fiftyone.utils.torch#

PyTorch utilities.

Copyright 2017-2025, Voxel51, Inc.
voxel51.com

Functions:

`load_torch_hub_image_model`(repo_or_dir, model)	Loads an image model from PyTorch Hub as a `TorchImageModel`.
`load_torch_hub_raw_model`(args, *kwargs)
`find_torch_hub_requirements`(repo_or_dir[, ...])
`load_torch_hub_requirements`(repo_or_dir[, ...])
`install_torch_hub_requirements`(repo_or_dir)
`ensure_torch_hub_requirements`(repo_or_dir[, ...])
`recommend_num_workers`()	Recommend a number of workers for running a `torch.utils.data.DataLoader`.
`from_image_classification_dir_tree`(dataset_dir)	Creates a `torch.utils.data.Dataset` for the given image classification dataset directory tree.
`get_local_size`(local_process_group)	Gets the number of processes per-machine in the local process group.
`get_world_size`()	Returns the world size of the current operation.
`get_local_rank`(local_process_group)	Gets the rank of the current process within the local processes group.
`get_rank`()	Gets the rank of the current process.
`local_scatter`(array, local_process_group)	Scatters the given array from the local leader to all local workers.
`all_gather`(data[, group])	Gathers arbitrary picklable data (not necessarily tensors).
`local_broadcast_process_authkey`(...)

Classes:

`GetItem`([field_mapping])	A class that defines how to load the input for a model.
`TorchEmbeddingsMixin`(model[, layer_name, ...])	Mixin for Torch models that can generate embeddings.
`TorchImageModelConfig`(d)	Configuration for running a `TorchImageModel`.
`ImageGetItem`([field_mapping, transform, ...])	A `GetItem` that loads images to feed to `TorchImageModel` instances.
`TorchImageModel`(config)	Wrapper for evaluating a Torch model on images.
`TorchSamplesMixin`()
`ToPILImage`()	Transform that converts a tensor or ndarray to a PIL image, while also allowing PIL images to passthrough.
`MinResize`(min_output_size[, interpolation])	Transform that resizes the PIL image or torch Tensor, if necessary, so that its minimum dimensions are at least the specified size.
`MaxResize`(max_output_size[, interpolation])	Transform that resizes the PIL image or torch Tensor, if necessary, so that its maximum dimensions are at most the specified size.
`PatchSize`(patch_size)	Transform that center crops the PIL image or torch Tensor, if necessary, so that its dimensions are multiples of the specified patch size.
`SaveLayerTensor`(model, layer_name)	Callback that saves the input/output tensor of the specified layer of a Torch model during each `forward()` call.
`OutputProcessor`([classes])	Interface for processing the outputs of Torch models.
`ClassifierOutputProcessor`([classes, ...])	Output processor for single label classifiers.
`DetectorOutputProcessor`([classes])	Output processor for object detectors.
`InstanceSegmenterOutputProcessor`([classes, ...])	Output processor for instance segementers.
`KeypointDetectorOutputProcessor`([classes])	Output processor for keypoint detection models.
`SemanticSegmenterOutputProcessor`([classes])	Output processor for semantic segementers.
`FiftyOneTorchDataset`(samples, get_item[, ...])	Constructs a `torch.utils.data.Dataset` that loads data from an arbitrary `fiftyone.core.collections.SampleCollection` via the provided `GetItem` instance.
`TorchImageDataset`([image_paths, samples, ...])	A `torch.utils.data.Dataset` of images.
`TorchImageClassificationDataset`([...])	A `torch.utils.data.Dataset` for image classification.
`TorchImagePatchesDataset`([image_paths, ...])	A `torch.utils.data.Dataset` of image patch tensors extracted from a list of images.
`NumpySerializedList`(lst)
`TorchSerializedList`(lst)
`TorchShmSerializedList`(lst, local_process_group)

fiftyone.utils.torch.load_torch_hub_image_model(repo_or_dir, model, hub_kwargs=None, **kwargs)#

Loads an image model from PyTorch Hub as a TorchImageModel.

Example usage:

import fiftyone.utils.torch as fout

model = fout.load_torch_hub_image_model(
    "facebookresearch/dinov2",
    "dinov2_vits14",
    image_patch_size=14,
    embeddings_layer="head",
)

assert model.has_embeddings is True

Parameters:

repo_or_dir – see torch:torch.hub.load
model – see torch:torch.hub.load
**kwargs – additional parameters for TorchImageModelConfig

Returns:

a TorchImageModel

fiftyone.utils.torch.load_torch_hub_raw_model(*args, **kwargs)#

Loads a raw model from PyTorch Hub as a torch.nn.Module.

Example usage:

import fiftyone.utils.torch as fout

model = fout.load_torch_hub_raw_model(
    "facebookresearch/dinov2",
    "dinov2_vits14",
)

print(type(model))
# <class 'dinov2.models.vision_transformer.DinoVisionTransformer'>

Parameters:

*args – positional arguments for torch:torch.hub.load
**kwargs – keyword arguments for torch:torch.hub.load

Returns:

a torch.nn.Module

fiftyone.utils.torch.find_torch_hub_requirements(repo_or_dir, source='github')#

Locates the requirements.txt file on disk associated with a downloaded PyTorch Hub model.

Example usage:

import fiftyone.utils.torch as fout

req_path = fout.find_torch_hub_requirements("facebookresearch/dinov2")

print(req_path)
# '~/.cache/torch/hub/facebookresearch_dinov2_main/requirements.txt'

Parameters:

repo_or_dir – see torch:torch.hub.load
source ("github") – see torch:torch.hub.load

Returns:

the path to the requirements file on disk

fiftyone.utils.torch.load_torch_hub_requirements(repo_or_dir, source='github')#

Loads the package requirements from the requirements.txt file on disk associated with a downloaded PyTorch Hub model.

Example usage:

import fiftyone.utils.torch as fout

requirements = fout.load_torch_hub_requirements("facebookresearch/dinov2")

print(requirements)
# ['torch==2.0.0', 'torchvision==0.15.0', ...]

Parameters:

repo_or_dir – see torch:torch.hub.load
source ("github") – see torch:torch.hub.load

Returns:

a list of requirement strings

fiftyone.utils.torch.install_torch_hub_requirements(repo_or_dir, source='github', error_level=None)#

Installs the package requirements from the requirements.txt file on disk associated with a downloaded PyTorch Hub model.

Example usage:

import fiftyone.utils.torch as fout

fout.install_torch_hub_requirements("facebookresearch/dinov2")

Parameters:

repo_or_dir – see torch:torch.hub.load
source ("github") – see torch:torch.hub.load
error_level (None) –
the error level to use, defined as:
- 0: raise error if the install fails
- 1: log warning if the install fails
- 2: ignore install fails
By default, fiftyone.config.requirement_error_level is used

fiftyone.utils.torch.ensure_torch_hub_requirements(repo_or_dir, source='github', error_level=None, log_success=False)#

Verifies that the package requirements from the requirements.txt file on disk associated with a downloaded PyTorch Hub model are installed.

Example usage:

import fiftyone.utils.torch as fout

fout.ensure_torch_hub_requirements("facebookresearch/dinov2")

Parameters:

repo_or_dir – see torch:torch.hub.load
source ("github") – see torch:torch.hub.load
error_level (None) –
the error level to use, defined as:
- 0: raise error if requirement is not satisfied
- 1: log warning if requirement is not satisfied
- 2: ignore unsatisifed requirements
By default, fiftyone.config.requirement_error_level is used
log_success (False) – whether to generate a log message if a requirement is satisfied

class fiftyone.utils.torch.GetItem(field_mapping=None, **kwargs)#

Bases: object

A class that defines how to load the input for a model.

Models that implement the fiftyone.core.models.SupportsGetItem mixin use this class to define how FiftyOneTorchDataset should load their inputs.

The __call__() method should accept a dictionary mapping the keys defined by required_keys to values extracted from the input fiftyone.core.sample.Sample instance according to the mapping defined by field_mapping.

Parameters:: field_mapping (None) – a user-supplied dict mapping keys in required_keys to field names of their dataset that contain the required values

Attributes:

`required_keys`	The list of keys that must exist on the dicts provided to the `__call__()` method at runtime.
`field_mapping`	A user-supplied dictionary mappings keys in `required_keys` to field names of their dataset that contain the required values.

property required_keys#

The list of keys that must exist on the dicts provided to the __call__() method at runtime.

The user supplies the field names from which to extract these values from their samples via field_mapping.

property field_mapping#: A user-supplied dictionary mappings keys in required_keys to field names of their dataset that contain the required values.

class fiftyone.utils.torch.TorchEmbeddingsMixin(model, layer_name=None, as_feature_extractor=False)#

Bases: EmbeddingsMixin

Mixin for Torch models that can generate embeddings.

Parameters:

model – the Torch model, a torch.nn.Module
layer_name (None) – the name of the embeddings layer whose output to save, or None if this model instance should not expose embeddings. Prepend "<" to save the input tensor instead
as_feature_extractor (False) – whether to operate the model as a feature extractor. If layer_name is provided, this layer is passed to torchvision’s create_feature_extractor() function. If no layer_name is provided, the model’s output is used as-is for feature extraction

Attributes:

has_embeddings

Whether this instance has embeddings.

Methods:

`embed`(arg)	Generates an embedding for the given data.
`embed_all`(args)	Generates embeddings for the given iterable of data.
`get_embeddings`()	Returns the embeddings generated by the last forward pass of the model.

property has_embeddings#: Whether this instance has embeddings.

embed(arg)#

Generates an embedding for the given data.

Subclasses can override this method to increase efficiency, but, by default, this method simply calls predict() and then returns get_embeddings().

Parameters:: arg – the data. See predict() for details
Returns:: a numpy array containing the embedding

embed_all(args)#

Generates embeddings for the given iterable of data.

Subclasses can override this method to increase efficiency, but, by default, this method simply iterates over the data and applies embed() to each.

Parameters:: args – an iterable of data. See predict_all() for details
Returns:: a numpy array containing the embeddings stacked along axis 0

get_embeddings()#

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

By convention, this method should always return an array whose first axis represents batch size (which will always be 1 when predict() was last used).

Returns:: a numpy array containing the embedding(s)

class fiftyone.utils.torch.TorchImageModelConfig(d)#

Bases: Config

Configuration for running a TorchImageModel.

Models are represented by this class via the following three components:

Model:

# Directly specify a model
model

# Load model from an entrypoint
model = entrypoint_fcn(**entrypoint_args)

Transforms:

# Directly provide transforms
transforms

# Load transforms from a function
transforms = transforms_fcn(**transforms_args)

# Use the `image_XXX` parameters defined below to build a transform
transforms = build_transforms(image_XXX, ...)

OutputProcessor:

# Directly provide an OutputProcessor
output_processor

# Load an OutputProcessor from a function
output_processor = output_processor_cls(**output_processor_args)

Given these components, inference happens as follows:

def predict_all(imgs):
    imgs = [transforms(img) for img in imgs]
    if not raw_inputs:
        imgs = torch.stack(imgs)

    output = model(imgs)
    return output_processor(output, ...)

Parameters:

model (None) – a torch.nn.Module instance to use
entrypoint_fcn (None) – a function or string like "torchvision.models.inception_v3" specifying the entrypoint function that loads the model
entrypoint_args (None) – a dictionary of arguments for entrypoint_fcn
transforms (None) – a preprocessing transform to apply
transforms_fcn (None) – a function or string like "torchvision.models.Inception_V3_Weights.DEFAULT.transforms" specifying a function that returns a preprocessing transform function to apply
transforms_args (None) – a dictionary of arguments for transforms_args
ragged_batches (None) – whether the provided transforms or transforms_fcn may return tensors of different sizes. This must be set to False to enable batch inference, if it is desired
raw_inputs (None) – whether to feed the raw list of images to the model rather than stacking them as a Torch tensor
output_processor (None) – an OutputProcessor instance to use
output_processor_cls (None) – a class or string like "fifytone.utils.torch.ClassifierOutputProcessor" specifying the OutputProcessor to use
output_processor_args (None) – a dictionary of arguments for output_processor_cls(classes=classes, **kwargs)
confidence_thresh (None) – an optional confidence threshold apply to any applicable predictions generated by the model
classes (None) – a list of class names for the model, if applicable
labels_string (None) – a comma-separated list of the class names for the model, if applicable
labels_path (None) – the path to the labels map for the model, if applicable
mask_targets (None) – a mask targets dict for the model, if applicable
mask_targets_path (None) – the path to a mask targets map for the model, if applicable
skeleton (None) – a keypoint skeleton dict for the model, if applicable
image_min_size (None) – resize the input images during preprocessing, if necessary, so that the image dimensions are at least this (width, height)
image_min_dim (None) – resize input images during preprocessing, if necessary, so that the smaller image dimension is at least this value
image_max_size (None) – resize the input images during preprocessing, if necessary, so that the image dimensions are at most this (width, height)
image_max_dim (None) – resize input images during preprocessing, if necessary, so that the largest image dimension is at most this value.
image_size (None) – a (width, height) to which to resize the input images during preprocessing
image_dim (None) – resize the smaller input dimension to this value during preprocessing
image_patch_size (None) – crop the input images during preprocessing, if necessary, so that the image dimensions are a multiple of this patch size
image_mean (None) – a 3-array of mean values in [0, 1] for preprocessing the input images
image_std (None) – a 3-array of std values in [0, 1] for preprocessing the input images inputs that are lists of Tensors
embeddings_layer (None) – the name of a layer whose output to expose as embeddings. Prepend "<" to save the input tensor instead
as_feature_extractor (False) – whether to operate the model as a feature extractor. If embeddings_layer is provided, this layer is passed to torchvision’s create_feature_extractor() function. If no embeddings_layer is provided, the model’s output is used as-is for feature extraction
use_half_precision (None) – whether to use half precision (only supported when using GPU)
cudnn_benchmark (None) – a value to use for torch.backends.cudnn.benchmark while the model is running
device (None) – a string specifying the device to use, eg ("cuda:0", "mps", "cpu"). By default, CUDA is used if available, else CPU is used

Methods:

`attributes`()	Returns a list of class attributes to be serialized.
`builder`()	Returns a ConfigBuilder instance for this class.
`copy`()	Returns a deep copy of the object.
`custom_attributes`([dynamic, private])	Returns a customizable list of class attributes.
`default`()	Returns the default config instance.
`from_dict`(d)	Constructs a Config object from a JSON dictionary.
`from_json`(path, args, *kwargs)	Constructs a Serializable object from a JSON file.
`from_kwargs`(**kwargs)	Constructs a Config object from keyword arguments.
`from_str`(s, args, *kwargs)	Constructs a Serializable object from a JSON string.
`get_class_name`()	Returns the fully-qualified class name string of this object.
`load_default`()	Loads the default config instance from file.
`parse_array`(d, key[, default])	Parses a raw array attribute.
`parse_bool`(d, key[, default])	Parses a boolean value.
`parse_categorical`(d, key, choices[, default])	Parses a categorical JSON field, which must take a value from among the given choices.
`parse_dict`(d, key[, default])	Parses a dictionary attribute.
`parse_int`(d, key[, default])	Parses an integer attribute.
`parse_mutually_exclusive_fields`(fields)	Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.
`parse_number`(d, key[, default])	Parses a number attribute.
`parse_object`(d, key, cls[, default])	Parses an object attribute.
`parse_object_array`(d, key, cls[, default])	Parses an array of objects.
`parse_object_dict`(d, key, cls[, default])	Parses a dictionary whose values are objects.
`parse_path`(d, key[, default])	Parses a path attribute.
`parse_raw`(d, key[, default])	Parses a raw (arbitrary) JSON field.
`parse_string`(d, key[, default])	Parses a string attribute.
`serialize`([reflective])	Serializes the object into a dictionary.
`to_str`([pretty_print])	Returns a string representation of this object.
`validate_all_or_nothing_fields`(fields)	Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.
`write_json`(path[, pretty_print])	Serializes the object and writes it to disk.

attributes()#

Returns a list of class attributes to be serialized.

This method is called internally by serialize() to determine the class attributes to serialize.

Subclasses can override this method, but, by default, all attributes in vars(self) are returned, minus private attributes, i.e., those starting with “_”. The order of the attributes in this list is preserved when serializing objects, so a common pattern is for subclasses to override this method if they want their JSON files to be organized in a particular way.

Returns:: a list of class attributes to be serialized

classmethod builder()#: Returns a ConfigBuilder instance for this class.

copy()#

Returns a deep copy of the object.

Returns:: a Serializable instance

custom_attributes(dynamic=False, private=False)#

Returns a customizable list of class attributes.

By default, all attributes in vars(self) are returned, minus private attributes (those starting with “_”).

Parameters:

dynamic – whether to include dynamic properties, e.g., those defined by getter/setter methods or the @property decorator. By default, this is False
private – whether to include private properties, i.e., those starting with “_”. By default, this is False

Returns:

a list of class attributes

classmethod default()#

Returns the default config instance.

By default, this method instantiates the class from an empty dictionary, which will only succeed if all attributes are optional. Otherwise, subclasses should override this method to provide the desired default configuration.

classmethod from_dict(d)#

Constructs a Config object from a JSON dictionary.

Config subclass constructors accept JSON dictionaries, so this method simply passes the dictionary to cls().

Parameters:: d – a dict of fields expected by cls
Returns:: an instance of cls

classmethod from_json(path, *args, **kwargs)#

Constructs a Serializable object from a JSON file.

Subclasses may override this method, but, by default, this method simply reads the JSON and calls from_dict(), which subclasses must implement.

Parameters:

path – the path to the JSON file on disk
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod from_kwargs(**kwargs)#

Constructs a Config object from keyword arguments.

Parameters:: **kwargs – keyword arguments that define the fields expected by cls
Returns:: an instance of cls

classmethod from_str(s, *args, **kwargs)#

Constructs a Serializable object from a JSON string.

Subclasses may override this method, but, by default, this method simply parses the string and calls from_dict(), which subclasses must implement.

Parameters:

s – a JSON string representation of a Serializable object
*args – optional positional arguments for self.from_dict()
**kwargs – optional keyword arguments for self.from_dict()

Returns:

an instance of the Serializable class

classmethod get_class_name()#: Returns the fully-qualified class name string of this object.

classmethod load_default()#

Loads the default config instance from file.

Subclasses must implement this method if they intend to support default instances.

static parse_array(d, key, default=<eta.core.config.NoDefault object>)#

Parses a raw array attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default list to return if key is not present

Returns:

a list of raw (untouched) values

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_bool(d, key, default=<eta.core.config.NoDefault object>)#

Parses a boolean value.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default bool to return if key is not present

Returns:

True/False

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_categorical(d, key, choices, default=<eta.core.config.NoDefault object>)#

Parses a categorical JSON field, which must take a value from among the given choices.

Parameters:

d – a JSON dictionary
key – the key to parse
choices – either an iterable of possible values or an enum-like class whose attributes define the possible values
default – a default value to return if key is not present

Returns:

the raw (untouched) value of the given field, which is equal to a value from choices

Raises:

ConfigError – if the key was present in the dictionary but its value was not an allowed choice, or if no default value was provided and the key was not found in the dictionary

static parse_dict(d, key, default=<eta.core.config.NoDefault object>)#

Parses a dictionary attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default dict to return if key is not present

Returns:

a dictionary

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_int(d, key, default=<eta.core.config.NoDefault object>)#

Parses an integer attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default integer value to return if key is not present

Returns:

an int

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_mutually_exclusive_fields(fields)#

Parses a mutually exclusive dictionary of pre-parsed fields, which must contain exactly one field with a truthy value.

Parameters:: fields – a dictionary of pre-parsed fields
Returns:: the (field, value) that was set
Raises:: ConfigError – if zero or more than one truthy value was found

static parse_number(d, key, default=<eta.core.config.NoDefault object>)#

Parses a number attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default numeric value to return if key is not present

Returns:

a number (e.g. int, float)

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses an object attribute.

The value of d[key] can be either an instance of cls or a serialized dict from an instance of cls.

Parameters:

d – a JSON dictionary
key – the key to parse
cls – the class of d[key]
default – a default cls instance to return if key is not present

Returns:

an instance of cls

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_array(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses an array of objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters:

d – a JSON dictionary
key – the key to parse
cls – the class of the elements of list d[key]
default – the default list to return if key is not present

Returns:

a list of cls instances

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_object_dict(d, key, cls, default=<eta.core.config.NoDefault object>)#

Parses a dictionary whose values are objects.

The values in d[key] can be either instances of cls or serialized dicts from instances of cls.

Parameters:

d – a JSON dictionary
key – the key to parse
cls – the class of the values of dictionary d[key]
default – the default dict of cls instances to return if key is not present

Returns:

a dictionary whose values are cls instances

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_path(d, key, default=<eta.core.config.NoDefault object>)#

Parses a path attribute.

The path is converted to an absolute path if necessary via os.path.abspath(os.path.expanduser(value)).

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default string to return if key is not present

Returns:

a path string

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

static parse_raw(d, key, default=<eta.core.config.NoDefault object>)#

Parses a raw (arbitrary) JSON field.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default value to return if key is not present

Returns:

the raw (untouched) value of the given field

Raises:

ConfigError – if no default value was provided and the key was not found in the dictionary

static parse_string(d, key, default=<eta.core.config.NoDefault object>)#

Parses a string attribute.

Parameters:

d – a JSON dictionary
key – the key to parse
default – a default string to return if key is not present

Returns:

a string

Raises:

ConfigError – if the field value was the wrong type or no default value was provided and the key was not found in the dictionary

serialize(reflective=False)#

Serializes the object into a dictionary.

Serialization is applied recursively to all attributes in the object, including element-wise serialization of lists and dictionary values.

Parameters:: reflective – whether to include reflective attributes when serializing the object. By default, this is False
Returns:: a JSON dictionary representation of the object

to_str(pretty_print=True, **kwargs)#

Returns a string representation of this object.

Parameters:

pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is True
**kwargs – optional keyword arguments for self.serialize()

Returns:

a string representation of the object

static validate_all_or_nothing_fields(fields)#

Validates a dictionary of pre-parsed fields checking that either all or none of the fields have a truthy value.

Parameters:: fields – a dictionary of pre-parsed fields
Raises:: ConfigError – if some values are truth and some are not

write_json(path, pretty_print=False, **kwargs)#

Serializes the object and writes it to disk.

Parameters:

path – the output path
pretty_print – whether to render the JSON in human readable format with newlines and indentations. By default, this is False
**kwargs – optional keyword arguments for self.serialize()

class fiftyone.utils.torch.ImageGetItem(field_mapping=None, transform=None, raw_inputs=False, using_half_precision=False, use_numpy=False, **kwargs)#

Bases: GetItem

A GetItem that loads images to feed to TorchImageModel instances.

By default, images are loaded from the "filepath" field of samples, but users can override this by providing field_mapping={"filepath": "another_field"}.

Parameters:

field_mapping (None) – the user-supplied dict mapping keys in required_keys to field names of their dataset that contain the required values
transform (None) – a torchvision.transforms function to apply
raw_inputs (False) – whether to feed the raw list of images to the model rather than stacking them as a Torch tensor
using_half_precision (False) – whether the model is using half precision
use_numpy (False) – whether to use numpy arrays rather than PIL images and Torch tensors when loading data

Attributes:

`required_keys`	The list of keys that must exist on the dicts provided to the `__call__()` method at runtime.
`field_mapping`	A user-supplied dictionary mappings keys in `required_keys` to field names of their dataset that contain the required values.

property required_keys#

The list of keys that must exist on the dicts provided to the __call__() method at runtime.

The user supplies the field names from which to extract these values from their samples via field_mapping.

property field_mapping#: A user-supplied dictionary mappings keys in required_keys to field names of their dataset that contain the required values.

class fiftyone.utils.torch.TorchImageModel(config)#

Bases: SupportsGetItem, TorchEmbeddingsMixin, TorchModelMixin, LogitsMixin, Model

Wrapper for evaluating a Torch model on images.

See this page for example usage.

Parameters:: config – an TorchImageModelConfig

Methods:

`build_get_item`([field_mapping])	Builds the `fiftyone.utils.torch.GetItem` instance that defines how the model's data should be loaded by data loaders.
`collate_fn`(batch)	The collate function to use when creating dataloaders for this model.
`predict`(img)	Performs prediction on the given image.
`predict_all`(imgs)	Performs prediction on the given batch of images.
`embed`(arg)	Generates an embedding for the given data.
`embed_all`(args)	Generates embeddings for the given iterable of data.
`from_config`(config)	Instantiates a Configurable class from a <cls>Config instance.
`from_dict`(d)	Instantiates a Configurable class from a <cls>Config dict.
`from_json`(json_path)	Instantiates a Configurable class from a <cls>Config JSON file.
`from_kwargs`(**kwargs)	Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.
`get_embeddings`()	Returns the embeddings generated by the last forward pass of the model.
`parse`(class_name[, module_name])	Parses a Configurable subclass name string.
`validate`(config)	Validates that the given config is an instance of <cls>Config.

Attributes:

`media_type`	The media type processed by the model.
`has_logits`	Whether this instance can generate logits.
`ragged_batches`	Whether `transforms()` may return tensors of different sizes.
`transforms`	A `torchvision.transforms` function that will be applied to each input before prediction, if any.
`has_collate_fn`	Whether this model has a custom collate function.
`preprocess`	Whether to apply preprocessing transforms for inference, if any.
`using_gpu`	Whether the model is using GPU.
`device`	The `torch:torch.torch.device` that the model is using.
`using_half_precision`	Whether the model is using half precision.
`classes`	The list of class labels for the model, if known.
`num_classes`	The number of classes for the model, if known.
`mask_targets`	The mask targets for the model, if any.
`skeleton`	The keypoint skeleton for the model, if any.
`can_embed_prompts`	Whether this instance can generate prompt embeddings.
`has_embeddings`	Whether this instance has embeddings.
`required_keys`	The required keys that must be provided as parameters to methods like `apply_model()` and `compute_embeddings()` at runtime.
`store_logits`	Whether the model should store logits in its predictions.

build_get_item(field_mapping=None)#

Builds the fiftyone.utils.torch.GetItem instance that defines how the model’s data should be loaded by data loaders.

Parameters:: field_mapping (None) – a user-provided dict mapping required keys to dataset field names
Returns:: a fiftyone.utils.torch.GetItem instance

property media_type#: The media type processed by the model.

property has_logits#: Whether this instance can generate logits.

property ragged_batches#: Whether transforms() may return tensors of different sizes. If True, then passing ragged lists of images to predict_all() may not be not allowed.

property transforms#: A torchvision.transforms function that will be applied to each input before prediction, if any.

property has_collate_fn#

Whether this model has a custom collate function.

Set this to True if you want collate_fn() to be used during inference.

static collate_fn(batch)#

The collate function to use when creating dataloaders for this model.

In order to enable this functionality, the model’s has_collate_fn() property must return True.

By default, this is the default collate function for torch.utils.data.DataLoader, but subclasses can override this method as necessary.

Note that this function must be serializable so it is compatible with multiprocessing for dataloaders.

Parameters:: batch – a list of items to collate
Returns:: the collated batch, which will be fed directly to the model

property preprocess#: Whether to apply preprocessing transforms for inference, if any.

property using_gpu#: Whether the model is using GPU.

property device#: The torch:torch.torch.device that the model is using.

property using_half_precision#: Whether the model is using half precision.

property classes#: The list of class labels for the model, if known.

property num_classes#: The number of classes for the model, if known.

property mask_targets#: The mask targets for the model, if any.

property skeleton#: The keypoint skeleton for the model, if any.

predict(img)#

Performs prediction on the given image.

Parameters:

img –

the image to process, which can be any of the following:

A PIL image
A uint8 numpy array (HWC)
A Torch tensor (CHW)

Returns:

a fiftyone.core.labels.Label instance or dict of fiftyone.core.labels.Label instances containing the predictions

predict_all(imgs)#

Performs prediction on the given batch of images.

Parameters:

imgs –

the batch of images to process, which can be any of the following:

A list of PIL images
A list of uint8 numpy arrays (HWC)
A list of Torch tensors (CHW)
A uint8 numpy tensor (NHWC)
A Torch tensor (NCHW)

Returns:

a list of fiftyone.core.labels.Label instances or a list of dicts of fiftyone.core.labels.Label instances containing the predictions

property can_embed_prompts#

Whether this instance can generate prompt embeddings.

This method returns False by default. Methods that can generate prompt embeddings will override this via implementing the PromptMixin interface.

embed(arg)#

Generates an embedding for the given data.

Subclasses can override this method to increase efficiency, but, by default, this method simply calls predict() and then returns get_embeddings().

Parameters:: arg – the data. See predict() for details
Returns:: a numpy array containing the embedding

embed_all(args)#

Generates embeddings for the given iterable of data.

Subclasses can override this method to increase efficiency, but, by default, this method simply iterates over the data and applies embed() to each.

Parameters:: args – an iterable of data. See predict_all() for details
Returns:: a numpy array containing the embeddings stacked along axis 0

classmethod from_config(config)#: Instantiates a Configurable class from a <cls>Config instance.

classmethod from_dict(d)#

Instantiates a Configurable class from a <cls>Config dict.

Parameters:: d – a dict to construct a <cls>Config
Returns:: an instance of cls

classmethod from_json(json_path)#

Instantiates a Configurable class from a <cls>Config JSON file.

Parameters:: json_path – path to a JSON file for type <cls>Config
Returns:: an instance of cls

classmethod from_kwargs(**kwargs)#

Instantiates a Configurable class from keyword arguments defining the attributes of a <cls>Config.

Parameters:: **kwargs – keyword arguments that define the fields of a <cls>Config dict
Returns:: an instance of cls

get_embeddings()#

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

By convention, this method should always return an array whose first axis represents batch size (which will always be 1 when predict() was last used).

Returns:: a numpy array containing the embedding(s)

property has_embeddings#: Whether this instance has embeddings.

static parse(class_name, module_name=None)#

Parses a Configurable subclass name string.

Assumes both the Configurable class and the Config class are defined in the same module. The module containing the classes will be loaded if necessary.

Parameters:

class_name – a string containing the name of the Configurable class, e.g. “ClassName”, or a fully-qualified class name, e.g. “eta.core.config.ClassName”
module_name – a string containing the fully-qualified module name, e.g. “eta.core.config”, or None if class_name includes the module name. Set module_name = __name__ to load a class from the calling module

Returns:

the Configurable class config_cls: the Config class associated with cls

Return type:

cls

property required_keys#: The required keys that must be provided as parameters to methods like apply_model() and compute_embeddings() at runtime.

property store_logits#: Whether the model should store logits in its predictions.

classmethod validate(config)#

Validates that the given config is an instance of <cls>Config.

Raises:: ConfigurableError – if config is not an instance of <cls>Config

class fiftyone.utils.torch.TorchSamplesMixin#

Bases: SamplesMixin

Methods:

`predict`(img[, sample])	Performs prediction on the given data.
`predict_all`(args[, samples])	Performs prediction on the given iterable of data.

Attributes:

needs_fields

A dict mapping model-specific keys to sample field names.

predict(img, sample=None)#

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
sample (None) – the fiftyone.core.sample.Sample associated with the data

Returns:

a fiftyone.core.labels.Label instance or dict of fiftyone.core.labels.Label instances containing the predictions

property needs_fields#: A dict mapping model-specific keys to sample field names.

predict_all(args, samples=None)#

Performs prediction on the given iterable of data.

Image models should support, at minimum, processing args values that are either lists of uint8 numpy arrays (HWC) or numpy array tensors (NHWC).

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

Subclasses can override this method to increase efficiency, but, by default, this method simply iterates over the data and applies predict() to each.

Parameters:

args – an iterable of data
samples (None) – an iterable of fiftyone.core.sample.Sample instances associated with the data

Returns:

a list of fiftyone.core.labels.Label instances or a list of dicts of fiftyone.core.labels.Label instances containing the predictions

class fiftyone.utils.torch.ToPILImage#

Bases: object

Transform that converts a tensor or ndarray to a PIL image, while also allowing PIL images to passthrough.

class fiftyone.utils.torch.MinResize(min_output_size, interpolation=None)#

Bases: object

Transform that resizes the PIL image or torch Tensor, if necessary, so that its minimum dimensions are at least the specified size.

Parameters:

min_output_size – desired minimum output dimensions. Can either be a (min_height, min_width) tuple or a single min_dim
interpolation (None) – optional interpolation mode. Passed directly to torchvision.transforms.functional.resize()

class fiftyone.utils.torch.MaxResize(max_output_size, interpolation=None)#

Bases: object

Transform that resizes the PIL image or torch Tensor, if necessary, so that its maximum dimensions are at most the specified size.

Parameters:

max_output_size – desired maximum output dimensions. Can either be a (max_height, max_width) tuple or a single max_dim
interpolation (None) – optional interpolation mode. Passed directly to torchvision.transforms.functional.resize()

class fiftyone.utils.torch.PatchSize(patch_size)#

Bases: object

Transform that center crops the PIL image or torch Tensor, if necessary, so that its dimensions are multiples of the specified patch size.

Parameters:: patch_size – the patch size

class fiftyone.utils.torch.SaveLayerTensor(model, layer_name)#

Bases: object

Callback that saves the input/output tensor of the specified layer of a Torch model during each forward() call.

Parameters:

model – the Torch model, a torch.nn.Module
layer_name – the name of the layer whose output to save. Prepend "<" to save the input tensor instead

Attributes:

tensor

The tensor saved from the last forward() call.

property tensor#: The tensor saved from the last forward() call.

class fiftyone.utils.torch.OutputProcessor(classes=None, **kwargs)#

Bases: object

Interface for processing the outputs of Torch models.

Parameters:: classes (None) – the list of class labels for the model. This may not be required or used by some models

class fiftyone.utils.torch.ClassifierOutputProcessor(classes=None, store_logits=False, logits_key='logits')#

Bases: OutputProcessor

Output processor for single label classifiers.

Parameters:

classes (None) – the list of class labels for the model
store_logits (False) – whether to store logits in the model outputs

class fiftyone.utils.torch.DetectorOutputProcessor(classes=None)#

Bases: OutputProcessor

Output processor for object detectors.

Parameters:: classes (None) – the list of class labels for the model

class fiftyone.utils.torch.InstanceSegmenterOutputProcessor(classes=None, mask_thresh=0.5)#

Bases: OutputProcessor

Output processor for instance segementers.

Parameters:

classes (None) – the list of class labels for the model
mask_thresh (0.5) – a threshold to use to convert soft masks to binary masks

class fiftyone.utils.torch.KeypointDetectorOutputProcessor(classes=None)#

Bases: OutputProcessor

Output processor for keypoint detection models.

Parameters:: classes (None) – the list of class labels for the model

class fiftyone.utils.torch.SemanticSegmenterOutputProcessor(classes=None)#

Bases: OutputProcessor

Output processor for semantic segementers.

Parameters:: classes (None) – the list of class labels for the model. This parameter is not used

fiftyone.utils.torch.recommend_num_workers()#

Recommend a number of workers for running a torch.utils.data.DataLoader.

Returns:: the recommended number of workers

class fiftyone.utils.torch.FiftyOneTorchDataset(samples, get_item, vectorize=False, skip_failures=False, local_process_group=None)#

Bases: Dataset

Constructs a torch.utils.data.Dataset that loads data from an arbitrary fiftyone.core.collections.SampleCollection via the provided GetItem instance.

Parameters:

samples – a fiftyone.core.collections.SampleCollection
get_item – a GetItem
vectorize (False) – whether to load and cache the required fields from the sample collection upfront (True) or lazily load the values from each sample when items are retrieved (False). Vectorizing gives faster data loading times, but you must have enough memory to store the required field values for the entire collection. When vectorize=True, all field values must be serializable; ie pickle.dumps(field_value) must not raise an error
skip_failures (False) – whether to skip failures that occur when calling get_item. If True, the exception will be returned rather than the intended field values
local_process_group (None) – the local process group. Only used during distributed training

Attributes:

samples

Methods:

`worker_init`(worker_id)	Initializes a worker during inference/training.
`distributed_init`(dataset_name, ...[, view_name])	Initializes a trainer process during distributed training.

property samples#

static worker_init(worker_id)#

Initializes a worker during inference/training.

This method is used as the worker_init_fn parameter for torch.utils.data.DataLoader.

Parameters:: worker_id – the worker ID

static distributed_init(dataset_name, local_process_group, view_name=None)#

Initializes a trainer process during distributed training.

This function should be called at the beginning of the training script. It facilitates communication between processes and safely creates a database connection for each trainer.

Parameters:

dataset_name – the name of the dataset to load
local_process_group – the process group with all the processes running the main training script
view_name (None) – the name of a saved view to load

Returns:

the loaded fiftyone.core.dataset.Dataset or fiftyone.core.view.DatasetView

class fiftyone.utils.torch.TorchImageDataset(image_paths=None, samples=None, sample_ids=None, include_ids=False, transform=None, use_numpy=False, force_rgb=False, skip_failures=False)#

Bases: Dataset

A torch.utils.data.Dataset of images.

Instances of this dataset emit images for each sample, or (img, sample_id) pairs if sample_ids are provided or include_ids == True.

By default, this class will load images in PIL format and emit Torch tensors, but you can use numpy images/tensors instead by passing use_numpy = True.

Parameters:

image_paths (None) – an iterable of image paths
samples (None) – a fiftyone.core.collections.SampleCollection from which to extract image paths
sample_ids (None) – an iterable of sample IDs corresponding to each image
include_ids (False) – whether to include the IDs of the samples in the returned items
transform (None) – an optional transform function to apply to each image patch. When use_numpy == False, this is typically a torchvision transform
use_numpy (False) – whether to use numpy arrays rather than PIL images and Torch tensors when loading data
force_rgb (False) – whether to force convert the images to RGB
skip_failures (False) – whether to return an Exception object rather than raising it if an error occurs while loading a sample

Attributes:

has_sample_ids

Whether this dataset has sample IDs.

property has_sample_ids#: Whether this dataset has sample IDs.

class fiftyone.utils.torch.TorchImageClassificationDataset(image_paths=None, targets=None, samples=None, sample_ids=None, include_ids=False, transform=None, use_numpy=False, force_rgb=False, skip_failures=False)#

Bases: Dataset

A torch.utils.data.Dataset for image classification.

Instances of this dataset emit images and their associated targets for each sample, either directly as (img, target) pairs or as (img, target, sample_id) pairs if sample_ids are provided or include_ids == True.

By default, this class will load images in PIL format and emit Torch tensors, but you can use numpy images/tensors instead by passing use_numpy = True.

Parameters:

image_paths (None) – an iterable of image paths
targets (None) – an iterable of targets, or the name of a field or embedded field of samples to use as targets
samples (None) – a fiftyone.core.collections.SampleCollection from which to extract image paths and targets
sample_ids (None) – an iterable of sample IDs corresponding to each image
include_ids (False) – whether to include the IDs of the samples in the returned items
transform (None) – an optional transform function to apply to each image patch. When use_numpy == False, this is typically a torchvision transform
use_numpy (False) – whether to use numpy arrays rather than PIL images and Torch tensors when loading data
force_rgb (False) – whether to force convert the images to RGB
skip_failures (False) – whether to return an Exception object rather than raising it if an error occurs while loading a sample

Attributes:

has_sample_ids

Whether this dataset has sample IDs.

property has_sample_ids#: Whether this dataset has sample IDs.

class fiftyone.utils.torch.TorchImagePatchesDataset(image_paths=None, patches=None, samples=None, patches_field=None, handle_missing='skip', transform=None, sample_ids=None, include_ids=False, ragged_batches=False, use_numpy=False, force_rgb=False, force_square=False, alpha=None, skip_failures=False)#

Bases: Dataset

A torch.utils.data.Dataset of image patch tensors extracted from a list of images.

Provide either image_paths and patches or samples and patches_field in order to use this dataset.

Instances of this dataset emit image patches for each sample, or (patches, sample_id) tuples if sample_ids are provided or include_ids == True.

By default, this class will load images in PIL format and emit Torch tensors, but you can use numpy images/tensors instead by passing use_numpy = True.

If ragged_batches = False (the default), this class will emit tensors containing the stacked (along axis 0) patches from each image. In this case, the provided transform must ensure that all image patches are resized to the same shape so they can be stacked.

If ragged_batches = True, lists of patch tensors will be returned.

Parameters:

image_paths (None) – an iterable of image paths
patches (None) – a list of labels of type fiftyone.core.labels.Detection, fiftyone.core.labels.Detections, fiftyone.core.labels.Polyline, or fiftyone.core.labels.Polylines specifying the image patch(es) to extract from each image. Elements can be None if an image has no patches
samples (None) – a fiftyone.core.collections.SampleCollection from which to extract patches
patches_field (None) – the name of the field defining the image patches in samples to extract. Must be of type fiftyone.core.labels.Detection, fiftyone.core.labels.Detections, fiftyone.core.labels.Polyline, or fiftyone.core.labels.Polylines
handle_missing ("skip") –
how to handle images with no patches. The supported values are:
- ”skip”: skip the image and assign its embedding as None
- ”image”: use the whole image as a single patch
- ”error”: raise an error
transform (None) – an optional transform function to apply to each image patch. When use_numpy == False, this is typically a torchvision transform
sample_ids (None) – an iterable of sample IDs corresponding to each image
include_ids (False) – whether to include the IDs of the samples in the returned items
ragged_batches (False) – whether the provided transform may return tensors of different dimensions and thus cannot be stacked
use_numpy (False) – whether to use numpy arrays rather than PIL images and Torch tensors when loading data
force_rgb (False) – whether to force convert the images to RGB
force_square (False) – whether to minimally manipulate the patch bounding boxes into squares prior to extraction
alpha (None) – an optional expansion/contraction to apply to the patches before extracting them, in [-1, inf). If provided, the length and width of the box are expanded (or contracted, when alpha < 0) by (100 * alpha)%. For example, set alpha = 0.1 to expand the boxes by 10%, and set alpha = -0.1 to contract the boxes by 10%
skip_failures (False) – whether to return an Exception object rather than raising it if an error occurs while loading a sample

Attributes:

has_sample_ids

Whether this dataset has sample IDs.

property has_sample_ids#: Whether this dataset has sample IDs.

fiftyone.utils.torch.from_image_classification_dir_tree(dataset_dir)#

Creates a torch.utils.data.Dataset for the given image classification dataset directory tree.

The directory should have the following format:

<dataset_dir>/
    <classA>/
        <image1>.<ext>
        <image2>.<ext>
        ...
    <classB>/
        <image1>.<ext>
        <image2>.<ext>
        ...

Parameters:: dataset_dir – the dataset directory
Returns:: a torchvision.datasets.ImageFolder

class fiftyone.utils.torch.NumpySerializedList(lst: list)#: Bases: object

class fiftyone.utils.torch.TorchSerializedList(lst: list)#: Bases: NumpySerializedList

class fiftyone.utils.torch.TorchShmSerializedList(lst: list, local_process_group)#: Bases: TorchSerializedList

fiftyone.utils.torch.get_local_size(local_process_group)#

Gets the number of processes per-machine in the local process group.

Parameters:: local_process_group – the local process group
Returns:: the number of processes per-machine

fiftyone.utils.torch.get_world_size()#

Returns the world size of the current operation.

Returns:: the world size

fiftyone.utils.torch.get_local_rank(local_process_group)#

Gets the rank of the current process within the local processes group.

Parameters:: local_process_group – the local process group
Returns:: the rank of the current process

fiftyone.utils.torch.get_rank()#

Gets the rank of the current process.

Returns:: the rank of the current process

fiftyone.utils.torch.local_scatter(array, local_process_group)#

Scatters the given array from the local leader to all local workers.

The worker with rank i gets array[i].

Parameters:

array – an array with same size as the local process group
local_process_group – the local process group

Returns:

the array element for the current rank

fiftyone.utils.torch.all_gather(data, group=None)#

Gathers arbitrary picklable data (not necessarily tensors).

Parameters:

data – any picklable object
group (None) – a torch process group. By default, uses a group which contains all ranks on gloo backend

Returns:

the list of data gathered from each rank

fiftyone.utils.torch.local_broadcast_process_authkey(local_process_group)#