def rf_lw152(num_classes, pretrained=False, **kwargs):
model = ResNetLW(Bottleneck, [3, 8, 36, 3],
num_classes=num_classes,
**kwargs)
if pretrained:
dataset = data_info.get(num_classes, None)
if dataset:
bname = '152_' + dataset.lower()
key = 'rf_lw' + bname
url = models_urls[bname]
model.load_state_dict(maybe_download(key, url), strict=False)
return model
def mbv2(num_classes, pretrained=False, **kwargs):
"""Constructs the network.
Args:
num_classes (int): the number of classes for the segmentation head to output.
"""
model = MBv2(num_classes, **kwargs)
if pretrained:
dataset = data_info.get(num_classes, None)
if dataset:
bname = 'mbv2_' + dataset.lower()
key = 'rf_lw' + bname
url = models_urls[bname]
model.load_state_dict(maybe_download(key, url), strict=False)
return model