a  d @sddlZddlmZmZmZmZddlmZddlm Z ddl m Z ddl m Z ddlmZdd lmZmZmZgd Zed d d ZdeedddZdeeeeeeeefefeeeefeeeeeeedddZdS)N)AnyDictOptionalUnion)urlsplit)set_layer_config)load_checkpoint)load_model_config_from_hf) PretrainedCfg)is_modelmodel_entrypointsplit_model_name_tag)parse_model_namesafe_model_name create_model) model_namecCs`|dr|dd}t|}|jdvs,J|jdkrB|j|jfStj|jd}d|fSdS)NZhf_hubhf-hub)timmrr) startswithreplacerschemepathossplit)rparsedr]/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/timm/models/_factory.pyrs    rT)r remove_sourcecCs dd}|rt|d}||S)NcSsddd|DdS)Nrcss|]}|r|ndVqdS)_N)isalnum).0crrr z5safe_model_name..make_safe..r!)joinrstrip)namerrr make_safesz"safe_model_name..make_safer)r)rr r*rrrrs rFr)r pretrainedpretrained_cfgpretrained_cfg_overlaycheckpoint_path scriptable exportableno_jitc Ksdd|D}t|\} }| dkr@|r2Jdt|\}}nt|\}} | rX|sX| }t|sltd|t|} t|||d&| f|||d|} Wdn1s0Y|rt| || S) a(Create a model. Lookup model's entrypoint function and pass relevant args to create a new model. **kwargs will be passed through entrypoint fn to ``timm.models.build_model_with_cfg()`` and then the model class __init__(). kwargs values set to None are pruned before passing. Args: model_name: Name of model to instantiate. pretrained: If set to `True`, load pretrained ImageNet-1k weights. pretrained_cfg: Pass in an external pretrained_cfg for model. pretrained_cfg_overlay: Replace key-values in base pretrained_cfg with these. checkpoint_path: Path of checkpoint to load _after_ the model is initialized. scriptable: Set layer config so that model is jit scriptable (not working for all models yet). exportable: Set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet). no_jit: Set layer config so that model doesn't utilize jit scripted layers (so far activations only). Keyword Args: drop_rate (float): Classifier dropout rate for training. drop_path_rate (float): Stochastic depth drop rate for training. global_pool (str): Classifier global pooling type. Example: ```py >>> from timm import create_model >>> # Create a MobileNetV3-Large model with no pretrained weights. >>> model = create_model('mobilenetv3_large_100') >>> # Create a MobileNetV3-Large model with pretrained weights. >>> model = create_model('mobilenetv3_large_100', pretrained=True) >>> model.num_classes 1000 >>> # Create a MobileNetV3-Large model with pretrained weights and a new head with 10 classes. >>> model = create_model('mobilenetv3_large_100', pretrained=True, num_classes=10) >>> model.num_classes 10 ``` cSsi|]\}}|dur||qS)Nr)r#kvrrr _r&z create_model..rzKpretrained_cfg should not be set when sourcing model from Hugging Face Hub.zUnknown model (%s))r/r0r1)r+r,r-N) itemsrr rr RuntimeErrorr rr ) rr+r,r-r.r/r0r1kwargsZ model_sourceZpretrained_tagZ create_fnmodelrrrr&s,9    $ r)T)FNNrNNN)rtypingrrrr urllib.parserZ timm.layersrZ_helpersr Z_hubr Z _pretrainedr _registryr r r__all__strrboolrrrrrrs6