alibi_detect.utils.pytorch.prediction

Functions

predict_batch

predict_batch(x: Union[list, numpy.ndarray, torch.Tensor], model: Union[Callable, torch.nn.modules.module.Module, torch.nn.modules.container.Sequential], device: Union[Literal[cuda, gpu, cpu], torch.device, None] = None, batch_size: int = 10000000000, preprocess_fn: Optional[Callable] = None, dtype: Union[type[numpy.generic], torch.dtype] = <class 'numpy.float32'>) -> Union[numpy.ndarray, torch.Tensor, tuple]

Make batch predictions on a model.

Name	Type	Default	Description
x	Union[list, numpy.ndarray, torch.Tensor]		Batch of instances.
model	Union[Callable, torch.nn.modules.module.Module, torch.nn.modules.container.Sequential]		PyTorch model.
device	Union[Literal[cuda, gpu, cpu], torch.device, None]	None	Device type used. The default tries to use the GPU and falls back on CPU if needed. Can be specified by passing either 'cuda', 'gpu', 'cpu' or an instance of torch.device.
batch_size	int	10000000000	Batch size used during prediction.
preprocess_fn	Optional[Callable]	None	Optional preprocessing function for each batch.
dtype	Union[type[numpy.generic], torch.dtype]	<class 'numpy.float32'>	Model output type, e.g. np.float32 or torch.float32.

Returns

• Type: Union[numpy.ndarray, torch.Tensor, tuple]

predict_batch_transformer

predict_batch_transformer(x: Union[list, numpy.ndarray], model: Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential], tokenizer: Callable, max_len: int, device: Union[Literal[cuda, gpu, cpu], torch.device, None] = None, batch_size: int = 10000000000, dtype: Union[type[numpy.generic], torch.dtype] = <class 'numpy.float32'>) -> Union[numpy.ndarray, torch.Tensor, tuple]

Make batch predictions using a transformers tokenizer and model.

Name	Type	Default	Description
x	Union[list, numpy.ndarray]		Batch of instances.
model	Union[torch.nn.modules.module.Module, torch.nn.modules.container.Sequential]		PyTorch model.
tokenizer	Callable		Tokenizer for model.
max_len	int		Max sequence length for tokens.
device	Union[Literal[cuda, gpu, cpu], torch.device, None]	None	Device type used. The default tries to use the GPU and falls back on CPU if needed. Can be specified by passing either 'cuda', 'gpu', 'cpu' or an instance of torch.device.
batch_size	int	10000000000	Batch size used during prediction.
dtype	Union[type[numpy.generic], torch.dtype]	<class 'numpy.float32'>	Model output type, e.g. np.float32 or torch.float32.

Returns

• Type: Union[numpy.ndarray, torch.Tensor, tuple]