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/*
* 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/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h"
#include "arm_compute/core/NEON/kernels/convolution/NEDirectConvolutionDetail.h"
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/AccessWindowTranspose.h"
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
using namespace arm_compute;
using namespace arm_compute::detail;
using namespace arm_compute::misc::shape_calculator;
namespace
{
template <typename T1, typename T2, unsigned int stridex>
class convolver_3x3
{
public:
static void convolve(const Window &window, unsigned int num_elems_written_per_iteration,
const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
{
const int input_offset = -input->info()->quantization_info().offset;
const int weights_offset = -weights->info()->quantization_info().offset;
const int input_stride_x = input->info()->strides_in_bytes().x();
const int input_stride_y = input->info()->strides_in_bytes().y();
const int output_stride_y = output->info()->strides_in_bytes().y();
const int kernel_stride_y = weights->info()->strides_in_bytes().y();
const int kernel_stride_z = weights->info()->strides_in_bytes().z();
const int output_w = output->info()->dimension(0);
const int output_h = output->info()->dimension(1);
const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
const unsigned int conv_pad_x = conv_info.pad_left();
const unsigned int conv_pad_y = conv_info.pad_top();
// setup output window for the iterator
Window window_out = window;
window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX)));
window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY)));
// setup input window for the iterator
Window window_in = window;
// we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0
window_in.set(Window::DimX, Window::Dimension(0, 0, 0));
window_in.set(Window::DimY, Window::Dimension(0, 0, 0));
Window window_k = calculate_max_window(*weights->info(), Steps(1u));
Iterator in(input, window_in);
Iterator out(output, window_out);
Iterator w(weights, window_k);
const uint8_t *weights_ptr = w.ptr();
execute_window_loop(window_out, [&](const Coordinates & id)
{
int ih = 0;
int oh = 0;
const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y;
const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z;
const auto ptr_weights_r0 = reinterpret_cast<const T1 *>(ptr_weights_base);
const auto ptr_weights_r1 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y);
const auto ptr_weights_r2 = reinterpret_cast<const T1 *>(ptr_weights_base + kernel_stride_y * 2);
const auto vw_r0 = load_matrix_row(ptr_weights_r0, weights_offset);
const auto vw_r1 = load_matrix_row(ptr_weights_r1, weights_offset);
const auto vw_r2 = load_matrix_row(ptr_weights_r2, weights_offset);
for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y)
{
auto in_top = reinterpret_cast<const T1 *>(input_ptr + (ih + 0) * input_stride_y);
auto in_mid = reinterpret_cast<const T1 *>(input_ptr + (ih + 1) * input_stride_y);
auto in_low = reinterpret_cast<const T1 *>(input_ptr + (ih + 2) * input_stride_y);
auto p_out = reinterpret_cast<T2 *>(out.ptr() + oh * output_stride_y);
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
in_top += delta_input, in_mid += delta_input, in_low += delta_input,
p_out += num_elems_written_per_iteration)
{
auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0, input_offset);
store_results<stridex>(p_out, vres);
}
}
},
in, out);
}
};
template <typename T1, typename T2>
inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration,
const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
{
const unsigned int conv_stride_x = std::get<0>(conv_info.stride());
switch(conv_stride_x)
{
case 1:
convolver_3x3<T1, T2, 1>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
break;
case 2:
convolver_3x3<T1, T2, 2>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
break;
case 3:
convolver_3x3<T1, T2, 3>::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info);
break;
default:
ARM_COMPUTE_ERROR("Not implemented");
}
}
} // namespace
NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel()
: _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0)
{
}
BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const
{
return _border_size;
}
void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3);
// Get convolved dimensions
const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info);
const DataType output_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type();
// Output auto inizialitation if not yet initialized
auto_init_if_empty(*output->info(),
input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt));
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
_input = input;
_output = output;
_weights = weights;
_conv_info = conv_info;
const unsigned int conv_stride_x = conv_info.stride().first;
const unsigned int conv_stride_y = conv_info.stride().second;
const unsigned int conv_pad_left = conv_info.pad_left();
const unsigned int conv_pad_top = conv_info.pad_top();
ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3);
unsigned int num_elems_read_per_iteration = 0;
switch(input->info()->data_type())
{
case DataType::QASYMM8:
num_elems_read_per_iteration = 16;
_num_elems_written_per_iteration = 16 >> conv_stride_x;
break;
case DataType::F32:
num_elems_read_per_iteration = 12;
_num_elems_written_per_iteration = 16 >> conv_stride_x;
break;
default:
ARM_COMPUTE_ERROR("Data type not supported.");
}
_border_size = BorderSize(conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), conv_pad_left);
// Configure kernel window
Window win = calculate_max_window(*output->info(), Steps(_num_elems_written_per_iteration));
const unsigned int num_x_steps = (output_shape.x() + _num_elems_written_per_iteration - 1) / _num_elems_written_per_iteration;
const int input_num_elems_processed = get_input_num_elems_processed(_num_elems_written_per_iteration, conv_stride_x);
AccessWindowStatic input_access(input->info(),
-conv_pad_left,
-conv_pad_top,
(num_x_steps - 1) * input_num_elems_processed + num_elems_read_per_iteration,
conv_stride_y * (output_shape.y() - 1) + 2);
AccessWindowStatic weights_access(weights->info(), 0, 0, weights->info()->dimension(0), weights->info()->dimension(1));
AccessWindowStatic output_access(output->info(), 0, 0, num_x_steps * _num_elems_written_per_iteration, output_shape.y());
update_window_and_padding(win, input_access, weights_access, output_access);
output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape()));
INEKernel::configure(win);
}
void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_UNUSED(info);
switch(_input->info()->data_type())
{
case DataType::F32:
convolve_3x3<float, float>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
break;
case DataType::QASYMM8:
convolve_3x3<uint8_t, int32_t>(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
break;
default:
ARM_COMPUTE_ERROR("Not implemented");
}
}
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