[Numpy] FFI: atleast_1/2/3d

benchmark/python/ffi/benchmark_ffi.py

@@ -62,6 +62,9 @@ def prepare_workloads():
     OpArgMngr.add_workload("split", pool['3x3'], (0, 1, 2), axis=1)
     OpArgMngr.add_workload("argmax", pool['3x2'], axis=-1)
     OpArgMngr.add_workload("argmin", pool['3x2'], axis=-1)
+    OpArgMngr.add_workload("atleast_1d", pool['2'], pool['2x2'])
+    OpArgMngr.add_workload("atleast_2d", pool['2'], pool['2x2'])
+    OpArgMngr.add_workload("atleast_3d", pool['2'], pool['2x2'])
     OpArgMngr.add_workload("indices", dimensions=(1, 2, 3))
     OpArgMngr.add_workload("subtract", pool['2x2'], pool['2x2'])
     OpArgMngr.add_workload("multiply", pool['2x2'], pool['2x2'])

python/mxnet/_numpy_op_doc.py

@@ -370,113 +370,6 @@ def _np_copy(a, out=None):
     pass
 
 
-def _np_atleast_1d(*arys):
-    """
-    Convert inputs to arrays with at least one dimension.
-
-    Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
-
-    Parameters
-    ----------
-    arys1, arys2, ... : ndarray
-        One or more input arrays.
-
-    Returns
-    -------
-    ret : ndarray
-        An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
-
-    See also
-    --------
-    atleast_2d, atleast_3d
-
-    Examples
-    --------
-    >>> np.atleast_1d(1.0)
-    array([1.])
-    >>> x = np.arange(9.0).reshape(3,3)
-    >>> np.atleast_1d(x)
-    array([[0., 1., 2.],
-           [3., 4., 5.],
-           [6., 7., 8.]])
-    >>> np.atleast_1d(np.array(1), np.array([3, 4]))
-    [array([1.]), array([3., 4.])]
-    """
-    pass
-
-
-def _np_atleast_2d(*arys):
-    """
-    Convert inputs to arrays with at least two dimensions.
-
-    Parameters
-    ----------
-    arys1, arys2, ... : ndarray
-        One or more input arrays.
-
-    Returns
-    -------
-    ret : ndarray
-        An array, or list of arrays, each with a.ndim >= 2. Copies are made only if necessary.
-
-    See also
-    --------
-    atleast_1d, atleast_3d
-
-    Examples
-    --------
-    >>> np.atleast_2d(3.0)
-    array([[3.]])
-    >>> x = np.arange(3.0)
-    >>> np.atleast_2d(x)
-    array([[0., 1., 2.]])
-    >>> np.atleast_2d(np.array(1), np.array([1, 2]), np.array([[1, 2]]))
-    [array([[1.]]), array([[1., 2.]]), array([[1., 2.]])]
-    """
-    pass
-
-def _np_atleast_3d(*arys):
-    """
-    Convert inputs to arrays with at least three dimension.
-
-    Parameters
-    ----------
-    arys1, arys2, ... : ndarray
-        One or more input arrays.
-
-    Returns
-    -------
-    ret : ndarray
-        An array, or list of arrays, 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).
-
-    See also
-    --------
-    atleast_1d, atleast_2d
-
-    Examples
-    --------
-    >>> np.atleast_3d(3.0)
-    array([[[3.]]])
-    >>> x = np.arange(3.0)
-    >>> np.atleast_3d(x).shape
-    (1, 3, 1)
-    >>> x = np.arange(12.0).reshape(4,3)
-    >>> np.atleast_3d(x).shape
-    (4, 3, 1)
-    >>> for arr in np.atleast_3d(np.array([1, 2]), np.array([[1, 2]]), np.array([[[1, 2]]])):
-    ...     print(arr, arr.shape)
-    ...
-    [[[1.]
-      [2.]]] (1, 2, 1)
-    [[[1.]
-      [2.]]] (1, 2, 1)
-    [[[1. 2.]]] (1, 1, 2)
-    """
-    pass
-
-
 def _np_reshape(a, newshape, order='C', out=None):
     """
     Gives a new shape to an array without changing its data.

python/mxnet/ndarray/numpy/_op.py

@@ -47,6 +47,7 @@
            'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal', 'roll', 'rot90', 'einsum',
            'true_divide', 'nonzero', 'quantile', 'percentile', 'shares_memory', 'may_share_memory',
            'diff', 'ediff1d', 'resize', 'polyval', 'nan_to_num', 'isnan', 'isinf', 'isposinf', 'isneginf', 'isfinite',
+           'atleast_1d', 'atleast_2d', 'atleast_3d',
            'where', 'bincount', 'pad', 'cumsum', 'diag', 'diagonal']
 
 
@@ -7564,6 +7565,123 @@ def isfinite(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.isfinite, _np.isfinite, out=out, **kwargs)
 
 
+@set_module('mxnet.ndarray.numpy')
+def atleast_1d(*arys):
+    """
+    Convert inputs to arrays with at least one dimension.
+
+    Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
+
+    See also
+    --------
+    atleast_2d, atleast_3d
+
+    Examples
+    --------
+    >>> np.atleast_1d(1.0)
+    array([1.])
+    >>> x = np.arange(9.0).reshape(3,3)
+    >>> np.atleast_1d(x)
+    array([[0., 1., 2.],
+           [3., 4., 5.],
+           [6., 7., 8.]])
+    >>> np.atleast_1d(np.array(1), np.array([3, 4]))
+    [array([1.]), array([3., 4.])]
+    """
+    if len(arys) == 1:
+        return _api_internal.atleast_1d(*arys)[0]
+    return list(_api_internal.atleast_1d(*arys))
+
+
+@set_module('mxnet.ndarray.numpy')
+def atleast_2d(*arys):
+    """
+    Convert inputs to arrays with at least two dimensions.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 2. Copies are made only if necessary.
+
+    See also
+    --------
+    atleast_1d, atleast_3d
+
+    Examples
+    --------
+    >>> np.atleast_2d(3.0)
+    array([[3.]])
+    >>> x = np.arange(3.0)
+    >>> np.atleast_2d(x)
+    array([[0., 1., 2.]])
+    >>> np.atleast_2d(np.array(1), np.array([1, 2]), np.array([[1, 2]]))
+    [array([[1.]]), array([[1., 2.]]), array([[1., 2.]])]
+    """
+    if len(arys) == 1:
+        return _api_internal.atleast_2d(*arys)[0]
+    return list(_api_internal.atleast_2d(*arys))
+
+
+@set_module('mxnet.ndarray.numpy')
+def atleast_3d(*arys):
+    """
+    Convert inputs to arrays with at least three dimension.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, 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).
+
+    See also
+    --------
+    atleast_1d, atleast_2d
+
+    Examples
+    --------
+    >>> np.atleast_3d(3.0)
+    array([[[3.]]])
+    >>> x = np.arange(3.0)
+    >>> np.atleast_3d(x).shape
+    (1, 3, 1)
+    >>> x = np.arange(12.0).reshape(4,3)
+    >>> np.atleast_3d(x).shape
+    (4, 3, 1)
+    >>> for arr in np.atleast_3d(np.array([1, 2]), np.array([[1, 2]]), np.array([[[1, 2]]])):
+    ...     print(arr, arr.shape)
+    ...
+    [[[1.]
+      [2.]]] (1, 2, 1)
+    [[[1.]
+      [2.]]] (1, 2, 1)
+    [[[1. 2.]]] (1, 1, 2)
+    """
+    if len(arys) == 1:
+        return _api_internal.atleast_3d(*arys)[0]
+    return list(_api_internal.atleast_3d(*arys))
+
+
 @set_module('mxnet.ndarray.numpy')
 def where(condition, x=None, y=None):  # pylint: disable=too-many-return-statements
     """where(condition, [x, y])

python/mxnet/numpy/multiarray.py

@@ -73,6 +73,7 @@
            'greater', 'less', 'greater_equal', 'less_equal', 'roll', 'rot90', 'einsum', 'true_divide', 'nonzero',
            'quantile', 'percentile', 'shares_memory', 'may_share_memory', 'diff', 'ediff1d', 'resize', 'matmul',
            'nan_to_num', 'isnan', 'isinf', 'isposinf', 'isneginf', 'isfinite', 'polyval', 'where', 'bincount',
+           'atleast_1d', 'atleast_2d', 'atleast_3d',
            'pad', 'cumsum', 'diag', 'diagonal']
 
 __all__ += fallback.__all__
@@ -9951,6 +9952,117 @@ def bincount(x, weights=None, minlength=0):
     return _mx_nd_np.bincount(x, weights=weights, minlength=minlength)
 
 
+@set_module('mxnet.numpy')
+def atleast_1d(*arys):
+    """
+    Convert inputs to arrays with at least one dimension.
+
+    Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
+
+    See also
+    --------
+    atleast_2d, atleast_3d
+
+    Examples
+    --------
+    >>> np.atleast_1d(1.0)
+    array([1.])
+    >>> x = np.arange(9.0).reshape(3,3)
+    >>> np.atleast_1d(x)
+    array([[0., 1., 2.],
+           [3., 4., 5.],
+           [6., 7., 8.]])
+    >>> np.atleast_1d(np.array(1), np.array([3, 4]))
+    [array([1.]), array([3., 4.])]
+    """
+    return _mx_nd_np.atleast_1d(*arys)
+
+
+@set_module('mxnet.numpy')
+def atleast_2d(*arys):
+    """
+    Convert inputs to arrays with at least two dimensions.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, each with a.ndim >= 2. Copies are made only if necessary.
+
+    See also
+    --------
+    atleast_1d, atleast_3d
+
+    Examples
+    --------
+    >>> np.atleast_2d(3.0)
+    array([[3.]])
+    >>> x = np.arange(3.0)
+    >>> np.atleast_2d(x)
+    array([[0., 1., 2.]])
+    >>> np.atleast_2d(np.array(1), np.array([1, 2]), np.array([[1, 2]]))
+    [array([[1.]]), array([[1., 2.]]), array([[1., 2.]])]
+    """
+    return _mx_nd_np.atleast_2d(*arys)
+
+
+@set_module('mxnet.numpy')
+def atleast_3d(*arys):
+    """
+    Convert inputs to arrays with at least three dimension.
+
+    Parameters
+    ----------
+    arys1, arys2, ... : ndarray
+        One or more input arrays.
+
+    Returns
+    -------
+    ret : ndarray
+        An array, or list of arrays, 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).
+
+    See also
+    --------
+    atleast_1d, atleast_2d
+
+    Examples
+    --------
+    >>> np.atleast_3d(3.0)
+    array([[[3.]]])
+    >>> x = np.arange(3.0)
+    >>> np.atleast_3d(x).shape
+    (1, 3, 1)
+    >>> x = np.arange(12.0).reshape(4,3)
+    >>> np.atleast_3d(x).shape
+    (4, 3, 1)
+    >>> for arr in np.atleast_3d(np.array([1, 2]), np.array([[1, 2]]), np.array([[[1, 2]]])):
+    ...     print(arr, arr.shape)
+    ...
+    [[[1.]
+      [2.]]] (1, 2, 1)
+    [[[1.]
+      [2.]]] (1, 2, 1)
+    [[[1. 2.]]] (1, 1, 2)
+    """
+    return _mx_nd_np.atleast_3d(*arys)
+
+
 @set_module('mxnet.numpy')
 def pad(x, pad_width=None, mode="constant", **kwargs): # pylint: disable=too-many-arguments
     """