diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEDeconvolutionLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEDeconvolutionLayer.cpp | 105 |
1 files changed, 54 insertions, 51 deletions
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp index 7b4e77b296..c4bca11d14 100644 --- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017, 2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -24,38 +24,41 @@ #include "arm_compute/runtime/NEON/functions/NEDeconvolutionLayer.h" #include "arm_compute/core/Helpers.h" -#include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" using namespace arm_compute; +using namespace arm_compute::misc::shape_calculator; NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT : _memory_group(std::move(memory_manager)), - _scale_f(), _conv_f(), - _scaled_output() + _scaled_output(), + _input(nullptr), + _info(), + _inner_border() { } void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info, - unsigned int ax, unsigned int ay, float upscalex, float upscaley) + unsigned int inner_border_right, unsigned int inner_border_top) { ARM_COMPUTE_ERROR_ON_NULLPTR(output); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1)); - ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) < 1); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5); - auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1), - info.pad().first, info.pad().second, ax, ay, upscalex, upscaley, info.round()); + _input = input; + _info = info; + _inner_border = std::make_pair(inner_border_right, inner_border_top); - const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape()); - - // Output auto initialization if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); + const unsigned int stride_x = info.stride().first; + const unsigned int stride_y = info.stride().second; + auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1), + info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights, bias); - ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output, weights, bias); + const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape()); ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid."); ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid."); @@ -64,51 +67,51 @@ void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, con _memory_group.manage(&_scaled_output); // configure scale function - //Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor - TensorShape scale_out_shape(input->info()->tensor_shape()); - scale_out_shape.set(0, output->info()->dimension(0)); - scale_out_shape.set(1, output->info()->dimension(1)); - TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type(), input->info()->fixed_point_position()); + // Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor + const TensorInfo scale_out_info(compute_deconvolution_shape(*input->info(), stride_x, stride_y, inner_border_right, inner_border_top, info), 1, input->info()->data_type(), + input->info()->fixed_point_position()); _scaled_output.allocator()->init(scale_out_info); - const unsigned int kernel_size = weights->info()->dimension(0); - // Padding for the upsampled image is calculated with the equiation: p' = k - p - 1, where k is kernel size and p is the input padding - ARM_COMPUTE_ERROR_ON(info.pad().first > (kernel_size - 1)); - const unsigned int tr_px = kernel_size - info.pad().first - 1; - const unsigned int tr_py = kernel_size - info.pad().second - 1; - const unsigned int tr_stride = 1; - const PadStrideInfo transposed_info(tr_stride, tr_stride, tr_px, tr_py); - _scale_f.configure(input, &_scaled_output, std::make_pair(ax, ay), std::make_pair(info.stride().first - 1u, info.stride().second - 1u), transposed_info); + // setup the function to convolve the upscaled output - switch(kernel_size) - { - case 1: - { - _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL)); - break; - } - case 3: - { - _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL)); - break; - } - case 5: - { - _conv_f.configure(&_scaled_output, weights, bias, output, PadStrideInfo(1, 1, 2, 2, DimensionRoundingType::CEIL)); - break; - } - default: - { - ARM_COMPUTE_ERROR("Not supported"); - break; - } - } + const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); + _conv_f.configure(&_scaled_output, weights, bias, output, conv_info); _scaled_output.allocator()->allocate(); } void NEDeconvolutionLayer::run() { _memory_group.acquire(); - _scale_f.run(); + + // Initialize _scaled_output buffer + const int width_in = _input->info()->dimension(0); + const int height_in = _input->info()->dimension(1); + const int width_scaled = _scaled_output.info()->dimension(0); + const int height_scaled = _scaled_output.info()->dimension(1); + const int num_2d_slices = _input->info()->tensor_shape().total_size() / (width_in * height_in); + const int stride_x = _info.stride().first; + const int stride_y = _info.stride().second; + + std::fill_n(reinterpret_cast<float *>(_scaled_output.buffer()), _scaled_output.info()->tensor_shape().total_size(), 0.f); + + // scaled_output is the input for the forward convolution. We copy the input elements to scaled_output + // and insert rows and columns with zeroes depending on the stride values. + for(int slice = 0; slice < num_2d_slices; ++slice) + { + const int start_x = _info.pad().first; + const int start_y = _inner_border.second + _info.pad().second; + const int end_y = height_scaled - _info.pad().second; + const int end_x = width_scaled - _inner_border.first - _info.pad().first; + + for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++) + { + for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++) + { + const auto in = *(reinterpret_cast<float *>(_input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(in_x, in_y, slice)))); + *(reinterpret_cast<float *>(_scaled_output.buffer() + _scaled_output.info()->offset_element_in_bytes(Coordinates(xi, yi, slice)))) = in; + } + } + } + _conv_f.run(); _memory_group.release(); } |