kaolin.ops.random¶

API¶

kaolin.ops.random.get_state()¶

Returns the generator states for generating random numbers.

Mostly used in pair with set_state() pytest –doctest-modules kaolin/ - https://pytorch.org/docs/stable/generated/torch.get_rng_state.html#torch.get_rng_state - https://docs.python.org/3/library/random.html#random.getstate - https://numpy.org/doc/stable/reference/random/generated/numpy.random.set_state.html#numpy.random.set_state

Returns: the states for the corresponding modules (torch, random, numpy).
Return type: (torch.ByteTensor, tuple, tuple)

Example

>>> torch_state, random_state, numpy_state = get_state()
>>> s = torch.randn((1, 3))
>>> set_state(torch_state, random_state, numpy_state)

kaolin.ops.random.manual_seed(torch_seed, random_seed=None, numpy_seed=None)¶

Set the seed for random and torch modules.

Parameters

torch_seed (int) – The desired seed for torch module.
random_seed (int) – The desired seed for random module. Default: torch_seed value.
numpy_seed (int) – The desired seed for numpy module. Default: torch_seed value.

kaolin.ops.random.random_shape_per_tensor(batch_size, min_shape=None, max_shape=None)¶

Generate random shape_per_tensor.

Parameters

min_shape (list, tuple or torch.LongTensor) – minimum values for each dimension of generated shapes. Default: 1 for each dimensions.
max_shape (list, tuple or torch.LongTensor) – maximu values for each dimension of generated shapes.

Returns

A shape_per_tensor (2D).

Return type

(torch.LongTensor)

Example

>>> _ = torch.random.manual_seed(1)
>>> random_shape_per_tensor(3, min_shape=(4, 4), max_shape=(10, 10))
tensor([[ 4,  7],
        [ 7,  7],
        [ 8, 10]])

kaolin.ops.random.random_spc_octrees(batch_size, max_level, device='cpu')¶

Generate random SPC octrees.

Parameters

batch_size (int) – The desired number of octrees.
max_level (int) – The desired max level of the octrees.
device (torch.device) – The desired output device.

Returns

A batch of randomly generated octrees.
The length of each octree.

Return type

(torch.ByteTensor, torch.IntTensor)

Example

>>> _ = torch.random.manual_seed(1)
>>> random_spc_octrees(2, 3, device='cpu')
(tensor([ 71, 180, 220,   9, 134,  59,  42, 102, 210, 193, 204, 190, 107,  24,
        104, 151,  13,   7,  18, 107,  16, 154,  57, 110,  19,  22, 230,  48,
        135,  65,  69, 147, 148, 184, 203, 229, 114, 232,  18, 231, 241, 195],
       dtype=torch.uint8), tensor([19, 23], dtype=torch.int32))

kaolin.ops.random.random_tensor(low, high, shape, dtype=torch.float32, device='cpu')¶

Generate a random tensor.

Parameters

low (float) – the lowest value to be drawn from the distribution.
high (float) – the highest value to be drawn from the distribution.
shape (list, tuple or torch.LongTensor) – the desired output shape.
dtype (torch.dtype) – the desired output dtype.
device (torch.device) – the desired output device.

Returns

a random generated tensor.

Return type

(torch.Tensor)

Example

>>> _ = torch.random.manual_seed(1)
>>> random_tensor(4., 5., (3, 3), dtype=torch.float, device='cpu')
tensor([[4.7576, 4.2793, 4.4031],
        [4.7347, 4.0293, 4.7999],
        [4.3971, 4.7544, 4.5695]])

kaolin.ops.random.set_state(torch_state, random_state, numpy_state)¶

Set the generator states for generating random numbers.

Mostly used in pair with get_state()

Parameters

torch_state (torch.ByteTensor) – the state of torch module.
random_state (tuple) – the state of random module.
numpy_state (tuple) – the state of numpy module.

Example

>>> torch_state, random_state, numpy_state = get_state()
>>> s = torch.randn((1, 3))
>>> set_state(torch_state, random_state, numpy_state)