COMPMID-2257: Add support for QASYMM8 in NEGenerateProposals
Change-Id: I7d9aa21ecac97847fce209f97dff0dea6e62790a
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2020
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h b/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h
index a7b2603..9ee9d5d 100644
--- a/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEGenerateProposalsLayerKernel.h
@@ -53,7 +53,7 @@
/** 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 @@
/** 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 @@
void run(const Window &window, const ThreadInfo &info) override;
private:
+ template <typename T>
+ 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 a272a81..1207520 100644
--- a/arm_compute/core/NEON/kernels/NEStridedSliceKernel.h
+++ b/arm_compute/core/NEON/kernels/NEStridedSliceKernel.h
@@ -58,7 +58,7 @@
*
* @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 @@
*
* @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 827f19d..e14e195 100644
--- a/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h
+++ b/arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h
@@ -50,7 +50,7 @@
* -# @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 c6d3628..cd370a0 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 @@
* -# @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 @@
/** 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 @@
/** 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 @@
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 4a585b7..ba5ca78 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 <arm_neon.h>
+
namespace arm_compute
{
namespace
@@ -39,7 +41,7 @@
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 @@
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 @@
// 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 @@
return Status{};
}
-void NEComputeAllAnchorsKernel::run(const Window &window, const ThreadInfo &info)
+template <>
+void NEComputeAllAnchorsKernel::internal_run<int16_t>(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 @@
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<float *>(all_anchors_it.ptr());
- const auto anchor_ptr = reinterpret_cast<float *>(_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<int16_t *>(all_anchors_it.ptr());
+ const auto anchor_ptr = reinterpret_cast<int16_t *>(_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 <typename T>
+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<T *>(all_anchors_it.ptr());
+ const auto anchor_ptr = reinterpret_cast<T *>(_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<int16_t>(window, info);
+ break;
+ }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ {
+ internal_run<float16_t>(window, info);
+ break;
+ }
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F32:
+ {
+ internal_run<float>(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 c33e699..2de49c6 100644
--- a/src/core/NEON/kernels/NEStridedSliceKernel.cpp
+++ b/src/core/NEON/kernels/NEStridedSliceKernel.cpp
@@ -45,7 +45,7 @@
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 b2a6ca8..7f25b63 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 @@
_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 @@
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 @@
}
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 @@
_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 @@
_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 @@
_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 @@
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 @@
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 @@
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 @@
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 @@
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 ea99bb3..4ca2d57 100644
--- a/tests/validation/NEON/GenerateProposalsLayer.cpp
+++ b/tests/validation/NEON/GenerateProposalsLayer.cpp
@@ -82,6 +82,8 @@
ComputeAnchorsInfo(100U, 100U, 1. / 4.f),
});
+
+constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1);
} // namespace
TEST_SUITE(NEON)
@@ -395,9 +397,24 @@
TEST_SUITE_END() // Float
+template <typename T>
+using NEComputeAllAnchorsQuantizedFixture = ComputeAllAnchorsQuantizedFixture<Tensor, Accessor, NEComputeAllAnchors, T>;
+
+TEST_SUITE(Quantized)
+TEST_SUITE(QASYMM8)
+FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsQuantizedFixture<int16_t>, 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