| /* |
| * 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 <set> |
| #include <string> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| namespace |
| { |
| // Internal window config info |
| using GCPoolingConfig = std::pair<unsigned int, BorderSize>; //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<Status, Window, GCPoolingConfig> 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<std::string> 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<GCKernel>(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<GCKernel>(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<GCKernel>(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)); |
| } |