diff options
Diffstat (limited to 'src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp')
| -rw-r--r-- | src/runtime/NEON/functions/NEGenerateProposalsLayer.cpp | 171 |
1 files changed, 133 insertions, 38 deletions
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<IMemoryManager> 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_ptr<IMemoryManage _compute_anchors_kernel(), _bounding_box_kernel(), _pad_kernel(), - _cpp_nms_kernel(), + _dequantize_anchors(), + _dequantize_deltas(), + _quantize_all_proposals(), + _cpp_nms(memory_manager), _is_nhwc(false), + _is_qasymm8(false), _deltas_permuted(), _deltas_flattened(), + _deltas_flattened_f32(), _scores_permuted(), _scores_flattened(), _all_anchors(), + _all_anchors_f32(), _all_proposals(), + _all_proposals_quantized(), _keeps_nms_unused(), _classes_nms_unused(), _proposals_4_roi_values(), + _all_proposals_to_use(nullptr), _num_valid_proposals(nullptr), _scores_out(nullptr) { @@ -60,25 +68,30 @@ void NEGenerateProposalsLayer::configure(const ITensor *scores, const ITensor *d 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(); + _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); |