o پi@sbddlZddlmZddlmZmZmZmZmZddl m Z ddl m Z ddl mZddlmZdd lmZmZdd lmZdd lmZmZmZgd Zd edeeeeffddZd!d ededefddZ        d"d ededeeeeeefefdeeeefdeeeefdeeeefdeedeedeedede jfdd Z dS)#N)Path)AnyDictOptionalTupleUnion)urlsplit)nn)set_layer_config)load_checkpoint)load_model_config_from_hfload_model_config_from_path) PretrainedCfg)is_modelmodel_entrypointsplit_model_name_tag)parse_model_namesafe_model_name create_model model_namereturncCsr|dr |dd}t|}|jdvsJ|jdkr!|j|jfS|jdkr,|j|jfStj|jd}d|fS)z;Parse source and name from potentially prefixed model name.hf_hubhf-hub)r local-dirrN) startswithreplacerschemepathossplit)rparsedr$H/home/ubuntu/.local/lib/python3.10/site-packages/timm/models/_factory.pyrs      rT remove_sourcecCs*dtdtfdd}|rt|d}||S)z)Return a filename / path safe model name.namercSsddd|DdS)Nrcss |] }|r |ndVqdS)_N)isalnum).0cr$r$r% &sz5safe_model_name..make_safe..r()joinrstrip)r'r$r$r% make_safe%sz"safe_model_name..make_safer)strr)rr&r/r$r$r%r#s rF pretrainedpretrained_cfgpretrained_cfg_overlaycheckpoint_path cache_dir scriptable exportableno_jitkwargsc Ks dd| D} t|\} } | rJ|rJd| dkr%t| |d\}}} n| dkr1t| \}}} nJd| | rI| D] \} }| | |q>n t| \}}|rV|sV|}t|s`td |t|}t |||d |d ||||d | }Wd n1swY|rt |||S)aCreate a model. Lookup model's entrypoint function and pass relevant args to create a new model. Tip: **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. cache_dir: Override model cache dir for Hugging Face Hub and Torch checkpoints. 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 >>> # Create a Dinov2 small model with pretrained weights and save weights in a custom directory. >>> model = create_model('vit_small_patch14_dinov2.lvd142m', pretrained=True, cache_dir="/data/my-models") >>> # Data will be stored at `/data/my-models/models--timm--vit_small_patch14_dinov2.lvd142m/` ``` cSsi|] \}}|dur||qS)Nr$)r*kvr$r$r% jsz create_model..zKpretrained_cfg should not be set when sourcing model from Hugging Face Hub.r)r5rFzUnknown model_source zUnknown model (%s))r6r7r8)r1r2r3r5Nr$) itemsrr r setdefaultrr RuntimeErrorrr r )rr1r2r3r4r5r6r7r8r9 model_sourcemodel_id model_argsr:r;pretrained_tag create_fnmodelr$r$r%r,sJ>      r)T)FNNNNNNN)!r!pathlibrtypingrrrrr urllib.parsertorchr timm.layersr _helpersr _hubr r _pretrainedr _registryrrr__all__r0rboolrModulerr$r$r$r%sX