NgdZddlmZmZmZmZmZmZmZddl Z ddl m Z ddl m Z m Z  ddlmZddlmZd Zn #e$rd ZYnwxYwdd lmZmZmZmZmZdd lmZmZdd lmZddlm Z m!Z!ddl"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)gdZ*eee eee!ee#e$e%e&e'e(e)hZ+ ddlm,Z,e+-e,n #e$rYnwxYwdee j.fdZ/dee j.fdZ0dZ1eehZ2defdZ3defdZ4dZ5de j.deee6ee6ffdZde j.deee6e6fee6ffdZGdd e j.Z7Gd!d"e j.Z8dS)#zm PyTorch FX Based Feature Extraction Helpers Using https://pytorch.org/vision/stable/feature_extraction.html )CallableDictListOptionalUnionTupleTypeN)nn)_get_feature_info_get_return_layers)create_feature_extractor)get_graph_node_namesTF) Conv2dSameScaledStdConv2dSame CondConv2d StdConv2dSameFormat)resample_abs_pos_embedresample_abs_pos_embed_nhwc)BilinearAttnTransform) MaxPool2dSame AvgPool2dSame)BatchNormAct2dSyncBatchNormActFrozenBatchNormAct2d GroupNormAct GroupNorm1Act LayerNormActLayerNormAct2d) register_notrace_moduleis_notrace_moduleget_notrace_modulesregister_notrace_functionis_notrace_functionget_notrace_functionsrrFeatureGraphNetGraphExtractNet) InplaceAbnmodulec:t||S)zq Any module not under timm.models.layers should get this decorator if we don't want to trace through it. ) _leaf_modulesaddr*s T/var/www/html/ai-engine/env/lib/python3.11/site-packages/timm/models/_features_fx.pyr!r!>sf Mc|tvSN)r,r.s r/r"r"Fs ] ""r0c*ttSr2)listr,r0r/r#r#Js   r0funcc:t||S)zF Decorator for functions which ought not to be traced through )_autowrap_functionsr-r6s r/r$r$UsD!!! Kr0c|tvSr2)r8r9s r/r%r%]s & &&r0c*ttSr2)r4r8r5r0r/r&r&as # $ $$r0modelreturncrt|ttttdS)N leaf_modulesautowrap_functions tracer_kwargs)_get_graph_node_namesr4r,r8)r<s r/rres9  '+M':':RVWjRkRkll   r0 return_nodescts Jdt||ttttdS)NKPlease update to PyTorch 1.10+, torchvision 0.11+ for FX feature extractionr?rB)has_fx_feature_extraction_create_feature_extractorr4r,r8)r<rEs r/rrlsP $ss&ssss $ |'+M':':RVWjRkRkll   r0c eZdZUdZejjeed< d de j de e dfd e ed edef fd Zd ZxZS)r'zX A FX Graph based feature extractor that works with the model feature_info metadata return_dictNNCHWFr< out_indices.out_map output_fmtcftts Jdt|||_|"t |t |ksJt ||_t|j|}t|||_ ||_ dS)NrG) super__init__rHr feature_infolenrrOr r graph_modulerK)selfr<rMrNrOrKrE __class__s r/rRzFeatureGraphNet.__init__ys (ww*wwww-e[AA  w<<3{#3#33333 ,,)$*;WEE 4ULII&r0c||}|jr|St|Sr2)rUrKr4valuesrVxouts r/forwardzFeatureGraphNet.forwards;""   JCJJLL!!!r0)NrLF)__name__ __module__ __qualname____doc__torchjitFinalbool__annotations__r ModulerintrrstrrRr] __classcell__rWs@r/r'r'ts&&&& '+$ % ''9'sCx'd^ '  '  ''''''$"""""""r0r'c eZdZUdZejjeed< d de j de e e e fee fdedeffd Zd e eejejffd ZxZS) r(a A standalone feature extraction wrapper that maps dict -> list or single tensor NOTE: * one can use feature_extractor directly if dictionary output is desired * unlike FeatureGraphNet, this is intended to be used standalone and not with model feature_info metadata for builtin feature extraction mode * create_feature_extractor can be used directly if dictionary output is acceptable Args: model: model to extract features from return_nodes: node names to return features from (dict or list) squeeze_out: if only one output, and output in list format, flatten to single tensor return_dict: return as dictionary from extractor with node names as keys, ignores squeeze_out arg rKTFr<rE squeeze_outct||_t|||_||_dSr2)rQrRrmrrUrK)rVr<rErmrKrWs r/rRzGraphExtractNet.__init__sC &4ULII&r0r=c||}|jr|St|}|jrt |dkr|dn|S)Nr r)rUrKr4rYrmrTrZs r/r]zGraphExtractNet.forwards^""   J3::<<  )Dc#hh!mms1vvDr0)TF)r^r_r`rarbrcrdrerfr rgrrrirrRTensorr]rjrks@r/r(r(s  &&&& !% % ' '9 ' S#XS 9: ' '  ' ' ' ' ' 'EE$u|"4el"BCEEEEEEEEr0r()9ratypingrrrrrrr rbr _featuresr r %torchvision.models.feature_extractionrrIrrDrH ImportError timm.layersrrrrrrrtimm.layers.non_local_attnrtimm.layers.pool2d_samerrtimm.layers.norm_actrrrrrrr __all__r,r)r-rgr!r"r#r8r$r%r&rir'r(r5r0r/rzsFEEEEEEEEEEEEEEEEE <<<<<<<<&kkkkkkcccccc $&&& %&[ZZZZZZZZZZZZZKKKKKKKK<<<<<<@@@@@@@@ e e e 2M=   &&&&&&j!!!!   D DO#d29o####  H'h''''%%% eDItCy4H.IBIU4S>SWX[S\C\=]"""""bi"""