/* * 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/GLES_COMPUTE/kernels/GCPoolingLayerKernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" #include "arm_compute/core/GLES_COMPUTE/GCKernelLibrary.h" #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h" #include "arm_compute/core/GLES_COMPUTE/OpenGLES.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 #include #include using namespace arm_compute; namespace { // Internal window config info using GCPoolingConfig = std::pair; //num_elems_processed_per_iteration, border_size void auto_init(const ITensorInfo *input, ITensorInfo *output, unsigned int pooled_w, unsigned int pooled_h) { TensorShape output_shape{ input->tensor_shape() }; output_shape.set(0, pooled_w); output_shape.set(1, pooled_h); auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); } Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, 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(!pool_info.pad_stride_info().padding_is_symmetric()); const bool is_global_pooling = pool_info.is_global_pooling(); const unsigned int pool_size = is_global_pooling ? input->tensor_shape().x() : pool_info.pool_size().width; ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_global_pooling && (input->tensor_shape().x() != input->tensor_shape().y()), "Global pooling is supported only with rectangular inputs!"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_global_pooling && ((pool_info.pad_stride_info().pad().first >= pool_size) || (pool_info.pad_stride_info().pad().second >= pool_size)), "Invalid pool size and pool pad combination!"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(pool_info.pool_size().width != pool_info.pool_size().height, "Invalid Pool size, width not equal to height!"); // Checks performed when output is configured if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); unsigned int pooled_w = 0; unsigned int pooled_h = 0; std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0), input->dimension(1), pool_size, pool_size, pool_info.pad_stride_info()); ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != pooled_w) || (output->dimension(1) != pooled_h), "Invalid output pooling dimensions!"); } return Status{}; } std::tuple validate_and_configure_window(ITensorInfo *input, ITensorInfo *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; int pool_size = pool_info.pool_size().width; const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); 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_NULLPTR(input, output); // Update pool size in case of global pooling pool_size = pool_info.is_global_pooling() ? input->dimension(0) : pool_size; // Check output dimensions std::tie(pooled_w, pooled_h) = scaled_dimensions(input->dimension(0), input->dimension(1), pool_size, pool_size, pad_stride_info); auto_init(input, output, pooled_w, pooled_h); BorderSize border_size = BorderSize(pool_pad_y, pool_pad_x); const int input_width = input->dimension(0); const int input_height = input->dimension(1); unsigned int num_elems_processed_per_iteration = 1; // Create kernel if(pool_size == 3) { // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where // each thread computes 4 output elements const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3); int num_elems_read_per_iteration = pool_size; if(input->data_type() == DataType::F32) { 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_elems_read_per_iteration = pool_size * (pool_stride_x + 1); } } else { if(is_pool3x3_stride_le3) { num_elems_processed_per_iteration = 4; } else { num_elems_processed_per_iteration = 2; } } const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elems_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); } else // Run general case { if(input->data_type() == DataType::F32) { num_elems_processed_per_iteration = 1; } else { num_elems_processed_per_iteration = 2; } 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); } // Configure kernel window Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); if(input->data_type() == DataType::F32) { AccessWindowStatic input_access(input, -pool_pad_x, -pool_pad_y, input_width + border_size.right, input_height + border_size.bottom); AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_tuple(err, win, GCPoolingConfig(num_elems_processed_per_iteration, border_size)); } else { // Calculate output right and bottom border const int output_width = output->dimension(0); const int output_height = output->dimension(1); const int output_padding_right = ceil_to_multiple(output_width, num_elems_processed_per_iteration) - output_width; const int output_padding_bottom = ceil_to_multiple(output_height, 1) - output_height; const int input_total_width = std::max(int(input->padding().left), int(pool_pad_x)) + input_width + std::max(int(input->padding().right), int(pool_pad_x)); const int input_padding_right = ceil_to_multiple(input_total_width, num_elems_processed_per_iteration) - input_width - pool_pad_x; const int input_total_height = std::max(int(input->padding().top), int(pool_pad_y)) + input_height + std::max(int(input->padding().bottom), int(pool_pad_y)); const int input_padding_bottom = input_total_height - input_height - pool_pad_y; // Configure kernel window AccessWindowStatic input_access(input, -pool_pad_x, -pool_pad_y, input_width + input_padding_right, input_height + input_padding_bottom); AccessWindowStatic output_access(output, 0, 0, output_width + output_padding_right, output_height + output_padding_bottom); bool window_changed = update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_tuple(err, win, GCPoolingConfig(num_elems_processed_per_iteration, border_size)); } } } // namespace GCPoolingLayerKernel::GCPoolingLayerKernel() : _input(nullptr), _output(nullptr), _pool_info(), _border_size(0), _num_elems_processed_per_iteration(1) { } BorderSize GCPoolingLayerKernel::border_size() const { return _border_size; } void GCPoolingLayerKernel::configure(const IGCTensor *input, IGCTensor *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(); int pool_size = pool_info.pool_size().width; const PadStrideInfo pad_stride_info = pool_info.pad_stride_info(); const 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_NULLPTR(input, output); // Update pool size in case of global pooling pool_size = pool_info.is_global_pooling() ? input->info()->dimension(0) : 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, pad_stride_info); auto_init(input->info(), output->info(), pooled_w, pooled_h); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), pool_info)); // Set instance variables _input = input; _output = output; _pool_info = pool_info; // Set build options std::set build_opts; build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1)); build_opts.emplace("#define LOCAL_SIZE_Y " + support::cpp11::to_string(1)); build_opts.emplace("#define LOCAL_SIZE_Z " + support::cpp11::to_string(1)); if(input->info()->data_type() == DataType::F32) { build_opts.insert("#define DATA_TYPE_FP32"); } else { build_opts.insert("#define DATA_TYPE_FP16"); } if(exclude_padding) { build_opts.emplace("#define EXCLUDE_PADDING"); } build_opts.emplace(("#define POOL_" + string_from_pooling_type(pool_type))); build_opts.emplace(("#define STRIDE_X " + support::cpp11::to_string(pool_stride_x))); build_opts.emplace(("#define MAX_WIDTH " + support::cpp11::to_string(input->info()->dimension(0) + (exclude_padding ? 0 : pool_pad_x)))); build_opts.emplace(("#define MAX_HEIGHT " + support::cpp11::to_string(input->info()->dimension(1) + (exclude_padding ? 0 : pool_pad_y)))); build_opts.emplace(("#define STRIDE_Y " + support::cpp11::to_string(pool_stride_y))); build_opts.emplace(("#define PAD_X " + support::cpp11::to_string(pool_pad_x))); build_opts.emplace(("#define PAD_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 OpenGLES kernel where // each thread computes 4 output elements const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3); std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size); if(is_pool3x3_stride_le3) { build_opts.insert("#define POOLING_LAYER_3_OPTIMIZED"); _kernel = static_cast(GCKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts)); } else { build_opts.insert("#define POOLING_LAYER_" + support::cpp11::to_string(pool_size)); _kernel = static_cast(GCKernelLibrary::get().create_kernel(kernel_name, build_opts)); } } else // Run general case { build_opts.emplace(("#define POOL_SIZE " + support::cpp11::to_string(pool_size))); build_opts.insert("#define POOLING_LAYER_N"); _kernel = static_cast(GCKernelLibrary::get().create_kernel("pooling_layer_n", build_opts)); } // 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)); IGCKernel::configure(std::get<1>(win_config)); GCPoolingConfig pooling_config = std::get<2>(win_config); _num_elems_processed_per_iteration = pooling_config.first; _border_size = pooling_config.second; } Status GCPoolingLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const PoolingLayerInfo &pool_info) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, pool_info)); ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), pool_info))); return Status{}; } void GCPoolingLayerKernel::run(const Window &window) { 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(); _kernel.use(); _output->set_needs_shifting(true); Window window_collapsed = window.collapse_if_possible(IGCKernel::window(), Window::DimZ); Window slice = window_collapsed.first_slice_window_3D(); Window slice_in_orig = window_collapsed.first_slice_window_3D(); slice.shift(Window::DimX, -(_output->info()->padding()).left); do { // Upsample input by pool size Window in_slice(slice_in_orig); // NOLINT 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, 1, in_slice); add_3D_tensor_argument(idx, _output, 2, slice); _kernel.update_shader_params(); enqueue(*this, slice); } while(window_collapsed.slide_window_slice_3D(slice) && window_collapsed.slide_window_slice_3D(slice_in_orig)); }