fix several numpy issues

python/mxnet/ndarray/numpy/_op.py

@@ -28,7 +28,7 @@
 from . import _internal as _npi
 from ..ndarray import NDArray
 
-__all__ = ['zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
+__all__ = ['shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide', 'mod', 'remainder', 'power',
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
@@ -43,7 +43,41 @@
 
 
 @set_module('mxnet.ndarray.numpy')
-def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+def shape(a):
+    """
+    Return the shape of an array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+
+    Returns
+    -------
+    shape : tuple of ints
+        The elements of the shape tuple give the lengths of the
+        corresponding array dimensions.
+
+    See Also
+    --------
+    ndarray.shape : Equivalent array method.
+
+    Examples
+    --------
+    >>> np.shape(np.eye(3))
+    (3, 3)
+    >>> np.shape([[1, 2]])
+    (1, 2)
+    >>> np.shape([0])
+    (1,)
+    >>> np.shape(0)
+    ()
+    """
+    return a.shape
+
+
+@set_module('mxnet.ndarray.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with zeros.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -77,7 +111,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
 
 
 @set_module('mxnet.ndarray.numpy')
-def ones(shape, dtype=_np.float32, order='C', ctx=None):
+def ones(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with ones.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -110,8 +144,9 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _npi.ones(shape=shape, ctx=ctx, dtype=dtype)
 
 
+# pylint: disable=too-many-arguments, redefined-outer-name
 @set_module('mxnet.ndarray.numpy')
-def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
     """
     Return a new array of given shape and type, filled with `fill_value`.
     Parameters
@@ -163,6 +198,7 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
         ctx = current_context()
     dtype = _np.float32 if dtype is None else dtype
     return _npi.full(shape=shape, value=fill_value, ctx=ctx, dtype=dtype, out=out)
+# pylint: enable=too-many-arguments, redefined-outer-name
 
 
 @set_module('mxnet.ndarray.numpy')

python/mxnet/numpy/multiarray.py

@@ -46,7 +46,7 @@
 from ..ndarray.numpy import _internal as _npi
 from ..ndarray.ndarray import _storage_type
 
-__all__ = ['ndarray', 'empty', 'array', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
+__all__ = ['ndarray', 'empty', 'array', 'shape', 'zeros', 'ones', 'full', 'add', 'subtract', 'multiply', 'divide',
            'mod', 'remainder', 'power', 'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10',
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
@@ -67,7 +67,7 @@
 
 # This function is copied from ndarray.py since pylint
 # keeps giving false alarm error of undefined-all-variable
-def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
+def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):  # pylint: disable=redefined-outer-name
     """Return a new handle with specified shape and context.
 
     Empty handle is only used to hold results.
@@ -89,7 +89,7 @@ def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
     return hdl
 
 
-def _reshape_view(a, *shape):
+def _reshape_view(a, *shape):  # pylint: disable=redefined-outer-name
     """Returns a **view** of this array with a new shape without altering any data.
 
     Parameters
@@ -462,7 +462,7 @@ def __getitem__(self, key):
         """
         # handling possible boolean indexing first
         ndim = self.ndim
-        shape = self.shape
+        shape = self.shape  # pylint: disable=redefined-outer-name
 
         if isinstance(key, list):
             try:
@@ -804,7 +804,7 @@ def __int__(self):
 
     def __len__(self):
         """Number of elements along the first axis."""
-        shape = self.shape
+        shape = self.shape  # pylint: disable=redefined-outer-name
         if len(shape) == 0:
             raise TypeError('len() of unsized object')
         return self.shape[0]
@@ -1165,7 +1165,7 @@ def reshape_like(self, *args, **kwargs):
         """
         raise AttributeError('mxnet.numpy.ndarray object has no attribute reshape_like')
 
-    def reshape_view(self, *shape, **kwargs):
+    def reshape_view(self, *shape, **kwargs):  # pylint: disable=redefined-outer-name
         """Returns a **view** of this array with a new shape without altering any data.
         Inheritated from NDArray.reshape.
         """
@@ -1854,7 +1854,7 @@ def squeeze(self, axis=None):  # pylint: disable=arguments-differ
         """Remove single-dimensional entries from the shape of a."""
         return _mx_np_op.squeeze(self, axis=axis)
 
-    def broadcast_to(self, shape):
+    def broadcast_to(self, shape):  # pylint: disable=redefined-outer-name
         return _mx_np_op.broadcast_to(self, shape)
 
     def broadcast_like(self, other):
@@ -1916,7 +1916,7 @@ def tostype(self, stype):
 
 
 @set_module('mxnet.numpy')
-def empty(shape, dtype=_np.float32, order='C', ctx=None):
+def empty(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, without initializing entries.
 
     Parameters
@@ -2020,7 +2020,41 @@ def array(object, dtype=None, ctx=None):
 
 
 @set_module('mxnet.numpy')
-def zeros(shape, dtype=_np.float32, order='C', ctx=None):
+def shape(a):
+    """
+    Return the shape of an array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+
+    Returns
+    -------
+    shape : tuple of ints
+        The elements of the shape tuple give the lengths of the
+        corresponding array dimensions.
+
+    See Also
+    --------
+    ndarray.shape : Equivalent array method.
+
+    Examples
+    --------
+    >>> np.shape(np.eye(3))
+    (3, 3)
+    >>> np.shape([[1, 2]])
+    (1, 2)
+    >>> np.shape([0])
+    (1,)
+    >>> np.shape(0)
+    ()
+    """
+    return _mx_nd_np.shape(a)
+
+
+@set_module('mxnet.numpy')
+def zeros(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with zeros.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -2061,7 +2095,7 @@ def zeros(shape, dtype=_np.float32, order='C', ctx=None):
 
 
 @set_module('mxnet.numpy')
-def ones(shape, dtype=_np.float32, order='C', ctx=None):
+def ones(shape, dtype=_np.float32, order='C', ctx=None):  # pylint: disable=redefined-outer-name
     """Return a new array of given shape and type, filled with ones.
     This function currently only supports storing multi-dimensional data
     in row-major (C-style).
@@ -2106,8 +2140,9 @@ def ones(shape, dtype=_np.float32, order='C', ctx=None):
     return _mx_nd_np.ones(shape, dtype, order, ctx)
 
 
+# pylint: disable=too-many-arguments, redefined-outer-name
 @set_module('mxnet.numpy')
-def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylint: disable=too-many-arguments
+def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):
     """
     Return a new array of given shape and type, filled with `fill_value`.
 
@@ -2160,6 +2195,7 @@ def full(shape, fill_value, dtype=None, order='C', ctx=None, out=None):  # pylin
            [2, 2]], dtype=int32)
     """
     return _mx_nd_np.full(shape, fill_value, order=order, ctx=ctx, dtype=dtype, out=out)
+# pylint: enable=too-many-arguments, redefined-outer-name
 
 
 @set_module('mxnet.numpy')

python/mxnet/util.py

@@ -35,6 +35,9 @@
     'subok': True,
 }
 
+_set_np_shape_logged = False
+_set_np_array_logged = False
+
 
 def makedirs(d):
     """Create directories recursively if they don't exist. os.makedirs(exist_ok=True) is not
@@ -87,13 +90,15 @@ def set_np_shape(active):
     >>> print(mx.is_np_shape())
     True
     """
-    if active:
+    global _set_np_shape_logged
+    if active and not _set_np_shape_logged:
         import logging
         logging.info('NumPy-shape semantics has been activated in your code. '
                      'This is required for creating and manipulating scalar and zero-size '
                      'tensors, which were not supported in MXNet before, as in the official '
                      'NumPy library. Please DO NOT manually deactivate this semantics while '
                      'using `mxnet.numpy` and `mxnet.numpy_extension` modules.')
+        _set_np_shape_logged = True
     elif is_np_array():
         raise ValueError('Deactivating NumPy shape semantics while NumPy array semantics is still'
                          ' active is not allowed. Please consider calling `npx.reset_np()` to'
@@ -678,11 +683,13 @@ def _set_np_array(active):
     -------
         A bool value indicating the previous state of NumPy array semantics.
     """
-    if active:
+    global _set_np_array_logged
+    if active and not _set_np_array_logged:
         import logging
         logging.info('NumPy array semantics has been activated in your code. This allows you'
                      ' to use operators from MXNet NumPy and NumPy Extension modules as well'
                      ' as MXNet NumPy `ndarray`s.')
+        _set_np_array_logged = True
     cur_state = is_np_array()
     _NumpyArrayScope._current.value = _NumpyArrayScope(active)
     return cur_state

tests/python/unittest/test_numpy_interoperability.py

@@ -66,24 +66,24 @@ def get_mat(n):
     A = np.array([[1, 2], [3, 4], [5, 6]])
     vals = (100 * np.arange(5)).astype('l')
     vals_c = (100 * np.array(get_mat(5)) + 1).astype('l')
-    vals_f = _np.array((100 * get_mat(5) + 1), order ='F', dtype ='l')
+    vals_f = _np.array((100 * get_mat(5) + 1), order='F', dtype='l')
     vals_f = np.array(vals_f)
 
-    OpArgMngr.add_workload('diag', A, k= 2)
-    OpArgMngr.add_workload('diag', A, k= 1)
-    OpArgMngr.add_workload('diag', A, k= 0)
-    OpArgMngr.add_workload('diag', A, k= -1)
-    OpArgMngr.add_workload('diag', A, k= -2)
-    OpArgMngr.add_workload('diag', A, k= -3)
-    OpArgMngr.add_workload('diag', vals, k= 0)
-    OpArgMngr.add_workload('diag', vals, k= 2)
-    OpArgMngr.add_workload('diag', vals, k= -2)
-    OpArgMngr.add_workload('diag', vals_c, k= 0)
-    OpArgMngr.add_workload('diag', vals_c, k= 2)
-    OpArgMngr.add_workload('diag', vals_c, k= -2)
-    OpArgMngr.add_workload('diag', vals_f, k= 0)
-    OpArgMngr.add_workload('diag', vals_f, k= 2)
-    OpArgMngr.add_workload('diag', vals_f, k= -2)
+    OpArgMngr.add_workload('diag', A, k=2)
+    OpArgMngr.add_workload('diag', A, k=1)
+    OpArgMngr.add_workload('diag', A, k=0)
+    OpArgMngr.add_workload('diag', A, k=-1)
+    OpArgMngr.add_workload('diag', A, k=-2)
+    OpArgMngr.add_workload('diag', A, k=-3)
+    OpArgMngr.add_workload('diag', vals, k=0)
+    OpArgMngr.add_workload('diag', vals, k=2)
+    OpArgMngr.add_workload('diag', vals, k=-2)
+    OpArgMngr.add_workload('diag', vals_c, k=0)
+    OpArgMngr.add_workload('diag', vals_c, k=2)
+    OpArgMngr.add_workload('diag', vals_c, k=-2)
+    OpArgMngr.add_workload('diag', vals_f, k=0)
+    OpArgMngr.add_workload('diag', vals_f, k=2)
+    OpArgMngr.add_workload('diag', vals_f, k=-2)
 
 
 def _add_workload_concatenate(array_pool):

tests/python/unittest/test_numpy_op.py

@@ -758,6 +758,26 @@ def legalize_shape(shape):
                                 assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol, use_broadcast=False, equal_nan=True)
 
 
+@with_seed()
+@use_np
+def test_np_shape():
+    shapes = [
+        (),
+        (0, 1),
+        (2, 3),
+        (2, 3, 4),
+    ]
+
+    for shape in shapes:
+        mx_a = np.random.uniform(size=shape)
+        np_a = _np.random.uniform(size=shape)
+
+        mx_shape = np.shape(mx_a)
+        np_shape = _np.shape(np_a)
+
+        assert mx_shape == np_shape
+
+
 @with_seed()
 @use_np
 def test_np_linspace():
@@ -4539,7 +4559,7 @@ def __init__(self, k=0):
 
         def hybrid_forward(self, F, a):
             return F.np.diag(a, k=self._k)
-            
+
     shapes = [(), (2,), (1, 5), (2, 2), (2, 5), (3, 3), (4, 3)]
     dtypes = [np.int8, np.uint8, np.int32, np.int64, np.float16, np.float32, np.float64]
     range_k = 6
@@ -4559,8 +4579,8 @@ def hybrid_forward(self, F, a):
             mx_out = test_diag(x)
         assert mx_out.shape == np_out.shape
         assert_almost_equal(mx_out.asnumpy(), np_out, rtol=rtol, atol=atol)
-        
-        # check backward function 
+
+        # check backward function
         mx_out.backward()
         if len(shape) == 0:
             np_backward = np.array(())
@@ -4593,7 +4613,6 @@ def hybrid_forward(self, F, a):
 @with_seed()
 @use_np
 def test_np_nan_to_num():
-
     def take_ele_grad(ele):
         if _np.isinf(ele) or _np.isnan(ele):
             return 0