a  d) @sddlZddlZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z mZddlZddlmZddlmZgdZd)ejed d d Zd*e ejeeejdddZd+ejeeedddZd,ejeeedddZdZd-e e eefee e feedddZd.ejeedddZd/ejeedddZd0e e eejfeeee ed!ffeee e ejfd"d#d$Z d1d%d&Z!d'd(Z"dS)2N) defaultdict)chain)AnyCallableDictIteratorTupleTypeUnion)nn) checkpoint) model_parameters named_apply named_modulesnamed_modules_with_paramsadapt_input_convgroup_with_matcher group_modulesgroup_parametersflatten_modulescheckpoint_seqFmodelZ exclude_headcCs*|rdd|DddS|SdS)NcSsg|]}|qSr).0prr`/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/timm/models/_manipulate.py z$model_parameters..) parametersrrrrr sr T)fnmodule depth_first include_rootreturncCsf|s|r|||d|D]0\}}|r6d||fn|}t||||ddq|rb|rb|||d|S)N)r#name.T)r"r#r'r$r%)named_childrenjoinr)r"r#r'r$r% child_name child_modulerrrrs  rr#r'r$r%ccsf|s|r||fV|D]4\}}|r4d||fn|}t|||ddEdHq|rb|rb||fVdSNr(Tr-)r)r*rr#r'r$r%r+r,rrrr(s rccsr|jr|s|r||fV|D]4\}}|r:d||fn|}t|||ddEdHq |jrn|rn|rn||fVdSr.) _parametersr)r*rr/rrrr8s r)i) named_objects group_matcher return_valuesreversecsjttrg}tD]h\}\}}|dur0qt|ttfrj|D]$}|t|d|f|dfg7}qBq|t||fdfg7}q|fdd} tt} |D]"\} } | | |  |r| n| qtt} d}t t dd| D]:} |dks| dt dkr|d7}| || | q|rf|r6Jdi}| D]\}}|D]}|||<qNqB|S| S) Nrc stttfrbD]D\}}}||}|r|||f}ttttt d|SqtdfS|}t|t j j s~|fSt|SdS)Ninf) isinstancelisttuplematchgroupsmapfloatr from_iterablefilter collectionsabcIterable)r'Zmatch_fnprefixsuffixrpartsordr2rr _get_grouping`s   z)group_with_matcher.._get_groupingcSs|duS)Nr)xrrrwrz$group_with_matcher..z-reverse mapping only sensible for name output)r7dict enumerateitemsr9r8recompilerappendsortedr?keysMATCH_PREV_GROUPextend)r1r2r3r4ZcompiledZ group_ordinalZ group_nameZmspecZsspecrIgroupingkvZlayer_id_to_paramZlidZparam_to_layer_idZlmnrrHrrKs8 $  r)r#r3r4cCst||||dSN)r3r4)rZnamed_parametersr#r2r3r4rrrrs rcCstt||||dSr[)rrr\rrrrs rr5 sequential.)rdepthrC module_typesccst|t}t|tr6|dkr.tjtjtjf}ntjf}|D]v\}}|rzt||rzt||d|rh|fn||dEdHq:|r||f}||fVq:|rd ||g}||fVq:dS)N containerr5)rCr_r() r7r9strr SequentialZ ModuleListZ ModuleDictrr)r*)rr^rCr_Zprefix_is_tupler'r#rrrrs&      rc Csdd}t|tjjr|}|r,t|}t|ttfsBt|}t |}|rV|d8}d}t d||D]0} t | |d|d}t || ||||d}qf|r||dt |d||S|S)aA helper function for checkpointing sequential models. Sequential models execute a list of modules/functions in order (sequentially). Therefore, we can divide such a sequence into segments and checkpoint each segment. All segments except run in :func:`torch.no_grad` manner, i.e., not storing the intermediate activations. The inputs of each checkpointed segment will be saved for re-running the segment in the backward pass. See :func:`~torch.utils.checkpoint.checkpoint` on how checkpointing works. .. warning:: Checkpointing currently only supports :func:`torch.autograd.backward` and only if its `inputs` argument is not passed. :func:`torch.autograd.grad` is not supported. .. warning: At least one of the inputs needs to have :code:`requires_grad=True` if grads are needed for model inputs, otherwise the checkpointed part of the model won't have gradients. Args: functions: A :class:`torch.nn.Sequential` or the list of modules or functions to run sequentially. x: A Tensor that is input to :attr:`functions` every: checkpoint every-n functions (default: 1) flatten (bool): flatten nn.Sequential of nn.Sequentials skip_last (bool): skip checkpointing the last function in the sequence if True preserve_rng_state (bool, optional, default=True): Omit stashing and restoring the RNG state during each checkpoint. Returns: Output of running :attr:`functions` sequentially on :attr:`*inputs` Example: >>> model = nn.Sequential(...) >>> input_var = checkpoint_seq(model, input_var, every=2) csfdd}|S)Ncs$tdD]}||}q|S)Nr5)range)_xjend functionsstartrrforwardsz5checkpoint_seq..run_function..forwardr)rirgrhrjrrfr run_functionsz$checkpoint_seq..run_functionr5rJr)preserve_rng_state) r7torchr rbchildrenrr>r9r8lenrcminr ) rhrKZeveryflattenZ skip_lastrlrkZnum_checkpointedrgrirrrrs", rcCs|j}|}|j\}}}}|dkrx|dkrh|jdddksBJ|||dd||}|jddd}q|jddd}nh|dkr|dkrtdnNtt|d}| d|ddddd|ddddf}|dt|9}| |}|S) Nr5rF)ZdimZkeepdimTz*Weight format not supported by conversion.) Zdtyper=shapeZreshapesumNotImplementedErrorintmathceilrepeatto)Zin_chansZ conv_weightZ conv_typeOIJKrzrrrrs" , r)F)r!TF)r!TF)r!TF)FF)FF)FF)r5r!r])r5FFT)#collections.abcr@rxrPr itertoolsrtypingrrrrrr r rmr Ztorch.utils.checkpointr __all__Moduleboolr rrarrrUrrrrwrrrrrrrs  $        @   ! F