ڧg* &ddlZddlZddlZddlmZddlmZmZmZm Z m Z m Z m Z ddl mZddlmZddlmZGd d ejZd#d ed ed e edefdZe dZdedefdedeffdZe deZe dejZe dZde ee e eeeeefge effffdZdeeefdededdfdZ dede ededefd Z!Gd!d"e"Z#dS)$N) OrderedDict)AnyCallableDictOptionalTupleTypeVarUnion)nn)sequence_to_str) WeightsEnumcleZdZdZdZdeeefiZdej deeefddffd Z dZ xZ S) IntermediateLayerGetterax Module wrapper that returns intermediate layers from a model It has a strong assumption that the modules have been registered into the model in the same order as they are used. This means that one should **not** reuse the same nn.Module twice in the forward if you want this to work. Additionally, it is only able to query submodules that are directly assigned to the model. So if `model` is passed, `model.feature1` can be returned, but not `model.feature1.layer2`. Args: model (nn.Module): model on which we will extract the features return_layers (Dict[name, new_name]): a dict containing the names of the modules for which the activations will be returned as the key of the dict, and the value of the dict is the name of the returned activation (which the user can specify). Examples:: >>> m = torchvision.models.resnet18(weights=ResNet18_Weights.DEFAULT) >>> # extract layer1 and layer3, giving as names `feat1` and feat2` >>> new_m = torchvision.models._utils.IntermediateLayerGetter(m, >>> {'layer1': 'feat1', 'layer3': 'feat2'}) >>> out = new_m(torch.rand(1, 3, 224, 224)) >>> print([(k, v.shape) for k, v in out.items()]) >>> [('feat1', torch.Size([1, 64, 56, 56])), >>> ('feat2', torch.Size([1, 256, 14, 14]))] r return_layersmodelreturnNct|d|Dstd|}d|D}t }|D]\}}|||<||vr||=|snt |||_dS)Ncg|]\}}|Sr).0name_s U/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/models/_utils.py z4IntermediateLayerGetter.__init__..3s+W+W+WWT1D+W+W+Wz&return_layers are not present in modelcNi|]"\}}t|t|#Sr)str)rkvs r z4IntermediateLayerGetter.__init__..6s*JJJDAqQQJJJr) setissubsetnamed_children ValueErroritemsrsuper__init__r)selfrrorig_return_layerslayersrmodule __class__s rr)z IntermediateLayerGetter.__init__2s=!!**+W+W@T@T@V@V+W+W+WXX GEFF F*JJM4G4G4I4IJJJ !0022  LD&!F4L}$$!$'       /rct}|D]+\}}||}||jvr|j|}|||<,|SN)rr'r)r*xoutrr-out_names rforwardzIntermediateLayerGetter.forwardBs]mm JJLL " "LD&q At)))-d3 !H  r) __name__ __module__ __qualname____doc___versionrr__annotations__r Moduler)r4 __classcell__r.s@rrr s>Hc3hO0bi0S#X04000000 rrr!divisor min_valuerc|||}t|t||dz z|z|z}|d|zkr||z }|S)z This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py Nr g?)maxint)r!r>r?new_vs r_make_divisiblerDLsT  3q7Q;//7:WD E EE sQw  LrDfn.ctjj} tdt |Dn'#t $rtdjddwxYwttjjdtj dtdtdtffd }|S) aDecorates a function that uses keyword only parameters to also allow them being passed as positionals. For example, consider the use case of changing the signature of ``old_fn`` into the one from ``new_fn``: .. code:: def old_fn(foo, bar, baz=None): ... def new_fn(foo, *, bar, baz=None): ... Calling ``old_fn("foo", "bar, "baz")`` was valid, but the same call is no longer valid with ``new_fn``. To keep BC and at the same time warn the user of the deprecation, this decorator can be used: .. code:: @kwonly_to_pos_or_kw def new_fn(foo, *, bar, baz=None): ... new_fn("foo", "bar, "baz") c3@K|]\}}|j|jk|VdSr0)kind KEYWORD_ONLY)ridxparams r z&kwonly_to_pos_or_kw..zs>& & C eN`@`@`C@`@`@`@`& & rz-Found no keyword-only parameter on function ''Nargskwargsrc 0|d|d}}|rxtt|}tjdt t |dd|||i|S)NzUsing zand ) separate_lastz as positional parameter(s) is deprecated since 0.13 and may be removed in the future. Please use keyword parameter(s) instead.)dictzipwarningswarnr tuplekeysupdate)rOrPkeyword_only_argskeyword_only_kwargsrFkeyword_only_paramskeyword_only_start_idxs rwrapperz$kwonly_to_pos_or_kw..wrappers"&'>(>'>"?F\F]F]A^  /"&s+>@Q'R'R"S"S  M/B/G/G/I/I)J)JZ`aaa    MM- . . .r4"6"""r)inspect signature parametersnext enumeratevalues StopIteration TypeErrorr5rW functoolswrapsrrE)rFparamsr^r\r]s` @@rkwonly_to_pos_or_kwrj_s"0 r " " -Fb!%& & "+FMMOO"<"<& & & " "  bbbV VVVWW]aab  1" 5 5 @AABXBYBYZ_R #s #c #a # # # # # # # # Ns 8A$A;W)boundMVweightsc ddtdtfdtdtfffd }|S)a0Decorates a model builder with the new interface to make it compatible with the old. In particular this handles two things: 1. Allows positional parameters again, but emits a deprecation warning in case they are used. See :func:`torchvision.prototype.utils._internal.kwonly_to_pos_or_kw` for details. 2. Handles the default value change from ``pretrained=False`` to ``weights=None`` and ``pretrained=True`` to ``weights=Weights`` and emits a deprecation warning with instructions for the new interface. Args: **weights (Tuple[str, Union[Optional[W], Callable[[Dict[str, Any]], Optional[W]]]]): Deprecated parameter name and default value for the legacy ``pretrained=True``. The default value can be a callable in which case it will be called with a dictionary of the keyword arguments. The only key that is guaranteed to be in the dictionary is the deprecated parameter name passed as first element in the tuple. All other parameters should be accessed with :meth:`~dict.get`. builder.rcttjdtdtdtffd }|S)NrOrPrc D]B\}\}}t}|||}||vr||vs2t|tst|t r|dks|h||u}|r||x||<}n||}|rIt|r ||n|} t| tstd j nd} |stj d|d|dd|d|d| d } |r| d |dt| j d } tj | ||=| ||<D |i|S) NlegacyzNo weights available for model The parameter 'zI' is deprecated since 0.13 and may be removed in the future, please use 'z ' instead.z2Arguments other than a weight enum or `None` for 'zm' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `=z`.z You can also use `z-.DEFAULT` to get the most up-to-date weights.) r'objectget isinstancerrpopcallabler&r5rUrVtype) rOrP weights_parampretrained_paramdefaultsentinel weights_argpretrained_positionalpretrained_argdefault_weights_argmsgrqros r inner_wrapperzEhandle_legacy_interface..outer_wrapper..inner_wrappersA?Fmmoo4 <4 <: : 0'"88$jjAA "&005EV5S5S!+{;;6T";446T9D9P9P"* )48(C%(>AG =@Y@YYF+,~~%+,<%=N!/=Eg=N=N*[''&///T['%&9;GG_()]7K[)])]^^^_+/',MA*:AA'4AAA nnnFSnnVinnn "??=??4H[C\C\Ce??? c"""+,(;}%%7D+F++ +r)rjrgrhrrm)rqrros` r outer_wrapperz.handle_legacy_interface..outer_wrapperse   ! !7 ,7 ,7 ,7 ,7 ,7 ,7 ,7 ,7 , " !  7 ,rr)rrm)rors` rhandle_legacy_interfacersJ$<xQ/<HS!V4D<<<<<<| rrPrL new_valuec r||vr-|||krtd|d|d||ddS|||<dSNruz' expected value z but got z instead.r&)rPrLrs r_ovewrite_named_paramrsb  %=I % %sussyss[abg[hssstt t & %"u ractualexpectedc H|||krtd|d|d|d|Srr)rLrrs r_ovewrite_value_paramrsC  X  kukkxkkZ`kkkll l OrceZdZfdZxZS) _ModelURLscntjdt|S)NzAccessing the model URLs via the internal dictionary of the module is deprecated since 0.13 and may be removed in the future. Please access them via the appropriate Weights Enum instead.)rUrVr( __getitem__)r*itemr.s rrz_ModelURLs.__getitem__s7  e   ww""4(((r)r5r6r7rr<r=s@rrrs8)))))))))rrr0)$rgr_rU collectionsrtypingrrrrrr r torchr _utilsr _apir ModuleDictrfloatrBrDrErjrkr;rmrnrrrrrSrrrrrs}######GGGGGGGGGGGGGGGGGG$$$$$$<<<<<bm<<<~  u s x} PS      GCLL1HS!V,1#q&1A1111h GC{### GCry!!! GCLLPuS% XtTWY\T\~N^`hij`kNkEl8l2m-m'nPPPPf"$sCx."""t""""hqkQ1))))))))))r