^jddlZddlmZddlmZmZmZmZmZddl m Z ddl m Z ddl mZddlmZdd lmZmZdd lmZdd lmZmZmZgd Zd edeeeeffdZdd ededefdZ dd ededeeeeeefefdeeeefdeeeefdeeeefdeedeedeedede j>fdZ y)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_namereturncz|jdr|jdd}t|}|jdvsJ|jdk(r|j|jfS|jdk(r|j|jfSt jj |jd}d|fS)z;Parse source and name from potentially prefixed model name.hf_hubhf-hub)r local-dirrN) startswithreplacer schemepathossplit)rparseds _/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/timm/models/_factory.pyrrsX&''(; j !F ==7 77 7 }} }}fkk)) + %}}fkk))WW]]6;;/3 Z remove_sourcecRdtdtfd}|rt|d}||S)z)Return a filename / path safe model name.namercPdjd|DjdS)Nrc3DK|]}|jr|ndyw)_N)isalnum).0cs r& z5safe_model_name..make_safe..&s?QAIIKqS0?s r-)joinrstrip)r*s r& make_safez"safe_model_name..make_safe%s"ww?$??FFsKKr'r)strr)rr(r4s r&rr#s5LLL%j1"5 Z  r' pretrainedpretrained_cfgpretrained_cfg_overlaycheckpoint_path cache_dir scriptable exportableno_jitkwargsc B| jD cic] \} } |  | |  } } } t|\} } | rl|rJd| dk(rt| |\}}}n| dk(rt| \}}}n Jd| |r?|jD]\} } | j | | nt | \}}|r|s|}t |std|zt|}t|||5|d ||||d | }ddd|r t|Scc} } w#1swYxYw) 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/` ``` NzKpretrained_cfg should not be set when sourcing model from Hugging Face Hub.r)r:rzUnknown model_source zUnknown model (%s))r;r<r=)r6r7r8r:) itemsrrr setdefaultrr RuntimeErrorrr r )rr6r7r8r9r:r;r<r=r>kv model_sourcemodel_id model_argspretrained_tag create_fnmodels r&rr,sn| &||~ ?tq!ad ?F ?-j9L(!p#pp! 8 #6O#6 2NJ [ (5P6 2NJ  A1,@ @5 "((* (1!!!Q' (&:(%C" N .+N J /*<== ,I ZJv V  !)#9     / LW@>  s DD)DD)T)FNNNNNNN)!r#pathlibrtypingrrrrr urllib.parser torchr timm.layersr _helpersr _hubrr _pretrainedr _registryrrr__all__r5rboolrModulerr@r'r&rXsS 44!(%H&GG B  x}c/A)B "!!D!C!!NR;?6:04%)%)!%iii!sDcNM'I!JKi!)c3h 8 i "%T "23 i E#t),- iTNiTNiiiYYir'