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docs updates
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@brimoor
brimoor committed Jun 15, 2021
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40 changes: 27 additions & 13 deletions fiftyone/core/collections.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1222,7 +1222,7 @@ def apply_model(
batch_size=None,
num_workers=None,
skip_failures=True,
**kwargs,
**trainer_kwargs,
):
"""Applies the :class:`FiftyOne model <fiftyone.core.models.Model>` or
:class:`Lightning Flash model <flash:flash.core.model.Task>` to the
Expand Down Expand Up @@ -1259,9 +1259,10 @@ def apply_model(
raising an error if predictions cannot be generated for a
sample. Only applicable to :class:`fiftyone.core.models.Model`
instances
kwargs: additional kwargs used to construct a
:class:`flash:flash.core.trainer.Trainer` for Flash
models
**trainer_kwargs: optional keyword arguments used to initialize the
:class:`flash:flash.core.trainer.Trainer` when using Flash
models. These can be used to, for example, configure the number
of GPUs to use and other distributed inference parameters
"""
fomo.apply_model(
self,
Expand All @@ -1272,7 +1273,7 @@ def apply_model(
batch_size=batch_size,
num_workers=num_workers,
skip_failures=skip_failures,
**kwargs,
**trainer_kwargs,
)

def compute_embeddings(
Expand All @@ -1282,25 +1283,33 @@ def compute_embeddings(
batch_size=None,
num_workers=None,
skip_failures=True,
**trainer_kwargs,
):
"""Computes embeddings for the samples in the collection using the
given :class:`fiftyone.core.models.Model`.
given :class:`FiftyOne model <fiftyone.core.models.Model>` or
:class:`Lightning Flash model <flash:flash.core.model.Task>`.
This method supports all the following cases:
- Using an image model to compute embeddings for an image collection
- Using an image model to compute frame embeddings for a video
collection
- Using a video model to compute embeddings for a video collection
The ``model`` must expose embeddings, i.e.,
- Using an image :class:`fiftyone.core.models.Model` to compute
embeddings for an image collection
- Using an image :class:`fiftyone.core.models.Model` to compute frame
embeddings for a video collection
- Using a video :class:`fiftyone.core.models.Model` to compute
embeddings for a video collection
- Using an :class:`flash:flash.image.ImageEmbeder` to compute
embeddings for an image collection
When using a :class:`FiftyOne model <fiftyone.core.models.Model>`, the
model must expose embeddings, i.e.,
:meth:`fiftyone.core.models.Model.has_embeddings` must return ``True``.
If an ``embeddings_field`` is provided, the embeddings are saved to the
samples; otherwise, the embeddings are returned in-memory.
Args:
model: a :class:`fiftyone.core.models.Model`
model: a :class:`fiftyone.core.models.Model` or
:class:`flash:flash.core.model.Task`
embeddings_field (None): the name of a field in which to store the
embeddings. When computing video frame embeddings, the
"frames." prefix is optional
Expand All @@ -1313,6 +1322,10 @@ def compute_embeddings(
raising an error if embeddings cannot be generated for a
sample. Only applicable to :class:`fiftyone.core.models.Model`
instances
**trainer_kwargs: optional keyword arguments used to initialize the
:class:`flash:flash.core.trainer.Trainer` when using Flash
models. These can be used to, for example, configure the number
of GPUs to use and other distributed inference parameters
Returns:
one of the following:
Expand Down Expand Up @@ -1340,6 +1353,7 @@ def compute_embeddings(
batch_size=batch_size,
num_workers=num_workers,
skip_failures=skip_failures,
**trainer_kwargs,
)

def compute_patch_embeddings(
Expand Down
25 changes: 16 additions & 9 deletions fiftyone/core/models.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ def apply_model(
batch_size=None,
num_workers=None,
skip_failures=True,
**kwargs,
**trainer_kwargs,
):
"""Applies the :class:`FiftyOne model <Model>` or
:class:`Lightning Flash model <flash:flash.core.model.Task>` to the samples
Expand Down Expand Up @@ -87,9 +87,10 @@ def apply_model(
skip_failures (True): whether to gracefully continue without raising an
error if predictions cannot be generated for a sample. Only
applicable to :class:`Model` instances
kwargs: additional kwargs used to construct a
:class:`flash:flash.core.trainer.Trainer` for Flash
models
**trainer_kwargs: optional keyword arguments used to initialize the
:class:`flash:flash.core.trainer.Trainer` when using Flash models.
These can be used to, for example, configure the number of GPUs to
use and other distributed inference parameters
"""
if _is_flash_model(model):
return fouf.apply_flash_model(
Expand All @@ -100,7 +101,7 @@ def apply_model(
store_logits=store_logits,
batch_size=batch_size,
num_workers=num_workers,
**kwargs,
**trainer_kwargs,
)

if not isinstance(model, Model):
Expand Down Expand Up @@ -619,6 +620,7 @@ def compute_embeddings(
batch_size=None,
num_workers=None,
skip_failures=True,
**trainer_kwargs,
):
"""Computes embeddings for the samples in the collection using the given
:class:`FiftyOne model <Model>` or
Expand All @@ -635,15 +637,15 @@ def compute_embeddings(
- Using an :class:`flash:flash.image.ImageEmbeder` to compute embeddings
for an image collection
When ``model`` is a FiftyOne model, it must expose embeddings, i.e.,
:meth:`Model.has_embeddings` must return ``True``.
When using a :class:`FiftyOne model <Model>`, the model must expose
embeddings, i.e., :meth:`Model.has_embeddings` must return ``True``.
If an ``embeddings_field`` is provided, the embeddings are saved to the
samples; otherwise, the embeddings are returned in-memory.
Args:
samples: a :class:`fiftyone.core.collections.SampleCollection`
model: a :class:`Model` or :class:`flash:flash.image.ImageEmbeder`
model: a :class:`Model` or :class:`flash:flash.core.model.Task>`
embeddings_field (None): the name of a field in which to store the
embeddings. When computing video frame embeddings, the "frames."
prefix is optional
Expand All @@ -653,7 +655,11 @@ def compute_embeddings(
Only applicable for Torch-based models
skip_failures (True): whether to gracefully continue without raising an
error if embeddings cannot be generated for a sample. Only
applicable to :class:`fiftyone.core.models.Model` instances
applicable to :class:`Model` instances
**trainer_kwargs: optional keyword arguments used to initialize the
:class:`flash:flash.core.trainer.Trainer` when using Flash models.
These can be used to, for example, configure the number of GPUs to
use and other distributed inference parameters
Returns:
one of the following:
Expand All @@ -680,6 +686,7 @@ def compute_embeddings(
embeddings_field=embeddings_field,
batch_size=batch_size,
num_workers=num_workers,
**trainer_kwargs,
)

if not isinstance(model, Model):
Expand Down
41 changes: 27 additions & 14 deletions fiftyone/utils/flash.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -59,8 +59,10 @@ def apply_flash_model(
batch_size (None): an optional batch size to use. If not provided, a
default batch size is used
num_workers (None): the number of workers for the data loader to use
trainer_kwargs: additional kwargs are passed into the Trainer()
constructor
**trainer_kwargs: optional keyword arguments used to initialize the
:class:`flash:flash.core.trainer.Trainer`. These can be used to,
for example, configure the number of GPUs to use and other
distributed inference parameters
"""
serializer = _get_serializer(model, confidence_thresh, store_logits)

Expand All @@ -71,12 +73,14 @@ def apply_flash_model(
with fou.SetAttributes(
model, data_pipeline=data_pipeline, serializer=serializer
):
kwargs = dict(preprocess=model.preprocess, num_workers=num_workers)
data_kwargs = dict(
preprocess=model.preprocess, num_workers=num_workers,
)
if batch_size is not None:
kwargs["batch_size"] = batch_size
data_kwargs["batch_size"] = batch_size

datamodule = fi.ImageClassificationData.from_fiftyone(
predict_dataset=samples, **kwargs
predict_dataset=samples, **data_kwargs
)
predictions = flash.Trainer(**trainer_kwargs).predict(
model, datamodule=datamodule
Expand All @@ -87,7 +91,12 @@ def apply_flash_model(


def compute_flash_embeddings(
samples, model, embeddings_field=None, batch_size=None, num_workers=None
samples,
model,
embeddings_field=None,
batch_size=None,
num_workers=None,
**trainer_kwargs,
):
"""Computes embeddings for the samples in the collection using the given
:class:`Lightning Flash model <flash:flash.core.model.Task>`.
Expand All @@ -106,6 +115,10 @@ def compute_flash_embeddings(
batch_size (None): an optional batch size to use. If not provided, a
default batch size is used
num_workers (None): the number of workers for the data loader to use
**trainer_kwargs: optional keyword arguments used to initialize the
:class:`flash:flash.core.trainer.Trainer`. These can be used to,
for example, configure the number of GPUs to use and other
distributed inference parameters
Returns:
one of the following:
Expand All @@ -125,18 +138,18 @@ def compute_flash_embeddings(
data_pipeline = model.data_pipeline

with fou.SetAttributes(model, data_pipeline=data_pipeline):
# equivalent(?) but no progress bar...
# filepaths = samples.values("filepath")
# embeddings = model.predict(filepaths)

kwargs = dict(preprocess=model.preprocess, num_workers=num_workers)
data_kwargs = dict(
preprocess=model.preprocess, num_workers=num_workers,
)
if batch_size is not None:
kwargs["batch_size"] = batch_size
data_kwargs["batch_size"] = batch_size

datamodule = fi.ImageClassificationData.from_fiftyone(
predict_dataset=samples, **kwargs
predict_dataset=samples, **data_kwargs
)
embeddings = flash.Trainer(**trainer_kwargs).predict(
model, datamodule=datamodule
)
embeddings = flash.Trainer().predict(model, datamodule=datamodule)
embeddings = list(itertools.chain.from_iterable(embeddings))

if embeddings_field is not None:
Expand Down

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