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43 lines
1.2 KiB
43 lines
1.2 KiB
from .inception_v4 import *
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from .inception_resnet_v2 import *
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from .densenet import *
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from .resnet import *
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from .dpn import *
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from .senet import *
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from .xception import *
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from .pnasnet import *
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from .gen_efficientnet import *
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from .inception_v3 import *
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from .gluon_resnet import *
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from .helpers import load_checkpoint
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def create_model(
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model_name,
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pretrained=False,
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num_classes=1000,
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in_chans=3,
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checkpoint_path='',
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**kwargs):
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margs = dict(pretrained=pretrained, num_classes=num_classes, in_chans=in_chans)
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# Not all models have support for batchnorm params passed as args, only gen_efficientnet variants
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supports_bn_params = model_name in gen_efficientnet_model_names()
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if not supports_bn_params and any([x in kwargs for x in ['bn_tf', 'bn_momentum', 'bn_eps']]):
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kwargs.pop('bn_tf', None)
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kwargs.pop('bn_momentum', None)
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kwargs.pop('bn_eps', None)
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if model_name in globals():
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create_fn = globals()[model_name]
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model = create_fn(**margs, **kwargs)
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else:
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raise RuntimeError('Unknown model (%s)' % model_name)
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if checkpoint_path:
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load_checkpoint(model, checkpoint_path)
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return model
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