From 58c71efe07031fc7ba82e61e2cdca8ae5ea13a8a Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Mon, 30 Sep 2019 15:03:21 +0100 Subject: COMPMID-2257: Add support for QASYMM8 in NEGenerateProposals Change-Id: I7d9aa21ecac97847fce209f97dff0dea6e62790a Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/2020 Tested-by: Arm Jenkins Reviewed-by: Pablo Marquez Comments-Addressed: Arm Jenkins --- .../NEON/kernels/NEGenerateProposalsLayerKernel.h | 7 +- .../core/NEON/kernels/NEStridedSliceKernel.h | 4 +- .../CL/functions/CLGenerateProposalsLayer.h | 2 +- .../NEON/functions/NEGenerateProposalsLayer.h | 43 ++++-- .../kernels/NEGenerateProposalsLayerKernel.cpp | 103 ++++++++++--- src/core/NEON/kernels/NEStridedSliceKernel.cpp | 2 +- .../NEON/functions/NEGenerateProposalsLayer.cpp | 171 ++++++++++++++++----- tests/validation/NEON/GenerateProposalsLayer.cpp | 19 ++- 8 files changed, 273 insertions(+), 78 deletions(-) diff --git a/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h b/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h index a7b2603648..9ee9d5dd08 100644 --- a/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h @@ -53,7 +53,7 @@ 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[in] anchors Source tensor. Original set of anchors of size (4, A), where A is the number of anchors. Data types supported: QSYMM16/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 * @@ -62,7 +62,7 @@ public: /** 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] anchors Source tensor info. Original set of anchors of size (4, A), where A is the number of anchors. Data types supported: QSYMM16/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 * @@ -74,6 +74,9 @@ public: void run(const Window &window, const ThreadInfo &info) override; private: + template + void internal_run(const Window &window, const ThreadInfo &info); + const ITensor *_anchors; ITensor *_all_anchors; ComputeAnchorsInfo _anchors_info; diff --git a/arm_compute/core/NEON/kernels/NEStridedSliceKernel.h b/arm_compute/core/NEON/kernels/NEStridedSliceKernel.h index a272a8118b..12075207b1 100644 --- a/arm_compute/core/NEON/kernels/NEStridedSliceKernel.h +++ b/arm_compute/core/NEON/kernels/NEStridedSliceKernel.h @@ -58,7 +58,7 @@ public: * * @note Supported tensor rank: up to 4 * - * @param[in] input Source tensor. Data type supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[in] input Source tensor. Data type supported: U8/S8/QASYMM8/U16/S16/QASYMM16/QSYMM16/U32/S32/F16/F32 * @param[out] output Destination tensor. Data type supported: Same as @p input * @param[in] starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input). * @param[in] ends The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input). @@ -76,7 +76,7 @@ public: * * @note Supported tensor rank: up to 4 * - * @param[in] input Source tensor info. Data type supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32 + * @param[in] input Source tensor info. Data type supported: U8/S8/QASYMM8/U16/S16/QASYMM16/QSYMM16/U32/S32/F16/F32 * @param[in] output Destination tensor info. Data type supported: Same as @p input * @param[in] starts The starts of the dimensions of the input tensor to be sliced. The length must be of rank(input). * @param[in] ends The ends of the dimensions of the input tensor to be sliced. The length must be of rank(input). diff --git a/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h b/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h index 827f19d130..e14e195ec6 100644 --- a/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h +++ b/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h @@ -50,7 +50,7 @@ class ICLTensor; * -# @ref CLReshapeLayer x 2 * -# @ref CLBoundingBoxTransform * -# @ref CLPadLayerKernel - * -# @ref CLDequantizationLayerKernel + * -# @ref CLDequantizationLayerKernel x 2 * -# @ref CLQuantizationLayerKernel * And the following CPP functions: * -# @ref CPPBoxWithNonMaximaSuppressionLimit diff --git a/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h b/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h index c6d3628e37..cd370a03dd 100644 --- a/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h +++ b/arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h @@ -23,15 +23,16 @@ */ #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/NEDequantizationLayerKernel.h" #include "arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h" #include "arm_compute/core/NEON/kernels/NEPadLayerKernel.h" #include "arm_compute/core/NEON/kernels/NEPermuteKernel.h" +#include "arm_compute/core/NEON/kernels/NEQuantizationLayerKernel.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/CPP/functions/CPPBoxWithNonMaximaSuppressionLimit.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/MemoryGroup.h" #include "arm_compute/runtime/Tensor.h" @@ -46,9 +47,10 @@ class ITensor; * -# @ref NEComputeAllAnchors * -# @ref NEPermute x 2 * -# @ref NEReshapeLayer x 2 - * -# @ref NEStridedSlice x 3 * -# @ref NEBoundingBoxTransform * -# @ref NEPadLayerKernel + * -# @ref NEDequantizationLayerKernel x 2 + * -# @ref NEQuantizationLayerKernel * And the following CPP kernels: * -# @ref CPPBoxWithNonMaximaSuppressionLimit */ @@ -71,10 +73,12 @@ public: /** 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] 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: QASYMM8/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[in] anchors Anchors tensor of size (4, A). Data types supported: QSYMM16 with scale of 0.125 if @p scores is QASYMM8, otherwise same as @p scores + * @param[out] proposals Box proposals output tensor of size (5, W*H*A). + * Data types supported: QASYMM16 with scale of 0.125 and 0 offset if @p scores is QASYMM8, otherwise same as @p scores * @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 @@ -87,12 +91,14 @@ public: /** 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] 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: QASYMM8/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] anchors Anchors tensor info of size (4, A). Data types supported: QSYMM16 with scale of 0.125 if @p scores is QASYMM8, otherwise same as @p scores + * @param[in] proposals Box proposals info output tensor of size (5, W*H*A). + * Data types supported: QASYMM16 with scale of 0.125 and 0 offset if @p scores is QASYMM8, otherwise same as @p scores * @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] num_valid_proposals Scalar output tensor info which says which of the first proposals are valid. Data types supported: U32 * @param[in] info Contains GenerateProposals operation information described in @ref GenerateProposalsInfo * * @return a Status @@ -116,29 +122,36 @@ private: NEComputeAllAnchorsKernel _compute_anchors_kernel; NEBoundingBoxTransformKernel _bounding_box_kernel; NEPadLayerKernel _pad_kernel; + NEDequantizationLayerKernel _dequantize_anchors; + NEDequantizationLayerKernel _dequantize_deltas; + NEQuantizationLayerKernel _quantize_all_proposals; - // CPP kernels - CPPBoxWithNonMaximaSuppressionLimitKernel _cpp_nms_kernel; + // CPP functions + CPPBoxWithNonMaximaSuppressionLimit _cpp_nms; bool _is_nhwc; + bool _is_qasymm8; // Temporary tensors Tensor _deltas_permuted; Tensor _deltas_flattened; + Tensor _deltas_flattened_f32; Tensor _scores_permuted; Tensor _scores_flattened; Tensor _all_anchors; + Tensor _all_anchors_f32; Tensor _all_proposals; + Tensor _all_proposals_quantized; Tensor _keeps_nms_unused; Tensor _classes_nms_unused; Tensor _proposals_4_roi_values; + // Temporary tensor pointers + Tensor *_all_proposals_to_use; + // 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/NEGenerateProposalsLayerKernel.cpp b/src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp index 4a585b70fd..ba5ca78955 100644 --- a/src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp +++ b/src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp @@ -30,6 +30,8 @@ #include "arm_compute/core/Utils.h" #include "arm_compute/core/Window.h" +#include + namespace arm_compute { namespace @@ -39,7 +41,7 @@ Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anc 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_DATA_TYPE_NOT_IN(anchors, DataType::QSYMM16, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON(anchors->num_dimensions() > 2); if(all_anchors->total_size() > 0) { @@ -50,6 +52,11 @@ Status validate_arguments(const ITensorInfo *anchors, const ITensorInfo *all_anc 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); + + if(is_data_type_quantized(anchors->data_type())) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(anchors, all_anchors); + } } return Status{}; } @@ -74,7 +81,7 @@ void NEComputeAllAnchorsKernel::configure(const ITensor *anchors, ITensor *all_a // 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); + auto_init_if_empty(*all_anchors->info(), TensorInfo(output_shape, 1, data_type, anchors->info()->quantization_info())); // Set instance variables _anchors = anchors; @@ -92,12 +99,9 @@ Status NEComputeAllAnchorsKernel::validate(const ITensorInfo *anchors, const ITe return Status{}; } -void NEComputeAllAnchorsKernel::run(const Window &window, const ThreadInfo &info) +template <> +void NEComputeAllAnchorsKernel::internal_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); @@ -105,27 +109,90 @@ void NEComputeAllAnchorsKernel::run(const Window &window, const ThreadInfo &info const float stride = 1.f / _anchors_info.spatial_scale(); const size_t feat_width = _anchors_info.feat_width(); + const UniformQuantizationInfo qinfo = _anchors->info()->quantization_info().uniform(); + 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 auto out_anchor_ptr = reinterpret_cast(all_anchors_it.ptr()); + const auto anchor_ptr = reinterpret_cast(_anchors->ptr_to_element(Coordinates(0, anchor_offset))); 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; + const float new_anchor_x1 = dequantize_qsymm16(*anchor_ptr, qinfo.scale) + shiftx; + const float new_anchor_y1 = dequantize_qsymm16(*(1 + anchor_ptr), qinfo.scale) + shifty; + const float new_anchor_x2 = dequantize_qsymm16(*(2 + anchor_ptr), qinfo.scale) + shiftx; + const float new_anchor_y2 = dequantize_qsymm16(*(3 + anchor_ptr), qinfo.scale) + shifty; + + *out_anchor_ptr = quantize_qsymm16(new_anchor_x1, qinfo.scale); + *(out_anchor_ptr + 1) = quantize_qsymm16(new_anchor_y1, qinfo.scale); + *(out_anchor_ptr + 2) = quantize_qsymm16(new_anchor_x2, qinfo.scale); + *(out_anchor_ptr + 3) = quantize_qsymm16(new_anchor_y2, qinfo.scale); + }, + all_anchors_it); +} + +template +void NEComputeAllAnchorsKernel::internal_run(const Window &window, const ThreadInfo &info) +{ + Iterator all_anchors_it(_all_anchors, window); + Iterator anchors_it(_all_anchors, window); + + const size_t num_anchors = _anchors->info()->dimension(1); + const T 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))); + + const size_t shift_idy = id.y() / num_anchors; + const T shiftx = (shift_idy % feat_width) * stride; + const T shifty = (shift_idy / feat_width) * stride; + + *out_anchor_ptr = *anchor_ptr + shiftx; + *(out_anchor_ptr + 1) = *(1 + anchor_ptr) + shifty; + *(out_anchor_ptr + 2) = *(2 + anchor_ptr) + shiftx; + *(out_anchor_ptr + 3) = *(3 + anchor_ptr) + shifty; }, all_anchors_it); } + +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); + + switch(_anchors->info()->data_type()) + { + case DataType::QSYMM16: + { + internal_run(window, info); + break; + } +#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F16: + { + internal_run(window, info); + break; + } +#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC + case DataType::F32: + { + internal_run(window, info); + break; + } + default: + { + ARM_COMPUTE_ERROR("Data type not supported"); + } + } +} } // namespace arm_compute diff --git a/src/core/NEON/kernels/NEStridedSliceKernel.cpp b/src/core/NEON/kernels/NEStridedSliceKernel.cpp index c33e699999..2de49c6864 100644 --- a/src/core/NEON/kernels/NEStridedSliceKernel.cpp +++ b/src/core/NEON/kernels/NEStridedSliceKernel.cpp @@ -45,7 +45,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, - DataType::U16, DataType::S16, DataType::QSYMM16, + DataType::U16, DataType::S16, DataType::QASYMM16, DataType::QSYMM16, DataType::U32, DataType::S32, DataType::F16, DataType::F32); diff --git a/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp index b2a6ca8c35..7f25b63758 100644 --- a/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp +++ b/src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp @@ -30,7 +30,7 @@ namespace arm_compute { NEGenerateProposalsLayer::NEGenerateProposalsLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), + : _memory_group(memory_manager), _permute_deltas_kernel(), _flatten_deltas_kernel(), _permute_scores_kernel(), @@ -38,17 +38,25 @@ NEGenerateProposalsLayer::NEGenerateProposalsLayer(std::shared_ptrinfo(), 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(); + _is_nhwc = scores->info()->data_layout() == DataLayout::NHWC; + const DataType scores_data_type = scores->info()->data_type(); + _is_qasymm8 = scores_data_type == DataType::QASYMM8; + 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(); + + const QuantizationInfo scores_qinfo = scores->info()->quantization_info(); + const DataType rois_data_type = (_is_qasymm8) ? DataType::QASYMM16 : scores_data_type; + const QuantizationInfo rois_qinfo = (_is_qasymm8) ? QuantizationInfo(0.125f, 0) : scores->info()->quantization_info(); // 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); + _deltas_flattened.allocator()->init(TensorInfo(flatten_shape_deltas, 1, scores_data_type, deltas->info()->quantization_info())); // Permute and reshape deltas + _memory_group.manage(&_deltas_flattened); if(!_is_nhwc) { _memory_group.manage(&_deltas_permuted); @@ -92,9 +105,10 @@ void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *d } 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); + _scores_flattened.allocator()->init(TensorInfo(flatten_shape_scores, 1, scores_data_type, scores_qinfo)); + // Permute and reshape scores + _memory_group.manage(&_scores_flattened); if(!_is_nhwc) { _memory_group.manage(&_scores_permuted); @@ -107,13 +121,40 @@ void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *d _flatten_scores_kernel.configure(scores, &_scores_flattened); } + Tensor *anchors_to_use = &_all_anchors; + Tensor *deltas_to_use = &_deltas_flattened; + if(_is_qasymm8) + { + _all_anchors_f32.allocator()->init(TensorInfo(_all_anchors.info()->tensor_shape(), 1, DataType::F32)); + _deltas_flattened_f32.allocator()->init(TensorInfo(_deltas_flattened.info()->tensor_shape(), 1, DataType::F32)); + _memory_group.manage(&_all_anchors_f32); + _memory_group.manage(&_deltas_flattened_f32); + // Dequantize anchors to float + _dequantize_anchors.configure(&_all_anchors, &_all_anchors_f32); + _all_anchors.allocator()->allocate(); + anchors_to_use = &_all_anchors_f32; + // Dequantize deltas to float + _dequantize_deltas.configure(&_deltas_flattened, &_deltas_flattened_f32); + _deltas_flattened.allocator()->allocate(); + deltas_to_use = &_deltas_flattened_f32; + } // 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(); + _bounding_box_kernel.configure(anchors_to_use, &_all_proposals, deltas_to_use, bbox_info); + deltas_to_use->allocator()->allocate(); + anchors_to_use->allocator()->allocate(); + _all_proposals_to_use = &_all_proposals; + if(_is_qasymm8) + { + _memory_group.manage(&_all_proposals_quantized); + // Requantize all_proposals to QASYMM16 with 0.125 scale and 0 offset + _all_proposals_quantized.allocator()->init(TensorInfo(_all_proposals.info()->tensor_shape(), 1, DataType::QASYMM16, QuantizationInfo(0.125f, 0))); + _quantize_all_proposals.configure(&_all_proposals, &_all_proposals_quantized); + _all_proposals.allocator()->allocate(); + _all_proposals_to_use = &_all_proposals_quantized; + } // 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) @@ -124,12 +165,12 @@ void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *d _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); + auto_init_if_empty(*scores_out->info(), TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo); + auto_init_if_empty(*_proposals_4_roi_values.info(), TensorShape(values_per_roi, scores_nms_size), 1, rois_data_type, rois_qinfo); + auto_init_if_empty(*num_valid_proposals->info(), TensorShape(1), 1, DataType::U32); // Initialize temporaries (unused) outputs - _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(scores_nms_size), 1, data_type)); + _classes_nms_unused.allocator()->init(TensorInfo(TensorShape(scores_nms_size), 1, scores_data_type, scores_qinfo)); _keeps_nms_unused.allocator()->init(*scores_out->info()); // Save the output (to map and unmap them at run) @@ -139,20 +180,20 @@ void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *d _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); + _cpp_nms.configure(&_scores_flattened /*scores_in*/, + _all_proposals_to_use /*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(); + _all_proposals_to_use->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 @@ -164,8 +205,10 @@ Status NEGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITens 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_TYPE_CHANNEL_NOT_IN(scores, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_DATA_LAYOUT_NOT_IN(scores, DataLayout::NCHW, DataLayout::NHWC); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(scores, deltas); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(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)); @@ -174,8 +217,17 @@ Status NEGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITens const int total_num_anchors = num_anchors * feat_width * feat_height; const int values_per_roi = info.values_per_roi(); + const bool is_qasymm8 = scores->data_type() == DataType::QASYMM8; + ARM_COMPUTE_RETURN_ERROR_ON(num_images > 1); + if(is_qasymm8) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(anchors, 1, DataType::QSYMM16); + const UniformQuantizationInfo anchors_qinfo = anchors->quantization_info().uniform(); + ARM_COMPUTE_RETURN_ERROR_ON(anchors_qinfo.scale != 0.125f); + } + 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()))); @@ -199,10 +251,32 @@ Status NEGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITens 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(NEPadLayerKernel::validate(&proposals_4_roi_values, proposals, PaddingList{ { 1, 0 } })); + TensorInfo *proposals_4_roi_values_to_use = &proposals_4_roi_values; + TensorInfo proposals_4_roi_values_quantized(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true)); + proposals_4_roi_values_quantized.set_data_type(DataType::QASYMM16).set_quantization_info(QuantizationInfo(0.125f, 0)); + if(is_qasymm8) + { + TensorInfo all_anchors_f32_info(anchors->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDequantizationLayerKernel::validate(&all_anchors_info, &all_anchors_f32_info)); + + TensorInfo deltas_flattened_f32_info(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDequantizationLayerKernel::validate(&deltas_flattened_info, &deltas_flattened_f32_info)); + + TensorInfo proposals_4_roi_values_f32(deltas->clone()->set_tensor_shape(TensorShape(values_per_roi, total_num_anchors)).set_is_resizable(true).set_data_type(DataType::F32)); + ARM_COMPUTE_RETURN_ON_ERROR(NEBoundingBoxTransformKernel::validate(&all_anchors_f32_info, &proposals_4_roi_values_f32, &deltas_flattened_f32_info, + BoundingBoxTransformInfo(info.im_width(), info.im_height(), 1.f))); + + ARM_COMPUTE_RETURN_ON_ERROR(NEQuantizationLayerKernel::validate(&proposals_4_roi_values_f32, &proposals_4_roi_values_quantized)); + proposals_4_roi_values_to_use = &proposals_4_roi_values_quantized; + } + else + { + 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(NEPadLayerKernel::validate(proposals_4_roi_values_to_use, proposals, PaddingList{ { 1, 0 } })); if(num_valid_proposals->total_size() > 0) { @@ -216,7 +290,17 @@ Status NEGenerateProposalsLayer::validate(const ITensorInfo *scores, const ITens 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(is_qasymm8) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(proposals, 1, DataType::QASYMM16); + const UniformQuantizationInfo proposals_qinfo = proposals->quantization_info().uniform(); + ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.scale != 0.125f); + ARM_COMPUTE_RETURN_ERROR_ON(proposals_qinfo.offset != 0); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(proposals, scores); + } } if(scores_out->total_size() > 0) @@ -247,11 +331,22 @@ void NEGenerateProposalsLayer::run() NEScheduler::get().schedule(&_flatten_deltas_kernel, Window::DimY); NEScheduler::get().schedule(&_flatten_scores_kernel, Window::DimY); + if(_is_qasymm8) + { + NEScheduler::get().schedule(&_dequantize_anchors, Window::DimY); + NEScheduler::get().schedule(&_dequantize_deltas, Window::DimY); + } + // Build the boxes NEScheduler::get().schedule(&_bounding_box_kernel, Window::DimY); + if(_is_qasymm8) + { + NEScheduler::get().schedule(&_quantize_all_proposals, Window::DimY); + } + // Non maxima suppression - CPPScheduler::get().schedule(&_cpp_nms_kernel, Window::DimX); + _cpp_nms.run(); // Add dummy batch indexes NEScheduler::get().schedule(&_pad_kernel, Window::DimY); diff --git a/tests/validation/NEON/GenerateProposalsLayer.cpp b/tests/validation/NEON/GenerateProposalsLayer.cpp index ea99bb3107..4ca2d57863 100644 --- a/tests/validation/NEON/GenerateProposalsLayer.cpp +++ b/tests/validation/NEON/GenerateProposalsLayer.cpp @@ -82,6 +82,8 @@ const auto ComputeAllInfoDataset = framework::dataset::make("ComputeAllInfo", ComputeAnchorsInfo(100U, 100U, 1. / 4.f), }); + +constexpr AbsoluteTolerance tolerance_qsymm16(1); } // namespace TEST_SUITE(NEON) @@ -395,9 +397,24 @@ TEST_SUITE_END() // FP16 TEST_SUITE_END() // Float +template +using NEComputeAllAnchorsQuantizedFixture = ComputeAllAnchorsQuantizedFixture; + +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8) +FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsQuantizedFixture, framework::DatasetMode::ALL, + combine(combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), + framework::dataset::make("DataType", { DataType::QSYMM16 })), + framework::dataset::make("QuantInfo", { QuantizationInfo(0.125f, 0) }))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_qsymm16); +} +TEST_SUITE_END() // QASYMM8 +TEST_SUITE_END() // Quantized + TEST_SUITE_END() // GenerateProposals TEST_SUITE_END() // NEON - } // namespace validation } // namespace test } // namespace arm_compute -- cgit v1.2.1