a  dx&@sdZddlZddlZddlmZddlmZmZmZm Z m Z ddl m Z ddZ e fd d Zd d Zd dZddZGdddZgdfddZgddfddZgdfddZgdfddZdS)zM Model / state_dict utils Hacked together by / Copyright 2020 Ross Wightman N)FrozenBatchNorm2d)BatchNormAct2dSyncBatchNormActFrozenBatchNormAct2dfreeze_batch_norm_2dunfreeze_batch_norm_2d)ModelEmacCs,t|trt|jSt|dr$|jS|SdS)Nmodule) isinstancer unwrap_modelZemahasattrr )modelrY/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/timm/utils/model.pyr s  r cCs ||SN)Z state_dict)rZ unwrap_fnrrrget_state_dictsrcCst|jgdddS)zA calculate average channel square mean of output activations rZaxisr)torchmeanitemrinputoutputrrravg_sq_ch_meansrcCst|jgddSz> calculate average channel variance of output activations rrrrvarrrrrr avg_ch_var sr!cCst|jgddSrrrrrravg_ch_var_residual&sr"c@s(eZdZdZddZddZddZdS) ActivationStatsHookaIterates through each of `model`'s modules and matches modules using unix pattern matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is a match. Arguments: model (nn.Module): model from which we will extract the activation stats hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string matching with the name of model's modules. hook_fns (List[Callable]): List of hook functions to be registered at every module in `layer_names`. Inspiration from https://docs.fast.ai/callback.hook.html. Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example on how to plot Signal Propogation Plots using `ActivationStatsHook`. cCsb||_||_||_t|t|kr*tdtdd|D|_t||D]\}}|||qHdS)Nz_Please provide `hook_fns` for each `hook_fn_locs`, their lengths are different.css|]}|jgfVqdSr)__name__).0hook_fnrrr Ez/ActivationStatsHook.__init__..) r hook_fn_locshook_fnslen ValueErrordictstatszip register_hook)selfrr)r* hook_fn_locr&rrr__init__>szActivationStatsHook.__init__csfdd}|S)Ncs"|||}jj|dSr)r.r$append)r rroutr&r1rrappend_activation_statsJs zAActivationStatsHook._create_hook..append_activation_statsr)r1r&r7rr6r _create_hookIsz ActivationStatsHook._create_hookcCs6|jD]&\}}t||s q |||q dSr)r named_modulesfnmatchZregister_forward_hookr8)r1r2r&namer rrrr0Ps z!ActivationStatsHook.register_hookN)r$ __module__ __qualname____doc__r3r8r0rrrrr#,s r#)rr@cCs*tdd|}t|||d}||}|jS)a Extract average square channel mean and variance of activations during forward pass to plot Signal Propogation Plots (SPP). Paper: https://arxiv.org/abs/2101.08692 Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 gg?)r)r*)rnormalr#r.)rr)r*Z input_shapexhook_rrrextract_spp_statsWs rETfreezec s"|dvsJdttjjjjtjjjjttfr:t dt|t rJ|g}|}fdd|D}t |s|t t \}}t ||D]\}}|D]}|dkrdnd|_q|rd d }|dkrt|} t|tjjjjtjjjjttfr||| qt|} t|ttfr||| qd S) a4 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"`. )rFunfreezez,`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 r(z$_freeze_unfreeze..rFFTcSsD|dd}t|dkr4||d|d|n |||dS)N.rr)rsplitr+rHZ add_module)r r; submodulesplitrrr_add_submodules  z(_freeze_unfreeze.._add_submoduleN)r rnnmodulesZ batchnormZ BatchNorm2dZ SyncBatchNormrrAssertionErrorstrr+listr/Znamed_children parametersZ requires_gradrrrr) rK submodulesinclude_bn_running_statsmoder9nrIprQresrrJr_freeze_unfreezeisB       r^cCst|||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) rFrYrZNr^rKrXrYrrrrFscCst|||dddS)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`. rGr_Nr`rarrrrGs rG)r>r:rZtorchvision.ops.miscrZ timm.layersrrrrrZ model_emar r rrr!r"r#rEr^rFrGrrrrs    / E"