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/*
* 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);
}
Error CLPoolingLayerKernel::validate(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::QS8, DataType::QS16, DataType::F16, DataType::F32);
int pool_pad_x = 0;
int pool_pad_y = 0;
int pool_size = pool_info.pool_size();
std::tie(pool_pad_x, pool_pad_y) = pool_info.pad_stride_info().pad();
ARM_COMPUTE_RETURN_ERROR_ON_MSG(((pool_pad_x >= pool_size) || (pool_pad_y >= pool_size)),
"Invalid pool size and pool pad combination");
// 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_FIXED_POINT(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 Error{};
}
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));
}
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