【Hackathon 5th No.33】为 Paddle 新增 atleast_1d / atleast_2d / atleast_3d API -part

python/paddle/__init__.py

@@ -199,6 +199,9 @@
 
 
 from .tensor.manipulation import (  # noqa: F401
+    atleast_1d,
+    atleast_2d,
+    atleast_3d,
     cast,
     cast_,
     concat,
@@ -833,6 +836,9 @@
     'logspace',
     'reshape',
     'reshape_',
+    'atleast_1d',
+    'atleast_2d',
+    'atleast_3d',
     'reverse',
     'nonzero',
     'CUDAPinnedPlace',

python/paddle/tensor/__init__.py

@@ -112,6 +112,9 @@
 from .logic import isclose  # noqa: F401
 from .logic import equal_all  # noqa: F401
 from .logic import is_tensor  # noqa: F401
+from .manipulation import atleast_1d  # noqa: F401
+from .manipulation import atleast_2d  # noqa: F401
+from .manipulation import atleast_3d  # noqa: F401
 from .manipulation import cast  # noqa: F401
 from .manipulation import cast_  # noqa: F401
 from .manipulation import concat  # noqa: F401
@@ -731,6 +734,9 @@
     'normal_',
     'index_fill',
     'index_fill_',
+    'atleast_1d',
+    'atleast_2d',
+    'atleast_3d',
 ]
 
 # this list used in math_op_patch.py for magic_method bind

python/paddle/tensor/manipulation.py

@@ -3957,6 +3957,184 @@ def reshape_(x, shape, name=None):
         return out
 
 
+def atleast_1d(*inputs, name=None):
+    """
+    Convert inputs to tensors and return the view with at least 1-dimension. Scalar inputs are converted,
+    one or high-dimensional inputs are preserved.
+
+    Args:
+        inputs (Tensor|list(Tensor)): One or more tensors. The data type is ``float16``, ``float32``, ``float64``, ``int16``, ``int32``, ``int64``, ``int8``, ``uint8``, ``complex64``, ``complex128``, ``bfloat16`` or ``bool``.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        One Tensor, if there is only one input.
+        List of Tensors, if there are more than one inputs.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> # one input
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> out = paddle.atleast_1d(x)
+            >>> print(out)
+            Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [123])
+
+            >>> # more than one inputs
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> y = paddle.to_tensor([1.23], dtype='float32')
+            >>> out = paddle.atleast_1d(x, y)
+            >>> print(out)
+            [Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [123]), Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [1.23000002])]
+
+            >>> # more than 1-D input
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> y = paddle.to_tensor([[1.23]], dtype='float32')
+            >>> out = paddle.atleast_1d(x, y)
+            >>> print(out)
+            [Tensor(shape=[1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [123]), Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[1.23000002]])]
+    """
+    out = []
+    for tensor in inputs:
+        tensor = paddle.to_tensor(tensor)
+        if tensor.dim() == 0:
+            result = tensor.reshape((1,))
+        else:
+            result = tensor
+        out.append(result)
+
+    if len(out) == 1:
+        return out[0]
+    else:
+        return out
+
+
+def atleast_2d(*inputs, name=None):
+    """
+    Convert inputs to tensors and return the view with at least 2-dimension. Two or high-dimensional inputs are preserved.
+
+    Args:
+        inputs (Tensor|list(Tensor)): One or more tensors. The data type is ``float16``, ``float32``, ``float64``, ``int16``, ``int32``, ``int64``, ``int8``, ``uint8``, ``complex64``, ``complex128``, ``bfloat16`` or ``bool``.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        One Tensor, if there is only one input.
+        List of Tensors, if there are more than one inputs.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> # one input
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> out = paddle.atleast_2d(x)
+            >>> print(out)
+            Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [[123]])
+
+            >>> # more than one inputs
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> y = paddle.to_tensor([1.23], dtype='float32')
+            >>> out = paddle.atleast_2d(x, y)
+            >>> print(out)
+            [Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [[123]]), Tensor(shape=[1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[1.23000002]])]
+
+            >>> # more than 2-D input
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> y = paddle.to_tensor([[[1.23]]], dtype='float32')
+            >>> out = paddle.atleast_2d(x, y)
+            >>> print(out)
+            [Tensor(shape=[1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [[123]]), Tensor(shape=[1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[[1.23000002]]])]
+    """
+    out = []
+    for tensor in inputs:
+        tensor = paddle.to_tensor(tensor)
+        if tensor.dim() == 0:
+            result = tensor.reshape((1, 1))
+        elif tensor.dim() == 1:
+            result = paddle.unsqueeze(tensor, axis=0)
+        else:
+            result = tensor
+        out.append(result)
+
+    if len(out) == 1:
+        return out[0]
+    else:
+        return out
+
+
+def atleast_3d(*inputs, name=None):
+    """
+    Convert inputs to tensors and return the view with at least 3-dimension. Three or high-dimensional inputs are preserved.
+
+    Args:
+        inputs (Tensor|list(Tensor)): One or more tensors. The data type is ``float16``, ``float32``, ``float64``, ``int16``, ``int32``, ``int64``, ``int8``, ``uint8``, ``complex64``, ``complex128``, ``bfloat16`` or ``bool``.
+        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        One Tensor, if there is only one input.
+        List of Tensors, if there are more than one inputs.
+
+    Examples:
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> # one input
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> out = paddle.atleast_3d(x)
+            >>> print(out)
+            Tensor(shape=[1, 1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [[[123]]])
+
+            >>> # more than one inputs
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> y = paddle.to_tensor([1.23], dtype='float32')
+            >>> out = paddle.atleast_3d(x, y)
+            >>> print(out)
+            [Tensor(shape=[1, 1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [[[123]]]), Tensor(shape=[1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[[1.23000002]]])]
+
+            >>> # more than 3-D input
+            >>> x = paddle.to_tensor(123, dtype='int32')
+            >>> y = paddle.to_tensor([[[[1.23]]]], dtype='float32')
+            >>> out = paddle.atleast_3d(x, y)
+            >>> print(out)
+            [Tensor(shape=[1, 1, 1], dtype=int32, place=Place(cpu), stop_gradient=True,
+            [[[123]]]), Tensor(shape=[1, 1, 1, 1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [[[[1.23000002]]]])]
+    """
+    out = []
+    for tensor in inputs:
+        tensor = paddle.to_tensor(tensor)
+        if tensor.dim() == 0:
+            result = tensor.reshape((1, 1, 1))
+        elif tensor.dim() == 1:
+            result = paddle.unsqueeze(tensor, axis=[0, 2])
+        elif tensor.dim() == 2:
+            result = paddle.unsqueeze(tensor, axis=2)
+        else:
+            result = tensor
+        out.append(result)
+
+    if len(out) == 1:
+        return out[0]
+    else:
+        return out
+
+
 def gather_nd(x, index, name=None):
     """