NgDBddlZddlmZmZmZmZddlmZddlm Z ddl m Z ddl m Z ddlmZdd lmZmZmZgd Zd efd Zdd edefdZ dd ededeeeeeefefdeeeefdedeedeedeefdZdS)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_namec|dr|dd}t|}|jdvsJ|jdkr|j|jfSt j|jd}d|fS)Nhf_hubhf-hub)timmrr) startswithreplacerschemepathossplit)rparseds P/var/www/html/ai-engine/env/lib/python3.11/site-packages/timm/models/_factory.pyrrsX&&<''(;; j ! !F =2 2 2 2 2 }  }fk))W]]6;//3 z!!T remove_sourcecLd}|rt|d}||S)Ncfdd|DdS)Nrc3FK|]}|r|ndVdS)_N)isalnum).0cs r! z5safe_model_name..make_safe.. s3??QAIIKK0qqS??????r"r')joinrstrip)names r! make_safez"safe_model_name..make_safes1ww??$?????FFsKKKr"r)r)rr#r/s r!rrs>LLL6%j11"5 9Z  r"Fr pretrainedpretrained_cfgpretrained_cfg_overlaycheckpoint_path scriptable exportableno_jitc 6d|D}t|\} }| dkrR|r Jdt|\}}} | r0| D]\} } || | nt |\}} | r|s| }t |st d|zt|}t|||5|d|||d|}dddn #1swxYwY|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 ``` ci|] \}}||| S)N)r)kvs r! z create_model.._s ? ? ?tq!ar"rzKpretrained_cfg should not be set when sourcing model from Hugging Face Hub.zUnknown model (%s))r4r5r6)r0r1r2Nr9) itemsrr setdefaultrr RuntimeErrorrrr )rr0r1r2r3r4r5r6kwargs model_source model_argsr:r;pretrained_tag create_fnmodels r!rr&sr@ ?v||~~ ? ? ?F/ ;;L*x!pp#pppp2K:1V1V. J  ("((** ( (1!!!Q''''%9*%E%E" N  ,. ,+N J  >/*<=== ,,I ZJv V V V    !)#9                    0/// Ls# C<<DD)T)FNNrNNN)rtypingrrrr urllib.parser timm.layersr_helpersr _hubr _pretrainedr _registryr rr__all__strrboolrrr9r"r!rPs ------------!!!!!!((((((%%%%%%++++++&&&&&&GGGGGGGGGG B A A " " " " "!!!D!!!!!NR<@!%)%)!%YYYY!sDcNM'I!JKY"*$sCx.!9 Y  Y TN YTNYYYYYYYr"