UnsortedSegmentMinD->UnsortedSegmentMin

mindspore/ccsrc/transform/convert.cc

@@ -343,7 +343,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimGelu->name(), ADPT_DESC(Gelu)},
     {prim::kPrimGeluGrad->name(), ADPT_DESC(GeluGrad)},
     {string(kNameStridedSlice), ADPT_DESC(StridedSlice)},
-    {prim::kPrimUnsortedSegmentMin->name(), ADPT_DESC(UnsortedSegmentMinD)},
+    {prim::kPrimUnsortedSegmentMin->name(), ADPT_DESC(UnsortedSegmentMin)},
     {prim::kPrimUnsortedSegmentSum->name(), ADPT_DESC(UnsortedSegmentSumD)},
     {string(kNameExpandDims), ADPT_DESC(ExpandDims)},
     {prim::kPrimSqueeze->name(), ADPT_DESC(Squeeze)},

mindspore/ccsrc/transform/op_declare.cc

@@ -1059,10 +1059,9 @@ ATTR_MAP(UnsortedSegmentSumD) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(UnsortedSegmentSumD) = {{0, OUTPUT_DESC(y)}};
 
 // UnsortedSegmentMin
-INPUT_MAP(UnsortedSegmentMinD) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(segment_ids)}};
-INPUT_ATTR_MAP(UnsortedSegmentMinD) = {{3, ATTR_DESC(num_segments, AnyTraits<int64_t>())}};
-ATTR_MAP(UnsortedSegmentMinD) = EMPTY_ATTR_MAP;
-OUTPUT_MAP(UnsortedSegmentMinD) = {{0, OUTPUT_DESC(y)}};
+INPUT_MAP(UnsortedSegmentMin) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(segment_ids)}, {3, INPUT_DESC(num_segments)}};
+ATTR_MAP(UnsortedSegmentMin) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(UnsortedSegmentMin) = {{0, OUTPUT_DESC(y)}};
 
 // ExpandDims
 INPUT_MAP(ExpandDims) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axis)}};

mindspore/ccsrc/transform/op_declare.h

@@ -285,9 +285,8 @@ DECLARE_OP_USE_OUTPUT(StridedSlice)
 DECLARE_OP_ADAPTER(UnsortedSegmentSumD)
 DECLARE_OP_USE_INPUT_ATTR(UnsortedSegmentSumD)
 DECLARE_OP_USE_OUTPUT(UnsortedSegmentSumD)
-DECLARE_OP_ADAPTER(UnsortedSegmentMinD)
-DECLARE_OP_USE_INPUT_ATTR(UnsortedSegmentMinD)
-DECLARE_OP_USE_OUTPUT(UnsortedSegmentMinD)
+DECLARE_OP_ADAPTER(UnsortedSegmentMin)
+DECLARE_OP_USE_OUTPUT(UnsortedSegmentMin)
 DECLARE_OP_ADAPTER(ExpandDims)
 DECLARE_OP_USE_OUTPUT(ExpandDims)
 DECLARE_OP_ADAPTER(Squeeze)

mindspore/ops/operations/array_ops.py

@@ -1271,7 +1271,7 @@ class UnsortedSegmentMin(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Tensor) - The shape is :math:`(x_1, x_2, ..., x_R)`.
-        - **segment_ids** (Tensor) - A `1-D` tensor whose shape is a prefix of `x_shape`.
+        - **segment_ids** (Tensor) - A `1-D` tensor whose shape is :math:`(x_1)`.
         - **num_segments** (int) - The value spcifies the number of distinct `segment_ids`.
 
     Outputs:
@@ -1279,7 +1279,7 @@ class UnsortedSegmentMin(PrimitiveWithInfer):
 
     Examples:
         >>> input_x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [4, 2, 1]]).astype(np.float32))
-        >>> segment_ids = Tensor(np.array([0, 1, 1]).np.int32)
+        >>> segment_ids = Tensor(np.array([0, 1, 1]).astype(np.int32))
         >>> num_segments = 2
         >>> unsorted_segment_min = P.UnsortedSegmentMin()
         >>> unsorted_segment_min(input_x, segment_ids, num_segments)
@@ -1299,6 +1299,8 @@ class UnsortedSegmentMin(PrimitiveWithInfer):
         validator.check_tensor_type_same({"x": x['dtype']}, valid_type, self.name)
         validator.check_tensor_type_same({"segment_ids": segment_ids['dtype']}, [mstype.int32], self.name)
         validator.check_integer("rank of segment_ids_shape", len(segment_ids_shape), 1, Rel.EQ, self.name)
+        validator.check(f'first shape of input_x', x_shape[0],
+                        'length of segments_id', segment_ids_shape[0], Rel.EQ, self.name)
         num_segments_v = num_segments['value']
         validator.check_value_type('num_segments', num_segments_v, [int], self.name)
         validator.check_integer("num_segments", num_segments_v, 0, Rel.GT, self.name)