alibi_detect.utils.pytorch.prediction module
- alibi_detect.utils.pytorch.prediction.predict_batch(x, model, device=None, batch_size=10000000000, preprocess_fn=None, dtype=numpy.float32)[source]
Make batch predictions on a model.
- Parameters
model (
Union[Callable,Module,Sequential]) – PyTorch model.device (
Optional[device]) – Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either torch.device(‘cuda’) or torch.device(‘cpu’).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
- 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=numpy.float32)[source]
Make batch predictions using a transformers tokenizer and model.
- Parameters
model (
Union[Module,Sequential]) – PyTorch model.tokenizer (
Callable) – Tokenizer for model.max_len (
int) – Max sequence length for tokens.device (
Optional[device]) – Device type used. The default None tries to use the GPU and falls back on CPU if needed. Can be specified by passing either torch.device(‘cuda’) or torch.device(‘cpu’).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
- Returns
Numpy array or torch tensor with model outputs.