From c9564cb3850b6675cef663d7cc0722567b55cc25 Mon Sep 17 00:00:00 2001 From: Pablo Tello Date: Fri, 13 Sep 2019 10:20:25 +0100 Subject: COMPMID-2257: Implement NEGenerateProposals. Change-Id: I8d751f8b09f842a214c305a4530a71d82f16db8f Signed-off-by: Pablo Tello Reviewed-on: https://review.mlplatform.org/c/1943 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio --- arm_compute/core/NEON/NEKernels.h | 1 + arm_compute/core/NEON/kernels/NECopyKernel.h | 17 +- .../NEON/kernels/NEGenerateProposalsLayerKernel.h | 82 +++++ arm_compute/runtime/NEON/NEFunctions.h | 2 + .../runtime/NEON/functions/NEComputeAllAnchors.h | 62 ++++ .../NEON/functions/NEGenerateProposalsLayer.h | 147 ++++++++ src/core/NEON/kernels/NECopyKernel.cpp | 116 +++++- .../kernels/NEGenerateProposalsLayerKernel.cpp | 131 +++++++ src/runtime/NEON/functions/NEComputeAllAnchors.cpp | 42 +++ .../NEON/functions/NEGenerateProposalsLayer.cpp | 264 ++++++++++++++ tests/validation/NEON/GenerateProposalsLayer.cpp | 403 +++++++++++++++++++++ .../validation/fixtures/ComputeAllAnchorsFixture.h | 2 +- 12 files changed, 1241 insertions(+), 28 deletions(-) create mode 100644 arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h create mode 100644 arm_compute/runtime/NEON/functions/NEComputeAllAnchors.h create mode 100644 arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h create mode 100644 src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp create mode 100644 src/runtime/NEON/functions/NEComputeAllAnchors.cpp create mode 100644 src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp create mode 100644 tests/validation/NEON/GenerateProposalsLayer.cpp diff --git a/arm_compute/core/NEON/NEKernels.h b/arm_compute/core/NEON/NEKernels.h index 80bc74e135..5eaf8ad445 100644 --- a/arm_compute/core/NEON/NEKernels.h +++ b/arm_compute/core/NEON/NEKernels.h @@ -94,6 +94,7 @@ #include "arm_compute/core/NEON/kernels/NEGaussian3x3Kernel.h" #include "arm_compute/core/NEON/kernels/NEGaussian5x5Kernel.h" #include "arm_compute/core/NEON/kernels/NEGaussianPyramidKernel.h" +#include "arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h" #include "arm_compute/core/NEON/kernels/NEHOGDescriptorKernel.h" #include "arm_compute/core/NEON/kernels/NEHOGDetectorKernel.h" #include "arm_compute/core/NEON/kernels/NEHarrisCornersKernel.h" diff --git a/arm_compute/core/NEON/kernels/NECopyKernel.h b/arm_compute/core/NEON/kernels/NECopyKernel.h index c6df9bafae..ddf1bb41fb 100644 --- a/arm_compute/core/NEON/kernels/NECopyKernel.h +++ b/arm_compute/core/NEON/kernels/NECopyKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -51,18 +51,20 @@ public: NECopyKernel &operator=(NECopyKernel &&) = default; /** Initialize the kernel's input, output. * - * @param[in] input Source tensor. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. - * @param[out] output Destination tensor. Data types supported: same as @p input. + * @param[in] input Source tensor. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[out] output Destination tensor. Data types supported: same as @p input. + * @param[in] padding (Optional) Padding to be applied to the input tensor */ - void configure(const ITensor *input, ITensor *output); + void configure(const ITensor *input, ITensor *output, const PaddingList &padding = PaddingList()); /** Static function to check if given info will lead to a valid configuration of @ref NECopyKernel * - * @param[in] input Source tensor. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. - * @param[in] output Destination tensor. Data types supported: same as @p input. + * @param[in] input Source tensor. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32. + * @param[in] output Destination tensor. Data types supported: same as @p input. + * @param[in] padding (Optional) Padding to be applied to the input tensor * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output); + static Status validate(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding = PaddingList()); // Inherited methods overridden: void run(const Window &window, const ThreadInfo &info) override; @@ -70,6 +72,7 @@ public: private: const ITensor *_input; ITensor *_output; + PaddingList _padding; }; } // namespace arm_compute #endif /*__ARM_COMPUTE_NECOPYKERNEL_H__ */ diff --git a/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h b/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h new file mode 100644 index 0000000000..a7b2603648 --- /dev/null +++ b/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h @@ -0,0 +1,82 @@ +/* + * Copyright (c) 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. + */ +#ifndef __ARM_COMPUTE_NEGENERATEPROPOSALSLAYERKERNEL_H__ +#define __ARM_COMPUTE_NEGENERATEPROPOSALSLAYERKERNEL_H__ + +#include "arm_compute/core/NEON/INEKernel.h" +namespace arm_compute +{ +class ITensor; + +/** Interface for Compute All Anchors kernel */ +class NEComputeAllAnchorsKernel : public INEKernel +{ +public: + const char *name() const override + { + return "NEComputeAllAnchorsKernel"; + } + + /** Default constructor */ + NEComputeAllAnchorsKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEComputeAllAnchorsKernel(const NEComputeAllAnchorsKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEComputeAllAnchorsKernel &operator=(const NEComputeAllAnchorsKernel &) = delete; + /** Allow instances of this class to be moved */ + NEComputeAllAnchorsKernel(NEComputeAllAnchorsKernel &&) = default; + /** Allow instances of this class to be moved */ + NEComputeAllAnchorsKernel &operator=(NEComputeAllAnchorsKernel &&) = default; + /** Default destructor */ + ~NEComputeAllAnchorsKernel() = default; + + /** Set the input and output tensors. + * + * @param[in] anchors Source tensor. Original set of anchors of size (4, A), where A is the number of anchors. Data types supported: F16/F32 + * @param[out] all_anchors Destination tensor. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input + * @param[in] info Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo + * + */ + void configure(const ITensor *anchors, ITensor *all_anchors, const ComputeAnchorsInfo &info); + + /** Static function to check if given info will lead to a valid configuration of @ref NEComputeAllAnchorsKernel + * + * @param[in] anchors Source tensor info. Original set of anchors of size (4, A), where A is the number of anchors. Data types supported: F16/F32 + * @param[in] all_anchors Destination tensor info. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input + * @param[in] info Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo + * + * @return a Status + */ + static Status validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info); + + // Inherited methods overridden: + void run(const Window &window, const ThreadInfo &info) override; + +private: + const ITensor *_anchors; + ITensor *_all_anchors; + ComputeAnchorsInfo _anchors_info; +}; +} // arm_compute +#endif // __ARM_COMPUTE_NEGENERATEPROPOSALSLAYERKERNEL_H__ diff --git a/arm_compute/runtime/NEON/NEFunctions.h b/arm_compute/runtime/NEON/NEFunctions.h index 09d3c65e25..28fd7f37b9 100644 --- a/arm_compute/runtime/NEON/NEFunctions.h +++ b/arm_compute/runtime/NEON/NEFunctions.h @@ -46,6 +46,7 @@ #include "arm_compute/runtime/NEON/functions/NEChannelShuffleLayer.h" #include "arm_compute/runtime/NEON/functions/NECol2Im.h" #include "arm_compute/runtime/NEON/functions/NEColorConvert.h" +#include "arm_compute/runtime/NEON/functions/NEComputeAllAnchors.h" #include "arm_compute/runtime/NEON/functions/NEConcatenateLayer.h" #include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h" #include "arm_compute/runtime/NEON/functions/NEConvolution.h" @@ -85,6 +86,7 @@ #include "arm_compute/runtime/NEON/functions/NEGaussian3x3.h" #include "arm_compute/runtime/NEON/functions/NEGaussian5x5.h" #include "arm_compute/runtime/NEON/functions/NEGaussianPyramid.h" +#include "arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h" #include "arm_compute/runtime/NEON/functions/NEHOGDescriptor.h" #include "arm_compute/runtime/NEON/functions/NEHOGDetector.h" #include "arm_compute/runtime/NEON/functions/NEHOGGradient.h" diff --git a/arm_compute/runtime/NEON/functions/NEComputeAllAnchors.h b/arm_compute/runtime/NEON/functions/NEComputeAllAnchors.h new file mode 100644 index 0000000000..5f24b3e618 --- /dev/null +++ b/arm_compute/runtime/NEON/functions/NEComputeAllAnchors.h @@ -0,0 +1,62 @@ +/* + * Copyright (c) 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. + */ +#ifndef __ARM_COMPUTE_NECOMPUTEALLANCHORS_H__ +#define __ARM_COMPUTE_NECOMPUTEALLANCHORS_H__ + +#include "arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h" +#include "arm_compute/runtime/NEON/INESimpleFunction.h" + +namespace arm_compute +{ +class ITensor; + +/** Basic function to run @ref NEComputeAllAnchorsKernel. + * + * This function calls the following NEON kernels: + * -# @ref NEComputeAllAnchorsKernel + */ +class NEComputeAllAnchors : public INESimpleFunction +{ +public: + /** Set the input and output tensors. + * + * @param[in] anchors Source tensor. Original set of anchors of size (4, A) where A is the number of anchors. Data types supported: F16/F32 + * @param[out] all_anchors Destination tensor. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input + * @param[in] info Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo + * + */ + void configure(const ITensor *anchors, ITensor *all_anchors, const ComputeAnchorsInfo &info); + + /** Static function to check if given info will lead to a valid configuration of @ref NEComputeAllAnchorsKernel + * + * @param[in] anchors Source tensor info. Original set of anchors of size (4, A) where A is the number of anchors. Data types supported: F16/F32 + * @param[in] all_anchors Destination tensor info. Destination anchors of size (4, H*W*A) where H and W are the height and width of the feature map and A is the number of anchors. Data types supported: Same as @p input + * @param[in] info Contains Compute Anchors operation information described in @ref ComputeAnchorsInfo + * + * @return a Status + */ + static Status validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info); +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_NECOMPUTEALLANCHORS_H__ */ diff --git a/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h b/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h new file mode 100644 index 0000000000..0e6601e4f9 --- /dev/null +++ b/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h @@ -0,0 +1,147 @@ +/* + * Copyright (c) 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. + */ +#ifndef __ARM_COMPUTE_NEGENERATEPROPOSALSLAYER_H__ +#define __ARM_COMPUTE_NEGENERATEPROPOSALSLAYER_H__ +#include "arm_compute/core/CPP/kernels/CPPBoxWithNonMaximaSuppressionLimitKernel.h" +#include "arm_compute/core/NEON/kernels/NEBoundingBoxTransformKernel.h" +#include "arm_compute/core/NEON/kernels/NECopyKernel.h" +#include "arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h" +#include "arm_compute/core/NEON/kernels/NEMemsetKernel.h" +#include "arm_compute/core/NEON/kernels/NEPermuteKernel.h" +#include "arm_compute/core/NEON/kernels/NEReshapeLayerKernel.h" +#include "arm_compute/core/NEON/kernels/NEStridedSliceKernel.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include "arm_compute/runtime/IFunction.h" +#include "arm_compute/runtime/MemoryGroup.h" +#include "arm_compute/runtime/Tensor.h" + +namespace arm_compute +{ +class ITensor; + +/** Basic function to generate proposals for a RPN (Region Proposal Network) + * + * This function calls the following Neon kernels: + * -# @ref NEComputeAllAnchors + * -# @ref NEPermute x 2 + * -# @ref NEReshapeLayer x 2 + * -# @ref NEStridedSlice x 3 + * -# @ref NEBoundingBoxTransform + * -# @ref NECopyKernel + * -# @ref NEMemsetKernel + * And the following CPP kernels: + * -# @ref CPPBoxWithNonMaximaSuppressionLimit + */ +class NEGenerateProposalsLayer : public IFunction +{ +public: + /** Default constructor + * + * @param[in] memory_manager (Optional) Memory manager. + */ + NEGenerateProposalsLayer(std::shared_ptr memory_manager = nullptr); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEGenerateProposalsLayer(const NEGenerateProposalsLayer &) = delete; + /** Default move constructor */ + NEGenerateProposalsLayer(NEGenerateProposalsLayer &&) = default; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + NEGenerateProposalsLayer &operator=(const NEGenerateProposalsLayer &) = delete; + /** Default move assignment operator */ + NEGenerateProposalsLayer &operator=(NEGenerateProposalsLayer &&) = default; + + /** Set the input and output tensors. + * + * @param[in] scores Scores from convolution layer of size (W, H, A), where H and W are the height and width of the feature map, and A is the number of anchors. Data types supported: F16/F32 + * @param[in] deltas Bounding box deltas from convolution layer of size (W, H, 4*A). Data types supported: Same as @p scores + * @param[in] anchors Anchors tensor of size (4, A). Data types supported: Same as @p input + * @param[out] proposals Box proposals output tensor of size (5, W*H*A). Data types supported: Same as @p input + * @param[out] scores_out Box scores output tensor of size (W*H*A). Data types supported: Same as @p input + * @param[out] num_valid_proposals Scalar output tensor which says which of the first proposals are valid. Data types supported: U32 + * @param[in] info Contains GenerateProposals operation information described in @ref GenerateProposalsInfo + * + * @note Only single image prediction is supported. Height and Width (and scale) of the image will be contained in the @ref GenerateProposalsInfo struct. + * @note Proposals contains all the proposals. Of those, only the first num_valid_proposals are valid. + */ + void configure(const ITensor *scores, const ITensor *deltas, const ITensor *anchors, ITensor *proposals, ITensor *scores_out, ITensor *num_valid_proposals, + const GenerateProposalsInfo &info); + + /** Static function to check if given info will lead to a valid configuration of @ref NEGenerateProposalsLayer + * + * @param[in] scores Scores info from convolution layer of size (W, H, A), where H and W are the height and width of the feature map, and A is the number of anchors. Data types supported: F16/F32 + * @param[in] deltas Bounding box deltas info from convolution layer of size (W, H, 4*A). Data types supported: Same as @p scores + * @param[in] anchors Anchors tensor info of size (4, A). Data types supported: Same as @p input + * @param[in] proposals Box proposals info output tensor of size (5, W*H*A). Data types supported: Data types supported: U32 + * @param[in] scores_out Box scores output tensor info of size (W*H*A). Data types supported: Same as @p input + * @param[in] num_valid_proposals Scalar output tensor info which says which of the first proposals are valid. Data types supported: Same as @p input + * @param[in] info Contains GenerateProposals operation information described in @ref GenerateProposalsInfo + * + * @return a Status + */ + static Status validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out, + const ITensorInfo *num_valid_proposals, + const GenerateProposalsInfo &info); + + // Inherited methods overridden: + void run() override; + +private: + // Memory group manager + MemoryGroup _memory_group; + + // Neon kernels + NEPermuteKernel _permute_deltas_kernel; + NEReshapeLayerKernel _flatten_deltas_kernel; + NEPermuteKernel _permute_scores_kernel; + NEReshapeLayerKernel _flatten_scores_kernel; + NEComputeAllAnchorsKernel _compute_anchors_kernel; + NEBoundingBoxTransformKernel _bounding_box_kernel; + NEMemsetKernel _memset_kernel; + NECopyKernel _padded_copy_kernel; + + // CPP kernels + CPPBoxWithNonMaximaSuppressionLimitKernel _cpp_nms_kernel; + + bool _is_nhwc; + + // Temporary tensors + Tensor _deltas_permuted; + Tensor _deltas_flattened; + Tensor _scores_permuted; + Tensor _scores_flattened; + Tensor _all_anchors; + Tensor _all_proposals; + Tensor _keeps_nms_unused; + Tensor _classes_nms_unused; + Tensor _proposals_4_roi_values; + + // Output tensor pointers + ITensor *_num_valid_proposals; + ITensor *_scores_out; + + /** Internal function to run the CPP BoxWithNMS kernel */ + void run_cpp_nms_kernel(); +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_NEGENERATEPROPOSALSLAYER_H__ */ diff --git a/src/core/NEON/kernels/NECopyKernel.cpp b/src/core/NEON/kernels/NECopyKernel.cpp index 4722c05507..83f3dded4f 100644 --- a/src/core/NEON/kernels/NECopyKernel.cpp +++ b/src/core/NEON/kernels/NECopyKernel.cpp @@ -29,28 +29,88 @@ #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" -using namespace arm_compute; +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PaddingList &padding = PaddingList()) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ERROR_ON(padding.size() > 4); + + // Validate output if initialized + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(misc::shape_calculator::compute_padded_shape(input->tensor_shape(), padding), output->tensor_shape()); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + } + + return Status{}; +} + +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *output) +{ + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, *input); + return std::make_pair(Status{}, calculate_max_window(*output)); +} + +std::pair validate_and_configure_window_with_padding(ITensorInfo *input, ITensorInfo *output, const PaddingList &padding) +{ + const TensorShape input_shape = input->tensor_shape(); + const TensorShape padded_shape = misc::shape_calculator::compute_padded_shape(input_shape, padding); + auto_init_if_empty(*output, input->clone()->set_tensor_shape(padded_shape)); + // Configure window + const Window win = calculate_max_window(*output, output->dimension(0)); + return std::make_pair(Status{}, win); +} + +} // namespace NECopyKernel::NECopyKernel() - : _input(nullptr), _output(nullptr) + : _input(nullptr), _output(nullptr), _padding() { } -void NECopyKernel::configure(const ITensor *input, ITensor *output) +void NECopyKernel::configure(const ITensor *input, ITensor *output, const PaddingList &padding) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), padding)); + + _input = input; + _output = output; + _padding = padding; - _input = input; - _output = output; + std::pair win_config; - INEKernel::configure(calculate_max_window(*output->info())); + if(padding.empty()) + { + win_config = validate_and_configure_window(input->info(), output->info()); + } + else + { + win_config = validate_and_configure_window_with_padding(input->info(), output->info(), padding); + } + + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + INEKernel::configure(win_config.second); } -Status NECopyKernel::validate(const arm_compute::ITensorInfo *input, const arm_compute::ITensorInfo *output) +Status NECopyKernel::validate(const arm_compute::ITensorInfo *input, const arm_compute::ITensorInfo *output, const PaddingList &padding) { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, padding)); + + if(padding.empty()) + { + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get()).first); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_with_padding(input->clone().get(), output->clone().get(), padding).first); + } + return Status{}; } @@ -60,22 +120,38 @@ void NECopyKernel::run(const Window &window, const ThreadInfo &info) ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window); - Window output_window{ window }; - output_window.set(Window::DimX, Window::Dimension(output_window.x().start(), output_window.x().end(), _input->info()->dimension(0))); - - Window out_slice = output_window.first_slice_window_1D(); + if(_padding.empty()) + { + Window output_window{ window }; + output_window.set(Window::DimX, Window::Dimension(output_window.x().start(), output_window.x().end(), _input->info()->dimension(0))); + Window out_slice = output_window.first_slice_window_1D(); + do + { + Iterator input_it(_input, out_slice); + Iterator output_it(_output, out_slice); - do + execute_window_loop(out_slice, [&](const Coordinates &) + { + memcpy(output_it.ptr(), input_it.ptr(), _output->info()->dimension(0) * _output->info()->element_size()); + }, + input_it, output_it); + } + while(output_window.slide_window_slice_1D(out_slice)); + } + else { - Iterator input_it(_input, out_slice); - Iterator output_it(_output, out_slice); + Window input_window{ window }; + input_window.set(Window::DimX, Window::Dimension(0, window.x().end() - _padding[0].first, _input->info()->dimension(0))); - execute_window_loop(out_slice, [&](const Coordinates &) + Iterator input_it(_input, input_window); + Iterator output_it(_output, window); + const size_t row_size_in_bytes = _input->info()->dimension(0) * _input->info()->element_size(); + execute_window_loop(window, [&](const Coordinates &) { - memcpy(output_it.ptr(), input_it.ptr(), _output->info()->dimension(0) * _output->info()->element_size()); + auto dst_ptr = output_it.ptr() + _padding[0].first * _output->info()->element_size(); + std::memcpy(dst_ptr, input_it.ptr(), row_size_in_bytes); }, input_it, output_it); - } - while(output_window.slide_window_slice_1D(out_slice)); } +} // namespace arm_compute diff --git a/src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp b/src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp new file mode 100644 index 0000000000..4a585b70fd --- /dev/null +++ b/src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp @@ -0,0 +1,131 @@ +/* + * Copyright (c) 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/NEGenerateProposalsLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CPP/Validate.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Window.h" + +namespace arm_compute +{ +namespace +{ +Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(anchors, all_anchors); + ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(anchors); + ARM_COMPUTE_RETURN_ERROR_ON(anchors->dimension(0) != info.values_per_roi()); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(anchors, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON(anchors->num_dimensions() > 2); + if(all_anchors->total_size() > 0) + { + const size_t feature_height = info.feat_height(); + const size_t feature_width = info.feat_width(); + const size_t num_anchors = anchors->dimension(1); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(all_anchors, anchors); + ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(0) != info.values_per_roi()); + ARM_COMPUTE_RETURN_ERROR_ON(all_anchors->dimension(1) != feature_height * feature_width * num_anchors); + } + return Status{}; +} + +} // namespace + +NEComputeAllAnchorsKernel::NEComputeAllAnchorsKernel() + : _anchors(nullptr), _all_anchors(nullptr), _anchors_info(0.f, 0.f, 0.f) +{ +} + +void NEComputeAllAnchorsKernel::configure(const ITensor *anchors, ITensor *all_anchors, const ComputeAnchorsInfo &info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(anchors, all_anchors); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(anchors->info(), all_anchors->info(), info)); + + // Metadata + const size_t num_anchors = anchors->info()->dimension(1); + const DataType data_type = anchors->info()->data_type(); + const float width = info.feat_width(); + const float height = info.feat_height(); + + // Initialize the output if empty + const TensorShape output_shape(info.values_per_roi(), width * height * num_anchors); + auto_init_if_empty(*all_anchors->info(), output_shape, 1, data_type); + + // Set instance variables + _anchors = anchors; + _all_anchors = all_anchors; + _anchors_info = info; + + Window win = calculate_max_window(*all_anchors->info(), Steps(info.values_per_roi())); + + INEKernel::configure(win); +} + +Status NEComputeAllAnchorsKernel::validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(anchors, all_anchors, info)); + return Status{}; +} + +void NEComputeAllAnchorsKernel::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); + + Iterator all_anchors_it(_all_anchors, window); + Iterator anchors_it(_all_anchors, window); + + const size_t num_anchors = _anchors->info()->dimension(1); + const float stride = 1.f / _anchors_info.spatial_scale(); + const size_t feat_width = _anchors_info.feat_width(); + + execute_window_loop(window, [&](const Coordinates & id) + { + const size_t anchor_offset = id.y() % num_anchors; + + const auto out_anchor_ptr = reinterpret_cast(all_anchors_it.ptr()); + const auto anchor_ptr = reinterpret_cast(_anchors->ptr_to_element(Coordinates(0, anchor_offset))); + + *out_anchor_ptr = *anchor_ptr; + *(1 + out_anchor_ptr) = *(1 + anchor_ptr); + *(2 + out_anchor_ptr) = *(2 + anchor_ptr); + *(3 + out_anchor_ptr) = *(3 + anchor_ptr); + + const size_t shift_idy = id.y() / num_anchors; + const float shiftx = (shift_idy % feat_width) * stride; + const float shifty = (shift_idy / feat_width) * stride; + + *out_anchor_ptr += shiftx; + *(out_anchor_ptr + 1) += shifty; + *(out_anchor_ptr + 2) += shiftx; + *(out_anchor_ptr + 3) += shifty; + }, + all_anchors_it); +} +} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEComputeAllAnchors.cpp b/src/runtime/NEON/functions/NEComputeAllAnchors.cpp new file mode 100644 index 0000000000..7702fb026d --- /dev/null +++ b/src/runtime/NEON/functions/NEComputeAllAnchors.cpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 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/runtime/NEON/functions/NEComputeAllAnchors.h" + +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +void NEComputeAllAnchors::configure(const ITensor *anchors, ITensor *all_anchors, const ComputeAnchorsInfo &info) +{ + // Configure ComputeAllAnchors kernel + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(anchors, all_anchors, info); + _kernel = std::move(k); +} + +Status NEComputeAllAnchors::validate(const ITensorInfo *anchors, const ITensorInfo *all_anchors, const ComputeAnchorsInfo &info) +{ + return NEComputeAllAnchorsKernel::validate(anchors, all_anchors, info); +} +} // namespace arm_compute diff --git a/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp new file mode 100644 index 0000000000..6e5da43a94 --- /dev/null +++ b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp @@ -0,0 +1,264 @@ +/* + * Copyright (c) 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/runtime/NEON/functions/NEGenerateProposalsLayer.h" + +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +NEGenerateProposalsLayer::NEGenerateProposalsLayer(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), + _permute_deltas_kernel(), + _flatten_deltas_kernel(), + _permute_scores_kernel(), + _flatten_scores_kernel(), + _compute_anchors_kernel(), + _bounding_box_kernel(), + _memset_kernel(), + _padded_copy_kernel(), + _cpp_nms_kernel(), + _is_nhwc(false), + _deltas_permuted(), + _deltas_flattened(), + _scores_permuted(), + _scores_flattened(), + _all_anchors(), + _all_proposals(), + _keeps_nms_unused(), + _classes_nms_unused(), + _proposals_4_roi_values(), + _num_valid_proposals(nullptr), + _scores_out(nullptr) +{ +} + +void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *deltas, const ITensor *anchors, ITensor *proposals, ITensor *scores_out, ITensor *num_valid_proposals, + const GenerateProposalsInfo &info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals); + ARM_COMPUTE_ERROR_THROW_ON(NEGenerateProposalsLayer::validate(scores->info(), deltas->info(), anchors->info(), proposals->info(), scores_out->info(), num_valid_proposals->info(), info)); + + _is_nhwc = scores->info()->data_layout() == DataLayout::NHWC; + const DataType data_type = deltas->info()->data_type(); + const int num_anchors = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::CHANNEL)); + const int feat_width = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::WIDTH)); + const int feat_height = scores->info()->dimension(get_data_layout_dimension_index(scores->info()->data_layout(), DataLayoutDimension::HEIGHT)); + const int total_num_anchors = num_anchors * feat_width * feat_height; + const int pre_nms_topN = info.pre_nms_topN(); + const int post_nms_topN = info.post_nms_topN(); + const size_t values_per_roi = info.values_per_roi(); + + // Compute all the anchors + _memory_group.manage(&_all_anchors); + _compute_anchors_kernel.configure(anchors, &_all_anchors, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale())); + + const TensorShape flatten_shape_deltas(values_per_roi, total_num_anchors); + _deltas_flattened.allocator()->init(TensorInfo(flatten_shape_deltas, 1, data_type)); + _memory_group.manage(&_deltas_flattened); + + // Permute and reshape deltas + if(!_is_nhwc) + { + _memory_group.manage(&_deltas_permuted); + _permute_deltas_kernel.configure(deltas, &_deltas_permuted, PermutationVector{ 2, 0, 1 }); + _flatten_deltas_kernel.configure(&_deltas_permuted, &_deltas_flattened); + _deltas_permuted.allocator()->allocate(); + } + else + { + _flatten_deltas_kernel.configure(deltas, &_deltas_flattened); + } + + const TensorShape flatten_shape_scores(1, total_num_anchors); + _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, data_type)); + _memory_group.manage(&_scores_flattened); + // Permute and reshape scores + if(!_is_nhwc) + { + _memory_group.manage(&_scores_permuted); + _permute_scores_kernel.configure(scores, &_scores_permuted, PermutationVector{ 2, 0, 1 }); + _flatten_scores_kernel.configure(&_scores_permuted, &_scores_flattened); + _scores_permuted.allocator()->allocate(); + } + else + { + _flatten_scores_kernel.configure(scores, &_scores_flattened); + } + + // Bounding box transform + _memory_group.manage(&_all_proposals); + BoundingBoxTransformInfo bbox_info(info.im_width(), info.im_height(), 1.f); + _bounding_box_kernel.configure(&_all_anchors, &_all_proposals, &_deltas_flattened, bbox_info); + _deltas_flattened.allocator()->allocate(); + _all_anchors.allocator()->allocate(); + + // The original layer implementation first selects the best pre_nms_topN anchors (thus having a lightweight sort) + // that are then transformed by bbox_transform. The boxes generated are then fed into a non-sorting NMS operation. + // Since we are reusing the NMS layer and we don't implement any CL/sort, we let NMS do the sorting (of all the input) + // and the filtering + const int scores_nms_size = std::min(std::min(post_nms_topN, pre_nms_topN), total_num_anchors); + const float min_size_scaled = info.min_size() * info.im_scale(); + _memory_group.manage(&_classes_nms_unused); + _memory_group.manage(&_keeps_nms_unused); + + // Note that NMS needs outputs preinitialized. + auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, data_type); + auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, data_type); + auto_init_if_empty(*num_valid_proposals->info(), TensorShape(scores_nms_size), 1, DataType::U32); + + // Initialize temporaries (unused) outputs + _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(8, 1), 1, data_type)); + _keeps_nms_unused.allocator()->init(*scores_out->info()); + + // Save the output (to map and unmap them at run) + _scores_out = scores_out; + _num_valid_proposals = num_valid_proposals; + + _memory_group.manage(&_proposals_4_roi_values); + + const BoxNMSLimitInfo box_nms_info(0.0f, info.nms_thres(), scores_nms_size, false, NMSType::LINEAR, 0.5f, 0.001f, true, min_size_scaled, info.im_width(), info.im_height()); + _cpp_nms_kernel.configure(&_scores_flattened /*scores_in*/, + &_all_proposals /*boxes_in,*/, + nullptr /* batch_splits_in*/, + scores_out /* scores_out*/, + &_proposals_4_roi_values /*boxes_out*/, + &_classes_nms_unused /*classes*/, + nullptr /*batch_splits_out*/, + &_keeps_nms_unused /*keeps*/, + num_valid_proposals /* keeps_size*/, + box_nms_info); + + _keeps_nms_unused.allocator()->allocate(); + _classes_nms_unused.allocator()->allocate(); + _all_proposals.allocator()->allocate(); + _scores_flattened.allocator()->allocate(); + + // Add the first column that represents the batch id. This will be all zeros, as we don't support multiple images + _padded_copy_kernel.configure(&_proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } }); + _proposals_4_roi_values.allocator()->allocate(); + + _memset_kernel.configure(proposals, PixelValue()); +} + +Status NEGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITensorInfo *deltas, const ITensorInfo *anchors, const ITensorInfo *proposals, const ITensorInfo *scores_out, + const ITensorInfo *num_valid_proposals, const GenerateProposalsInfo &info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(scores, deltas, anchors, proposals, scores_out, num_valid_proposals); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(scores, deltas); + + const int num_anchors = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::CHANNEL)); + const int feat_width = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::WIDTH)); + const int feat_height = scores->dimension(get_data_layout_dimension_index(scores->data_layout(), DataLayoutDimension::HEIGHT)); + const int num_images = scores->dimension(3); + const int total_num_anchors = num_anchors * feat_width * feat_height; + const int values_per_roi = info.values_per_roi(); + + ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1); + + TensorInfo all_anchors_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true)); + ARM_COMPUTE_RETURN_ON_ERROR(NEComputeAllAnchorsKernel::validate(anchors, &all_anchors_info, ComputeAnchorsInfo(feat_width, feat_height, info.spatial_scale()))); + + TensorInfo deltas_permuted_info = deltas->clone()->set_tensor_shape(TensorShape(values_per_roi * num_anchors, feat_width, feat_height)).set_is_resizable(true); + TensorInfo scores_permuted_info = scores->clone()->set_tensor_shape(TensorShape(num_anchors, feat_width, feat_height)).set_is_resizable(true); + if(scores->data_layout() == DataLayout::NHWC) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(deltas, &deltas_permuted_info); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(scores, &scores_permuted_info); + } + else + { + ARM_COMPUTE_RETURN_ON_ERROR(NEPermuteKernel::validate(deltas, &deltas_permuted_info, PermutationVector{ 2, 0, 1 })); + ARM_COMPUTE_RETURN_ON_ERROR(NEPermuteKernel::validate(scores, &scores_permuted_info, PermutationVector{ 2, 0, 1 })); + } + + TensorInfo deltas_flattened_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true)); + ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(&deltas_permuted_info, &deltas_flattened_info)); + + TensorInfo scores_flattened_info(scores->clone()->set_tensor_shape(TensorShape(1, total_num_anchors)).set_is_resizable(true)); + TensorInfo proposals_4_roi_values(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true)); + + ARM_COMPUTE_RETURN_ON_ERROR(NEReshapeLayerKernel::validate(&scores_permuted_info, &scores_flattened_info)); + ARM_COMPUTE_RETURN_ON_ERROR(NEBoundingBoxTransformKernel::validate(&all_anchors_info, &proposals_4_roi_values, &deltas_flattened_info, BoundingBoxTransformInfo(info.im_width(), info.im_height(), + 1.f))); + + ARM_COMPUTE_RETURN_ON_ERROR(NECopyKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 0, 1 } })); + + if(num_valid_proposals->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(num_valid_proposals->dimension(0) > 1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(num_valid_proposals, 1, DataType::U32); + } + + if(proposals->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON(proposals->num_dimensions() > 2); + ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(0) != size_t(values_per_roi) + 1); + ARM_COMPUTE_RETURN_ERROR_ON(proposals->dimension(1) != size_t(total_num_anchors)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, deltas); + } + + if(scores_out->total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON(scores_out->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(scores_out->dimension(0) != size_t(total_num_anchors)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(scores_out, scores); + } + + return Status{}; +} + +void NEGenerateProposalsLayer::run() +{ + // Acquire all the temporaries + MemoryGroupResourceScope scope_mg(_memory_group); + + // Compute all the anchors + NEScheduler::get().schedule(&_compute_anchors_kernel, Window::DimY); + + // Transpose and reshape the inputs + if(!_is_nhwc) + { + NEScheduler::get().schedule(&_permute_deltas_kernel, Window::DimY); + NEScheduler::get().schedule(&_permute_scores_kernel, Window::DimY); + } + + NEScheduler::get().schedule(&_flatten_deltas_kernel, Window::DimY); + NEScheduler::get().schedule(&_flatten_scores_kernel, Window::DimY); + + // Build the boxes + NEScheduler::get().schedule(&_bounding_box_kernel, Window::DimY); + + // Non maxima suppression + CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX); + + // Add dummy batch indexes + + NEScheduler::get().schedule(&_memset_kernel, Window::DimY); + NEScheduler::get().schedule(&_padded_copy_kernel, Window::DimY); +} +} // namespace arm_compute diff --git a/tests/validation/NEON/GenerateProposalsLayer.cpp b/tests/validation/NEON/GenerateProposalsLayer.cpp new file mode 100644 index 0000000000..ea99bb3107 --- /dev/null +++ b/tests/validation/NEON/GenerateProposalsLayer.cpp @@ -0,0 +1,403 @@ +/* + * Copyright (c) 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/runtime/NEON/NEScheduler.h" +#include "arm_compute/runtime/NEON/functions/NEComputeAllAnchors.h" +#include "arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h" +#include "arm_compute/runtime/NEON/functions/NEPermute.h" +#include "arm_compute/runtime/NEON/functions/NESlice.h" +#include "tests/Globals.h" +#include "tests/NEON/Accessor.h" +#include "tests/NEON/ArrayAccessor.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/ComputeAllAnchorsFixture.h" +#include "utils/TypePrinter.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +template +inline void fill_tensor(U &&tensor, const std::vector &v) +{ + std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); +} + +template +inline void fill_tensor(Accessor &&tensor, const std::vector &v) +{ + if(tensor.data_layout() == DataLayout::NCHW) + { + std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size()); + } + else + { + const int channels = tensor.shape()[0]; + const int width = tensor.shape()[1]; + const int height = tensor.shape()[2]; + for(int x = 0; x < width; ++x) + { + for(int y = 0; y < height; ++y) + { + for(int c = 0; c < channels; ++c) + { + *(reinterpret_cast(tensor(Coordinates(c, x, y)))) = *(reinterpret_cast(v.data() + x + y * width + c * height * width)); + } + } + } + } +} + +const auto ComputeAllInfoDataset = framework::dataset::make("ComputeAllInfo", +{ + ComputeAnchorsInfo(10U, 10U, 1. / 16.f), + ComputeAnchorsInfo(100U, 1U, 1. / 2.f), + ComputeAnchorsInfo(100U, 1U, 1. / 4.f), + ComputeAnchorsInfo(100U, 100U, 1. / 4.f), + +}); +} // namespace + +TEST_SUITE(NEON) +TEST_SUITE(GenerateProposals) + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( + framework::dataset::make("scores", { TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F32), + TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Mismatching types + TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong deltas (number of transformation non multiple of 4) + TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong anchors (number of values per roi != 5) + TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Output tensor num_valid_proposals not scalar + TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16)}), // num_valid_proposals not U32 + framework::dataset::make("deltas",{ TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32), + TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32), + TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32), + TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32), + TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32), + TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32)})), + framework::dataset::make("anchors", { TensorInfo(TensorShape(4U, 9U), 1, DataType::F32), + TensorInfo(TensorShape(4U, 9U), 1, DataType::F32), + TensorInfo(TensorShape(4U, 9U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 9U), 1, DataType::F32), + TensorInfo(TensorShape(4U, 9U), 1, DataType::F32), + TensorInfo(TensorShape(4U, 9U), 1, DataType::F32)})), + framework::dataset::make("proposals", { TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32)})), + framework::dataset::make("scores_out", { TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32), + TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32)})), + framework::dataset::make("num_valid_proposals", { TensorInfo(TensorShape(1U, 1U), 1, DataType::U32), + TensorInfo(TensorShape(1U, 1U), 1, DataType::U32), + TensorInfo(TensorShape(1U, 1U), 1, DataType::U32), + TensorInfo(TensorShape(1U, 1U), 1, DataType::U32), + TensorInfo(TensorShape(1U, 10U), 1, DataType::U32), + TensorInfo(TensorShape(1U, 1U), 1, DataType::F16)})), + framework::dataset::make("generate_proposals_info", { GenerateProposalsInfo(10.f, 10.f, 1.f), + GenerateProposalsInfo(10.f, 10.f, 1.f), + GenerateProposalsInfo(10.f, 10.f, 1.f), + GenerateProposalsInfo(10.f, 10.f, 1.f), + GenerateProposalsInfo(10.f, 10.f, 1.f), + GenerateProposalsInfo(10.f, 10.f, 1.f)})), + framework::dataset::make("Expected", { true, false, false, false, false, false })), + scores, deltas, anchors, proposals, scores_out, num_valid_proposals, generate_proposals_info, expected) +{ + ARM_COMPUTE_EXPECT(bool(NEGenerateProposalsLayer::validate(&scores.clone()->set_is_resizable(true), + &deltas.clone()->set_is_resizable(true), + &anchors.clone()->set_is_resizable(true), + &proposals.clone()->set_is_resizable(true), + &scores_out.clone()->set_is_resizable(true), + &num_valid_proposals.clone()->set_is_resizable(true), + generate_proposals_info)) == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* + +template +using NEComputeAllAnchorsFixture = ComputeAllAnchorsFixture; + +TEST_SUITE(Float) +TEST_SUITE(FP32) +DATA_TEST_CASE(IntegrationTestCaseAllAnchors, framework::DatasetMode::ALL, framework::dataset::make("DataType", { DataType::F32 }), + data_type) +{ + const int values_per_roi = 4; + const int num_anchors = 3; + const int feature_height = 4; + const int feature_width = 3; + + SimpleTensor anchors_expected(TensorShape(values_per_roi, feature_width * feature_height * num_anchors), DataType::F32); + fill_tensor(anchors_expected, std::vector { -26, -19, 87, 86, + -81, -27, 58, 63, + -44, -15, 55, 36, + -10, -19, 103, 86, + -65, -27, 74, 63, + -28, -15, 71, 36, + 6, -19, 119, 86, + -49, -27, 90, 63, + -12, -15, 87, 36, + -26, -3, 87, 102, + -81, -11, 58, 79, + -44, 1, 55, 52, + -10, -3, 103, 102, + -65, -11, 74, 79, + -28, 1, 71, 52, + 6, -3, 119, 102, + -49, -11, 90, 79, + -12, 1, 87, 52, + -26, 13, 87, 118, + -81, 5, 58, 95, + -44, 17, 55, 68, + -10, 13, 103, 118, + -65, 5, 74, 95, + -28, 17, 71, 68, + 6, 13, 119, 118, + -49, 5, 90, 95, + -12, 17, 87, 68, + -26, 29, 87, 134, + -81, 21, 58, 111, + -44, 33, 55, 84, + -10, 29, 103, 134, + -65, 21, 74, 111, + -28, 33, 71, 84, + 6, 29, 119, 134, + -49, 21, 90, 111, + -12, 33, 87, 84 + }); + + Tensor all_anchors; + Tensor anchors = create_tensor(TensorShape(4, num_anchors), data_type); + + // Create and configure function + NEComputeAllAnchors compute_anchors; + compute_anchors.configure(&anchors, &all_anchors, ComputeAnchorsInfo(feature_width, feature_height, 1. / 16.0)); + anchors.allocator()->allocate(); + all_anchors.allocator()->allocate(); + + fill_tensor(Accessor(anchors), std::vector { -26, -19, 87, 86, + -81, -27, 58, 63, + -44, -15, 55, 36 + }); + // Compute function + compute_anchors.run(); + validate(Accessor(all_anchors), anchors_expected); +} + +DATA_TEST_CASE(IntegrationTestCaseGenerateProposals, framework::DatasetMode::ALL, combine(framework::dataset::make("DataType", { DataType::F32 }), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + data_type, data_layout) +{ + const int values_per_roi = 4; + const int num_anchors = 2; + const int feature_height = 4; + const int feature_width = 5; + + std::vector scores_vector + { + 5.055894435664012e-04f, 1.270304909820112e-03f, 2.492271113912067e-03f, 5.951663827809190e-03f, + 7.846917156877404e-03f, 6.776275276294789e-03f, 6.761571012891965e-03f, 4.898292096237725e-03f, + 6.044472332578605e-04f, 3.203334118759474e-03f, 2.947527908919908e-03f, 6.313238560015770e-03f, + 7.931767757095738e-03f, 8.764345805102866e-03f, 7.325012199914913e-03f, 4.317069470446271e-03f, + 2.372537409795522e-03f, 1.589227460352735e-03f, 7.419477503600818e-03f, 3.157690354133824e-05f, + 1.125915135986472e-03f, 9.865363483872330e-03f, 2.429454743386769e-03f, 2.724460564167563e-03f, + 7.670409838207963e-03f, 5.558891552328172e-03f, 7.876904873099614e-03f, 6.824746047239291e-03f, + 7.023817548067892e-03f, 3.651314909238673e-04f, 6.720443709032501e-03f, 5.935615511606155e-03f, + 2.837349642759774e-03f, 1.787235113610299e-03f, 4.538568889918262e-03f, 3.391510678188818e-03f, + 7.328474239481874e-03f, 6.306967923936016e-03f, 8.102218904895860e-04f, 3.366646521610209e-03f + }; + + std::vector bbx_vector + { + 5.066650471856862e-03, -7.638671742936328e-03, 2.549596503988635e-03, -8.316416756423296e-03, + -2.397471917924575e-04, 7.370595187754891e-03, -2.771880178185262e-03, 3.958364873973579e-03, + 4.493661094712284e-03, 2.016487051533088e-03, -5.893883038142033e-03, 7.570636080807809e-03, + -1.395511229386785e-03, 3.686686052704696e-03, -7.738166245767079e-03, -1.947306329828059e-03, + -9.299719716045681e-03, -3.476410493413708e-03, -2.390761190919604e-03, 4.359281254364210e-03, + -2.135251160164030e-04, 9.203299843371962e-03, 4.042322775006053e-03, -9.464271243910754e-03, + 2.566239543229305e-03, -9.691093900220627e-03, -4.019283034310979e-03, 8.145470429508792e-03, + 7.345087308315662e-04, 7.049642787384043e-03, -2.768492313674294e-03, 6.997160053405803e-03, + 6.675346697112969e-03, 2.353293365652274e-03, -3.612002585241749e-04, 1.592076522068768e-03, + -8.354188900818149e-04, -5.232515333564140e-04, 6.946683728847089e-03, -8.469757407935994e-03, + -8.985324496496555e-03, 4.885832859017961e-03, -7.662967577576512e-03, 7.284124004335807e-03, + -5.812167510299458e-03, -5.760336800482398e-03, 6.040416930336549e-03, 5.861508595443691e-03, + -5.509243096133549e-04, -2.006142470055888e-03, -7.205925340416066e-03, -1.117459082969758e-03, + 4.233247017623154e-03, 8.079257498201178e-03, 2.962639022639513e-03, 7.069474943472751e-03, + -8.562946284971293e-03, -8.228634642768271e-03, -6.116245322799971e-04, -7.213122000180859e-03, + 1.693094399433209e-03, -4.287504459132290e-03, 8.740365683925144e-03, 3.751788160720638e-03, + 7.006764222862830e-03, 9.676754678358187e-03, -6.458757235812945e-03, -4.486506575589758e-03, + -4.371087196816259e-03, 3.542166755953152e-03, -2.504808998699504e-03, 5.666601724512010e-03, + -3.691862724546129e-03, 3.689809719085287e-03, 9.079930264704458e-03, 6.365127787359476e-03, + 2.881681788246101e-06, 9.991866069315165e-03, -1.104757466496565e-03, -2.668455405633477e-03, + -1.225748887087659e-03, 6.530536159094015e-03, 3.629468917975644e-03, 1.374426066950348e-03, + -2.404098881570632e-03, -4.791365049441602e-03, -2.970654027009094e-03, 7.807553690294366e-03, + -1.198321129505323e-03, -3.574885336949881e-03, -5.380848303732298e-03, 9.705151282165116e-03, + -1.005217683242201e-03, 9.178094036278405e-03, -5.615977269541644e-03, 5.333533158509859e-03, + -2.817116206168516e-03, 6.672609782000503e-03, 6.575769501651313e-03, 8.987596634989362e-03, + -1.283530791296188e-03, 1.687717120057778e-03, 3.242391851439037e-03, -7.312060454341677e-03, + 4.735335326324270e-03, -6.832367028817463e-03, -5.414854835884652e-03, -9.352380213755996e-03, + -3.682662043703889e-03, -6.127508590419776e-04, -7.682256596819467e-03, 9.569532628790246e-03, + -1.572157284518933e-03, -6.023034366859191e-03, -5.110873282582924e-03, -8.697072236660256e-03, + -3.235150419663566e-03, -8.286320236471386e-03, -5.229472409112913e-03, 9.920785896115053e-03, + -2.478413362126123e-03, -9.261324796935007e-03, 1.718512310840434e-04, 3.015875488208480e-03, + -6.172932549255669e-03, -4.031715551985103e-03, -9.263878005853677e-03, -2.815310738453385e-03, + 7.075307462133643e-03, 1.404611747938669e-03, -1.518548732533266e-03, -9.293430941655778e-03, + 6.382186966633246e-03, 8.256835789169248e-03, 3.196907843506736e-03, 8.821615689753433e-03, + -7.661543424832439e-03, 1.636273081822326e-03, -8.792373335756125e-03, 2.958775812049877e-03, + -6.269300278071262e-03, 6.248285790856450e-03, -3.675414624536002e-03, -1.692616700318762e-03, + 4.126007647815893e-03, -9.155291689759584e-03, -8.432616039924004e-03, 4.899980636213323e-03, + 3.511535019681671e-03, -1.582745757177339e-03, -2.703657774917963e-03, 6.738168990840388e-03, + 4.300455303937919e-03, 9.618312854781494e-03, 2.762142918402472e-03, -6.590025003382154e-03, + -2.071168373801788e-03, 8.613893943683627e-03, 9.411190295341036e-03, -6.129018930548372e-03 + }; + + const std::vector anchors_vector{ -26, -19, 87, 86, -81, -27, 58, 63 }; + ; + + SimpleTensor proposals_expected(TensorShape(5, 9), DataType::F32); + fill_tensor(proposals_expected, std::vector + { + 0, 0, 0, 75.269, 64.4388, + 0, 21.9579, 13.0535, 119, 99, + 0, 38.303, 0, 119, 87.6447, + 0, 0, 0, 119, 64.619, + 0, 0, 20.7997, 74.0714, 99, + 0, 0, 0, 91.8963, 79.3724, + 0, 0, 4.42377, 58.1405, 95.1781, + 0, 0, 13.4405, 104.799, 99, + 0, 38.9066, 28.2434, 119, 99, + + }); + + SimpleTensor scores_expected(TensorShape(9), DataType::F32); + fill_tensor(scores_expected, std::vector + { + 0.00986536, + 0.00876435, + 0.00784692, + 0.00767041, + 0.00732847, + 0.00682475, + 0.00672044, + 0.00631324, + 3.15769e-05 + }); + + TensorShape scores_shape = TensorShape(feature_width, feature_height, num_anchors); + TensorShape deltas_shape = TensorShape(feature_width, feature_height, values_per_roi * num_anchors); + if(data_layout == DataLayout::NHWC) + { + permute(scores_shape, PermutationVector(2U, 0U, 1U)); + permute(deltas_shape, PermutationVector(2U, 0U, 1U)); + } + // Inputs + Tensor scores = create_tensor(scores_shape, data_type, 1, QuantizationInfo(), data_layout); + Tensor bbox_deltas = create_tensor(deltas_shape, data_type, 1, QuantizationInfo(), data_layout); + Tensor anchors = create_tensor(TensorShape(values_per_roi, num_anchors), data_type); + + // Outputs + Tensor proposals; + Tensor num_valid_proposals; + Tensor scores_out; + num_valid_proposals.allocator()->init(TensorInfo(TensorShape(1), 1, DataType::U32)); + + NEGenerateProposalsLayer generate_proposals; + generate_proposals.configure(&scores, &bbox_deltas, &anchors, &proposals, &scores_out, &num_valid_proposals, + GenerateProposalsInfo(120, 100, 0.166667f, 1 / 16.0, 6000, 300, 0.7f, 16.0f)); + + // Allocate memory for input/output tensors + scores.allocator()->allocate(); + bbox_deltas.allocator()->allocate(); + anchors.allocator()->allocate(); + proposals.allocator()->allocate(); + num_valid_proposals.allocator()->allocate(); + scores_out.allocator()->allocate(); + // Fill inputs + fill_tensor(Accessor(scores), scores_vector); + fill_tensor(Accessor(bbox_deltas), bbx_vector); + fill_tensor(Accessor(anchors), anchors_vector); + + // Run operator + generate_proposals.run(); + // Gather num_valid_proposals + const uint32_t N = *reinterpret_cast(num_valid_proposals.ptr_to_element(Coordinates(0, 0))); + + // Select the first N entries of the proposals + Tensor proposals_final; + NESlice select_proposals; + select_proposals.configure(&proposals, &proposals_final, Coordinates(0, 0), Coordinates(values_per_roi + 1, N)); + + proposals_final.allocator()->allocate(); + select_proposals.run(); + + // Select the first N entries of the proposals + Tensor scores_final; + NESlice select_scores; + select_scores.configure(&scores_out, &scores_final, Coordinates(0), Coordinates(N)); + scores_final.allocator()->allocate(); + select_scores.run(); + + const RelativeTolerance tolerance_f32(1e-5f); + // Validate the output + validate(Accessor(proposals_final), proposals_expected, tolerance_f32); + validate(Accessor(scores_final), scores_expected, tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsFixture, framework::DatasetMode::ALL, + combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F32 }))) +{ + // Validate output + validate(Accessor(_target), _reference); +} +TEST_SUITE_END() // FP32 +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsFixture, framework::DatasetMode::ALL, + combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F16 }))) +{ + // Validate output + validate(Accessor(_target), _reference); +} +TEST_SUITE_END() // FP16 +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + +TEST_SUITE_END() // Float + +TEST_SUITE_END() // GenerateProposals +TEST_SUITE_END() // NEON + +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/fixtures/ComputeAllAnchorsFixture.h b/tests/validation/fixtures/ComputeAllAnchorsFixture.h index bfa43ceafc..6f2db3e623 100644 --- a/tests/validation/fixtures/ComputeAllAnchorsFixture.h +++ b/tests/validation/fixtures/ComputeAllAnchorsFixture.h @@ -78,7 +78,7 @@ protected: ARM_COMPUTE_EXPECT(!all_anchors.info()->is_resizable(), framework::LogLevel::ERRORS); // Fill tensors - fill(CLAccessor(anchors)); + fill(AccessorType(anchors)); // Compute function compute_all_anchors.run(); -- cgit v1.2.1