numpy api bug fix

mindspore/numpy/array_creations.py

@@ -27,12 +27,12 @@ from .._c_expression import Tensor as Tensor_
 from .._c_expression.typing import Float
 
 from .utils import _check_input_for_asarray, _deep_list, _deep_tensor_to_nparray, \
-    _expand, _broadcast_to_shape, _check_input_tensor, _convert_64_to_32, _get_dtype_from_scalar
+    _broadcast_to_shape, _check_input_tensor, _convert_64_to_32, _get_dtype_from_scalar
 from .utils_const import _raise_value_error, _empty, _check_axis_valid, _max, _min, \
     _check_same_type, _is_shape_empty, _check_shape, _check_dtype, _tile_size, _abs, \
     _raise_type_error, _expanded_shape, _check_is_float, _iota, \
     _type_convert, _canonicalize_axis, _list_comprehensions, _ceil
-from .array_ops import transpose, ravel, concatenate, broadcast_arrays, reshape
+from .array_ops import transpose, ravel, concatenate, broadcast_arrays, reshape, broadcast_to
 from .dtypes import nan
 
 # According to official numpy reference, the dimension of a numpy array must be less
@@ -355,9 +355,7 @@ def full(shape, fill_value, dtype=None):
             return F.fill(dtype, shape, fill_value)
         if isinstance(fill_value, (list, tuple)):
             fill_value = asarray_const(fill_value)
-        if isinstance(fill_value, Tensor):
-            fill_value = _expand(fill_value, len(shape))
-        return F.tile(fill_value, _tile_size(fill_value.shape, shape, len(shape)))
+        return broadcast_to(fill_value, shape)
     # if shape contains zero, use c.Tensor()
     return _convert_64_to_32(empty_compile(dtype, shape))
 
@@ -366,9 +364,6 @@ def arange(start, stop=None, step=None, dtype=None):
     """
     Returns evenly spaced values within a given interval.
 
-    Returns `num` evenly spaced samples, calculated over the interval :math:`[start, stop]`.
-    The endpoint of the interval can optionally be excluded.
-
     Args:
         start(Union[int, float]): Start of interval. The interval includes this value.
             When `stop` is provided as a position argument, `start` must be given, when `stop`

mindspore/numpy/array_ops.py

@@ -92,7 +92,7 @@ def expand_dims(a, axis):
             the new axis is placed,
 
     Returns:
-        View of `a` with the number of dimensions increased.
+        Tensor, with the number of dimensions increased at specified axis.
 
     Raises:
         TypeError: If input arguments have types not specified above.
@@ -822,7 +822,7 @@ def atleast_1d(*arys):
 
 def atleast_2d(*arys):
     """
-    Views inputs as arrays with at least two dimensions.
+    Reshapes inputs as arrays with at least two dimensions.
 
     Note:
         In graph mode, returns a tuple of tensor instead of a list of
@@ -859,7 +859,7 @@ def atleast_2d(*arys):
 
 def atleast_3d(*arys):
     """
-    Views inputs as arrays with at least three dimensions.
+    Reshapes inputs as arrays with at least three dimensions.
 
     Note:
         In graph mode, returns a tuple of tensor instead of a list of
@@ -870,8 +870,8 @@ def atleast_3d(*arys):
 
     Returns:
         Tensor, or list of tensors, each with ``a.ndim >= 3``. For example,
-        a 1-D array of shape `(N,)` becomes a view of shape `(1, N, 1)`, and
-        a 2-D array of shape `(M, N)` becomes a view of shape `(M, N, 1)`.
+        a 1-D array of shape `(N,)` becomes a tensor of shape `(1, N, 1)`, and
+        a 2-D array of shape `(M, N)` becomes a tensor of shape `(M, N, 1)`.
 
     Raises:
         TypeError: if the input is not a tensor.
@@ -1591,7 +1591,7 @@ def flip(m, axis=None):
             all of the axes specified in the tuple.
 
     Returns:
-        Tensor, a view of `m` with the entries of `axis` reversed.
+        Tensor, with the entries of `axis` reversed.
 
     Raises:
         TypeError: if the input is not a tensor.

mindspore/numpy/logic_ops.py

@@ -95,7 +95,7 @@ def less_equal(x1, x2, out=None, where=True, dtype=None):
 
     Returns:
        Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
-       bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are
+       bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
        scalars.
 
     Raises:
@@ -142,7 +142,7 @@ def less(x1, x2, out=None, where=True, dtype=None):
 
     Returns:
        Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
-       bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are
+       bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
        scalars.
 
     Raises:
@@ -188,7 +188,7 @@ def greater_equal(x1, x2, out=None, where=True, dtype=None):
 
     Returns:
        Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
-       bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are
+       bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
        scalars.
 
     Raises:
@@ -234,7 +234,7 @@ def greater(x1, x2, out=None, where=True, dtype=None):
 
     Returns:
        Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
-       bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are
+       bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
        scalars.
 
     Raises:
@@ -280,7 +280,7 @@ def equal(x1, x2, out=None, where=True, dtype=None):
 
     Returns:
        Tensor or scalar, element-wise comparison of `x1` and `x2`. Typically of type
-       bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are
+       bool, unless `dtype` is passed. This is a scalar if both `x1` and `x2` are
        scalars.
 
     Raises:

mindspore/numpy/math_ops.py

@@ -1692,7 +1692,7 @@ def amin(a, axis=None, keepdims=False, initial=None, where=True):
         a (Tensor): Input data.
         axis (None or int or tuple of ints, optional): defaults to None. Axis or
             axes along which to operate. By default, flattened input is used. If
-            this is a tuple of ints, the maximum is selected over multiple axes,
+            this is a tuple of ints, the minimum is selected over multiple axes,
             instead of a single axis or all the axes as before.
         keepdims (boolean, optional): defaults to False.
             If this is set to True, the axes which are reduced are left in the

mindspore/ops/composite/math_ops.py

@@ -58,7 +58,7 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
           Tensor, number of nonzero element. The data type is dtype.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
@@ -77,7 +77,7 @@ def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
     reduce_sum = P.ReduceSum(keep_dims)
     nonzero_bool = not_equal(x, 0)
     # ReduceSum only support float16 or float32 tensor.
-    nonzero_val = cast(nonzero_bool, mstype.float16)
+    nonzero_val = cast(nonzero_bool, mstype.float32)
     nonzero_num = cast(reduce_sum(nonzero_val, axis), dtype)
 
     return nonzero_num