kaolin.utils.testing¶
API¶
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kaolin.utils.testing.
check_packed_tensor
(tensor, total_numel=None, last_dim=None, dtype=None, device=None, throw=True)¶ Check if packed tensor is valid given set of criteria.
Parameters: - tensor (torch.Tensor) – the packed tensor to be tested.
- total_numel (int, optional) – the expected number of elements.
- last_dim (int, optional) – the expected last dimension size.
- dtype (torch.dtype, optional) – the expected dtype.
- device (torch.device, optional) – the expected device.
- throw (bool) – if True the check will raise an error if failing.
Returns: status of the check.
Return type: (bool)
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kaolin.utils.testing.
check_padded_tensor
(tensor, padding_value=None, shape_per_tensor=None, batch_size=None, max_shape=None, last_dim=None, dtype=None, device=None, throw=True)¶ Check if padded tensor is valid given set of criteria.
Parameters: - tensor (torch.Tensor) – the padded tensor to be tested.
- padding_value (int, optional) – the expected number of elements,
shape_per_tensor
must be provided with padding_value. - shape_per_tensor (torch.LongTensor, optional) – the expected
shape_per_tensor
. - batch_size (int, optional) – the expected batch size.
- last_dim (int, optional) – the expected last dimension size.
- dtype (torch.dtype, optional) – the expected dtype.
- device (torch.device, optional) – the expected device.
- throw (bool) – if True the check will raise an error if failing.
Returns: status of the check.
Return type: (bool)
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kaolin.utils.testing.
check_tensor
(tensor, shape=None, dtype=None, device=None, throw=True)¶ Check if
torch.Tensor
is valid given set of criteria.Parameters: - tensor (torch.Tensor) – the tensor to be tested.
- shape (list or tuple of int, optional) – the expected shape,
if a dimension is set at
None
then it’s not verified. - dtype (torch.dtype, optional) – the expected dtype.
- device (torch.device, optional) – the expected device.
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kaolin.utils.testing.
tensor_info
(t, name='', print_stats=False, detailed=False)¶ Convenience method to format diagnostic tensor information, including shape, type, and optional attributes if specified as string. This information can then be logged as: logger.debug(tensor_info(my_tensor, ‘my tensor’))
Log output: my_tensor: [10, 2, 100, 100] (torch.float32)
Parameters: - t – input pytorch tensor or numpy array or None
- name – human readable name of the tensor (optional)
- print_stats – if True, includes mean/max/min statistics (takes compute time)
- detailed – if True, includes details about tensor properties
Returns: formatted string
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kaolin.utils.testing.
with_seed
(torch_seed=0, numpy_seed=None, random_seed=None)¶ Decorator to fix the seed of a function.
Parameters: