[feat][assistant][I40FG0] add new Ascend operator NonMaxSuppression

mindspore/ccsrc/backend/kernel_compiler/aicpu/aicpu_util.h

@@ -63,6 +63,7 @@ constexpr auto kPadAndShift = "PadAndShift";
 constexpr auto kCpuRunApi = "RunCpuKernel";
 constexpr auto kDropout2D = "Dropout2D";
 constexpr auto kDropout3D = "Dropout3D";
+constexpr auto kNonMaxSuppressionV3 = "NonMaxSuppressionV3";
 constexpr auto kMaskedSelect = "MaskedSelect";
 constexpr auto kMaskedSelectGrad = "MaskedSelectGrad";
 constexpr auto kDynamicStitch = "DynamicStitch";
@@ -72,8 +73,8 @@ constexpr auto kResizeBilinearGrad = "ResizeBilinearGrad";
 constexpr auto kScatterElements = "ScatterElements";
 const std::set<std::string> kCpuKernelOps{kIdentity,     kMaskedSelect,   kMaskedSelectGrad,   kDynamicStitch,
                                           kSearchSorted, kResizeBilinear, kResizeBilinearGrad, kScatterElements};
-const std::set<std::string> kCacheKernelOps{kUpdateCache, kCacheSwapTable, kSubAndFilter,
-                                            kPadAndShift, kDropout3D,      kDropout2D};
+const std::set<std::string> kCacheKernelOps{kUpdateCache, kCacheSwapTable, kSubAndFilter,       kPadAndShift,
+                                            kDropout3D,   kDropout2D,      kNonMaxSuppressionV3};
 const std::set<std::string> kCpuKernelBaseOps{kGetNext, kInitData, kRandomChoiceWithMask};
 const std::set<std::string> kDynamicInputOps{
   kPrint, kPack, kMeshgrid, kStackInitOpName, kStackDestroyOpName, kStackPushOpName, kStackPopOpName, kDynamicStitch};

mindspore/ccsrc/utils/utils.h

@@ -81,6 +81,7 @@ constexpr auto kBNTrainingReduceOpName = "BNTrainingReduce";
 constexpr auto kBNTrainingUpdateOpName = "BNTrainingUpdate";
 constexpr auto kBNTrainingUpdateV2OpName = "BNTrainingUpdateV2";
 constexpr auto kBNTrainingUpdateV3OpName = "BNTrainingUpdateV3";
+constexpr auto kNonMaxSuppressionV3OpName = "NonMaxSuppressionV3";
 constexpr auto kSimpleMeanGradOpName = "SimpleMeanGrad";
 constexpr auto kMeanGradOpName = "MeanGrad";
 constexpr auto kSliceOpName = "Slice";
@@ -716,9 +717,10 @@ const std::set<std::string> kHWSpecialFormatSet = {
 
 const std::set<TypeId> kFloatDataTypeSet = {kNumberTypeFloat16, kNumberTypeFloat32};
 
-const std::set<std::string> kComputeDepend = {kUniqueOpName,       kComputeAccidentalHitsOpName, kSubAndFilterOpName,
-                                              kPadAndShiftOpName,  kCTCGreedyDecoderOpName,      kDropoutGenMaskOpName,
-                                              kMaskedSelectOpName, kDynamicStitchOpName,         kGetNextOpName};
+const std::set<std::string> kComputeDepend = {
+  kUniqueOpName,           kComputeAccidentalHitsOpName, kSubAndFilterOpName, kPadAndShiftOpName,
+  kCTCGreedyDecoderOpName, kDropoutGenMaskOpName,        kMaskedSelectOpName, kDynamicStitchOpName,
+  kGetNextOpName,          kNonMaxSuppressionV3OpName};
 
 const std::set<std::string> k3DFormatSet = {kOpFormat_NCDHW, kOpFormat_NDC1HWC0, kOpFormat_FRACTAL_Z_3D,
                                             kOpFormat_NDHWC, kOpFormat_DHWCN,    kOpFormat_DHWNC};

mindspore/core/base/core_ops.h

@@ -590,6 +590,9 @@ inline const PrimitivePtr kPrimComplex = std::make_shared<Primitive>("Complex");
 inline const PrimitivePtr kPrimXdivy = std::make_shared<Primitive>("Xdivy");
 inline const PrimitivePtr kPrimInv = std::make_shared<Primitive>("Inv");
 
+// Image
+inline const PrimitivePtr kPrimNonMaxSuppressionV3 = std::make_shared<Primitive>("NonMaxSuppressionV3");
+
 // Statements
 inline const PrimitivePtr kPrimReturn = std::make_shared<Primitive>("Return");
 inline const PrimitivePtr kPrimUnroll = std::make_shared<Primitive>("Unroll");

mindspore/core/ops/non_max_suppression_v3.cc

@@ -0,0 +1,115 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <set>
+
+#include "ops/non_max_suppression_v3.h"
+
+namespace mindspore {
+namespace ops {
+namespace {
+abstract::ShapePtr InferShape(const PrimitivePtr &primitive, const std::vector<AbstractBasePtr> &input_args) {
+  auto prim_name = primitive->name();
+  MS_EXCEPTION_IF_NULL(primitive);
+  const int input_num = 5;
+  (void)CheckAndConvertUtils::CheckInteger("input number", SizeToLong(input_args.size()), kEqual, input_num, prim_name);
+  for (const auto &item : input_args) {
+    MS_EXCEPTION_IF_NULL(item);
+  }
+  CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, 0);
+  CheckAndConvertUtils::CheckArgs<abstract::AbstractTensor>(prim_name, input_args, 1);
+  auto boxes_shape = std::make_shared<abstract::Shape>(
+    CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[0]->GetShapeTrack())[kShape]);
+  auto scores_shape = std::make_shared<abstract::Shape>(
+    CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->GetShapeTrack())[kShape]);
+  auto max_output_size_shape = std::make_shared<abstract::Shape>(
+    CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[2]->GetShapeTrack())[kShape]);
+  auto iou_threshold_shape = std::make_shared<abstract::Shape>(
+    CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[3]->GetShapeTrack())[kShape]);
+  auto score_threshold_shape = std::make_shared<abstract::Shape>(
+    CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[4]->GetShapeTrack())[kShape]);
+  // boxes second dimension must euqal 4
+  (void)CheckAndConvertUtils::CheckInteger("boxes second dimension", boxes_shape->shape()[1], kEqual, 4, prim_name);
+  // boxes must be rank 2
+  (void)CheckAndConvertUtils::CheckInteger("boxes rank", boxes_shape->shape().size(), kEqual, 2, prim_name);
+  // score must be rank 1
+  (void)CheckAndConvertUtils::CheckInteger("scores rank", scores_shape->shape().size(), kEqual, 1, prim_name);
+  // score length must be equal with boxes first dimension
+  (void)CheckAndConvertUtils::CheckInteger("scores length", scores_shape->shape()[0], kEqual, boxes_shape->shape()[0],
+                                           prim_name);
+  // max_output_size,iou_threshold,score_threshold must be scalar
+  (void)CheckAndConvertUtils::CheckInteger("max_output_size size", max_output_size_shape->shape().size(), kEqual, 0,
+                                           prim_name);
+  (void)CheckAndConvertUtils::CheckInteger("iou_threshold size", iou_threshold_shape->shape().size(), kEqual, 0,
+                                           prim_name);
+  (void)CheckAndConvertUtils::CheckInteger("score_threshold size", score_threshold_shape->shape().size(), kEqual, 0,
+                                           prim_name);
+  auto scores_shape_map = CheckAndConvertUtils::ConvertShapePtrToShapeMap(input_args[1]->BuildShape());
+  // calculate output shape
+  ShapeVector selected_indices_shape = {abstract::Shape::SHP_ANY};
+  ShapeVector selected_indices_min_shape = {0};
+  ShapeVector selected_indices_max_shape;
+  if (scores_shape_map[kShape].size() > 0 && scores_shape_map[kShape][0] == -1) {
+    selected_indices_max_shape = scores_shape_map[kMaxShape];
+    return std::make_shared<abstract::Shape>(selected_indices_shape, selected_indices_min_shape,
+                                             selected_indices_max_shape);
+  }
+  selected_indices_max_shape = scores_shape_map[kShape];
+  return std::make_shared<abstract::Shape>(selected_indices_shape, selected_indices_min_shape,
+                                           selected_indices_max_shape);
+}
+
+TypePtr InferType(const PrimitivePtr &prim, const std::vector<AbstractBasePtr> &input_args) {
+  auto prim_name = prim->name();
+  MS_EXCEPTION_IF_NULL(prim);
+  const int input_num = 5;
+  (void)CheckAndConvertUtils::CheckInteger("input number", SizeToLong(input_args.size()), kEqual, input_num, prim_name);
+  for (const auto &item : input_args) {
+    MS_EXCEPTION_IF_NULL(item);
+  }
+  auto boxes_type = input_args[0]->BuildType();
+  auto scores_type = input_args[1]->BuildType();
+  auto max_output_size_type = input_args[2]->BuildType();
+  auto iou_threshold_type = input_args[3]->BuildType();
+  auto score_threshold_type = input_args[4]->BuildType();
+  // boxes and scores must have same type
+  const std::set<TypePtr> valid_types = {kFloat16, kFloat32};
+  std::map<std::string, TypePtr> args;
+  args.insert({"boxes_type", boxes_type});
+  args.insert({"scores_type", scores_type});
+  (void)CheckAndConvertUtils::CheckTensorTypeSame(args, valid_types, prim_name);
+  // iou_threshold,score_threshold must be scalar
+  std::map<std::string, TypePtr> args2;
+  args2.insert({"iou_threshold_type", iou_threshold_type});
+  args2.insert({"score_threshold_type", score_threshold_type});
+  (void)CheckAndConvertUtils::CheckScalarOrTensorTypesSame(args2, valid_types, prim_name);
+  // max_output_size must be scalar
+  const std::set<TypePtr> valid_types2 = {kInt32, kInt64};
+  std::map<std::string, TypePtr> args3;
+  args3.insert({"max_output_size_type", max_output_size_type});
+  (void)CheckAndConvertUtils::CheckScalarOrTensorTypesSame(args3, valid_types2, prim_name);
+  return max_output_size_type;
+}
+}  // namespace
+AbstractBasePtr NonMaxSuppressionV3Infer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                         const std::vector<AbstractBasePtr> &input_args) {
+  MS_EXCEPTION_IF_NULL(primitive);
+  return abstract::MakeAbstract(InferShape(primitive, input_args), InferType(primitive, input_args));
+}
+REGISTER_PRIMITIVE_EVAL_IMPL(NonMaxSuppressionV3, prim::kPrimNonMaxSuppressionV3, NonMaxSuppressionV3Infer, nullptr,
+                             true);
+}  // namespace ops
+}  // namespace mindspore

mindspore/core/ops/non_max_suppression_v3.h

@@ -0,0 +1,48 @@
+/**
+ * Copyright 2021 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef MINDSPORE_CORE_OPS_NON_MAX_SUPPRESSION_V3_H_
+#define MINDSPORE_CORE_OPS_NON_MAX_SUPPRESSION_V3_H_
+
+#include <map>
+#include <vector>
+#include <string>
+#include <memory>
+#include <algorithm>
+#include "ops/op_utils.h"
+#include "ops/primitive_c.h"
+#include "abstract/primitive_infer_map.h"
+#include "abstract/abstract_value.h"
+#include "abstract/dshape.h"
+#include "utils/check_convert_utils.h"
+
+namespace mindspore {
+namespace ops {
+constexpr auto kNameNonMaxSuppressionV3 = "NonMaxSuppressionV3";
+class NonMaxSuppressionV3 : public PrimitiveC {
+ public:
+  NonMaxSuppressionV3() : PrimitiveC(kNameNonMaxSuppressionV3) {
+    InitIOName({"boxes", "score", "max_output_size", "iou_threshold", "score_threshold"}, {"selected_indices"});
+  }
+  ~NonMaxSuppressionV3() = default;
+  MS_DECLARE_PARENT(NonMaxSuppressionV3, PrimitiveC);
+};
+AbstractBasePtr NonMaxSuppressionV3Infer(const abstract::AnalysisEnginePtr &, const PrimitivePtr &primitive,
+                                         const std::vector<AbstractBasePtr> &input_args);
+using PrimNonMaxSuppressionV3Ptr = std::shared_ptr<NonMaxSuppressionV3>;
+}  // namespace ops
+}  // namespace mindspore
+#endif  // MINDSPORE_CORE_OPS_NON_MAX_SUPPRESSION_V3_H_

mindspore/python/mindspore/ops/_op_impl/aicpu/__init__.py

@@ -82,3 +82,4 @@ from .ctc_greedy_decoder import _ctc_greedy_decoder_aicpu
 from .resize_bilinear import _resize_bilinear_aicpu
 from .resize_bilinear_grad import _resize_bilinear_grad_aicpu
 from .scatter_elements import _scatter_elements_aicpu
+from .non_max_suppression import _non_max_suppression_aicpu

mindspore/python/mindspore/ops/_op_impl/aicpu/non_max_suppression.py

@@ -0,0 +1,36 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""NonMaxSuppressionV3 op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+non_max_suppression_op_info = AiCPURegOp("NonMaxSuppressionV3")\
+    .fusion_type("OPAQUE")\
+    .input(0, "boxes", "required")\
+    .input(1, "scores", "required")\
+    .input(2, "max_output_size", "required")\
+    .input(3, "iou_threshold", "required")\
+    .input(4, "score_threshold", "required")\
+    .output(0, "selected_indices", "required")\
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, \
+    DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.I32_Default)\
+    .dtype_format(DataType.F16_Default, DataType.F16_Default, \
+    DataType.I32_Default, DataType.F16_Default, DataType.F16_Default, DataType.I32_Default)\
+    .get_op_info()
+
+@op_info_register(non_max_suppression_op_info)
+def _non_max_suppression_aicpu():
+    """NonMaxSuppression AiCPU register"""
+    return

mindspore/python/mindspore/ops/operations/__init__.py

@@ -19,7 +19,7 @@ Primitive operator classes.
 A collection of operators to build neural networks or to compute functions.
 """
 
-from .image_ops import (CropAndResize)
+from .image_ops import (CropAndResize, NonMaxSuppressionV3)
 from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Stack, Unpack, Unstack,
                         Diag, DiagPart, DType, ExpandDims, Eye,
                         Fill, Ones, Zeros, GatherNd, GatherV2, Gather, SparseGatherV2, InvertPermutation,
@@ -207,6 +207,7 @@ __all__ = [
     'UniqueWithPad',
     'Concat',
     'Pack',
+    'NonMaxSuppressionV3',
     'Stack',
     'Unpack',
     'Unstack',

mindspore/python/mindspore/ops/operations/image_ops.py

@@ -18,7 +18,7 @@ from ... import context
 from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
-from ..primitive import PrimitiveWithInfer, prim_attr_register
+from ..primitive import PrimitiveWithInfer, prim_attr_register, Primitive
 
 
 class CropAndResize(PrimitiveWithInfer):
@@ -144,3 +144,65 @@ class CropAndResize(PrimitiveWithInfer):
         return {'shape': out_shape,
                 'dtype': mstype.float32,
                 'value': None}
+
+class NonMaxSuppressionV3(Primitive):
+    r"""
+        Greedily selects a subset of bounding boxes in descending order of score.
+
+    .. warning::
+        When input "max_output_size" is negative, it will be treated as 0.
+
+    Note:
+        This algorithm is agnostic to where the origin is in the coordinate system.
+        This algorithm is invariant to orthogonal transformations and translations of the coordinate system;
+        thus translating or reflections of the coordinate system result in the same boxes being
+        selected by the algorithm.
+
+    Inputs:
+        - **boxes** (Tensor) - A 2-D Tensor of shape [num_boxes, 4].
+        - **scores** (Tensor) - A 1-D Tensor of shape [num_boxes] representing a single score
+          corresponding to each box (each row of boxes), the num_boxes of "scores" must be equal to
+          the num_boxes of "boxes".
+        - **max_output_size** (Union[Tensor, Number.Int]) - A scalar integer Tensor representing the maximum
+          number of boxes to be selected by non max suppression.
+        - **iou_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for
+          deciding whether boxes overlap too much with respect to IOU, and iou_threshold must be equal or greater
+          than 0 and be equal or smaller than 1.
+        - **score_threshold** (Union[Tensor, Number.Float]) - A 0-D float tensor representing the threshold for
+          deciding when to remove boxes based on score.
+
+    Outputs:
+        A 1-D integer Tensor of shape [M] representing the selected indices from the boxes tensor,
+        where M <= max_output_size.
+
+    Raises:
+        TypeError: If the dtype of `boxes` and `scores` is different.
+        TypeError: If the dtype of `iou_threshold` and `score_threshold` is different.
+        TypeError: If `boxes` is not tensor or its dtype is not float16 or float32.
+        TypeEroor: If `scores` is not tensor or its dtype is not float16 or float32.
+        TypeError: If `max_output_size` is not tensor or scalar.If `max_output_size` is not int32 or int64.
+        TypeError: If `iou_threshold` is not tensor or scalar. If its type is not float16 or float32.
+        TypeError: If `score_threshold` is not tensor or scalar. If its type is not float16 or float32.
+        ValueError: If the size of shape of `boxes` is not 2 or the second value of its shape is not 4.
+        ValueError: If the size of shape of `scores` is not 1.
+        ValueError: If each of the size of shape of `max_output_size`, `iou_threshold`, `score_threshold` is not 0.
+
+    Supported Platforms:
+        ``Ascend``
+
+    Examples:
+        >>> boxes = Tensor(np.array([[1, 2, 3, 4], [1, 3, 3, 4], [1, 3, 4, 4],
+        ...                          [1, 1, 4, 4], [1, 1, 3, 4]]), mstype.float32)
+        >>> scores = Tensor(np.array([0.4, 0.5, 0.72, 0.9, 0.45]), mstype.float32)
+        >>> max_output_size = Tensor(5, mstype.int32)
+        >>> iou_threshold = Tensor(0.5, mstype.float32)
+        >>> score_threshold = Tensor(0, mstype.float32)
+        >>> nonmaxsuppression = ops.NonMaxSuppressionV3()
+        >>> output = nonmaxsuppression(boxes, scores, max_output_size, iou_threshold, score_threshold)
+        >>> print(output)
+        [3 2 0]
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """Initialize NonMaxSuppressionV3"""

tests/ut/python/ops/test_ops.py

@@ -2660,6 +2660,22 @@ test_case_array_ops = [
     }),
 ]
 
+test_case_image_ops = [
+    ('NonMaxSuppressionV3', {
+        'block': P.NonMaxSuppressionV3(),
+        'desc_inputs': [Tensor(np.array([[20, 5, 200, 100],
+                                         [50, 50, 200, 200],
+                                         [20, 120, 150, 150],
+                                         [250, 250, 400, 350],
+                                         [90, 10, 300, 300],
+                                         [40, 220, 280, 380]]).astype(np.float32)),
+                        Tensor(np.array([0.353, 0.624, 0.667, 0.5, 0.3, 0.46]).astype(np.float32)),
+                        Tensor(4, mstype.int32),
+                        Tensor(0.1, mstype.float32),
+                        Tensor(0, mstype.float32)],
+        'skip': ['backward']}),
+]
+
 test_case_other_ops = [
     ('ScalarLog', {
         'block': F.scalar_log,
@@ -2983,7 +2999,7 @@ test_case_quant_ops = [
 ]
 
 test_case_lists = [test_case_nn_ops, test_case_math_ops, test_case_array_ops,
-                   test_case_other_ops, test_case_quant_ops]
+                   test_case_other_ops, test_case_quant_ops, test_case_image_ops]
 test_case = functools.reduce(lambda x, y: x + y, test_case_lists)
 # use -k to select certain testcast
 # pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm