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/*
* Copyright (c) 2017-2020 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/runtime/NEON/functions/NEDeconvolutionLayer.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
using namespace arm_compute::misc::shape_calculator;
namespace arm_compute
{
namespace
{
PadStrideInfo compute_upsample_info(const PadStrideInfo &info, uint32_t deconv_pad_x, uint32_t deconv_pad_y)
{
const unsigned int pad_left = info.pad_left();
const unsigned int pad_right = info.pad_right();
const unsigned int pad_top = info.pad_top();
const unsigned int pad_bottom = info.pad_bottom();
const unsigned int stride_x = info.stride().first;
const unsigned int stride_y = info.stride().second;
// Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape
unsigned int deconv_pad_left = pad_right > pad_left ? pad_right - pad_left : 0;
unsigned int deconv_pad_right = pad_left > pad_right ? pad_left - pad_right : 0;
deconv_pad_x -= deconv_pad_left + deconv_pad_right;
ARM_COMPUTE_ERROR_ON((deconv_pad_x % 2) != 0);
deconv_pad_left += deconv_pad_x / 2;
deconv_pad_right += deconv_pad_x / 2;
unsigned int deconv_pad_top = pad_bottom > pad_top ? pad_bottom - pad_top : 0;
unsigned int deconv_pad_bottom = pad_top > pad_bottom ? pad_top - pad_bottom : 0;
deconv_pad_y -= deconv_pad_top + deconv_pad_bottom;
ARM_COMPUTE_ERROR_ON((deconv_pad_y % 2) != 0);
deconv_pad_top += deconv_pad_y / 2;
deconv_pad_bottom += deconv_pad_y / 2;
return PadStrideInfo(stride_x, stride_y, deconv_pad_left, deconv_pad_right, deconv_pad_top, deconv_pad_bottom, DimensionRoundingType::FLOOR);
}
} // namespace
NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
_conv_f(),
_upsample_f(),
_flip_weights(),
_scaled_output(),
_weights_flipped(),
_flip_axis(),
_original_weights(nullptr),
_input(nullptr),
_info(),
_is_prepared(false)
{
}
Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, const ITensorInfo *output, const PadStrideInfo &info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, input);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(weights, input);
const unsigned int width_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::WIDTH);
const unsigned int height_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::HEIGHT);
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != weights->dimension(height_idx));
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) < 1);
auto out_dims = deconvolution_output_dimensions(input->dimension(width_idx), input->dimension(height_idx), weights->dimension(width_idx), weights->dimension(height_idx), info);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
if(bias != nullptr)
{
if(is_data_type_quantized_asymmetric(input->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
}
else
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
}
}
if(output->tensor_shape().total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
}
uint32_t deconv_pad_x = 0;
uint32_t deconv_pad_y = 0;
const unsigned int stride_x = info.stride().first;
const unsigned int stride_y = info.stride().second;
const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input, *weights, stride_x, stride_y, out_dims, deconv_pad_x, deconv_pad_y);
TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(scale_out_shape));
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
const unsigned int batches_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::BATCHES);
const unsigned int channel_idx = get_data_layout_dimension_index(weights->data_layout(), DataLayoutDimension::CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(batches_idx) != scale_out_info.dimension(batches_idx));
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(channel_idx) != scale_out_info.dimension(channel_idx));
ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, WeightsInfo()));
return Status{};
}
void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info)
{
// Perform validation step
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_ERROR_THROW_ON(NEDeconvolutionLayer::validate(input->info(), weights->info(), (bias == nullptr) ? nullptr : bias->info(), output->info(), info));
const DataLayout data_layout = input->info()->data_layout();
const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
auto out_dims = deconvolution_output_dimensions(input->info()->dimension(width_idx), input->info()->dimension(height_idx),
weights->info()->dimension(width_idx), weights->info()->dimension(height_idx), info);
const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info());
_input = input;
_original_weights = weights;
_info = info;
_is_prepared = false;
const unsigned int stride_x = info.stride().first;
const unsigned int stride_y = info.stride().second;
// Output auto initialization if not yet initialized
auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->quantization_info());
_flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32));
_memory_group.manage(&_scaled_output);
_weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout));
_flip_weights.configure(weights, &_weights_flipped, &_flip_axis);
// setup the function to convolve the upscaled output
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
uint32_t deconv_pad_x = 0;
uint32_t deconv_pad_y = 0;
const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*input->info(), *weights->info(),
stride_x, stride_y,
out_dims, deconv_pad_x, deconv_pad_y);
const PadStrideInfo upsample_info = compute_upsample_info(info, deconv_pad_x, deconv_pad_y);
TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->quantization_info());
scale_out_info.set_data_layout(data_layout);
_scaled_output.allocator()->init(scale_out_info);
_upsample_f.configure(input, &_scaled_output, upsample_info);
_conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info);
// Setup flip axis data
_flip_axis.allocator()->allocate();
auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer());
axis_data[0] = static_cast<uint32_t>(width_idx);
axis_data[1] = static_cast<uint32_t>(height_idx);
_scaled_output.allocator()->allocate();
}
void NEDeconvolutionLayer::run()
{
prepare();
MemoryGroupResourceScope scope_mg(_memory_group);
_upsample_f.run();
_conv_f.run();
}
void NEDeconvolutionLayer::prepare()
{
if(!_is_prepared)
{
ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
// Run weights flipping and mark original weights tensor as unused
_weights_flipped.allocator()->allocate();
_flip_weights.run();
_original_weights->mark_as_unused();
// Prepare convolution
_conv_f.prepare();
_is_prepared = true;
}
}
} // namespace arm_compute
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