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45 lines
1.6 KiB
45 lines
1.6 KiB
from .registry import is_model, is_model_in_modules, model_entrypoint
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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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"""Create a model
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Args:
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model_name (str): name of model to instantiate
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pretrained (bool): load pretrained ImageNet-1k weights if true
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num_classes (int): number of classes for final fully connected layer (default: 1000)
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in_chans (int): number of input channels / colors (default: 3)
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checkpoint_path (str): path of checkpoint to load after model is initialized
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Keyword Args:
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drop_rate (float): dropout rate for training (default: 0.0)
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global_pool (str): global pool type (default: 'avg')
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**: other kwargs are model specific
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"""
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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 = is_model_in_modules(model_name, ['gen_efficientnet'])
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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 is_model(model_name):
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create_fn = model_entrypoint(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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