o پiQ)@sdZddlZddlmZddlZddlmZddlmZm Z m Z m Z m Z ddl mZdd Zefd d Zd d ZddZddZGdddZgdfddZgddfddZgdfddZgdfddZd#dejjd ejjfd!d"ZdS)$zM Model / state_dict utils Hacked together by / Copyright 2020 Ross Wightman Ndeepcopy)FrozenBatchNorm2d)BatchNormAct2dSyncBatchNormActFrozenBatchNormAct2dfreeze_batch_norm_2dunfreeze_batch_norm_2d)ModelEmacCs@t|tr t|jSt|drt|jSt|drt|jS|S)Nmodule _orig_mod) isinstancer unwrap_modelemahasattrr r )modelrD/home/ubuntu/.local/lib/python3.10/site-packages/timm/utils/model.pyrs      rcCs ||SN) state_dict)r unwrap_fnrrrget_state_dicts rcCst|jgdddS)zA calculate average channel square mean of output activations raxisr)torchmeanitemrinputoutputrrravg_sq_ch_mean sr$cCt|jgddSz> calculate average channel variance of output activations rrrrvarr r!rrr avg_ch_var&r)cCr%r&r'r!rrravg_ch_var_residual,r*r+c@s(eZdZdZddZddZddZdS) ActivationStatsHookaIterates through each of `model`'s modules and matches modules using unix pattern matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is a match. Arguments: model (nn.Module): model from which we will extract the activation stats hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string matching with the name of model's modules. hook_fns (List[Callable]): List of hook functions to be registered at every module in `layer_names`. Inspiration from https://docs.fast.ai/callback.hook.html. Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example on how to plot Signal Propagation Plots using `ActivationStatsHook`. cCsb||_||_||_t|t|krtdtdd|D|_t||D] \}}|||q$dS)Nz_Please provide `hook_fns` for each `hook_fn_locs`, their lengths are different.css|]}|jgfVqdSr)__name__).0hook_fnrrr Ksz/ActivationStatsHook.__init__..) r hook_fn_locshook_fnslen ValueErrordictstatszip register_hook)selfrr1r2 hook_fn_locr/rrr__init__DszActivationStatsHook.__init__csfdd}|S)Ncs"|||}jj|dSr)r6r-append)r r"r#outr/r9rrappend_activation_statsPs zAActivationStatsHook._create_hook..append_activation_statsr)r9r/r?rr>r _create_hookOsz ActivationStatsHook._create_hookcCs6|jD]\}}t||sq|||qdSr)r named_modulesfnmatchregister_forward_hookr@)r9r:r/namer rrrr8Vs  z!ActivationStatsHook.register_hookN)r- __module__ __qualname____doc__r;r@r8rrrrr,2s  r,)rrIcCs*tdd|}t|||d}||}|jS)aExtract average square channel mean and variance of activations during forward pass to plot Signal Propagation Plots (SPP). Paper: https://arxiv.org/abs/2101.08692 Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 gg?)r1r2)rnormalr,r6)rr1r2 input_shapexhook_rrrextract_spp_stats]s rOTfreezec s"|dvsJdttjjjjtjjjjttfrt dt|t r%|g}|}fdd|D}t |s>t t \}}t ||D]K\}}|D] }|dkrSdnd|_qK|rd d }|dkr}t|} t|tjjjjtjjjjttfr|||| qCt|} t|ttfr||| qCd S) a9 Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place. Args: root_module (nn.Module, optional): Root module relative to which the `submodules` are referenced. submodules (list[str]): List of modules for which the parameters will be (un)frozen. They are to be provided as named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list means that the whole root module will be (un)frozen. Defaults to [] include_bn_running_stats (bool): Whether to also (un)freeze the running statistics of batch norm 2d layers. Defaults to `True`. mode (bool): Whether to freeze ("freeze") or unfreeze ("unfreeze"). Defaults to `"freeze"`. )rPunfreezez,`mode` must be one of "freeze" or "unfreeze"zYou have provided a batch norm layer as the `root module`. Please use `timm.utils.model.freeze_batch_norm_2d` or `timm.utils.model.unfreeze_batch_norm_2d` instead.csg|]}|qSr) get_submodule)r.m root_modulerr sz$_freeze_unfreeze..rPFTcSsF|dd}t|dkr||d|d|dS|||dS)N.r r)rsplitr3rR add_module)r rD submodulesplitrrr_add_submodules  z(_freeze_unfreeze.._add_submoduleN)rrnnmodules batchnorm BatchNorm2d SyncBatchNormrrAssertionErrorstrr3listr7named_children parameters requires_gradrr rr) rU submodulesinclude_bn_running_statsmoderAnrSpr\resrrTr_freeze_unfreezeosH        rncCt|||dddS)a Freeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place. Args: root_module (nn.Module): Root module relative to which `submodules` are referenced. submodules (list[str]): List of modules for which the parameters will be frozen. They are to be provided as named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list means that the whole root module will be frozen. Defaults to `[]`. include_bn_running_stats (bool): Whether to also freeze the running statistics of `BatchNorm2d` and `SyncBatchNorm` layers. These will be converted to `FrozenBatchNorm2d` in place. Hint: During fine tuning, it's good practice to freeze batch norm stats. And note that these are different to the affine parameters which are just normal PyTorch parameters. Defaults to `True`. Hint: If you want to freeze batch norm ONLY, use `timm.utils.model.freeze_batch_norm_2d`. Examples:: >>> model = timm.create_model('resnet18') >>> # Freeze up to and including layer2 >>> submodules = [n for n, _ in model.named_children()] >>> print(submodules) ['conv1', 'bn1', 'act1', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'global_pool', 'fc'] >>> freeze(model, submodules[:submodules.index('layer2') + 1]) >>> # Check for yourself that it works as expected >>> print(model.layer2[0].conv1.weight.requires_grad) False >>> print(model.layer3[0].conv1.weight.requires_grad) True >>> # Unfreeze >>> unfreeze(model) rPrirjNrnrUrhrirrrrPs cCro)a Unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place. Args: root_module (nn.Module): Root module relative to which `submodules` are referenced. submodules (list[str]): List of submodules for which the parameters will be (un)frozen. They are to be provided as named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list means that the whole root module will be unfrozen. Defaults to `[]`. include_bn_running_stats (bool): Whether to also unfreeze the running statistics of `FrozenBatchNorm2d` layers. These will be converted to `BatchNorm2d` in place. Defaults to `True`. See example in docstring for `freeze`. rQrpNrqrrrrrrQsrQFrreturncs$|st|}fdd||S)Ncs`|D])\}}t|drt|||nt|dr |n t|dr)||qdS)Nfusereparameterizeswitch_to_deploy)rersetattrrtrurv)rS child_namechild_fuserrr{s     z#reparameterize_model.._fuser)rinplacerrzrreparameterize_models   r})F)rGrBcopyrrtorchvision.ops.miscr timm.layersrrrrr model_emar rrr$r)r+r,rOrnrPrQr]Moduler}rrrrs&    / F#