fiftyone.utils.eval.activitynet¶
ActivityNet-style temporal detection evaluation.
Classes:
|
ActivityNet-style evaluation config. |
|
ActivityNet-style evaluation. |
|
Class that stores the results of a ActivityNet detection evaluation. |
-
class
fiftyone.utils.eval.activitynet.
ActivityNetEvaluationConfig
(pred_field, gt_field, iou=None, classwise=None, compute_mAP=False, iou_threshs=None, **kwargs)¶ Bases:
fiftyone.utils.eval.detection.DetectionEvaluationConfig
ActivityNet-style evaluation config.
- Parameters
pred_field – the name of the field containing the predicted
fiftyone.core.labels.TemporalDetection
instancesgt_field – the name of the field containing the ground truth
fiftyone.core.labels.TemporalDetection
instancesiou (None) – the IoU threshold to use to determine matches
classwise (None) – whether to only match segments with the same class label (True) or allow matches between classes (False)
compute_mAP (False) – whether to perform the necessary computations so that mAP and PR curves can be generated
iou_threshs (None) – a list of IoU thresholds to use when computing mAP and PR curves. Only applicable when
compute_mAP
is True
Attributes:
The name of the method.
The fully-qualified name of this
BaseRunConfig
class.Whether fields besides
pred_field
andgt_field
are required in order to perform evaluation.The
BaseRun
class associated with this config.The type of run.
Methods:
Returns the list of class attributes that will be serialized by
serialize()
.base_config_cls
(type)Returns the config class for the given run type.
build
()Builds the
BaseRun
instance associated with this config.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
BaseRunConfig
from a serialized JSON dict representation of it.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.
Returns the fully-qualified class name string of this object.
load_credentials
(**kwargs)Loads any necessary credentials from the given keyword arguments or the relevant FiftyOne config.
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.
-
property
method
¶ The name of the method.
-
attributes
()¶ Returns the list of class attributes that will be serialized by
serialize()
.- Returns
a list of attributes
-
static
base_config_cls
(type)¶ Returns the config class for the given run type.
- Parameters
type – a
BaseRunConfig.type
- Returns
a
BaseRunConfig
subclass
-
build
()¶ Builds the
BaseRun
instance associated with this config.- Returns
a
BaseRun
instance
-
classmethod
builder
()¶ Returns a ConfigBuilder instance for this class.
-
property
cls
¶ The fully-qualified name of this
BaseRunConfig
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
BaseRunConfig
from a serialized JSON dict representation of it.- Parameters
d – a JSON dict
- Returns
a
BaseRunConfig
-
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.
-
load_credentials
(**kwargs)¶ Loads any necessary credentials from the given keyword arguments or the relevant FiftyOne config.
- Parameters
**kwargs – subclass-specific credentials
-
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
-
property
requires_additional_fields
¶ Whether fields besides
pred_field
andgt_field
are required in order to perform evaluation.If True then the entire samples will be loaded rather than using
select_fields()
to optimize.
-
property
run_cls
¶ The
BaseRun
class associated with this config.
-
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
-
property
type
¶ The type of run.
-
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.eval.activitynet.
ActivityNetEvaluation
(config)¶ Bases:
fiftyone.utils.eval.detection.DetectionEvaluation
ActivityNet-style evaluation.
- Parameters
config – a
ActivityNetEvaluationConfig
Methods:
evaluate
(sample[, eval_key])Performs ActivityNet-style evaluation on the given video.
generate_results
(samples, matches[, …])Generates aggregate evaluation results for the samples.
cleanup
(samples, eval_key)Cleans up the results of the run with the given key from the collection.
delete_run
(samples, key[, cleanup])Deletes the results associated with the given run key from the collection.
delete_runs
(samples[, cleanup])Deletes all runs from the collection.
Ensures that any necessary packages to execute this run are installed.
Ensures that any necessary packages to use existing results for this run are installed.
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_fields
(samples, eval_key)Gets the fields that were involved in the given run.
get_run_info
(samples, key)Gets the
BaseRunInfo
for the given key on the collection.has_cached_run_results
(samples, key)Determines whether
BaseRunResults
for the given key are cached on the collection.list_runs
(samples[, type, method])Returns the list of run keys on the given collection.
load_run_results
(samples, key[, cache, …])Loads the
BaseRunResults
for the given key on the collection.load_run_view
(samples, key[, select_fields])Loads the
fiftyone.core.view.DatasetView
on which the specified run was performed.parse
(class_name[, module_name])Parses a Configurable subclass name string.
register_run
(samples, key[, overwrite, cleanup])Registers a run of this method under the given key on the given collection.
register_samples
(samples, eval_key[, dynamic])Registers the collection on which evaluation will be performed.
rename
(samples, eval_key, new_eval_key)Performs any necessary operations required to rename this run’s key.
The
BaseRunInfo
class associated with this class.save_run_info
(samples, run_info[, …])Saves the run information on the collection.
save_run_results
(samples, key, run_results)Saves the run results on the collection.
update_run_config
(samples, key, config)Updates the
BaseRunConfig
for the given run on the collection.update_run_key
(samples, key, new_key)Replaces the key for the given run with a new key.
validate
(config)Validates that the given config is an instance of <cls>Config.
validate_run
(samples, key[, overwrite])Validates that the collection can accept this run.
-
evaluate
(sample, eval_key=None)¶ Performs ActivityNet-style evaluation on the given video.
Predicted segments are matched to ground truth segments in descending order of confidence, with matches requiring a minimum IoU of
self.config.iou
.The
self.config.classwise
parameter controls whether to only match segments with the same class label (True) or allow matches between classes (False).- Parameters
sample – a
fiftyone.core.sample.Sample
eval_key (None) – the evaluation key for this evaluation
- Returns
a list of matched
(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)
tuples
-
generate_results
(samples, matches, eval_key=None, classes=None, missing=None, progress=None)¶ Generates aggregate evaluation results for the samples.
If
self.config.compute_mAP
is True, this method performs ActivityNet-style evaluation as inevaluate()
to generate precision and recall sweeps over the range of IoU thresholds inself.config.iou_threshs
. In this case, anActivityNetDetectionResults
instance is returned that can compute mAP and PR curves.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
matches – a list of
(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)
matches. Either label can beNone
to indicate an unmatched segmenteval_key (None) – the evaluation key for this evaluation
classes (None) – the list of possible classes. If not provided, the observed ground truth/predicted labels are used for results purposes
missing (None) – a missing label string. Any unmatched segments are given this label for results purposes
progress (None) – whether to render a progress bar (True/False), use the default value
fiftyone.config.show_progress_bars
(None), or a progress callback function to invoke instead
- Returns
a
DetectionResults
-
cleanup
(samples, eval_key)¶ Cleans up the results of the run with the given key from the collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
-
classmethod
delete_run
(samples, key, cleanup=True)¶ Deletes the results associated with the given run key from the collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
cleanup (True) – whether to execute the run’s
BaseRun.cleanup()
method
-
classmethod
delete_runs
(samples, cleanup=True)¶ Deletes all runs from the collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
cleanup (True) – whether to execute the run’s
BaseRun.cleanup()
methods
-
ensure_requirements
()¶ Ensures that any necessary packages to execute this run are installed.
Runs should respect
fiftyone.config.requirement_error_level
when handling errors.
-
ensure_usage_requirements
()¶ Ensures that any necessary packages to use existing results for this run are installed.
Runs should respect
fiftyone.config.requirement_error_level
when handling errors.
-
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_fields
(samples, eval_key)¶ Gets the fields that were involved in the given run.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
- Returns
a list of fields
-
classmethod
get_run_info
(samples, key)¶ Gets the
BaseRunInfo
for the given key on the collection.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
- Returns
a
BaseRunInfo
-
classmethod
has_cached_run_results
(samples, key)¶ Determines whether
BaseRunResults
for the given key are cached on the collection.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
- Returns
True/False
-
classmethod
list_runs
(samples, type=None, method=None, **kwargs)¶ Returns the list of run keys on the given collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
type (None) –
a specific run type to match, which can be:
a
fiftyone.core.runs.BaseRun
class or its fully-qualified class name string
method (None) – a specific
fiftyone.core.runs.BaseRunConfig.method
string to match**kwargs – optional config parameters to match
- Returns
a list of run keys
-
classmethod
load_run_results
(samples, key, cache=True, load_view=True, **kwargs)¶ Loads the
BaseRunResults
for the given key on the collection.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
cache (True) – whether to cache the results on the collection
load_view (True) – whether to load the run view in the results (True) or the full dataset (False)
**kwargs – keyword arguments for the run’s
BaseRunConfig.load_credentials()
method
- Returns
a
BaseRunResults
, or None if the run did not save results
-
classmethod
load_run_view
(samples, key, select_fields=False)¶ Loads the
fiftyone.core.view.DatasetView
on which the specified run was performed.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
select_fields (False) – whether to exclude fields involved in other runs of the same type
- Returns
-
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
-
register_run
(samples, key, overwrite=True, cleanup=True)¶ Registers a run of this method under the given key on the given collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
overwrite (True) – whether to allow overwriting an existing run of the same type
cleanup (True) – whether to execute an existing run’s
BaseRun.cleanup()
method when overwriting it
-
register_samples
(samples, eval_key, dynamic=True)¶ Registers the collection on which evaluation will be performed.
This method will be called before the first call to
evaluate()
. Subclasses can extend this method to perform any setup required for an evaluation run.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
eval_key – the evaluation key for this evaluation
dynamic (True) – whether to declare the dynamic object-level attributes that are populated on the dataset’s schema
-
rename
(samples, eval_key, new_eval_key)¶ Performs any necessary operations required to rename this run’s key.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
new_key – a new run key
-
classmethod
run_info_cls
()¶ The
BaseRunInfo
class associated with this class.
-
classmethod
save_run_info
(samples, run_info, overwrite=True, cleanup=True)¶ Saves the run information on the collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
run_info – a
BaseRunInfo
overwrite (True) – whether to overwrite an existing run with the same key
cleanup (True) – whether to execute an existing run’s
BaseRun.cleanup()
method when overwriting it
-
classmethod
save_run_results
(samples, key, run_results, overwrite=True, cache=True)¶ Saves the run results on the collection.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
run_results – a
BaseRunResults
, or Noneoverwrite (True) – whether to overwrite an existing result with the same key
cache (True) – whether to cache the results on the collection
-
classmethod
update_run_config
(samples, key, config)¶ Updates the
BaseRunConfig
for the given run on the collection.- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
config – a
BaseRunConfig
-
classmethod
update_run_key
(samples, key, new_key)¶ Replaces the key for the given run with a new key.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
new_key – a new run key
-
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
-
validate_run
(samples, key, overwrite=True)¶ Validates that the collection can accept this run.
The run may be invalid if, for example, a run of a different type has already been run under the same key and thus overwriting it would cause ambiguity on how to cleanup the results.
- Parameters
samples – a
fiftyone.core.collections.SampleCollection
key – a run key
overwrite (True) – whether to allow overwriting an existing run of the same type
- Raises
ValueError – if the run is invalid
-
class
fiftyone.utils.eval.activitynet.
ActivityNetDetectionResults
(samples, config, eval_key, matches, precision, recall, classwise_AP, iou_threshs, classes, thresholds=None, missing=None, backend=None)¶ Bases:
fiftyone.utils.eval.detection.DetectionResults
Class that stores the results of a ActivityNet detection evaluation.
- Parameters
samples – the
fiftyone.core.collections.SampleCollection
usedconfig – the
ActivityNetEvaluationConfig
usedeval_key – the evaluation key
matches – a list of
(gt_label, pred_label, iou, pred_confidence, gt_id, pred_id)
matches. Either label can beNone
to indicate an unmatched segmentprecision – an array of precision values of shape
num_iou_threshs x num_classes x num_recall
recall – an array of recall values
classwise_AP – an array of average precision values of shape
num_iou_threshs x num_classes
iou_threshs – an array of IoU thresholds
classes – the list of possible classes
thresholds (None) – an optional array of decision thresholds of shape
num_iou_threshs x num_classes x num_recall
missing (None) – a missing label string. Any unmatched segments are given this label for evaluation purposes
backend (None) – a
ActivityNetEvaluation
backend
Methods:
plot_pr_curves
([classes, iou_thresh, backend])Plots precision-recall (PR) curves for the results.
mAP
([classes])Computes ActivityNet-style mean average precision (mAP) for the specified classes.
Returns the list of class attributes that will be serialized by
serialize()
.base_results_cls
(type)Returns the results class for the given run type.
confusion_matrix
([classes, include_other])Generates a confusion matrix for the results via
sklearn.metrics.confusion_matrix()
.copy
()Returns a deep copy of the object.
custom_attributes
([dynamic, private])Returns a customizable list of class attributes.
from_dict
(d, samples, config, key)Builds a
BaseRunResults
from a JSON dict representation of it.from_json
(path, *args, **kwargs)Constructs a Serializable object from a JSON file.
from_str
(s, *args, **kwargs)Constructs a Serializable object from a JSON string.
Returns the fully-qualified class name string of this object.
metrics
([classes, average, beta])Computes classification metrics for the results, including accuracy, precision, recall, and F-beta score.
plot_confusion_matrix
([classes, …])Plots a confusion matrix for the evaluation results.
print_report
([classes, digits])Prints a classification report for the results via
sklearn.metrics.classification_report()
.report
([classes])Generates a classification report for the results via
sklearn.metrics.classification_report()
.save
()Saves the results to the database.
Saves these results config to the database.
serialize
([reflective])Serializes the object into a dictionary.
to_str
([pretty_print])Returns a string representation of this object.
write_json
(path[, pretty_print])Serializes the object and writes it to disk.
Attributes:
The
BaseRun
for these results.The fully-qualified name of this
BaseRunResults
class.The
BaseRunConfig
for these results.The run key for these results.
The
fiftyone.core.collections.SampleCollection
associated with these results.-
plot_pr_curves
(classes=None, iou_thresh=None, backend='plotly', **kwargs)¶ Plots precision-recall (PR) curves for the results.
- Parameters
classes (None) – a list of classes to generate curves for. By default, the top 3 AP classes will be plotted
iou_thresh (None) – an optional IoU threshold or list of IoU thresholds for which to plot curves. If multiple thresholds are provided, precision data is averaged across these thresholds. By default, precision data is averaged over all IoU thresholds. Refer to
iou_threshs
to see the available thresholdsbackend ("plotly") – the plotting backend to use. Supported values are
("plotly", "matplotlib")
**kwargs –
keyword arguments for the backend plotting method:
”plotly” backend:
fiftyone.core.plots.plotly.plot_pr_curves()
”matplotlib” backend:
fiftyone.core.plots.matplotlib.plot_pr_curves()
- Returns
a
fiftyone.core.plots.plotly.PlotlyNotebookPlot
, if you are working in a notebook context and the plotly backend is useda plotly or matplotlib figure, otherwise
- Return type
one of the following
-
mAP
(classes=None)¶ Computes ActivityNet-style mean average precision (mAP) for the specified classes.
See this page for more details about ActivityNet-style mAP.
- Parameters
classes (None) – a list of classes for which to compute mAP
- Returns
the mAP in
[0, 1]
-
attributes
()¶ Returns the list of class attributes that will be serialized by
serialize()
.- Returns
a list of attributes
-
property
backend
¶ The
BaseRun
for these results.
-
static
base_results_cls
(type)¶ Returns the results class for the given run type.
- Parameters
type – a
BaseRunConfig.type
- Returns
a
BaseRunResults
subclass
-
property
cls
¶ The fully-qualified name of this
BaseRunResults
class.
-
property
config
¶ The
BaseRunConfig
for these results.
-
confusion_matrix
(classes=None, include_other=False)¶ Generates a confusion matrix for the results via
sklearn.metrics.confusion_matrix()
.The rows of the confusion matrix represent ground truth and the columns represent predictions.
- Parameters
classes (None) – an optional list of classes to include in the confusion matrix. Include
self.missing
in this list if you would like to include a row/column for unmatched examplesinclude_other (False) – whether to include an extra row/column at the end of the matrix for labels that do not appear in
classes
. Only applicable ifclasses
are provided
- Returns
a
num_classes x num_classes
confusion matrix
-
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
from_dict
(d, samples, config, key)¶ Builds a
BaseRunResults
from a JSON dict representation of it.- Parameters
d – a JSON dict
samples – the
fiftyone.core.collections.SampleCollection
for the runconfig – the
BaseRunConfig
for the runkey – the run key
- Returns
a
BaseRunResults
-
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_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.
-
property
key
¶ The run key for these results.
-
metrics
(classes=None, average='micro', beta=1.0)¶ Computes classification metrics for the results, including accuracy, precision, recall, and F-beta score.
See
sklearn.metrics.precision_recall_fscore_support()
for details.- Parameters
classes (None) – an optional list of classes to include in the calculations
average ("micro") – the averaging strategy to use
beta (1.0) – the F-beta value to use
- Returns
a dict
-
plot_confusion_matrix
(classes=None, include_other=None, include_missing=None, other_label='(other)', backend='plotly', **kwargs)¶ Plots a confusion matrix for the evaluation results.
If you are working in a notebook environment with the default plotly backend, this method returns an interactive
fiftyone.core.plots.plotly.InteractiveHeatmap
that you can attach to an App session via itsfiftyone.core.session.Session.plots
attribute, which will automatically sync the session’s view with the currently selected cells in the confusion matrix.- Parameters
classes (None) – an optional list of classes to include in the confusion matrix
include_other (None) –
whether to include a row/column for examples whose label is in
classes
but are matched to labels that do not appear inclasses
. Only applicable ifclasses
are provided. The supported values are:None (default): only include a row/column for other labels if there are any
True: do include a row/column for other labels
False: do not include a row/column for other labels
include_missing (None) –
whether to include a row/column for missing ground truth/predictions in the confusion matrix. The supported values are:
None (default): only include a row/column for missing labels if there are any
True: do include a row/column for missing labels
False: do not include a row/column for missing labels
other_label ("(other)") – the label to use for “other” predictions
backend ("plotly") – the plotting backend to use. Supported values are
("plotly", "matplotlib")
**kwargs –
keyword arguments for the backend plotting method:
”plotly” backend:
fiftyone.core.plots.plotly.plot_confusion_matrix()
”matplotlib” backend:
fiftyone.core.plots.matplotlib.plot_confusion_matrix()
- Returns
a
fiftyone.core.plots.plotly.InteractiveHeatmap
, if the plotly backend is useda matplotlib figure, otherwise
- Return type
one of the following
-
print_report
(classes=None, digits=2)¶ Prints a classification report for the results via
sklearn.metrics.classification_report()
.- Parameters
classes (None) – an optional list of classes to include in the report
digits (2) – the number of digits of precision to print
-
report
(classes=None)¶ Generates a classification report for the results via
sklearn.metrics.classification_report()
.- Parameters
classes (None) – an optional list of classes to include in the report
- Returns
a dict
-
property
samples
¶ The
fiftyone.core.collections.SampleCollection
associated with these results.
-
save
()¶ Saves the results to the database.
-
save_config
()¶ Saves these results config to the database.
-
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
-
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()