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/*
* Copyright (c) 2017-2019 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/NEWeightsReshapeKernel.h"
#include "arm_compute/core/Dimensions.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
using namespace arm_compute;
namespace
{
template <typename T>
void weights_reshape(const ITensor *input, const ITensor *bias, ITensor *output, const Window &window)
{
const unsigned int kernel_size_x = input->info()->dimension(0);
const unsigned int kernel_size_y = input->info()->dimension(1);
const unsigned int kernel_depth = input->info()->dimension(2);
const unsigned int input_stride_x = input->info()->strides_in_bytes().x();
const unsigned int input_stride_y = input->info()->strides_in_bytes().y();
const unsigned int input_stride_z = input->info()->strides_in_bytes().z();
const unsigned int output_stride_y = output->info()->strides_in_bytes().y();
// Create iterators
Iterator in(input, window);
execute_window_loop(window, [&](const Coordinates & id)
{
// Get column index
const int kernel_idx = id[3];
const int kernel_idz = id[4];
// Setup pointers
const uint8_t *tmp_input_ptr = in.ptr();
uint8_t *tmp_output_ptr = output->ptr_to_element(Coordinates(kernel_idx, 0, kernel_idz));
const uint8_t *curr_input_row_ptr = tmp_input_ptr;
const uint8_t *curr_input_depth_ptr = tmp_input_ptr;
// Linearize volume
for(unsigned int d = 0; d < kernel_depth; ++d)
{
for(unsigned int j = 0; j < kernel_size_y; ++j)
{
for(unsigned int i = 0; i < kernel_size_x; ++i)
{
*(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(tmp_input_ptr));
tmp_input_ptr += input_stride_x;
tmp_output_ptr += output_stride_y;
}
curr_input_row_ptr += input_stride_y;
tmp_input_ptr = curr_input_row_ptr;
}
curr_input_depth_ptr += input_stride_z;
curr_input_row_ptr = curr_input_depth_ptr;
tmp_input_ptr = curr_input_depth_ptr;
}
// Add bias
if(bias != nullptr)
{
*(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(bias->ptr_to_element(Coordinates(kernel_idx, kernel_idz))));
}
},
in);
}
TensorShape get_output_shape(const ITensorInfo *input, bool has_bias)
{
TensorShape output_shape{ input->tensor_shape() };
output_shape.collapse(3);
const size_t tmp_dim = output_shape[0];
output_shape.set(0, output_shape[1]);
output_shape.set(1, tmp_dim + (has_bias ? 1 : 0));
return output_shape;
}
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output)
{
//Note: ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input) is not needed here as this kernel doesn't use NEON FP16 instructions.
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->num_dimensions() != 1));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 5) && (biases->num_dimensions() != 2));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->dimension(0) != input->tensor_shape()[3]));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 5) && (biases->dimension(0) != input->tensor_shape()[3] || biases->dimension(1) != input->tensor_shape()[4]));
}
// Checks performed when output is configured
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input, biases != nullptr));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
}
return Status{};
}
std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
Window window = calculate_max_window(*input, Steps());
window.set(Window::DimX, Window::Dimension(0, input->dimension(0), input->dimension(0)));
window.set(Window::DimY, Window::Dimension(0, input->dimension(1), input->dimension(1)));
window.set(Window::DimZ, Window::Dimension(0, input->dimension(2), input->dimension(2)));
// The NEConvolutionLayerWeightsReshapeKernel doesn't need padding so update_window_and_padding() can be skipped
output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
return std::make_pair(Status{}, window);
}
} // namespace
NEWeightsReshapeKernel::NEWeightsReshapeKernel()
: _func(nullptr), _input(nullptr), _bias(nullptr), _output(nullptr)
{
}
void NEWeightsReshapeKernel::configure(const ITensor *input, const ITensor *bias, ITensor *output)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output tensor auto inizialitation if not yet initialized
auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(get_output_shape(input->info(), (bias != nullptr))));
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(),
(bias != nullptr) ? bias->info() : nullptr,
output->info()));
_input = input;
_bias = bias;
_output = output;
switch(_input->info()->element_size())
{
case 4:
{
_func = &weights_reshape<uint32_t>;
break;
}
case 2:
{
_func = &weights_reshape<uint16_t>;
break;
}
case 1:
{
_func = &weights_reshape<uint8_t>;
break;
}
default:
{
ARM_COMPUTE_ERROR_ON("Element size not supported");
break;
}
}
// Configure kernel
auto win_config = validate_and_configure_window(input->info(), output->info());
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
INEKernel::configure(win_config.second);
}
Status NEWeightsReshapeKernel::validate(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, biases, output));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first);
return Status{};
}
void NEWeightsReshapeKernel::run(const Window &window, const ThreadInfo &info)
{
ARM_COMPUTE_UNUSED(info);
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
(*_func)(_input, _bias, _output, window);
}
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