def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if 'extra' in kwargs:
extra = kwargs.pop('extra')
else:
extra = True
if model_name == 'dpn68':
model = dpn68(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn68b':
model = dpn68b(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn92':
model = dpn92(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool,
extra=extra)
elif model_name == 'dpn98':
model = dpn98(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn131':
model = dpn131(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'dpn107':
model = dpn107(num_classes=num_classes,
pretrained=pretrained,
test_time_pool=test_time_pool)
elif model_name == 'resnet18':
model = resnet18(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet34':
model = resnet34(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet50':
model = resnet50(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet101':
model = resnet101(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'resnet152':
model = resnet152(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet121':
model = densenet121(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet161':
model = densenet161(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet169':
model = densenet169(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'densenet201':
model = densenet201(num_classes=num_classes,
pretrained=pretrained,
**kwargs)
elif model_name == 'inception_v3':
model = inception_v3(num_classes=num_classes,
pretrained=pretrained,
transform_input=False,
**kwargs)
else:
assert False, "Unknown model architecture (%s)" % model_name
return model
def dpn68_warp(pretrained=True):
model = dpn68(num_classes=1000, pretrained=pretrained, test_time_pool=True)
return model