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:
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 oftorch.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:
- 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:
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 oftorch.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:
- Returns:
Numpy array or torch tensor with model outputs.