乐奇 Rokid 开放社区

numpy.expand_dims(a, axis)

Expand the shape of an array.

Insert a new axis that will appear at the axis position in the expanded array shape.

 

Parameters:	a  : array_like Input array. axis  : int Position in the expanded axes where the new axis is placed.
Returns:	res  : ndarray Output array. The number of dimensions is one greater than that of the input array.

 

Examples

>>> x = np.array([1,2]) >>> x.shape (2,) 

The following is equivalent to x[np.newaxis,:] or x[np.newaxis]:

>>> y = np.expand_dims(x, axis=0) >>> y array([[1, 2]]) >>> y.shape (1, 2)

>>> y = np.expand_dims(x, axis=1) # Equivalent to x[:,np.newaxis] >>> y array([[1],  [2]]) >>> y.shape (2, 1) 

Note that some examples may use None instead of np.newaxis. These are the same objects:

>>> np.newaxis is None True

 

torch.unsqueeze(input, dim, out=None) → Tensor

Returns a new tensor with a dimension of size one inserted at the specified position.

The returned tensor shares the same underlying data with this tensor.

A dim value within the range [-input.dim() - 1, input.dim() + 1) can be used. Negative dimwill correspond to unsqueeze() applied at dim = dim + input.dim() + 1.

Parameters:	input (Tensor) – the input tensor dim (int) – the index at which to insert the singleton dimension out (Tensor, optional) – the output tensor

Example:

>>> x = torch.tensor([1, 2, 3, 4]) >>> torch.unsqueeze(x, 0) tensor([[ 1, 2, 3, 4]]) >>> torch.unsqueeze(x, 1) tensor([[ 1],  [ 2],  [ 3],  [ 4]])