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from .registry import is_model, is_model_in_modules, model_entrypoint
from .helpers import load_checkpoint
from .layers import set_layer_config
def create_model(
model_name,
pretrained=False,
num_classes=1000,
in_chans=3,
checkpoint_path='',
scriptable=None,
exportable=None,
no_jit=None,
**kwargs):
"""Create a model
Args:
model_name (str): name of model to instantiate
pretrained (bool): load pretrained ImageNet-1k weights if true
num_classes (int): number of classes for final fully connected layer (default: 1000)
in_chans (int): number of input channels / colors (default: 3)
checkpoint_path (str): path of checkpoint to load after model is initialized
scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
Keyword Args:
drop_rate (float): dropout rate for training (default: 0.0)
global_pool (str): global pool type (default: 'avg')
**: other kwargs are model specific
"""
model_args = dict(pretrained=pretrained, num_classes=num_classes, in_chans=in_chans)
# Only EfficientNet and MobileNetV3 models have support for batchnorm params or drop_connect_rate passed as args
is_efficientnet = is_model_in_modules(model_name, ['efficientnet', 'mobilenetv3'])
if not is_efficientnet:
kwargs.pop('bn_tf', None)
kwargs.pop('bn_momentum', None)
kwargs.pop('bn_eps', None)
# handle backwards compat with drop_connect -> drop_path change
drop_connect_rate = kwargs.pop('drop_connect_rate', None)
if drop_connect_rate is not None and kwargs.get('drop_path_rate', None) is None:
print("WARNING: 'drop_connect' as an argument is deprecated, please use 'drop_path'."
" Setting drop_path to %f." % drop_connect_rate)
kwargs['drop_path_rate'] = drop_connect_rate
# Parameters that aren't supported by all models or are intended to only override model defaults if set
# should default to None in command line args/cfg. Remove them if they are present and not set so that
# non-supporting models don't break and default args remain in effect.
kwargs = {k: v for k, v in kwargs.items() if v is not None}
with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
if is_model(model_name):
create_fn = model_entrypoint(model_name)
model = create_fn(**model_args, **kwargs)
else:
raise RuntimeError('Unknown model (%s)' % model_name)
if checkpoint_path:
load_checkpoint(model, checkpoint_path)
return model
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