a Ú þd,ã@sŽddlmZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z ddl Z ddlZGdd „d ƒZdd d „Zdd d„ZdS)é)Úabsolute_importN)Únn)Ú OrderedDict)ÚVariable)Úzoom)Útqdmc@s¸eZdZdd„Zddddddddddd d d d gfd d„Zd-dd„Zdd„Zdd„Zdd„Zdd„Z dd„Z dd„Z dd„Z dd „Z d!d"„Zd#d$„Zd%d&„Zd'd(„Zd)d*„Zd.d+d,„ZdS)/ÚTrainercCs|jS©N)Ú model_name©Úself©r úV/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/lpips/trainer.pyÚnamesz Trainer.nameÚlpipsZalexZLabFNTg-Cëâ6?çà?z0.1rc Csª||_||_||_||_| |_| |_d||f|_|jdkr`tj| || d| ||d|dd |_np|jdkr~tj||dd|_nR|jdvr tj ||d |_d |_n0|jd vrÂtj ||d |_d |_nt d |jƒ‚t |j  ¡ƒ|_ |jr4t ¡|_|j t |jj  ¡ƒ7_ | |_| |_tjj|j | | dfd|_n |j ¡|r„|j |d¡tjj|j|d|_|jr„|jj|dd|_|r¦tdƒt |j¡tdƒdS)aÚ INPUTS model - ['lpips'] for linearly calibrated network ['baseline'] for off-the-shelf network ['L2'] for L2 distance in Lab colorspace ['SSIM'] for ssim in RGB colorspace net - ['squeeze','alex','vgg'] model_path - if None, will look in weights/[NET_NAME].pth colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM use_gpu - bool - whether or not to use a GPU printNet - bool - whether or not to print network architecture out spatial - bool - whether to output an array containing varying distances across spatial dimensions is_train - bool - [True] for training mode lr - float - initial learning rate beta1 - float - initial momentum term for adam version - 0.1 for latest, 0.0 was original (with a bug) gpu_ids - int array - [0] by default, gpus to use z%s [%s]rTF) Z pretrainedÚnetÚversionrÚspatialÚ pnet_randÚ pnet_tuneZ use_dropoutÚ model_pathZ eval_modeZbaseline)rrr)ÚL2Úl2)Úuse_gpuÚ colorspacer)ÚDSSIMZdssimÚSSIMZssimrzModel [%s] not recognized.g+‡ÙÎ÷ï?)ÚlrZbetasr)Z device_ids©Zdevicez----------- Networks initialized -------------z/-----------------------------------------------N)rÚgpu_idsÚmodelrÚis_trainrr rZLPIPSrrÚ ValueErrorÚlistÚ parametersZBCERankingLossÚrankLossrÚold_lrÚtorchZoptimZAdamÚ optimizer_netÚevalÚtorZ DataParallelÚprintZnetworksZ print_network)r r!rrrrrrZprintNetrr"rZbeta1rr r r rÚ initializesL þ       zTrainer.initializecCs|jj|||dS)zö Function computes the distance between image patches in0 and in1 INPUTS in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] OUTPUT computed distances between in0 and in1 )Ú retPerLayer)rÚforward)r Zin0Zin1r.r r rr/VszTrainer.forwardcCs0| ¡|j ¡| ¡|j ¡| ¡dSr )Ú forward_trainr)Z zero_gradÚbackward_trainÚstepÚ clamp_weightsr r r rÚoptimize_parametersas   zTrainer.optimize_parameterscCs>|j ¡D].}t|dƒr |jdkr tj|jjdd|j_q dS)NÚweight)ér6r)Úmin)rÚmodulesÚhasattrZ kernel_sizer(Úclampr5Údata)r Úmoduler r rr3hszTrainer.clamp_weightscCsº|d|_|d|_|d|_|d|_|jr†|jj|jdd|_|jj|jdd|_|jj|jdd|_|jj|jdd|_t|jdd|_t|jdd|_ t|jdd|_ dS) NÚrefÚp0Úp1ÚjudgerrT)Z requires_grad) Z input_refZinput_p0Zinput_p1Ú input_judgerr+r rÚvar_refÚvar_p0Úvar_p1)r r;r r rÚ set_inputms    zTrainer.set_inputcCs|| |j|j¡|_| |j|j¡|_| |j|j|j¡|_t d|jƒ  |j  ¡¡|_ |j  |j|j|j dd¡|_|jS)Nçð?g@)r/rBrCÚd0rDÚd1Úcompute_accuracyrAÚacc_rrÚviewÚsizeZ var_judger&Ú loss_totalr r r rr0}s  zTrainer.forward_traincCst |j¡ ¡dSr )r(ÚmeanrMZbackwardr r r rr1ˆszTrainer.backward_traincCs>||k ¡j ¡ ¡}| ¡ ¡ ¡}||d|d|S)z) d0, d1 are Variables, judge is a Tensor r6)Úcpur;ÚnumpyÚflatten)r rGrHr@Zd1_lt_d0Z judge_perr r rrI‹szTrainer.compute_accuracycCsFtd|jj ¡ ¡fd|jfgƒ}| ¡D]}t ||¡||<q*|S)NrMrJ) rrMr;rOrPrJÚkeysÚnprN)r ZretDictÚkeyr r rÚget_current_errors‘s ÿ zTrainer.get_current_errorscCs”d|jj ¡d}t |jj¡}t |jj¡}t |jj¡}t|||dgdd}t|||dgdd}t|||dgdd}td|fd|fd|fgƒS) Néér6r)Úorderr=r>r?) rBr;rLrZ tensor2imrCrDrr)r Z zoom_factorZref_imgZp0_imgZp1_imgZ ref_img_visZ p0_img_visZ p1_img_visr r rÚget_current_visualsšsþzTrainer.get_current_visualscCsF|jr| |jj|d|¡n| |j|d|¡| |jj|d|¡dS)NÚZrank)rÚ save_networkrr<r&)r ÚpathÚlabelr r rÚsave©sz Trainer.savecCs.d||f}tj ||¡}t | ¡|¡dS)Nú %s_net_%s.pth)Úosr\Újoinr(r^Z state_dict)r Únetworkr\Ú network_labelÚ epoch_labelÚ save_filenameÚ save_pathr r rr[±s zTrainer.save_networkcCs<d||f}tj |j|¡}td|ƒ| t |¡¡dS)Nr_zLoading network from %s)r`r\raÚsave_dirr,Zload_state_dictr(Úload)r rbrcrdrerfr r rÚ load_network·s  zTrainer.load_networkcCsH|j|}|j|}|jjD] }||d<qtdt|j|fƒ||_dS)Nrzupdate lr [%s] decay: %f -> %f)rr'r)Z param_groupsr,Útype)r Z nepoch_decayZlrdrZ param_groupr r rÚupdate_learning_rate½s     zTrainer.update_learning_ratecCs|jSr )Z image_pathsr r r rÚget_image_pathsÈszTrainer.get_image_pathscCs:t tj |jd¡|¡tjtj |jd¡|gdddS)NZ done_flagz%i)Úfmt)rSr^r`r\rargZsavetxt)r Úflagr r rÚ save_doneËszTrainer.save_done)F)F)Ú__name__Ú __module__Ú __qualname__rr-r/r4r3rEr0r1rIrUrYr^r[rirkrlror r r rrs(  þ C    rrZcCsîg}g}g}t| ¡|dD]p}|||d|dƒj ¡ ¡ ¡ ¡7}|||d|dƒj ¡ ¡ ¡ ¡7}||d ¡ ¡ ¡ ¡7}qt |¡}t |¡}t |¡}||kd|||k|||kd}t  |¡t ||||dfS) a Function computes Two Alternative Forced Choice (2AFC) score using distance function 'func' in dataset 'data_loader' INPUTS data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside func - callable distance function - calling d=func(in0,in1) should take 2 pytorch tensors with shape Nx3xXxY, and return numpy array of length N OUTPUTS [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators [1] - dictionary with following elements d0s,d1s - N arrays containing distances between reference patch to perturbed patches gts - N array in [0,1], preferred patch selected by human evaluators (closer to "0" for left patch p0, "1" for right patch p1, "0.6" means 60pct people preferred right patch, 40pct preferred left) scores - N array in [0,1], corresponding to what percentage function agreed with humans CONSTS N - number of test triplets in data_loader ©Údescr=r>r?r@rFr)Úd0sÚd1sÚgtsÚscores) rÚ load_datar;rOrPrQÚtolistrSÚarrayrNÚdict)Ú data_loaderÚfuncrrurvrwr;rxr r rÚscore_2afc_datasetÐs((   (rcCsæg}g}t| ¡|dD]D}|||d|dƒj ¡ ¡ ¡7}||d ¡ ¡ ¡ ¡7}qt |¡}t |¡}t  |¡}||}||} t  | ¡} t  d| ¡} t  | ¡| } | | | } | | | }t   || ¡}|t||dfS)aê Function computes JND score using distance function 'func' in dataset 'data_loader' INPUTS data_loader - CustomDatasetDataLoader object - contains a JNDDataset inside func - callable distance function - calling d=func(in0,in1) should take 2 pytorch tensors with shape Nx3xXxY, and return pytorch array of length N OUTPUTS [0] - JND score in [0,1], mAP score (area under precision-recall curve) [1] - dictionary with following elements ds - N array containing distances between two patches shown to human evaluator sames - N array containing fraction of people who thought the two patches were identical CONSTS N - number of test triplets in data_loader rsr>r?Zsamer6)ÚdsÚsames)rryr;rOrPrzrQrSr{ZargsortZcumsumÚsumrZvoc_apr|)r}r~rr€rwr;rZ sorted_indsZ ds_sortedZ sames_sortedZTPsZFPsZFNsZprecsZrecsZscorer r rÚscore_jnd_datasetós"$       rƒ)rZ)rZ)Ú __future__rrPrSr(rÚ collectionsrZtorch.autogradrZ scipy.ndimagerrrr`rrrƒr r r rÚs      B #