def __init__(self):
self.sample_options = (
# Do nothing
None,
# min IoU, max IoU
(0.1, None),
(0.3, None),
(0.5, None),
(0.7, None),
(0.9, None),
# no IoU requirements
(None, None),
)
self.dboxes = dboxes640_coco()
def __init__(self, backbone=None, num_classes=21):
super(RetinaNet640, self).__init__()
if backbone is None:
raise Exception("backbone is None")
if not hasattr(backbone, "out_channels"):
raise Exception("the backbone not has attribute: out_channel")
self.feature_extractor = backbone
self.num_classes = num_classes
# out_channels = [1024, 512, 512, 256, 256, 256] for resnet50
self.predictor = Predictor(num_features=5,
in_channels=256,
num_layers_before_predictor=4,
num_classes=num_classes,
num_boxes=6)
default_box = dboxes640_coco()
self.compute_loss = Loss(default_box)
self.encoder = Encoder(default_box)
self.postprocess = PostProcess(default_box)
def __init__(self):
self.default_box = dboxes640_coco()
self.encoder = Encoder(self.default_box)