fiftyone.utils.torch

PyTorch utilities.

Copyright 2017-2024, 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)	Loads a raw model from PyTorch Hub as a torch.nn.Module.
find_torch_hub_requirements(repo_or_dir[, …])	Locates the requirements.txt file on disk associated with a downloaded PyTorch Hub model.
load_torch_hub_requirements(repo_or_dir[, …])	Loads the package requirements from the requirements.txt file on disk associated with a downloaded PyTorch Hub model.
install_torch_hub_requirements(repo_or_dir)	Installs the package requirements from the requirements.txt file on disk associated with a downloaded PyTorch Hub model.
ensure_torch_hub_requirements(repo_or_dir[, …])	Verifies that the package requirements from the requirements.txt file on disk associated with a downloaded PyTorch Hub model are installed.
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.

Classes:

TorchEmbeddingsMixin(model[, layer_name, …])	Mixin for Torch models that can generate embeddings.
TorchImageModelConfig(d)	Configuration for running a TorchImageModel.
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.
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.

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.hub.load

• model – see 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.hub.load

• **kwargs – keyword arguments for 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.hub.load

• source ("github") – see 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.hub.load

• source ("github") – see 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.hub.load

• source ("github") – see 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.hub.load

• source ("github") – see 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.TorchEmbeddingsMixin(model, layer_name=None, as_feature_extractor=False)

Bases: fiftyone.core.models.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: fiftyone.core.config.Config

Configuration for running a TorchImageModel.

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

1. Model:

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

2. 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, ...)
   

3. 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.TorchImageModel(config)

Bases: fiftyone.utils.torch.TorchEmbeddingsMixin, fiftyone.core.models.TorchModelMixin, fiftyone.core.models.LogitsMixin, fiftyone.core.models.Model

Wrapper for evaluating a Torch model on images.

See this page for example usage.

Parameters

config – an TorchImageModelConfig

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.
preprocess	Whether to apply preprocessing transforms for inference, if any.
using_gpu	Whether the model is using GPU.
device	The 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.
store_logits	Whether the model should store logits in its predictions.

Methods:

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.

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 preprocess

Whether to apply preprocessing transforms for inference, if any.

property using_gpu

Whether the model is using GPU.

property device

The 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 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: fiftyone.core.models.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)

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

Bases: Generic[torch.utils.data.dataset._T_co]

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: Generic[torch.utils.data.dataset._T_co]

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: Generic[torch.utils.data.dataset._T_co]

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