Tensor Like
- class towhee.hub.builtin.operators.tensor_like.tensor_stack(axis=0)[source]
Stack the sequence of inputs along a new axis.
- Parameters
axis (int, optional) – the axis of the result array along which the inputs are stacked. Defaults to 0.
- Returns
the stacked array.
- Return type
ndarray
Examples:
>>> import numpy as np >>> from towhee.functional import DataCollection >>> dc = ( ... DataCollection.range(10) ... .map(lambda x: np.array([x])) ... .batch(2) ... .tensor_stack(axis=1) ... ) >>> dc.to_list() [array([[0, 1]]), array([[2, 3]]), array([[4, 5]]), array([[6, 7]]), array([[8, 9]])]
- class towhee.hub.builtin.operators.tensor_like.tensor_unstack(axis=0)[source]
Unstack an array along given axis.
- Parameters
axis (int, optional) – the axis along which to unstack the array. Defaults to 0.
- Returns
sequence of result arrays.
- Return type
[ndarray]
Examples:
>>> import numpy as np >>> from towhee.functional import DataCollection >>> dc = ( ... DataCollection.range(3) ... .map(lambda x: np.array([x, x*2])) ... .tensor_unstack(axis=0) ... ) >>> dc.to_list() [[array(0), array(0)], [array(1), array(2)], [array(2), array(4)]]
- class towhee.hub.builtin.operators.tensor_like.tensor_concat(axis=0)[source]
Concat the sequence of inputs along a axis (no new axis)
- Parameters
axis (int, optional) – the axis alone which to concat inputs. Defaults to 0.
- Returns
the stacked array.
- Return type
ndarray
Examples:
>>> import numpy as np >>> from towhee.functional import DataCollection >>> dc = ( ... DataCollection.range(10) ... .map(lambda x: np.array([x])) ... .batch(2) ... .tensor_concat(axis=0) ... ) >>> dc.to_list() [array([0, 1]), array([2, 3]), array([4, 5]), array([6, 7]), array([8, 9])]
- class towhee.hub.builtin.operators.tensor_like.tensor_split(axis=0)[source]
Split the input array along a axis.
- Parameters
axis (int, optional) – the axis along which to split the array. Defaults to 0.
- Returns
list of result arrays.
- Return type
[ndarray]
Examples:
>>> import numpy as np >>> from towhee.functional import DataCollection >>> dc = ( ... DataCollection.range(3) ... .map(lambda x: np.array([[x, x*2]])) ... .tensor_split(axis=1) ... ) >>> dc.to_list() [[array([[0]]), array([[0]])], [array([[1]]), array([[2]])], [array([[2]]), array([[4]])]]
- class towhee.hub.builtin.operators.tensor_like.tensor_normalize(axis=0)[source]
Normalize input tensor.
Examples:
>>> import numpy >>> from towhee.functional import DataCollection >>> dc = DataCollection([numpy.array([3, 4]), numpy.array([6,8])]) >>> dc.tensor_normalize().to_list() [array([0.6, 0.8]), array([0.6, 0.8])]
- class towhee.hub.builtin.operators.tensor_like.tensor_random(shape)[source]
Return a random tensor filled with random numbers from [0.0, 1.0).
- Parameters
shape ([int], optional) – tensor shape. Defaults to None.
- Returns
output tensor.
- Return type
ndarray
Examples:
>>> from towhee.functional import DataCollection >>> DataCollection.range(5).tensor_random(shape=[1,2,3]).map(lambda x: x.shape).to_list() [(1, 2, 3), (1, 2, 3), (1, 2, 3), (1, 2, 3), (1, 2, 3)]