o پi|+ @s<ddlZddlZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z mZmZddlZddlZddlmZddlmZddlmZgdZdde e eefdee e fded efd!d"Z d>dejded efd#d$Z d>dejded efd%d&Z! '  (d?de e eejfd)e"d*eee ed+ffd,eee eejffd-d.Z#dd/d0e efd1d2Z$ ' d@d3e"d4ed5ed0e efd6d7Z%d8e"d9edefd:d;Z&dS)AN) defaultdict)chain)AnyCallableDictIteratorOptionalTupleTypeUnion)nn)Tensor)use_reentrant_ckpt) model_parameters named_apply named_modulesnamed_modules_with_paramsadapt_input_convgroup_with_matcher group_modulesgroup_parametersflatten_modulescheckpoint_seq checkpointFmodel exclude_headcCs&|rdd|DddS|S)NcSsg|]}|qSr).0prrK/home/ubuntu/.local/lib/python3.10/site-packages/timm/models/_manipulate.py sz$model_parameters..) parameters)rrrrrrsrTfnmodule depth_first include_rootreturncCsf|s |r |||d|D]\}}|rd||fn|}t||||ddq|r1|r1|||d|S)N)r%name.T)r$r%r)r&r')named_childrenjoinr)r$r%r)r&r' child_name child_modulerrrrs  rr)ccsp|s |r ||fV|D]\}}|rd||fn|}t|||ddEdHq|r4|r6||fVdSdSdSNr*T)r%r)r&r')r+r,rr%r)r&r'r-r.rrrr,s rccs|jr |s |r ||fV|D]\}}|rd||fn|}t|||ddEdHq|jr:|r<|r>||fVdSdSdSdSr/) _parametersr+r,rr0rrrr<s  r)i named_objects group_matcher return_valuesreversecs^ttrDg}tD]4\}\}}|durq t|ttfr5|D]}|t|d|f|dfg7}q!q |t||fdfg7}q |fdd} tt} |D]\} } | | |  |r^| n| qPtt} d}t t dd| D]} |dks| dt dkr|d7}| || | qs|r|rJdi}| D] \}}|D]}|||<qq|S| S) Nrc stttfr1D]"\}}}||}|r+|||f}ttttt d|Sq tdfS|}t|t j j s?|fSt|S)Ninf) isinstancelisttuplematchgroupsmapfloatr from_iterablefilter collectionsabcIterable)r)match_fnprefixsuffixrpartsordr3rr _get_groupingds  z)group_with_matcher.._get_groupingcSs|duS)Nr)xrrr{sz$group_with_matcher..z-reverse mapping only sensible for name output)r8dict enumerateitemsr:r9recompilerappendsortedr@keysMATCH_PREV_GROUPextend)r2r3r4r5compiled group_ordinal group_namemspecsspecrKgroupingkvlayer_id_to_paramlidparam_to_layer_idlmnrrJrrOs< "    rcCst||||dSN)r4r5)rnamed_parametersr%r3r4r5rrrr rcCstt||||dSrf)rrrhrrrrrirr6 sequentialdepthrE. module_typesccst|t}t|tr|dkrtjtjtjf}ntjf}|D];\}}|r>t||r>t||d|r5|fn||dEdHq|rK||f}||fVq|rTd ||g}||fVqdS)N containerr6)rErlr*) r8r:strr Sequential ModuleList ModuleDictrr+r,)rrkrErlprefix_is_tupler)r%rrrrs*       r use_reentrantrtcOs.|durt}tjjj|g|Rd|i|S)z~ checkpoint wrapper fn A thin wrapper around torch.utils.checkpoint.checkpoint to default use_reentrant to False Nrt)rtorchutilsr)functionrtargskwargsrrrrs reveryflatten skip_lastc Cs|durt}dd}t|tjjr|}|rt|}t|tt fs(t|}t |}|r2|d8}d}t d||D]} t | |d|d}tj jj|| ||||d}q:|rf||dt |d||S|S)a,A 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: flatten nn.Sequential of nn.Sequentials skip_last: skip checkpointing the last function in the sequence if True use_reentrant: Use re-entrant checkpointing Returns: Output of running :attr:`functions` sequentially on :attr:`*inputs` Example: >>> model = nn.Sequential(...) >>> input_var = checkpoint_seq(model, input_var, every=2) Ncsfdd}|S)Ncs$tdD]}||}q|S)Nr6)range)_xjend functionsstartrrforwardsz5checkpoint_seq..run_function..forwardr)rrrrrrr run_functionsz$checkpoint_seq..run_functionr6rLrrs)rr8rur rochildrenrr?r:r9lenr}minrvr) rrMrzr{r|rtrnum_checkpointedrrrrrrs.+  rin_chans conv_weightcCs|j}|}|j\}}}}|dkr<|dkr4|jdddks!J|||dd||}|jddd}n;|jddd}n3|dkro|dkrHtdtt|d}| d|ddddd|ddddf}|dt|9}| |}|S) Nr6rF)dimkeepdimTz*Weight format not supported by conversion.) dtyper>shapereshapesumNotImplementedErrorintmathceilrepeatto)rr conv_typeOIJKrrrrr s", r)F)r#TF)FF)r6r#rj)r6FFN)'collections.abcrArrRr itertoolsrtypingrrrrrr r r rutorch.utils.checkpointr r timm.layersr__all__ModuleboolrrrnrrrWrrrrrrrrrrrrs  (         @     !  L