diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEDeconvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEDeconvolutionLayer.cpp | 218 |
1 files changed, 68 insertions, 150 deletions
diff --git a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp index 06885d59e5..c87dac60dc 100644 --- a/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDeconvolutionLayer.cpp @@ -33,21 +33,45 @@ 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(), - _permute_input(), - _permute_weights(), - _permute_output(), _scaled_output(), _weights_flipped(), - _permuted_input(), - _permuted_weights(), - _permuted_output(), _flip_axis(), - _is_nchw(false), _original_weights(nullptr), _input(nullptr), _info(), @@ -92,8 +116,8 @@ Status NEDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInf ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid."); } - unsigned int deconv_pad_x = 0; - unsigned int deconv_pad_y = 0; + 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); @@ -116,136 +140,58 @@ void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, con 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 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; - _is_nchw = data_layout == DataLayout::NCHW; - _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32)); - - 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; - 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 unsigned int stride_x = info.stride().first; + const unsigned int stride_y = info.stride().second; - const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input->info(), *weights->info()); // 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); + _memory_group.manage(&_flip_axis); - if(!_is_nchw) - { - _memory_group.manage(&_permuted_input); - _memory_group.manage(&_permuted_output); - - // Configure the function to transform the input tensor from NHWC -> NCHW - _permuted_input.info()->set_quantization_info(input->info()->quantization_info()); - _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); - _permuted_input.info()->set_data_layout(DataLayout::NCHW); - - // Configure the function to transform the weights tensor from NHWC -> NCHW - _permuted_weights.info()->set_quantization_info(weights->info()->quantization_info()); - _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); - _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - - // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape - unsigned int deconv_pad_x = 0; - unsigned int deconv_pad_y = 0; - const TensorShape scale_out_shape = compute_deconvolution_upsampled_shape(*_permuted_input.info(), *_permuted_weights.info(), stride_x, stride_y, out_dims, - deconv_pad_x, deconv_pad_y); - - 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; - - TensorInfo scale_out_info(scale_out_shape, 1, _permuted_input.info()->data_type(), _permuted_input.info()->quantization_info()); - scale_out_info.set_data_layout(DataLayout::NCHW); - _scaled_output.allocator()->init(scale_out_info); - - const PadStrideInfo upsample_info(stride_x, stride_y, deconv_pad_left, deconv_pad_right, deconv_pad_top, deconv_pad_bottom, DimensionRoundingType::FLOOR); - _upsample_f.configure(&_permuted_input, &_scaled_output, upsample_info); - - _weights_flipped.allocator()->init(*_permuted_weights.info()->clone()); - _weights_flipped.info()->set_quantization_info(weights->info()->quantization_info()); - _flip_weights.configure(&_permuted_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); - - _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); - _conv_f.configure(&_scaled_output, &_weights_flipped, bias, &_permuted_output, conv_info); - - // Configure the function to transform the convoluted output to NHWC - _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); - _permuted_output.info()->set_data_layout(DataLayout::NCHW); - - _permuted_input.allocator()->allocate(); - _permuted_output.allocator()->allocate(); - } - else - { - // Find the upsampled dimensions and the padding needed for the convolution with stride 1 in order to match output shape - unsigned int deconv_pad_x = 0; - unsigned int 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); - - 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; - - 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); - - const PadStrideInfo upsample_info(stride_x, stride_y, deconv_pad_left, deconv_pad_right, deconv_pad_top, deconv_pad_bottom, DimensionRoundingType::FLOOR); - _upsample_f.configure(input, &_scaled_output, upsample_info); - - _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); - _conv_f.configure(&_scaled_output, &_weights_flipped, bias, output, conv_info); - } - _scaled_output.allocator()->allocate(); + _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] = 0; - axis_data[1] = 1; + 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() @@ -254,20 +200,8 @@ void NEDeconvolutionLayer::run() MemoryGroupResourceScope scope_mg(_memory_group); - // Permute input - if(!_is_nchw) - { - _permute_input.run(); - } - _upsample_f.run(); _conv_f.run(); - - // Permute output - if(!_is_nchw) - { - _permute_output.run(); - } } void NEDeconvolutionLayer::prepare() @@ -275,13 +209,6 @@ void NEDeconvolutionLayer::prepare() if(!_is_prepared) { ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); - // Permute weights - if(!_is_nchw) - { - // Manually manage _permuted_weights - _permuted_weights.allocator()->allocate(); - _permute_weights.run(); - } // Run weights flipping and mark original weights tensor as unused _weights_flipped.allocator()->allocate(); @@ -291,15 +218,6 @@ void NEDeconvolutionLayer::prepare() // Prepare convolution _conv_f.prepare(); - // Unused weights are already released in _conv_f - - if(!_is_nchw) - { - // Manually manage _permuted_weights - // Free _permuted_weights as it not used after this method (prepare) - _permuted_weights.allocator()->free(); - } - _is_prepared = true; } } |