fix mindpsore.numpy packaging issue and format API comments

cmake/package.cmake

@@ -281,6 +281,7 @@ install(
         ${CMAKE_SOURCE_DIR}/mindspore/_extends
         ${CMAKE_SOURCE_DIR}/mindspore/parallel
         ${CMAKE_SOURCE_DIR}/mindspore/mindrecord
+        ${CMAKE_SOURCE_DIR}/mindspore/numpy
         ${CMAKE_SOURCE_DIR}/mindspore/train
         ${CMAKE_SOURCE_DIR}/mindspore/common
         ${CMAKE_SOURCE_DIR}/mindspore/ops

mindspore/numpy/__init__.py

@@ -42,3 +42,5 @@ array_ops_module = ['array', 'asarray', 'asfarray', 'copy', 'ones', 'zeros', 'ar
 math_module = ['mean', 'inner']
 
 __all__ = array_ops_module + math_module + numeric_types
+
+__all__.sort()

mindspore/numpy/dtypes.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """Dtypes and utilities"""
 
-from mindspore import (int8, int16, int32, int64, uint8, uint16, uint32, uint64, \
+from ..common.dtype import (int8, int16, int32, int64, uint8, uint16, uint32, uint64, \
     float16, float32, float64, bool_)
 
 # original numpy has int->int64, float->float64, uint->uint64 mapping. we map

mindspore/numpy/math_ops.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 # ============================================================================
 """math operations, the function docs are adapted from Numpy API."""
-from mindspore.ops import operations as P
-from mindspore.ops import functional as F
+from ..ops import operations as P
+from ..ops import functional as F
 from .array_ops import squeeze
 from .utils import _infer_out_shape, _is_scalar, _check_axis_valid, _get_device_compile, \
     _check_shape_aligned
@@ -22,7 +22,7 @@ from .utils import _infer_out_shape, _is_scalar, _check_axis_valid, _get_device_
 
 def mean(a, axis=None, keepdims=False):
     """
-    Compute the arithmetic mean along the specified axis.
+    Computes the arithmetic mean along the specified axis.
 
     Returns the average of the array elements. The average is taken
     over the flattened array by default, otherwise over the specified
@@ -30,8 +30,8 @@ def mean(a, axis=None, keepdims=False):
 
     Note:
         Numpy arguments dtype and out are not supported.
-        On GPU, the supported dtypes are np.float16, and np.float32.
-        On CPU, the supported dtypes are np.float16, and np.float32.
+        On GPU, the supported dtypes are mstype.float16, and mstype.float32.
+        On CPU, the supported dtypes are mstype.float16, and mstype.float32.
 
     Args:
         a (Tensor): input tensor containing numbers whose mean is desired.
@@ -56,6 +56,7 @@ def mean(a, axis=None, keepdims=False):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> a = np.arange(6, dtype='float32')
         >>> output = np.mean(a, 0)
         >>> print(output)
@@ -83,8 +84,8 @@ def inner(a, b):
 
     Note:
         Numpy argument out is not supported.
-        On GPU, the supported dtypes are np.float16, and np.float32.
-        On CPU, the supported dtype is np.float32.
+        On GPU, the supported dtypes are mstype.float16, and mstype.float32.
+        On CPU, the supported dtype is mstype.float32.
 
     Args:
         a (Tensor): input tensor. If a and b are nonscalar, their last
@@ -103,6 +104,7 @@ def inner(a, b):
         ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
+        >>> import mindspore.numpy as np
         >>> a = np.ones((5, 3))
         >>> b = np.ones((2, 7, 3))
         >>> output = np.inner(a, b)

mindspore/numpy/utils.py

@@ -17,13 +17,12 @@ from functools import partial
 
 import numpy as onp
 
-import mindspore
 import mindspore.context as context
-from mindspore import Tensor
-from mindspore.ops import operations as P
-from mindspore.ops import functional as F
-from mindspore.ops.primitive import constexpr
-from mindspore.common import dtype as mstype
+from ..common import Tensor
+from ..ops import operations as P
+from ..ops import functional as F
+from ..ops.primitive import constexpr
+from ..common import dtype as mstype
 
 from .dtypes import dtype_tuple, all_types, dtype_map
 
@@ -119,17 +118,17 @@ def _check_shape(shape):
 
 def _check_dtype(dtype):
     """check the input dtype and make conversions"""
-    # convert the string dtype to mindspore.dtype
+    # convert the string dtype to mstype.dtype
     if isinstance(dtype, str):
         dtype = dtype.lower()
         dtype = dtype_map[dtype]
     elif isinstance(dtype, type):
         if dtype is int:
-            dtype = mindspore.int32
+            dtype = mstype.int32
         if dtype is float:
-            dtype = mindspore.float32
+            dtype = mstype.float32
         if dtype is bool:
-            dtype = mindspore.bool_
+            dtype = mstype.bool_
     if dtype not in dtype_tuple:
         raise TypeError(
             f"only {all_types} are allowed for dtype, but got {type(dtype)}")

tests/ut/python/numpy_native/test_array_ops.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""unit tests for array operations"""
+"""unit tests for numpy array operations"""
 
 import functools
 

tests/ut/python/numpy_native/test_math_ops.py

@@ -1,8 +1,23 @@
 
+# Copyright 2020 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.
+# ============================================================================
+"""unit tests for numpy math operations"""
+
 import pytest
 import numpy as onp
 
-import mindspore.context as context
 import mindspore.numpy as mnp
 
 
@@ -16,7 +31,6 @@ def rand_int(*shape):
 
 class Cases():
     def __init__(self):
-        self.device_cpu = context.get_context('device_target') == 'CPU'
 
         self.arrs = [
             rand_int(2),