def load_data( self, paths: Union[str, List[str]], targets: Optional[List[str]] = None, sampling_rate: int = 16000, ) -> Sequence: self.sampling_rate = sampling_rate if targets is None: return to_samples(list_valid_files(paths, AUDIO_EXTENSIONS)) return to_samples(*filter_valid_files( paths, targets, valid_extensions=AUDIO_EXTENSIONS))
def load_data( self, array: Any, masks: Any = None, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) return to_samples(array, masks)
def load_data( self, tensor: Any, targets: Optional[List[Any]] = None, target_formatter: Optional[TargetFormatter] = None ) -> List[Dict[str, Any]]: if targets is not None: self.load_target_metadata(targets, target_formatter=target_formatter) return to_samples(tensor, targets)
def load_data( self, files: List[PATH_TYPE], targets: Optional[List[Any]] = None, sampling_rate: int = 16000, n_fft: int = 400, target_formatter: Optional[TargetFormatter] = None, ) -> List[Dict[str, Any]]: self.sampling_rate = sampling_rate self.n_fft = n_fft if targets is None: files = filter_valid_files(files, valid_extensions=AUDIO_EXTENSIONS + IMG_EXTENSIONS + NP_EXTENSIONS) return to_samples(files) files, targets = filter_valid_files(files, targets, valid_extensions=AUDIO_EXTENSIONS + IMG_EXTENSIONS + NP_EXTENSIONS) self.load_target_metadata(targets, target_formatter=target_formatter) return to_samples(files, targets)
def load_data( self, files: Union[PATH_TYPE, List[PATH_TYPE]], mask_files: Optional[Union[PATH_TYPE, List[PATH_TYPE]]] = None, num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) if mask_files is None: files = filter_valid_files(files, valid_extensions=IMG_EXTENSIONS) else: files, mask_files = filter_valid_files( files, mask_files, valid_extensions=IMG_EXTENSIONS) return to_samples(files, mask_files)
def load_data( self, files: List[PATH_TYPE], targets: Optional[List[Any]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> List[Dict[str, Any]]: if targets is None: return super().load_data(files) files, targets = filter_valid_files(files, targets, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS) self.load_target_metadata(targets, target_formatter=target_formatter) return to_samples(files, targets)
def load_data( self, sample_collection: SampleCollection, label_field: str = "ground_truth", num_classes: Optional[int] = None, labels_map: Optional[Dict[int, Tuple[int, int, int]]] = None, ) -> List[Dict[str, Any]]: self.load_labels_map(num_classes, labels_map) self.label_field = label_field label_utilities = FiftyOneLabelUtilities(label_field, fo.Segmentation) label_utilities.validate(sample_collection) self._fo_dataset_name = sample_collection.name return to_samples(sample_collection.values("filepath"))
def load_data( self, examples: Collection[np.ndarray], targets: Optional[Sequence[Any]] = None, target_formatter: Optional[TargetFormatter] = None, ) -> Sequence[Dict[str, Any]]: """Sets the ``num_features`` attribute and calls ``super().load_data``. Args: examples: The ``np.ndarray`` (num_examples x num_features). targets: Associated targets. target_formatter: Optionally provide a ``TargetFormatter`` to control how targets are formatted. Returns: A sequence of samples / sample metadata. """ if not self.predicting and isinstance(examples, np.ndarray): self.num_features = examples.shape[1] if targets is not None: self.load_target_metadata(targets, target_formatter=target_formatter) return to_samples(examples, targets)
def predict_load_data( self, sample_collection: SampleCollection, ) -> List[Dict[str, Any]]: return to_samples(sample_collection.values("filepath"))
def load_data(self, tensor: Any) -> List[Dict[str, Any]]: return to_samples(tensor)
def load_data(self, files: List[PATH_TYPE]) -> List[Dict[str, Any]]: files = filter_valid_files(files, valid_extensions=IMG_EXTENSIONS + NP_EXTENSIONS) return to_samples(files)
def load_data(self, array: Any) -> List[Dict[str, Any]]: return to_samples(array)