fiftyone.utils.transformers.FiftyOneTransformer

class documentation

class FiftyOneTransformer(TransformerEmbeddingsMixin, Model): (source)

Known subclasses: fiftyone.utils.transformers.FiftyOneTransformerForDepthEstimation, fiftyone.utils.transformers.FiftyOneTransformerForImageClassification, fiftyone.utils.transformers.FiftyOneTransformerForObjectDetection, fiftyone.utils.transformers.FiftyOneTransformerForSemanticSegmentation

Constructor: FiftyOneTransformer(config)

View In Hierarchy

FiftyOne wrapper around a transformers model.

Parameters
config	a `FiftyOneTransformerConfig`

Method	`__init__`	Undocumented
Method	`predict`	Performs prediction on the given data.
Instance Variable	`config`	Undocumented
Instance Variable	`device`	Undocumented
Instance Variable	`image_processor`	Undocumented
Instance Variable	`model`	Undocumented
Property	`media_type`	The media type processed by the model.
Property	`preprocess`	Whether to apply `transforms` during inference (True) or to assume that they have already been applied (False).
Property	`ragged_batches`	True/False whether `transforms` may return tensors of different sizes. If True, then passing ragged lists of data to `predict_all` is not allowed.
Property	`transforms`	The preprocessing function that will/must be applied to each input before prediction, or `None` if no preprocessing is performed.
Method	`_load_image_processor`	Undocumented
Method	`_load_model`	Undocumented

Inherited from TransformerEmbeddingsMixin:

Method	`embed`	Generates an embedding for the given data.
Method	`embed_all`	Generates embeddings for the given iterable of data.
Property	`has_embeddings`	Whether this instance has embeddings.
Method	`_embed`	Undocumented

Inherited from EmbeddingsMixin (via TransformerEmbeddingsMixin):

Method get_embeddings Returns the embeddings generated by the last forward pass of the model.

Inherited from Model (via TransformerEmbeddingsMixin, EmbeddingsMixin):

Method	`__enter__`	Undocumented
Method	`__exit__`	Undocumented
Method	`predict_all`	Performs prediction on the given iterable of data.
Method	`preprocess.setter`	Undocumented
Property	`can_embed_prompts`	Whether this instance can generate prompt embeddings.
Property	`has_logits`	Whether this instance can generate logits for its predictions.

def __init__(self, config): (source) ¶

Undocumented

def predict(self, arg): (source) ¶

overrides fiftyone.core.models.Model.predict

overridden in fiftyone.utils.transformers.FiftyOneTransformerForDepthEstimation, fiftyone.utils.transformers.FiftyOneTransformerForImageClassification, fiftyone.utils.transformers.FiftyOneTransformerForObjectDetection, fiftyone.utils.transformers.FiftyOneTransformerForSemanticSegmentation

Performs prediction on the given data.

Image models should support, at minimum, processing arg values that are uint8 numpy arrays (HWC).

Video models should support, at minimum, processing arg values that are eta.core.video.VideoReader instances.

Parameters
arg	the data
Returns
a `fiftyone.core.labels.Label` instance or dict of `fiftyone.core.labels.Label` instances containing the predictions

config = (source) ¶

Undocumented

device = (source) ¶

Undocumented

image_processor = (source) ¶

Undocumented

model = (source) ¶

Undocumented

@property
media_type = (source) ¶

overrides fiftyone.core.models.Model.media_type

The media type processed by the model.

Supported values are "image" and "video".

@property
preprocess = (source) ¶

overrides fiftyone.core.models.Model.preprocess

Whether to apply transforms during inference (True) or to assume that they have already been applied (False).

@property
ragged_batches = (source) ¶

overrides fiftyone.core.models.Model.ragged_batches

True/False whether transforms may return tensors of different sizes. If True, then passing ragged lists of data to predict_all is not allowed.

@property
transforms = (source) ¶

overrides fiftyone.core.models.Model.transforms

The preprocessing function that will/must be applied to each input before prediction, or None if no preprocessing is performed.

def _load_image_processor(self): (source) ¶

Undocumented

def _load_model(self, config): (source) ¶

Undocumented