iteration, pool_size_y, |
| pool_stride_x, pool_stride_y); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| window_changed = update_window_and_padding(win, input_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| break; |
| } |
| case DataLayout::NHWC: |
| { |
| num_elems_processed_per_iteration = 8; |
| win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowStatic input_access(input, |
| 0, -1, |
| ceil_to_multiple(input->dimension(0), num_elems_processed_per_iteration), input->dimension(1)); |
| AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); |
| window_changed = update_window_and_padding(win, input_access, output_access); |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_tuple(err, win, CLPoolingConfig(num_elems_processed_per_iteration, border_size)); |
| } |
| } // namespace |
| |
| CLPoolingLayerKernel::CLPoolingLayerKernel() |
| : _input(nullptr), _output(nullptr), _indices(nullptr), _pool_info(), _data_layout(DataLayout::UNKNOWN), _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, ICLTensor *indices) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, output, pool_info, indices); |
| } |
| |
| void CLPoolingLayerKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, const PoolingLayerInfo &pool_info, ICLTensor *indices) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| // Set instance variables |
| _input = input; |
| _output = output; |
| _pool_info = pool_info; |
| _data_layout = pool_info.data_layout == DataLayout::UNKNOWN ? input->info()->data_layout() : pool_info.data_layout; |
| _indices = indices; |
| int pool_stride_x = 0; |
| int pool_stride_y = 0; |
| const PoolingType pool_type = pool_info.pool_type; |
| const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); |
| const int pool_size_x = pool_info.is_global_pooling ? input->info()->dimension(idx_width) : pool_info.pool_size.width; |
| const int pool_size_y = pool_info.is_global_pooling ? input->info()->dimension(idx_height) : pool_info.pool_size.height; |
| const PadStrideInfo pad_stride_info = pool_info.pad_stride_info; |
| const bool exclude_padding = pool_info.exclude_padding; |
| std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride(); |
| const int pool_pad_top = pad_stride_info.pad_top(); |
| const int pool_pad_left = pad_stride_info.pad_left(); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| |
| if(is_data_type_quantized_asymmetric(input->info()->data_type()) && input->info()->quantization_info() != output->info()->quantization_info()) |
| { |
| const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); |
| |
| build_opts.add_option("-DOFFSET_IN1=" + float_to_string_with_full_precision(iq_info.offset)); |
| build_opts.add_option("-DOFFSET_OUT=" + float_to_string_with_full_precision(oq_info.offset)); |
| build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq_info.scale)); |
| build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale)); |
| } |
| |
| // Check output dimensions |
| auto_init(input->info(), output->info(), pool_info); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info, (indices) ? indices->info() : nullptr)); |
| |
| const DataType data_type = input->info()->data_type(); |
| |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type)); |
| build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x)); |
| build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y)); |
| build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left)); |
| build_opts.add_option("-DPAD_Y=" + support::cpp11::to_string(pool_pad_top)); |
| build_opts.add_option("-DPOOL_SIZE_X=" + support::cpp11::to_string(pool_size_x)); |
| build_opts.add_option("-DPOOL_SIZE_Y=" + support::cpp11::to_string(pool_size_y)); |
| |
| // Set the initial value for the pooling operation accordingly with the data type |
| if(pool_type == PoolingType::MAX) |
| { |
| if(is_data_type_quantized(data_type)) |
| { |
| PixelValue type_min{}; |
| std::tie(type_min, std::ignore) = get_min_max(data_type); |
| build_opts.add_option("-DINITIAL_VALUE=" + support::cpp11::to_string(type_min.get<int32_t>())); |
| } |
| else |
| { |
| build_opts.add_option("-DINITIAL_VALUE=" + float_to_string_with_full_precision(std::numeric_limits<float>::lowest())); |
| } |
| } |
| else |
| { |
| // Pool AVG and Pool L2 initial value |
| build_opts.add_option("-DINITIAL_VALUE=0"); |
| } |
| |
| const auto use_fp_mixed_precision = (data_type == DataType::F16) && pool_info.fp_mixed_precision; |
| const auto use_wider_accumulator = use_fp_mixed_precision && (pool_type != PoolingType::MAX); |
| const auto acc_data_type = get_cl_type_from_data_type(use_wider_accumulator ? DataType::F32 : data_type); |
| build_opts.add_option("-DACC_DATA_TYPE=" + acc_data_type); |
| build_opts.add_option_if(use_wider_accumulator, "-DFP_MIXED_PRECISION"); |
| |
| // Create kernel |
| switch(_data_layout) |
| { |
| case DataLayout::NCHW: |
| { |
| build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width) + (exclude_padding ? 0 : pool_pad_left))); |
| build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height) + (exclude_padding ? 0 : pool_pad_top))); |
| if(pool_type != PoolingType::MAX) |
| { |
| build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING"); |
| } |
| |
| if((pool_size_x == 3) && (pool_size_y == 3) && !is_data_type_quantized_asymmetric(data_type)) |
| { |
| // 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_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3); |
| |
| std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_") |
| + support::cpp11::to_string(pool_size_x); |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| } |
| else // Run general case |
| { |
| std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nchw" : "pooling_layer_MxN_nchw"; |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| } |
| break; |
| } |
| case DataLayout::NHWC: |
| { |
| build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING"); |
| build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width))); |
| build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height))); |
| build_opts.add_option_if(output->info()->tensor_shape().total_size_upper(3) > 1, |
| "-DDST_DEPTH=" + support::cpp11::to_string(output->info()->dimension(idx_height))); |
| std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc"; |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), output->info(), pool_info); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); |
| ICLKernel::configure_internal(std::get<1>(win_config)); |
| |
| if(_data_layout == DataLayout::NCHW) |
| { |
| CLPoolingConfig pooling_config = std::get<2>(win_config); |
| _num_elems_processed_per_iteration = pooling_config.first; |
| _border_size = pooling_config.second; |
| } |
| else |
| { |
| _border_size = BorderSize(1, 0, 0, 0); |
| _num_elems_processed_per_iteration = 8; |
| } |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = "pooling_layer_"; |
| _config_id += lower_string(string_from_data_type(data_type)); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_layout(_data_layout)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(idx_width)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(idx_height)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(output->info()->dimension(idx_channel)); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_layout(input->info()->data_layout())); |
| } |
| |
| Status CLPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info, const ITensorInfo *indices) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info, indices)); |
| ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info))); |
| |
| return Status{}; |
| } |
| |
| 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_stride_x = 0; |
| unsigned int pool_stride_y = 0; |
| std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info.stride(); |
| |
| // Collapse window |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| |
| switch(_data_layout) |
| { |
| case DataLayout::NCHW: |
| { |
| 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_info.pad_stride_info.pad_left(), |
| (in_slice.x().end() - _pool_info.pad_stride_info.pad_left()) * pool_stride_x, |
| pool_stride_x * _num_elems_processed_per_iteration)); |
| in_slice.set(Window::DimY, Window::Dimension(in_slice.y().start() - _pool_info.pad_stride_info.pad_top(), |
| (in_slice.y().end() - _pool_info.pad_stride_info.pad_top()) * 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, lws_hint()); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| break; |
| } |
| case DataLayout::NHWC: |
| { |
| const size_t total_batches = _output->info()->tensor_shape().total_size_upper(3); |
| |
| Window slice = window_collapsed.first_slice_window_4D(); |
| Window in_slice = window_collapsed.first_slice_window_4D(); |
| in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration)); |
| in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x)); |
| in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y)); |
| in_slice.set(3, Window::Dimension(0, total_batches, 1)); |
| do |
| { |
| // Set inputs |
| unsigned int idx = 0; |
| add_4D_tensor_argument(idx, _input, in_slice); |
| add_4D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice)); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("Not implemented"); |
| } |
| } |
| } // namespace arm_compute |