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61 lines
2.6 KiB
61 lines
2.6 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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from .layers import set_layer_config
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def create_model(
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model_name,
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pretrained=False,
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checkpoint_path='',
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scriptable=None,
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exportable=None,
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no_jit=None,
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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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checkpoint_path (str): path of checkpoint to load after model is initialized
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scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
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exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
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no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
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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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model_args = dict(pretrained=pretrained)
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# Only EfficientNet and MobileNetV3 models have support for batchnorm params or drop_connect_rate passed as args
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is_efficientnet = is_model_in_modules(model_name, ['efficientnet', 'mobilenetv3'])
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if not is_efficientnet:
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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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# handle backwards compat with drop_connect -> drop_path change
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drop_connect_rate = kwargs.pop('drop_connect_rate', None)
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if drop_connect_rate is not None and kwargs.get('drop_path_rate', None) is None:
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print("WARNING: 'drop_connect' as an argument is deprecated, please use 'drop_path'."
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" Setting drop_path to %f." % drop_connect_rate)
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kwargs['drop_path_rate'] = drop_connect_rate
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# Parameters that aren't supported by all models or are intended to only override model defaults if set
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# should default to None in command line args/cfg. Remove them if they are present and not set so that
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# non-supporting models don't break and default args remain in effect.
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kwargs = {k: v for k, v in kwargs.items() if v is not None}
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with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
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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(**model_args, **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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