diff options
Diffstat (limited to 'src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp | 86 |
1 files changed, 58 insertions, 28 deletions
diff --git a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp index 8d1a91e420..7cd268ab0b 100644 --- a/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDirectDeconvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021 Arm Limited. + * Copyright (c) 2019-2021, 2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -26,12 +26,13 @@ #include "arm_compute/core/CL/CLKernelLibrary.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/core/Validate.h" #include "arm_compute/runtime/CL/CLScheduler.h" + +#include "src/common/utils/Log.h" #include "src/core/CL/kernels/CLDeconvolutionLayerUpsampleKernel.h" #include "src/core/CL/kernels/CLFillBorderKernel.h" -#include "src/core/CL/kernels/CLWeightsReshapeKernel.h" #include "src/core/helpers/AutoConfiguration.h" #include <memory> @@ -54,11 +55,16 @@ CLDirectDeconvolutionLayer::CLDirectDeconvolutionLayer(std::shared_ptr<IMemoryMa { } -Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info, - const WeightsInfo &weights_info) +Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *bias, + ITensorInfo *output, + const PadStrideInfo &info, + const WeightsInfo &weights_info) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8_SIGNED, DataType::QASYMM8, + DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights); const DataLayout data_layout = input->data_layout(); @@ -66,23 +72,25 @@ Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, const ITen const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const size_t idx_c = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) != weights->dimension(idx_h)); ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) < 1); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) < 1); - auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), weights->dimension(idx_w), weights->dimension(idx_h), info); + auto out_dims = deconvolution_output_dimensions(input->dimension(idx_w), input->dimension(idx_h), + weights->dimension(idx_w), weights->dimension(idx_h), info); const TensorShape output_shape = compute_deconvolution_output_shape(out_dims, *input, *weights); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - if(input->data_type() != weights->data_type()) + if (input->data_type() != weights->data_type()) { - ARM_COMPUTE_RETURN_ERROR_ON(weights->data_type() != DataType::QSYMM8_PER_CHANNEL || !is_data_type_quantized_asymmetric(input->data_type())); + ARM_COMPUTE_RETURN_ERROR_ON(weights->data_type() != DataType::QSYMM8_PER_CHANNEL || + !is_data_type_quantized_asymmetric(input->data_type())); } - if(bias != nullptr) + if (bias != nullptr) { - if(is_data_type_quantized_asymmetric(input->data_type())) + if (is_data_type_quantized_asymmetric(input->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); } @@ -101,26 +109,42 @@ Status CLDirectDeconvolutionLayer::validate(const ITensorInfo *input, const ITen unsigned int 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).set_data_layout(data_layout)); + 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) + .set_data_layout(data_layout)); const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL); ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, info)); - ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info)); + ARM_COMPUTE_RETURN_ON_ERROR( + CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info)); return Status{}; } -void CLDirectDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, - const WeightsInfo &weights_info) +void CLDirectDeconvolutionLayer::configure(ICLTensor *input, + ICLTensor *weights, + const ICLTensor *bias, + ICLTensor *output, + const PadStrideInfo &info, + const WeightsInfo &weights_info) { configure(CLKernelLibrary::get().get_compile_context(), input, weights, bias, output, info, weights_info); } -void CLDirectDeconvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, - const WeightsInfo &weights_info) +void CLDirectDeconvolutionLayer::configure(const CLCompileContext &compile_context, + ICLTensor *input, + ICLTensor *weights, + const ICLTensor *bias, + ICLTensor *output, + const PadStrideInfo &info, + const WeightsInfo &weights_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_LOG_PARAMS(input, weights, bias, output, info, weights_info); const unsigned int pad_left = info.pad_left(); const unsigned int pad_right = info.pad_right(); @@ -137,17 +161,21 @@ void CLDirectDeconvolutionLayer::configure(const CLCompileContext &compile_conte _original_weights = weights; _flip_axis.allocator()->init(TensorInfo(TensorShape(2U), 1, DataType::U32)); _weights_flipped.allocator()->init(weights->info()->clone()->set_data_layout(data_layout)); - _flip_weights.configure(compile_context, weights, &_weights_flipped, &_flip_axis); + _flip_weights.configure(compile_context, weights, &_weights_flipped, &_flip_axis, /* use_inverted_axis */ false); - auto out_dims = deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), weights->info()->dimension(idx_w), weights->info()->dimension(idx_h), info); + auto out_dims = + deconvolution_output_dimensions(input->info()->dimension(idx_w), input->info()->dimension(idx_h), + weights->info()->dimension(idx_w), weights->info()->dimension(idx_h), info); 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(), input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout)); + auto_init_if_empty(*output->info(), + input->info()->clone()->set_tensor_shape(output_shape).set_data_layout(data_layout)); // Perform validation step - ARM_COMPUTE_ERROR_THROW_ON(CLDirectDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info)); + ARM_COMPUTE_ERROR_THROW_ON(CLDirectDeconvolutionLayer::validate( + input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info)); _is_prepared = weights_info.retain_internal_weights(); @@ -156,7 +184,8 @@ void CLDirectDeconvolutionLayer::configure(const CLCompileContext &compile_conte // 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); + 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; @@ -177,7 +206,8 @@ void CLDirectDeconvolutionLayer::configure(const CLCompileContext &compile_conte _scaled_output.allocator()->init(scale_out_info); // configure scale function - const PadStrideInfo upsample_info(stride_x, stride_y, deconv_pad_left, deconv_pad_right, deconv_pad_top, deconv_pad_bottom, DimensionRoundingType::FLOOR); + const PadStrideInfo upsample_info(stride_x, stride_y, deconv_pad_left, deconv_pad_right, deconv_pad_top, + deconv_pad_bottom, DimensionRoundingType::FLOOR); _scale_f.configure(compile_context, input, &_scaled_output, upsample_info); // Setup the function to convolve the upscaled output @@ -189,7 +219,7 @@ void CLDirectDeconvolutionLayer::configure(const CLCompileContext &compile_conte _flip_axis.allocator()->allocate(); _flip_axis.map(true); auto axis_data = reinterpret_cast<uint32_t *>(_flip_axis.buffer()); - if(weights->info()->data_layout() == DataLayout::NHWC) + if (weights->info()->data_layout() == DataLayout::NHWC) { axis_data[0] = 1; axis_data[1] = 2; @@ -214,7 +244,7 @@ void CLDirectDeconvolutionLayer::run() void CLDirectDeconvolutionLayer::prepare() { - if(!_is_prepared) + if (!_is_prepared) { ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); @@ -227,7 +257,7 @@ void CLDirectDeconvolutionLayer::prepare() _conv_f.prepare(); // Free flipped weights - if(!_weights_flipped.is_used()) + if (!_weights_flipped.is_used()) { _weights_flipped.allocator()->free(); } |