def __init__(self, model, settings):
self._preprocessors = model['preprocess']
self._model = deepnet_model(model, settings)
outex = get_output_exposition(model)
try:
self._classes = outex['values']
except KeyError:
self._classes = None
def image_model(network, input_settings):
settings = ensure_settings(input_settings)
if is_yolo_model(network):
model = box_detector(network, settings)
else:
model = deepnet_model(network, settings)
if settings.load_pretrained_weights:
load_pretrained_weights(model, network['image_network'])
return model
def __init__(self, model, settings):
super().__init__(model, settings)
if isinstance(model, dict):
outex = get_output_exposition(model)
try:
self._classes = outex["values"]
except KeyError:
self._classes = None
self._preprocessors = model["preprocess"]
self._model = deepnet_model(model, settings)
# Pretrained image networks should be the only thing missing
# this `_preprocessors` attribute
if getattr(self, "_preprocessors", None) is None:
self._preprocessors = [{"type": IMAGE, "index": 0}]
self._ncolumns, self._nimages = count_types(self._preprocessors)