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77 lines
3.1 KiB
77 lines
3.1 KiB
from urllib.parse import urlsplit, urlunsplit
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import os
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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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from .hub import load_model_config_from_hf
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def parse_model_name(model_name):
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model_name = model_name.replace('hf_hub', 'hf-hub') # NOTE for backwards compat, to deprecate hf_hub use
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parsed = urlsplit(model_name)
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assert parsed.scheme in ('', 'timm', 'hf-hub')
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if parsed.scheme == 'hf-hub':
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# FIXME may use fragment as revision, currently `@` in URI path
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return parsed.scheme, parsed.path
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else:
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model_name = os.path.split(parsed.path)[-1]
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return 'timm', model_name
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def safe_model_name(model_name, remove_source=True):
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def make_safe(name):
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return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
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if remove_source:
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model_name = parse_model_name(model_name)[-1]
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return make_safe(model_name)
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def create_model(
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model_name,
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pretrained=False,
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pretrained_cfg=None,
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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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# 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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model_source, model_name = parse_model_name(model_name)
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if model_source == 'hf-hub':
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# FIXME hf-hub source overrides any passed in pretrained_cfg, warn?
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# For model names specified in the form `hf-hub:path/architecture_name@revision`,
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# load model weights + pretrained_cfg from Hugging Face hub.
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pretrained_cfg, model_name = load_model_config_from_hf(model_name)
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if not is_model(model_name):
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raise RuntimeError('Unknown model (%s)' % model_name)
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create_fn = model_entrypoint(model_name)
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with set_layer_config(scriptable=scriptable, exportable=exportable, no_jit=no_jit):
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model = create_fn(pretrained=pretrained, pretrained_cfg=pretrained_cfg, **kwargs)
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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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