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@ -42,11 +42,32 @@ from ..fluid import layers
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import paddle
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__all__ = [
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'cast', 'concat', 'expand', 'expand_as', 'flatten', 'gather', 'gather_nd',
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'reshape', 'reverse', 'scatter', 'scatter_nd_add', 'scatter_nd',
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'shard_index', 'slice', 'split', 'squeeze', 'stack', 'strided_slice',
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'transpose', 'unique', 'unique_with_counts', 'unsqueeze', 'unstack', 'flip',
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'unbind', 'roll'
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'cast',
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'concat',
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'expand',
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'expand_as',
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'flatten',
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'gather',
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'gather_nd',
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'reshape',
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'reverse',
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'scatter',
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'scatter_nd_add',
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'scatter_nd',
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'shard_index',
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'slice',
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'split',
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'squeeze',
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'stack',
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'strided_slice',
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'transpose',
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'unique',
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'unique_with_counts',
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'unsqueeze',
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'unstack',
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'flip',
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'unbind',
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'roll',
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]
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@ -417,7 +438,7 @@ def stack(x, axis=0, name=None):
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Args:
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x (Tensor|list[Tensor]): Input ``x`` can be a single tensor, or a ``list`` of tensors.
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If ``x`` is a ``list``, the Tensors in ``x``
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must be of the same shape and dtype. Support data types: float32, float64, int32, int64.
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must be of the same shape and dtype. Supported data types: float32, float64, int32, int64.
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axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is ``[-(R+1), R+1)``,
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where ``R`` is the number of dimensions of the first input tensor ``x[0]``.
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If ``axis < 0``, ``axis = axis+R+1``. The default value of axis is 0.
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@ -559,18 +580,19 @@ def squeeze(x, axis=None, name=None):
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out.shape = [1, 3, 5]
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Args:
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input (Tensor): The input Tensor. Support data type: float32, float64, int8, int32, int64.
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x (Tensor): The input Tensor. Supported data type: float32, float64, bool, int8, int32, int64.
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axis (int|list|tuple, optional): An integer or list of integers, indicating the dimensions to be squeezed. Default is None.
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The range of axis is :math:`[-ndim(input), ndim(input))`.
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If axis is negative, :math:`axis = axis + ndim(input)`.
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If axis is None, all the dimensions of input of size 1 will be removed.
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The range of axis is :math:`[-ndim(x), ndim(x))`.
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If axis is negative, :math:`axis = axis + ndim(x)`.
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If axis is None, all the dimensions of x of size 1 will be removed.
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name (str, optional): Please refer to :ref:`api_guide_Name`, Default None.
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Returns:
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Tensor: Output squeezed Tensor. Data type is same as input Tensor.
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Tensor: Squeezed Tensor with the same data type as input Tensor.
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Examples:
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.. code-block:: python
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import paddle
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paddle.enable_imperative()
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@ -590,87 +612,50 @@ def squeeze(x, axis=None, name=None):
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return layers.squeeze(x, axis, name)
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def unsqueeze(input, axes, out=None, name=None):
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def unsqueeze(x, axis, name=None):
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"""
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:alias_main: paddle.unsqueeze
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:alias: paddle.unsqueeze,paddle.tensor.unsqueeze,paddle.tensor.manipulation.unsqueeze
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Insert single-dimensional entries to the shape of a Tensor. Takes one
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required argument axes, a list of dimensions that will be inserted.
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Dimension indices in axes are as seen in the output tensor.
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For example:
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.. code-block:: text
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:alias: paddle.unsqueeze, paddle.tensor.unsqueeze, paddle.tensor.manipulation.unsqueeze
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Given a tensor such that tensor with shape [3, 4, 5],
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then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
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Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one
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required argument axis, a dimension or list of dimensions that will be inserted.
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Dimension indices in axis are as seen in the output tensor.
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Args:
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input (Variable): The input Tensor to be unsqueezed. It is a N-D Tensor of data types float32, float64, int32.
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axes (int|list|tuple|Variable): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axes`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axes`` is an Variable, it should be an 1-D Tensor .
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name (str|None): Name for this layer.
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x (Tensor): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64.
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axis (int|list|tuple|Tensor): Indicates the dimensions to be inserted. The data type is ``int32`` .
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If ``axis`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
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If ``axis`` is a Tensor, it should be an 1-D Tensor .
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If ``axis`` is negative, ``axis = axis + ndim(x) + 1``.
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name (str|None): Name for this layer. Please refer to :ref:`api_guide_Name`, Default None.
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Returns:
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Variable: Output unsqueezed Tensor, with data type being float32, float64, int32, int64.
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Tensor: Unsqueezed Tensor with the same data type as input Tensor.
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Examples:
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.. code-block:: python
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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with fluid.dygraph.guard():
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input_1 = np.random.random([5, 10]).astype("int32")
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# input is a variable which shape is [5, 10]
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input = fluid.dygraph.to_variable(input_1)
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paddle.enable_imperative()
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x = paddle.rand([5, 10])
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print(x.shape) # [5, 10]
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out1 = paddle.unsqueeze(x, axis=0)
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print(out1.shape) # [1, 5, 10]
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out2 = paddle.unsqueeze(x, axis=[0, 2])
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print(out2.shape) # [1, 5, 1, 10]
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output = paddle.unsqueeze(input, axes=[1])
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# output.shape [5, 1, 10]
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axis = paddle.fluid.dygraph.to_variable([0, 1, 2])
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out3 = paddle.unsqueeze(x, axis=axis)
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print(out3.shape) # [1, 1, 1, 5, 10]
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"""
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if not isinstance(axes, (int, list, tuple, Variable)):
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raise TypeError(
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"The type of 'axes' in unsqueeze must be int, list, tuple or Variable, but "
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"received %s." % (type(axes)))
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helper = LayerHelper("unsqueeze2", **locals())
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inputs = {"X": input}
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attrs = {}
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def _to_Variable_list(one_list):
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Variable_list = []
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for ele in one_list:
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if isinstance(ele, Variable):
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ele.stop_gradient = True
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Variable_list.append(ele)
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else:
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assert (isinstance(ele, int))
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temp_out = helper.create_variable_for_type_inference('int32')
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fill_constant([1], 'int32', ele, force_cpu=True, out=temp_out)
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Variable_list.append(temp_out)
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return Variable_list
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if isinstance(axes, int):
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axes = [axes]
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if isinstance(axes, Variable):
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axes.stop_gradient = True
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inputs["AxesTensor"] = axes
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elif isinstance(axes, (list, tuple)):
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contain_var = not all(not isinstance(ele, Variable) for ele in axes)
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if contain_var:
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inputs["AxesTensorList"] = _to_Variable_list(axes)
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else:
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attrs["axes"] = axes
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out = helper.create_variable_for_type_inference(dtype=input.dtype)
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x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
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helper.append_op(
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type="unsqueeze2",
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inputs=inputs,
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attrs=attrs,
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outputs={"Out": out,
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"XShape": x_shape})
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if isinstance(axis, int):
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axis = [axis]
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return out
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return layers.unsqueeze(x, axis, name)
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def gather(input, index, overwrite=True):
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Leo Chen
5 years ago
committed by
GitHub