alibi_detect.utils.pytorch.prediction module

alibi_detect.utils.pytorch.prediction.predict_batch(x, model, device=None, batch_size=10000000000, preprocess_fn=None, dtype=<class 'numpy.float32'>)[source]

Make batch predictions on a model.

Parameters:
  • x (Union[list, ndarray, Tensor]) – Batch of instances.

  • model (Union[Callable, Module, Sequential]) – PyTorch model.

  • device (Union[Literal[‘cuda’, ‘gpu’, ‘cpu’], device, 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) – Batch size used during prediction.

  • preprocess_fn (Optional[Callable]) – Optional preprocessing function for each batch.

  • dtype (Union[Type[generic], dtype]) – Model output type, e.g. np.float32 or torch.float32.

Return type:

Union[ndarray, Tensor, tuple]

Returns:

Numpy array, torch tensor or tuples of those with model outputs.

alibi_detect.utils.pytorch.prediction.predict_batch_transformer(x, model, tokenizer, max_len, device=None, batch_size=10000000000, dtype=<class 'numpy.float32'>)[source]

Make batch predictions using a transformers tokenizer and model.

Parameters:
  • x (Union[list, ndarray]) – Batch of instances.

  • model (Union[Module, Sequential]) – PyTorch model.

  • tokenizer (Callable) – Tokenizer for model.

  • max_len (int) – Max sequence length for tokens.

  • device (Union[Literal[‘cuda’, ‘gpu’, ‘cpu’], device, 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) – Batch size used during prediction.

  • dtype (Union[Type[generic], dtype]) – Model output type, e.g. np.float32 or torch.float32.

Return type:

Union[ndarray, Tensor, tuple]

Returns:

Numpy array or torch tensor with model outputs.