Add TransShape Operator.

mindspore/ccsrc/transform/convert.cc

@@ -134,6 +134,7 @@ const char kNameAssignSub[] = "AssignSub";
 const char kNameNPUAllocFloatStatus[] = "NPUAllocFloatStatus";
 const char kNameNPUClearFloatStatus[] = "NPUClearFloatStatus";
 const char kNameReshape[] = "Reshape";
+const char kNameTransShape[] = "TransShape";
 const char kNameRealDiv[] = "RealDiv";
 const char kNameTile[] = "Tile";
 const char kNameCos[] = "Cos";
@@ -242,6 +243,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameBatchNorm), ADPT_DESC(BatchNorm)},
     {string(kNameBatchNormGrad), ADPT_DESC(BatchNormGrad)},
     {string(kNameReshape), ADPT_DESC(Reshape)},
+    {string(kNameTransShape), ADPT_DESC(TransShape)},
     {string(kNameFlattenGrad), ADPT_DESC(Reshape)},
     {prim::kPrimFlatten->name(), ADPT_DESC(Flatten)},
     {string(kNameAddN), ADPT_DESC(AddN)},

mindspore/ccsrc/transform/op_declare.cc

@@ -442,6 +442,12 @@ INPUT_MAP(Reshape) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(shape)}};
 ATTR_MAP(Reshape) = EMPTY_ATTR_MAP;
 OUTPUT_MAP(Reshape) = {{0, OUTPUT_DESC(y)}};
 
+// TransShape
+INPUT_MAP(TransShape) = {{1, INPUT_DESC(x)}};
+INPUT_ATTR_MAP(TransShape) = {{2, ATTR_DESC(outShape, AnyTraits<int>(), AnyTraits<std::vector<int64_t>>())}};
+ATTR_MAP(TransShape) = EMPTY_ATTR_MAP;
+OUTPUT_MAP(TransShape) = {{0, OUTPUT_DESC(y)}};
+
 // BiasAdd
 INPUT_MAP(BiasAdd) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(bias)}};
 ATTR_MAP(BiasAdd) = {{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())}};

mindspore/ccsrc/transform/op_declare.h

@@ -112,6 +112,9 @@ DECLARE_OP_USE_INPUT_ATTR(DepthwiseConv2DBackpropInputD)
 DECLARE_OP_USE_OUTPUT(DepthwiseConv2DBackpropInputD)
 DECLARE_OP_ADAPTER(Reshape)
 DECLARE_OP_USE_OUTPUT(Reshape)
+DECLARE_OP_ADAPTER(TransShape)
+DECLARE_OP_USE_INPUT_ATTR(TransShape)
+DECLARE_OP_USE_OUTPUT(TransShape)
 DECLARE_OP_ADAPTER(Iou)
 DECLARE_OP_USE_OUTPUT(Iou)
 DECLARE_OP_ADAPTER(ResizeNearestNeighborV2D)

mindspore/ops/_grad/grad_array_ops.py

@@ -696,3 +696,13 @@ def get_bprop_reverse_sequence(self):
         dx = reverse_sequence_grad(dout, seq_lengths)
         return dx, zeros_like(seq_lengths)
     return bprop
+
+
+@bprop_getters.register(P.TransShape)
+def get_bprop_trans_shape(self):
+    """Generate bprop for TransShape"""
+    op = P.TransShape()
+    def bprop(x, shape, out, dout):
+        dx = op(dout, shape_op(x))
+        return (dx, zeros_like(shape))
+    return bprop

mindspore/ops/operations/__init__.py

@@ -27,7 +27,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
                         SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin,
                         ScatterUpdate, ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
-                        Shape, Size, Slice, Split,
+                        Shape, Size, Slice, Split, TransShape,
                         Squeeze, StridedSlice, Tile, TensorScatterUpdate,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin,
                         UnsortedSegmentSum, SpaceToDepth, DepthToSpace, SpaceToBatch, BatchToSpace,

mindspore/ops/operations/array_ops.py

@@ -3106,3 +3106,28 @@ class ReverseSequence(PrimitiveWithInfer):
         validator.check_tensor_type_same({"x_dtype": x}, mstype.number_type + (mstype.bool_,), self.name)
         validator.check_tensor_type_same({"seq_lengths_dtype": seq_lengths}, [mstype.int32, mstype.int64], self.name)
         return x
+
+
+class TransShape(PrimitiveWithInfer):
+    """
+    Transform the shape of input tensor to target shape.
+
+    Inputs:
+        - **input_x** (Tensor) - A input tensor.
+        - **out_shape** (tuple[int]) - The shape of output data.
+
+    Outputs:
+        Tensor, a tensor whose data type is same as 'input_x', and the shape is same as the `out_shape`.
+    """
+    @prim_attr_register
+    def __init__(self):
+        self.__setattr_flag__ = True
+
+    def __infer__(self, x, shape):
+        shp = shape['value']
+        dtype = x['dtype']
+        validator.check_tensor_type_same({'x': dtype}, mstype.number_type + (mstype.bool_,), self.name)
+        self.add_prim_attr('out_shape', tuple(shp))
+        return {'shape': shp,
+                'dtype': dtype,
+                'value': None}

tests/ut/python/ops/test_ops.py

@@ -1865,6 +1865,12 @@ test_case_array_ops = [
                         Tensor(np.arange(-12, 0).reshape(3, 2, 2), mstype.float32)],
         'skip': ['backward'],
     }),
+    ('TransShape', {
+        'block': P.TransShape(),
+        'desc_const': [(1, 12, 24, 24)],
+        'desc_inputs': [[1, 3, 24, 24]],
+        'desc_bprop': [[1, 12, 24, 24]],
+    }),
 ]
 
 test_case_other_ops = [