def __init__(
self,
hparams: OTEClassificationParameters,
label_schema: LabelSchemaEntity,
model_file: Union[str, bytes],
weight_file: Union[str, bytes, None] = None,
device: str = "CPU",
num_requests: int = 1,
):
"""
Inferencer implementation for OTEDetection using OpenVINO backend.
:param model: Path to model to load, `.xml`, `.bin` or `.onnx` file.
:param hparams: Hyper parameters that the model should use.
:param num_requests: Maximum number of requests that the inferencer can make.
Good value is the number of available cores. Defaults to 1.
:param device: Device to run inference on, such as CPU, GPU or MYRIAD. Defaults to "CPU".
"""
multilabel = len(label_schema.get_groups(False)) > 1 and \
len(label_schema.get_groups(False)) == len(label_schema.get_labels(include_empty=False))
self.label_schema = label_schema
model_adapter = OpenvinoAdapter(create_core(),
model_file,
weight_file,
device=device,
max_num_requests=num_requests)
self.configuration = {'multilabel': multilabel}
self.model = Model.create_model("ote_classification",
model_adapter,
self.configuration,
preload=True)
self.converter = ClassificationToAnnotationConverter(self.label_schema)
def __init__(self, label_schema: LabelSchemaEntity):
if len(label_schema.get_labels(False)) == 1:
self.labels = label_schema.get_labels(include_empty=True)
else:
self.labels = label_schema.get_labels(include_empty=False)
self.empty_label = get_empty_label(label_schema)
multilabel = len(label_schema.get_groups(False)) > 1 and len(
label_schema.get_groups(False)) == len(
label_schema.get_labels(include_empty=False))
self.hierarchical = False
if not multilabel and len(label_schema.get_groups(False)) > 1:
self.labels = get_leaf_labels(label_schema)
self.hierarchical = True
self.label_schema = label_schema
def forward(
instance: LabelSchemaEntity,
) -> dict:
"""Serializes to dict."""
label_groups = [
LabelGroupMapper().forward(group)
for group in instance.get_groups(include_empty=True)
]
output_dict = {
"label_tree": LabelGraphMapper().forward(instance.label_tree),
"exclusivity_graph": LabelGraphMapper().forward(instance.exclusivity_graph),
"label_groups": label_groups,
}
output_dict["all_labels"] = {
IDMapper().forward(label.id): LabelMapper().forward(label)
for label in instance.get_labels(True)
}
return output_dict