!14013 Enable zero dimension using attribute not parameter

From: @liangzhibo
Reviewed-by: @zh_qh,@ginfung
Signed-off-by: @zh_qh

mindspore/common/tensor.py

@@ -74,7 +74,7 @@ class Tensor(Tensor_):
         >>> assert t3.dtype == ms.float32
     """
 
-    def __init__(self, input_data=None, dtype=None, shape=None, init=None, check_zero_dims=True):
+    def __init__(self, input_data=None, dtype=None, shape=None, init=None):
         self.init_finished = False
         # If input data is numpy number, convert it to np array
         if isinstance(input_data, np_types):
@@ -92,13 +92,12 @@ class Tensor(Tensor_):
         if isinstance(shape, numbers.Number):
             shape = (shape,)
 
-        if check_zero_dims:
+        if input_data is not None and isinstance(input_data, (tuple, list, np.ndarray)) \
-            if input_data is not None and isinstance(input_data, (tuple, list, np.ndarray)) \
+                and np.array(input_data).ndim > 1 and np.array(input_data).size == 0:
-                    and np.array(input_data).ndim > 1 and np.array(input_data).size == 0:
+            raise ValueError("input_data can not contain zero dimension.")
-                raise ValueError("input_data can not contain zero dimension.")
+        if shape is not None and not (hasattr(init, "__enable_zero_dim__") and init.__enable_zero_dim__):
-            if shape is not None:
+            if 0 in shape:
-                if 0 in shape:
+                raise ValueError("Shape can not contain zero value.")
-                    raise ValueError("Shape can not contain zero value.")
 
         # If input_data is tuple/list/numpy.ndarray, it's support in check_type method.
         if init is None:

mindspore/ops/operations/array_ops.py

@@ -26,6 +26,7 @@ import numbers
 import numpy as np
 
 from mindspore import log as logger
+from mindspore.common.initializer import Zero
 from .._utils import get_concat_offset
 from ..operations.math_ops import _infer_shape_reduce
 from ..primitive import Primitive, PrimitiveWithInfer, PrimitiveWithCheck, prim_attr_register, _run_op
@@ -38,6 +39,7 @@ from ...common.parameter import Parameter
 from ...common.tensor import Tensor
 
 
+
 class _ScatterOp(PrimitiveWithInfer):
     """
     Defines Scatter operators
@@ -3015,8 +3017,13 @@ class StridedSlice(PrimitiveWithInfer):
 
         ret_shape = self._compute_slicing_shape(x['shape'], begin_v, end_v, strides_v)
 
-        value = None if all(ret_shape) else Tensor(np.array([]).reshape(ret_shape), x['dtype'].element_type(),
+        if all(ret_shape):
-                                                   check_zero_dims=False)
+            value = None
+        else:
+            init_func = Zero()
+            init_func.__enable_zero_dim__ = True
+            value = Tensor(dtype=x['dtype'].element_type(), shape=ret_shape, init=init_func)
+
         if "max_value" in x and "min_value" in x:
             validator.check_value_type("min_value", x["min_value"], [tuple, list], self.name)
             validator.check_value_type("max_value", x["max_value"], [tuple, list], self.name)