OrderedDict([('name', 'Eval_monorec'), ('n_gpu', 8), ('timestamp_replacement', '00'), ('models', [OrderedDict([('type', 'MonoRecModel'), ('args', OrderedDict([('inv_depth_min_max', [0.33, 0.0025]), ('checkpoint_location', ['saved/checkpoints/monorec_depth_ref.pth']), ('pretrain_mode', 0), ('pretrain_dropout', 0), ('use_stereo', False), ('use_mono', True), ('use_ssim', 1)]))])]), ('data_loader', OrderedDict([('type', 'KittiOdometryDataloader'), ('args', OrderedDict([('dataset_dir', '../data/dataset'), ('depth_folder', 'image_depth_annotated'), ('batch_size', 2), ('frame_count', 2), ('shuffle', False), ('validation_split', 0), ('num_workers', 8), ('sequences', ['00', '04', '05', '07']), ('target_image_size', [256, 512]), ('use_color', True), ('use_color_augmentation', False), ('use_dso_poses', True), ('lidar_depth', True), ('dso_depth', False), ('return_stereo', False)]))])), ('loss', 'depth_loss'), ('metrics', ['abs_rel_sparse_metric', 'sq_rel_sparse_metric', 'rmse_sparse_metric', 'rmse_log_sparse_metric', 'a1_sparse_metric', 'a2_sparse_metric', 'a3_sparse_metric']), ('evaluater', OrderedDict([('save_dir', 'saved/'), ('max_distance', 80), ('verbosity', 2), ('log_step', 20), ('tensorboard', False)]))])
Ground truth poses are not avaialble for sequence 00.
Ground truth poses are not avaialble for sequence 04.
Ground truth poses are not avaialble for sequence 05.
Ground truth poses are not avaialble for sequence 07.
{'training': True, 'inv_depth_min_max': [0.33, 0.0025], 'cv_depth_steps': 32, 'use_mono': True, 'use_stereo': False, 'use_ssim': 1, 'sfcv_mult_mask': True, 'pretrain_mode': 0, 'pretrain_dropout': 0, 'pretrain_dropout_mode': 0, 'augmentation': None, 'simple_mask': False, 'mask_use_cv': True, 'mask_use_feats': True, 'cv_patch_size': 3, 'no_cv': False, 'depth_large_model': False, 'checkpoint_location': ['saved/checkpoints/monorec_depth_ref.pth'], 'mask_cp_loc': None, 'depth_cp_loc': None, 'freeze_module': (), 'freeze_resnet': True, 'augmenter': None}
{'dataset_dir': '../data/dataset', 'frame_count': 2, 'sequences': ['00', '04', '05', '07'], 'depth_folder': 'image_depth_annotated', 'lidar_depth': True, 'annotated_lidar': True, 'dso_depth': False, 'target_image_size': [256, 512], 'use_index_mask': (), 'offset_d': 0, 'length': 8634, 'dilation': 1, 'use_color': True, 'use_dso_poses': True, 'use_color_augmentation': False, 'return_stereo': False, 'return_mvobj_mask': False}
Warning: The number of GPU's configured to use is 8, but only 1 are available on this machine.
Evaluating [0/8634 (0%)] Loss: 0.000000 Metrics: [0.03889954835176468, 0.15210138261318207, 2.2974374294281006, 0.06517335772514343, 0.9886399507522583, 0.9977246522903442, 0.9988455772399902]
Evaluating [40/8634 (0%)] Loss: 0.000000 Metrics: [0.03358750604093075, 0.09767412642637889, 1.893045533271063, 0.05471042454952285, 0.9915229025341216, 0.9984956922985259, 0.9994336253120786]
Evaluating [80/8634 (1%)] Loss: 0.000000 Metrics: [0.03293997662641653, 0.08875621073856586, 1.7992511987686157, 0.05379983710079658, 0.9918638991146553, 0.9984056615247959, 0.9994241726107713]
Evaluating [120/8634 (1%)] Loss: 0.000000 Metrics: [0.04002390554571738, 0.12060390070813601, 2.011894046283159, 0.06291638100977803, 0.9869123765679656, 0.9977484316122337, 0.9992948418757954]
Evaluating [160/8634 (2%)] Loss: 0.000000 Metrics: [0.03897419774237974, 0.12317223189237678, 2.0787455608815324, 0.06364018345872562, 0.9868870635091522, 0.997635613988947, 0.9991389395278177]
Evaluating [200/8634 (2%)] Loss: 0.000000 Metrics: [0.04070509789456235, 0.12746708002863544, 2.0777154240277733, 0.06741223790415443, 0.985897951196916, 0.9973767547324153, 0.9989329954185108]
Evaluating [240/8634 (3%)] Loss: 0.000000 Metrics: [0.041888178195342546, 0.13049679960716853, 2.125969006995524, 0.06862825084446876, 0.9852578043937683, 0.997312118199246, 0.9989322813089229]
Evaluating [280/8634 (3%)] Loss: 0.000000 Metrics: [0.042014615517770144, 0.13544157039083488, 2.1835166993716086, 0.07031662862562964, 0.9847687191151558, 0.9970327755238148, 0.9987326581427391]
Evaluating [320/8634 (4%)] Loss: 0.000000 Metrics: [0.04168298928448873, 0.1315943178784403, 2.1314746156242323, 0.07097600800743015, 0.9843861879028889, 0.9967850413381684, 0.9985760535512652]
Evaluating [360/8634 (4%)] Loss: 0.000000 Metrics: [0.04134184997837517, 0.13027346656961336, 2.1255849244186233, 0.07116829686237304, 0.9843599522969999, 0.9965882416588167, 0.99844438825523]
Evaluating [400/8634 (5%)] Loss: 0.000000 Metrics: [0.04135749682747013, 0.13298827487586148, 2.1447495067890605, 0.07232075822145785, 0.9839851197911732, 0.9963632881344847, 0.9983206898418825]
Evaluating [440/8634 (5%)] Loss: 0.000000 Metrics: [0.04384080154560001, 0.14867289868347786, 2.20538890685431, 0.0749497104823859, 0.9805283654329464, 0.9961084515800304, 0.9983504870358635]
Evaluating [480/8634 (6%)] Loss: 0.000000 Metrics: [0.04357530890782344, 0.14646121740526677, 2.1903477625233503, 0.0747823239423922, 0.9804922797373221, 0.9961248495272086, 0.998359700703522]
Evaluating [520/8634 (6%)] Loss: 0.000000 Metrics: [0.04333962197504738, 0.14361002531716194, 2.168438423182316, 0.07406763838054577, 0.9812280524279423, 0.9962364177137499, 0.9983885288238525]
Evaluating [560/8634 (6%)] Loss: 0.000000 Metrics: [0.04947052718375501, 0.22598797155815936, 2.4145189786720955, 0.0824254139780574, 0.9704541996704726, 0.9917799671349576, 0.9968352434473954]
Evaluating [600/8634 (7%)] Loss: 0.000000 Metrics: [0.04975941374996968, 0.22421713063239654, 2.419887606487718, 0.08249196759193046, 0.9704951142947935, 0.9921244269985693, 0.9970039024305503]
Evaluating [640/8634 (7%)] Loss: 0.000000 Metrics: [0.048589953868374273, 0.2154541185642143, 2.3838264544804892, 0.08059359491567746, 0.9717672470945435, 0.9925395116627773, 0.9971747168127992]
Evaluating [680/8634 (8%)] Loss: 0.000000 Metrics: [0.047623330688983466, 0.20833888974401257, 2.3529802550318655, 0.07964613787895423, 0.9726293148183404, 0.9927439950078814, 0.9972364986397304]
Evaluating [720/8634 (8%)] Loss: 0.000000 Metrics: [0.04691044725684083, 0.20144452442546631, 2.3236894300439683, 0.07829160884105267, 0.9737297828177666, 0.9930677214157548, 0.9973698539086656]
Evaluating [760/8634 (9%)] Loss: 0.000000 Metrics: [0.047525017392721386, 0.20193443937212463, 2.3224167742441333, 0.07890933494907351, 0.9735341194107776, 0.9931932581378407, 0.9974329997235396]
Evaluating [800/8634 (9%)] Loss: 0.000000 Metrics: [0.04669988554370522, 0.19581288002672934, 2.28733691372479, 0.07806620928934982, 0.974330086065944, 0.9933118218198381, 0.9974467604237601]
Evaluating [840/8634 (10%)] Loss: 0.000000 Metrics: [0.046180833675268144, 0.19147041697506384, 2.275775022574672, 0.07708203725322126, 0.9751537759343689, 0.9935573345125429, 0.9975393513602485]
Evaluating [880/8634 (10%)] Loss: 0.000000 Metrics: [0.045732495617839486, 0.1870956209798654, 2.25669954017717, 0.07606982668680129, 0.9759443683418828, 0.993821063144407, 0.997641743445883]
Evaluating [920/8634 (11%)] Loss: 0.000000 Metrics: [0.045150364924892945, 0.18307599570771876, 2.23800175412358, 0.07557630646416266, 0.9764684525332585, 0.9938954113620998, 0.9976273631584102]
Evaluating [960/8634 (11%)] Loss: 0.000000 Metrics: [0.04545048654094927, 0.1831794030453212, 2.2408911906755886, 0.07580590714350063, 0.9764206414411073, 0.994000120742901, 0.9976919509021259]
Evaluating [1000/8634 (12%)] Loss: 0.000000 Metrics: [0.045050844892175375, 0.18092946700766652, 2.233907596317832, 0.07572162579633519, 0.9767714519938547, 0.9940258722581311, 0.9976693011091616]
Evaluating [1040/8634 (12%)] Loss: 0.000000 Metrics: [0.04478444021745744, 0.17966534105807028, 2.2314534628917526, 0.07602499709991942, 0.9769443969122508, 0.9939790205626021, 0.9976118347695144]
Evaluating [1080/8634 (13%)] Loss: 0.000000 Metrics: [0.044549495763319256, 0.1803768856275324, 2.243750997700224, 0.07638814063422561, 0.9771526364434008, 0.99394930587899, 0.9975574961872066]
Evaluating [1120/8634 (13%)] Loss: 0.000000 Metrics: [0.04436319959208821, 0.17741464189985856, 2.2130555424460754, 0.07608826504273211, 0.9777020665413556, 0.994057264026261, 0.9975731649075812]
Evaluating [1160/8634 (13%)] Loss: 0.000000 Metrics: [0.0444694552955293, 0.1771573862589883, 2.2213395226637798, 0.07601017436964787, 0.9778869018702416, 0.9941836624260408, 0.9976276296756764]
Evaluating [1200/8634 (14%)] Loss: 0.000000 Metrics: [0.04421006465724324, 0.175267902932776, 2.2102302402109157, 0.07572126297432055, 0.9783523767046047, 0.9942848656617861, 0.9976532399753564]
Evaluating [1240/8634 (14%)] Loss: 0.000000 Metrics: [0.044782742680966756, 0.17644312169144888, 2.208744590313154, 0.07616163958193983, 0.9781179990553434, 0.9942773308922895, 0.997675109024785]
Evaluating [1280/8634 (15%)] Loss: 0.000000 Metrics: [0.045815510187129524, 0.17964011600441568, 2.222855191985828, 0.07715945032987505, 0.9771271226186649, 0.994257360836273, 0.9977066891613691]
Evaluating [1320/8634 (15%)] Loss: 0.000000 Metrics: [0.04559056680655155, 0.177664591847678, 2.2172957675900653, 0.07702674170034556, 0.9772884004415434, 0.9943122117133436, 0.9977134812437279]
Evaluating [1360/8634 (16%)] Loss: 0.000000 Metrics: [0.04527389698780843, 0.17590254762930668, 2.2082881797077722, 0.0768807425810552, 0.9775009460736301, 0.9943146490315509, 0.9977062313042023]
Evaluating [1400/8634 (16%)] Loss: 0.000000 Metrics: [0.04639158864769973, 0.18302573839710035, 2.2239385517619645, 0.07793879672119688, 0.9760119772161465, 0.9940288824463707, 0.9976640302002345]
Evaluating [1440/8634 (17%)] Loss: 0.000000 Metrics: [0.046391414426752826, 0.1820848087280286, 2.220034284416416, 0.07769934657377277, 0.9761497601556712, 0.9941224209478593, 0.9977179854984257]
Evaluating [1480/8634 (17%)] Loss: 0.000000 Metrics: [0.04595796276240857, 0.17879581356901228, 2.200143928067726, 0.07693575426192097, 0.9766388082632973, 0.9942506124616152, 0.9977710732886023]
Evaluating [1520/8634 (18%)] Loss: 0.000000 Metrics: [0.045656489137359424, 0.17632172103486612, 2.1892224139045635, 0.07637152359692402, 0.9770261531747124, 0.9943596449694089, 0.9978234756948443]
Evaluating [1560/8634 (18%)] Loss: 0.000000 Metrics: [0.04571851189802764, 0.17499036533178502, 2.1811901247760854, 0.07627206187928989, 0.9772742959204465, 0.9944402339669104, 0.9978560643525801]
Evaluating [1600/8634 (19%)] Loss: 0.000000 Metrics: [0.0455466393432516, 0.17437545203146862, 2.1831202035986084, 0.07615603371226833, 0.9774672700075918, 0.9944928586855065, 0.9978664059019863]
Evaluating [1640/8634 (19%)] Loss: 0.000000 Metrics: [0.04533395650552702, 0.1733886479342245, 2.1802595511859284, 0.07612736264059954, 0.9776601018191255, 0.9945024788452269, 0.9978576538478559]
Evaluating [1680/8634 (19%)] Loss: 0.000000 Metrics: [0.04502384040579997, 0.1719032469893253, 2.1754743069871, 0.07597315705222976, 0.977836811443288, 0.9945277380319612, 0.9978610951891977]
Evaluating [1720/8634 (20%)] Loss: 0.000000 Metrics: [0.044743979637458076, 0.17029677742051608, 2.1724399609986635, 0.07566607080218268, 0.9780683770135443, 0.9945992965869925, 0.9978862671348138]
Evaluating [1760/8634 (20%)] Loss: 0.000000 Metrics: [0.04450989029159882, 0.1685152964351464, 2.1621768560501557, 0.07544389020791119, 0.9782643073939303, 0.9946398693099872, 0.9979055999491732]
Evaluating [1800/8634 (21%)] Loss: 0.000000 Metrics: [0.04444643714311186, 0.16730163740479986, 2.1509921592427674, 0.0753976529573693, 0.9784638414636966, 0.9946766626689331, 0.9979058541812326]
Evaluating [1840/8634 (21%)] Loss: 0.000000 Metrics: [0.04436763886657587, 0.16782794700658413, 2.1603240243904493, 0.07542175226455532, 0.9784861638672836, 0.99467177700401, 0.997919724831493]
Evaluating [1880/8634 (22%)] Loss: 0.000000 Metrics: [0.044158318168092736, 0.16653334903238812, 2.158161093717427, 0.0752754168274172, 0.978572633390599, 0.9946852095828933, 0.9979346718848955]
Evaluating [1920/8634 (22%)] Loss: 0.000000 Metrics: [0.044078749537654026, 0.16607640001779417, 2.15860579617934, 0.07514249799728517, 0.9786553692991354, 0.9947150767035589, 0.9979460593564949]
Evaluating [1960/8634 (23%)] Loss: 0.000000 Metrics: [0.04504043631688535, 0.17050219669360878, 2.169904919270954, 0.07609707988593556, 0.9775525806633096, 0.9945499509360326, 0.9979078242902727]
Evaluating [2000/8634 (23%)] Loss: 0.000000 Metrics: [0.04484151338750904, 0.16928463315704723, 2.1673735031833896, 0.07583840175361543, 0.9777684340943823, 0.9946084783508347, 0.9979265170259314]
Evaluating [2040/8634 (24%)] Loss: 0.000000 Metrics: [0.04459328966052887, 0.1680192558111278, 2.1585437503664315, 0.07566696929359529, 0.9779563797209102, 0.9946279075657118, 0.9979332150137973]
Evaluating [2080/8634 (24%)] Loss: 0.000000 Metrics: [0.04437739006623869, 0.16702682442612424, 2.1542778051208695, 0.07535255014338137, 0.9782040168984822, 0.9946874462561923, 0.9979567871886188]
Evaluating [2120/8634 (25%)] Loss: 0.000000 Metrics: [0.044412266886130355, 0.1660039416083348, 2.144099682316704, 0.07523462097322907, 0.9782653262770254, 0.9947605800448893, 0.9979902973835699]
Evaluating [2160/8634 (25%)] Loss: 0.000000 Metrics: [0.044417179828022055, 0.16561684169902172, 2.1413060646714377, 0.07531742780546034, 0.9782837957391907, 0.9947429729985705, 0.9979696798721581]
Evaluating [2200/8634 (25%)] Loss: 0.000000 Metrics: [0.04425151266333572, 0.16499772624575365, 2.138069577751974, 0.07535256663646944, 0.9783361704646187, 0.9947073025989273, 0.9979452528702357]
Evaluating [2240/8634 (26%)] Loss: 0.000000 Metrics: [0.044228713563996484, 0.16481982204102705, 2.1358591823786313, 0.07538113659987164, 0.9785156358468756, 0.994735339302553, 0.9979359177369076]
Evaluating [2280/8634 (26%)] Loss: 0.000000 Metrics: [0.04434306831095012, 0.16541777427106652, 2.144139716282526, 0.07541343092095633, 0.9784472181962103, 0.9947666484571986, 0.9979558225580729]
Evaluating [2320/8634 (27%)] Loss: 0.000000 Metrics: [0.04423794155165515, 0.16603527800159965, 2.155936578088131, 0.07542167325776061, 0.9785126507230918, 0.9947841942156316, 0.997961071983275]
Evaluating [2360/8634 (27%)] Loss: 0.000000 Metrics: [0.044248585285623636, 0.16684860729192091, 2.1660291604022675, 0.0755186769937833, 0.9784851500182511, 0.9947764278467583, 0.9979625052561909]
Evaluating [2400/8634 (28%)] Loss: 0.000000 Metrics: [0.04413918369945191, 0.16608783113867118, 2.1580205020757637, 0.07563946906672628, 0.9785863293596946, 0.9947438835105133, 0.9979181248480632]
Evaluating [2440/8634 (28%)] Loss: 0.000000 Metrics: [0.04443056739402979, 0.16630522972682185, 2.1497722405278226, 0.0758561893754519, 0.97844212012248, 0.9947390382061426, 0.9979210700000729]
Evaluating [2480/8634 (29%)] Loss: 0.000000 Metrics: [0.04449369184864951, 0.16689125579946182, 2.1544225164039976, 0.0761470263140947, 0.9783780894675244, 0.9946934816147607, 0.9978849495446653]
Evaluating [2520/8634 (29%)] Loss: 0.000000 Metrics: [0.044344804593133696, 0.16605405780670662, 2.1500937410236634, 0.07616153725738284, 0.9784523207549535, 0.9946421126256757, 0.9978710259831585]
Evaluating [2560/8634 (30%)] Loss: 0.000000 Metrics: [0.04410366982128544, 0.16474398461975315, 2.1434320317498416, 0.07589106735170678, 0.9786458886944625, 0.9946701811869381, 0.9978768101527671]
Evaluating [2600/8634 (30%)] Loss: 0.000000 Metrics: [0.04389869371467742, 0.16349726809986181, 2.1360987258085737, 0.0756812074195184, 0.978834285496016, 0.9947067874381398, 0.9978820909728094]
Evaluating [2640/8634 (31%)] Loss: 0.000000 Metrics: [0.04367820905305778, 0.1621009711709147, 2.1276328327256926, 0.07544861547507904, 0.9789957271481354, 0.9947276632502439, 0.9978829023514978]
Evaluating [2680/8634 (31%)] Loss: 0.000000 Metrics: [0.04349269981563402, 0.16099887053460704, 2.122733505780265, 0.07521378671462904, 0.9791469102382304, 0.9947568702217716, 0.9978921064950032]
Evaluating [2720/8634 (32%)] Loss: 0.000000 Metrics: [0.0438011499158737, 0.1624925029323786, 2.130783188001449, 0.07551361783287877, 0.9788171443581843, 0.9947439380466369, 0.9979055584747067]
Evaluating [2760/8634 (32%)] Loss: 0.000000 Metrics: [0.0436859579216001, 0.1617002172434701, 2.127992131155872, 0.07547178054410508, 0.9788987498884181, 0.9947442746870246, 0.9978983709986533]
Evaluating [2800/8634 (32%)] Loss: 0.000000 Metrics: [0.04350949185558041, 0.1604418199719445, 2.1195053594781195, 0.075236127737758, 0.9790848111953163, 0.9947817536186611, 0.9979119525476492]
Evaluating [2840/8634 (33%)] Loss: 0.000000 Metrics: [0.04339119471232082, 0.15922904594746967, 2.109496900936317, 0.0750760920581727, 0.979247919360489, 0.9948127939615511, 0.9979204701588743]
Evaluating [2880/8634 (33%)] Loss: 0.000000 Metrics: [0.04360711243001022, 0.16000394694801837, 2.109009781411586, 0.07522540484179564, 0.9791022580502183, 0.994798651964279, 0.9979351017724963]
Evaluating [2920/8634 (34%)] Loss: 0.000000 Metrics: [0.043502984630947324, 0.15913627464464475, 2.102209153075809, 0.07510584071890529, 0.9792028877279841, 0.9948080681024717, 0.9979450679004885]
Evaluating [2960/8634 (34%)] Loss: 0.000000 Metrics: [0.04332115787165057, 0.1582597217080482, 2.0988634401765087, 0.0748910884788558, 0.9793241635759806, 0.9948335248099075, 0.9979587397327462]
Evaluating [3000/8634 (35%)] Loss: 0.000000 Metrics: [0.04354286128326228, 0.1604152778380716, 2.1111043352909205, 0.0751444430975895, 0.9790648962560294, 0.9948115953201456, 0.9979637376790361]
Evaluating [3040/8634 (35%)] Loss: 0.000000 Metrics: [0.043427994967065384, 0.1603198620263563, 2.10769505476497, 0.07501338422788278, 0.9792063768406905, 0.9948342211696366, 0.9979692172408496]
Evaluating [3080/8634 (36%)] Loss: 0.000000 Metrics: [0.04330133690845069, 0.1596444842250296, 2.102406969904358, 0.07490590369687848, 0.9793235801938754, 0.9948480338496407, 0.9979708471675727]
Evaluating [3120/8634 (36%)] Loss: 0.000000 Metrics: [0.043346963338386856, 0.15962328576625423, 2.1032571746400346, 0.07490735991233285, 0.9793119997187045, 0.9948654155712873, 0.9979831180764955]
Evaluating [3160/8634 (37%)] Loss: 0.000000 Metrics: [0.04320316070633022, 0.15863813549412117, 2.0981762574715828, 0.07474343758842222, 0.9794462852505171, 0.994884145252317, 0.9979851302808029]
Evaluating [3200/8634 (37%)] Loss: 0.000000 Metrics: [0.043025184620513836, 0.15771708753353206, 2.0936310088388774, 0.07453422201826451, 0.9795909746597142, 0.9949046059149195, 0.9979849375835588]
Evaluating [3240/8634 (38%)] Loss: 0.000000 Metrics: [0.042929709059939494, 0.15725273043744584, 2.0910557435295747, 0.07455885566122954, 0.9796629663899238, 0.9948889271923232, 0.997965023335228]
Evaluating [3280/8634 (38%)] Loss: 0.000000 Metrics: [0.04311058277662646, 0.15696751418035979, 2.082929628422753, 0.07467452456889757, 0.9795575439021909, 0.9949095256077234, 0.9979753985701357]
Evaluating [3320/8634 (38%)] Loss: 0.000000 Metrics: [0.043123387392388274, 0.15781340393485038, 2.0883023581685682, 0.07486659255437217, 0.97952289691824, 0.9948720231535634, 0.9979455153099533]
Evaluating [3360/8634 (39%)] Loss: 0.000000 Metrics: [0.043107510874006875, 0.157477545960275, 2.0806557409186084, 0.07494411098503201, 0.9795847001847308, 0.994856269036213, 0.9979213569080596]
Evaluating [3400/8634 (39%)] Loss: 0.000000 Metrics: [0.04352078825314209, 0.16016096243528602, 2.0843698090836975, 0.07540020653249796, 0.9792386053380793, 0.9947454566678042, 0.9978725740588601]
Evaluating [3440/8634 (40%)] Loss: 0.000000 Metrics: [0.04372012060198376, 0.16084476221160166, 2.089695447132658, 0.07556914361945978, 0.9791616095075214, 0.9947622425330371, 0.9978841088803129]
Evaluating [3480/8634 (40%)] Loss: 0.000000 Metrics: [0.04373117515550887, 0.16082868886611915, 2.090526527197179, 0.07564654518164896, 0.9790595211495209, 0.9947562503924252, 0.9978820798316268]
Evaluating [3520/8634 (41%)] Loss: 0.000000 Metrics: [0.04369339590584399, 0.16024135577575446, 2.087901359522363, 0.07552353281491796, 0.979177309531234, 0.994786810495852, 0.9978942833523531]
Evaluating [3560/8634 (41%)] Loss: 0.000000 Metrics: [0.04381362475551025, 0.16098491916749866, 2.093945570235705, 0.07563479741065773, 0.9790611957847771, 0.9947944370834162, 0.9979086519291668]
Evaluating [3600/8634 (42%)] Loss: 0.000000 Metrics: [0.04367161439581754, 0.16003270220965032, 2.0894859922653697, 0.07537776818380033, 0.9792134961374993, 0.9948351362690139, 0.9979262236818084]
Evaluating [3640/8634 (42%)] Loss: 0.000000 Metrics: [0.04355270334840115, 0.15928134206786776, 2.0869159170849385, 0.07521666804679406, 0.9793058619729639, 0.9948554425236944, 0.997939816384051]
Evaluating [3680/8634 (43%)] Loss: 0.000000 Metrics: [0.04373483589988471, 0.16255476327369162, 2.09239738897938, 0.07551726858328374, 0.9791718613253153, 0.9948182214154684, 0.9979261094820623]
Evaluating [3720/8634 (43%)] Loss: 0.000000 Metrics: [0.04370894012617399, 0.1623577607504339, 2.091537864601529, 0.07546979917162276, 0.9791877033055821, 0.9948289075056627, 0.9979290097455451]
Evaluating [3760/8634 (44%)] Loss: 0.000000 Metrics: [0.04359797722323818, 0.1617981468301102, 2.0888319514901763, 0.07531949807827869, 0.9793013323500459, 0.9948478923840196, 0.9979364390008187]
Evaluating [3800/8634 (44%)] Loss: 0.000000 Metrics: [0.0434858684145357, 0.16095764567190757, 2.0849660481985213, 0.07507867687261775, 0.9794451580556802, 0.9948920796006557, 0.9979545183711024]
Evaluating [3840/8634 (44%)] Loss: 0.000000 Metrics: [0.04339812785573207, 0.1604236408366101, 2.0831811079316185, 0.07502468932131068, 0.9795229860980458, 0.9948904742367997, 0.9979449522675232]
Evaluating [3880/8634 (45%)] Loss: 0.000000 Metrics: [0.0433525453172413, 0.1602673562495964, 2.085152195324178, 0.07499879297101271, 0.9795468106888914, 0.9949008339400883, 0.9979485865415586]
Evaluating [3920/8634 (45%)] Loss: 0.000000 Metrics: [0.04338933990803803, 0.1600564498243467, 2.082041074401684, 0.07510045667172754, 0.979510087282423, 0.9948860809609696, 0.9979491600389689]
Evaluating [3960/8634 (46%)] Loss: 0.000000 Metrics: [0.04341075639534273, 0.15898784183074824, 2.0693470508025666, 0.07497454999538457, 0.9796599675344133, 0.9949313003206903, 0.9979661122408255]
Evaluating [4000/8634 (46%)] Loss: 0.000000 Metrics: [0.04330625298662462, 0.15830326643118497, 2.0647945400478243, 0.07477476475001692, 0.9798032240769912, 0.994969297027302, 0.9979794581254562]
Evaluating [4040/8634 (47%)] Loss: 0.000000 Metrics: [0.04313966518803465, 0.1572415995117109, 2.057593057262962, 0.07447296275560482, 0.979967217230667, 0.9950099711604545, 0.9979960743535101]
Evaluating [4080/8634 (47%)] Loss: 0.000000 Metrics: [0.043001816228820613, 0.15632007185630736, 2.0519528283259847, 0.07425075242663645, 0.980111565647144, 0.9950421090689084, 0.9980075529309711]
Evaluating [4120/8634 (48%)] Loss: 0.000000 Metrics: [0.04286949425548937, 0.15533175342569416, 2.045178366887344, 0.07400696314889386, 0.9802617145822443, 0.9950778934632847, 0.9980216135046309]
Evaluating [4160/8634 (48%)] Loss: 0.000000 Metrics: [0.042813232348464764, 0.15463152294195312, 2.041425549943879, 0.0738815540668914, 0.9803645987169265, 0.9951053620072181, 0.9980348068782195]
Evaluating [4200/8634 (49%)] Loss: 0.000000 Metrics: [0.04273592401517538, 0.15410891958095244, 2.0381542317700467, 0.07383936534377293, 0.9804200913609918, 0.9951150424703991, 0.9980353147344666]
Evaluating [4240/8634 (49%)] Loss: 0.000000 Metrics: [0.042604899337758496, 0.15317522937265954, 2.0315395132206007, 0.07364422559583597, 0.9805258683357976, 0.9951419453472531, 0.9980452736837018]
Evaluating [4280/8634 (50%)] Loss: 0.000000 Metrics: [0.042475754458636446, 0.15228551673880808, 2.025779106415415, 0.07344437597275115, 0.9806346565081326, 0.9951717263346017, 0.9980605473478282]
Evaluating [4320/8634 (50%)] Loss: 0.000000 Metrics: [0.04239159866250372, 0.15167345395005727, 2.024008193097695, 0.07330698242573923, 0.9807266009069052, 0.9952003282840911, 0.9980704783014211]
Evaluating [4360/8634 (50%)] Loss: 0.000000 Metrics: [0.04235004991333947, 0.1513567254893526, 2.024096356625406, 0.07322687340378488, 0.9807763694077115, 0.9952224529101071, 0.9980817001820267]
Evaluating [4400/8634 (51%)] Loss: 0.000000 Metrics: [0.04237378497135363, 0.15166196829450235, 2.026697354498694, 0.07324014120362445, 0.9807766029532526, 0.995234587906383, 0.9980887441622133]
Evaluating [4440/8634 (51%)] Loss: 0.000000 Metrics: [0.04256538619555261, 0.15222717583253276, 2.0258788893427844, 0.07338935341426028, 0.9806484018881436, 0.995229147834211, 0.9980948359136482]
Evaluating [4480/8634 (52%)] Loss: 0.000000 Metrics: [0.04253393207917332, 0.15193757298121968, 2.025217651576136, 0.07335839475912741, 0.980649875057217, 0.9952312134994241, 0.9980996150568308]
Evaluating [4520/8634 (52%)] Loss: 0.000000 Metrics: [0.04243396255791135, 0.15121566062016278, 2.021415170079467, 0.0731730202607537, 0.9807463792877248, 0.9952596581019115, 0.9981125680180263]
Evaluating [4560/8634 (53%)] Loss: 0.000000 Metrics: [0.042423814777668656, 0.15292326307045895, 2.029304225786586, 0.07325864174304851, 0.9807819939924405, 0.9952474093029552, 0.9980936134556624]
Evaluating [4600/8634 (53%)] Loss: 0.000000 Metrics: [0.04250930549122753, 0.15530108721009653, 2.039127026211433, 0.0734672655895035, 0.9807232908195436, 0.9952231334220425, 0.9980740876417479]
Evaluating [4640/8634 (54%)] Loss: 0.000000 Metrics: [0.042520634350894544, 0.15629378379110323, 2.044617196754462, 0.07353506069060596, 0.9807754279158846, 0.9952171491374747, 0.9980630202572801]
Evaluating [4680/8634 (54%)] Loss: 0.000000 Metrics: [0.042588786831120044, 0.15784545590331645, 2.051644861621767, 0.07370594102652192, 0.9807739240498891, 0.9951764308250343, 0.9980234000632927]
Evaluating [4720/8634 (55%)] Loss: 0.000000 Metrics: [0.04267449194485734, 0.15885652614972795, 2.059411378645988, 0.07388711476928095, 0.9806674334115267, 0.9951347786963163, 0.9980074757996478]
Evaluating [4760/8634 (55%)] Loss: 0.000000 Metrics: [0.04282690521763033, 0.16111005280058907, 2.073900299330611, 0.07420916534750614, 0.9805153971056836, 0.9950556937059101, 0.9979624512665215]
Evaluating [4800/8634 (56%)] Loss: 0.000000 Metrics: [0.04291583483620566, 0.1629615757165974, 2.0856488043444696, 0.07461404816240928, 0.9803239279913436, 0.9949336376154437, 0.9978749443222612]
Evaluating [4840/8634 (56%)] Loss: 0.000000 Metrics: [0.042808089854610404, 0.16210600499560743, 2.080652446354878, 0.07438462735674983, 0.9804532780769393, 0.994968909387104, 0.9978894264872337]
Evaluating [4880/8634 (57%)] Loss: 0.000000 Metrics: [0.042723778041597764, 0.16157043992935854, 2.07779102245348, 0.07425736423333079, 0.9805357072110159, 0.994982856260563, 0.9978967746082558]
Evaluating [4920/8634 (57%)] Loss: 0.000000 Metrics: [0.042915216184265484, 0.16207918559557924, 2.0786466216324113, 0.07437326540668676, 0.9803310048042493, 0.9949919037923538, 0.9979046351877667]
Evaluating [4960/8634 (57%)] Loss: 0.000000 Metrics: [0.04312604311395681, 0.16319577458679843, 2.085561444758408, 0.07459430062466935, 0.9801976551263863, 0.9949679718484613, 0.9979010651016466]
Evaluating [5000/8634 (58%)] Loss: 0.000000 Metrics: [0.04310526030777884, 0.16273537086920947, 2.0834050825813777, 0.07454713095859831, 0.9802371362408177, 0.9949782055790355, 0.9979080432226829]
Evaluating [5040/8634 (58%)] Loss: 0.000000 Metrics: [0.04311799073133001, 0.16274616618754134, 2.0864868458657453, 0.07454017204266132, 0.9802206238788633, 0.9949895418952827, 0.9979178539725913]
Evaluating [5080/8634 (59%)] Loss: 0.000000 Metrics: [0.04309390580214909, 0.16248789387734938, 2.0856910865168925, 0.07450171490117398, 0.9802476579352, 0.9950048790724904, 0.9979225420098002]
Evaluating [5120/8634 (59%)] Loss: 0.000000 Metrics: [0.04305379553736187, 0.16209865433892343, 2.085604894445167, 0.07443372329223533, 0.98030066781098, 0.9950181104298077, 0.9979295035164508]
Evaluating [5160/8634 (60%)] Loss: 0.000000 Metrics: [0.043058906350225634, 0.16203049579415152, 2.0830525788444274, 0.07443985470571707, 0.9803175342632605, 0.995025948769637, 0.9979342916639034]
Evaluating [5200/8634 (60%)] Loss: 0.000000 Metrics: [0.04309911427747992, 0.16188764846992923, 2.0814579686308217, 0.0744592951942251, 0.9803374703917674, 0.9950355652523517, 0.9979370590449754]
Evaluating [5240/8634 (61%)] Loss: 0.000000 Metrics: [0.04324282625550102, 0.16191993799886373, 2.0804971752617933, 0.07452167251503727, 0.9803179673728303, 0.9950555586396442, 0.9979484838777168]
Evaluating [5280/8634 (61%)] Loss: 0.000000 Metrics: [0.04317955927488164, 0.1613949384753314, 2.0772514496730703, 0.07439506423632469, 0.9804122274412165, 0.9950771049449318, 0.9979561584233244]
Evaluating [5320/8634 (62%)] Loss: 0.000000 Metrics: [0.0432397613616164, 0.16099034233683035, 2.0723371947676292, 0.07435205097638185, 0.9804313493957648, 0.9950955645655475, 0.9979668521692949]
Evaluating [5360/8634 (62%)] Loss: 0.000000 Metrics: [0.04337791334497244, 0.16182633233282692, 2.074936521426816, 0.07448455124276425, 0.9803494281263683, 0.9950730160117728, 0.9979626467004724]
Evaluating [5400/8634 (63%)] Loss: 0.000000 Metrics: [0.04337840188696004, 0.16166909869452709, 2.0734158684394397, 0.07455703678483303, 0.9803246104333277, 0.9950548110075326, 0.9979532556991054]
Evaluating [5440/8634 (63%)] Loss: 0.000000 Metrics: [0.04338267212545377, 0.16144441533799514, 2.0697065815904567, 0.07461787413330649, 0.980309493442705, 0.9950359445729268, 0.9979438192701567]
Evaluating [5480/8634 (63%)] Loss: 0.000000 Metrics: [0.043329397047231426, 0.1610247776012858, 2.0676025602557457, 0.07457471341682977, 0.9803622059907325, 0.9950363466401293, 0.9979432366138739]
Evaluating [5520/8634 (64%)] Loss: 0.000000 Metrics: [0.0432900835263075, 0.16092205716027855, 2.066292387577383, 0.07453375132143908, 0.9803991754259678, 0.9950378634971969, 0.9979486658883155]
Evaluating [5560/8634 (64%)] Loss: 0.000000 Metrics: [0.04329124231699215, 0.16070542295109408, 2.065207796810943, 0.0745186463335957, 0.9804265722659199, 0.9950479652246353, 0.9979529299353661]
Evaluating [5600/8634 (65%)] Loss: 0.000000 Metrics: [0.043527414715692256, 0.16264667271003685, 2.07317232176986, 0.07473539770639015, 0.98018411600943, 0.9950134171540718, 0.9979541313005574]
Evaluating [5640/8634 (65%)] Loss: 0.000000 Metrics: [0.043508109114654775, 0.1624239563323935, 2.073563115113342, 0.07462517974204266, 0.9802441343015409, 0.9950361262606797, 0.9979660184786532]
Evaluating [5680/8634 (66%)] Loss: 0.000000 Metrics: [0.0435872081915013, 0.16292044710510845, 2.0756429366476308, 0.0746948309321401, 0.980201096486055, 0.9950313136133666, 0.9979662558680813]
Evaluating [5720/8634 (66%)] Loss: 0.000000 Metrics: [0.0436890214051412, 0.16308483875160323, 2.0758612801544487, 0.07479619585454193, 0.980160534986038, 0.9950284487465302, 0.9979667710866265]
Evaluating [5760/8634 (67%)] Loss: 0.000000 Metrics: [0.043617931568619484, 0.1626729438031601, 2.074532789618801, 0.07469363010455154, 0.9802222187135916, 0.9950447604913589, 0.997974208559025]
Evaluating [5800/8634 (67%)] Loss: 0.000000 Metrics: [0.04359531158151831, 0.16239551997460072, 2.074583282535629, 0.07465189389604529, 0.98026550021594, 0.995058858016572, 0.9979810879091606]
Evaluating [5840/8634 (68%)] Loss: 0.000000 Metrics: [0.04355854589080145, 0.16198091974908543, 2.0730574273646836, 0.07460398313827565, 0.9802966184585072, 0.9950654109795181, 0.9979890857155868]
Evaluating [5880/8634 (68%)] Loss: 0.000000 Metrics: [0.04350948161092472, 0.16163395870121025, 2.072776876372247, 0.07454683901333597, 0.98032631220683, 0.9950764470666251, 0.9979955232982447]
Evaluating [5920/8634 (69%)] Loss: 0.000000 Metrics: [0.04347942695298416, 0.16141308125451947, 2.0726409829858587, 0.0745568276898497, 0.9803465962893091, 0.9950691537050249, 0.9979928864976998]
Evaluating [5960/8634 (69%)] Loss: 0.000000 Metrics: [0.043485923638053085, 0.16137056169904587, 2.07131723014351, 0.07460290841515814, 0.9803370026004911, 0.9950595833598588, 0.9979881615656329]
Evaluating [6000/8634 (69%)] Loss: 0.000000 Metrics: [0.04361268355623161, 0.1618198710917354, 2.073673718117031, 0.07467303250291593, 0.9802554991991271, 0.9950693588938486, 0.9979970330836097]
Evaluating [6040/8634 (70%)] Loss: 0.000000 Metrics: [0.043591236923160005, 0.16171444196578327, 2.074489660094961, 0.07460793526642806, 0.9802890658496982, 0.9950887213594588, 0.9980066251139181]
Evaluating [6080/8634 (70%)] Loss: 0.000000 Metrics: [0.04356262621625067, 0.16163992501283234, 2.0748831935518472, 0.07457616702653161, 0.9802752287322775, 0.9950902198184991, 0.998008794276505]
Evaluating [6120/8634 (71%)] Loss: 0.000000 Metrics: [0.04363824144475544, 0.16188206353274406, 2.0752307202877542, 0.07469841873298202, 0.9802029596283248, 0.9950757024530412, 0.9980012436039266]
Evaluating [6160/8634 (71%)] Loss: 0.000000 Metrics: [0.04363011051826626, 0.1616441266044747, 2.073393708446203, 0.07475161895213944, 0.9801777428096473, 0.995065179709992, 0.9979953491544615]
Evaluating [6200/8634 (72%)] Loss: 0.000000 Metrics: [0.04360178326998688, 0.16135951400917986, 2.069788806690627, 0.0747727095715579, 0.9801788431565557, 0.9950539871632227, 0.9979920175643707]
Evaluating [6240/8634 (72%)] Loss: 0.000000 Metrics: [0.04355698128704439, 0.16110752333159709, 2.0684931840662846, 0.07475627597823657, 0.9802039716534796, 0.9950515247048265, 0.9979915729664488]
Evaluating [6280/8634 (73%)] Loss: 0.000000 Metrics: [0.04352392747272393, 0.16125353734936732, 2.066580882974963, 0.07472684421060344, 0.9802367115468593, 0.9950556314056066, 0.9979939124873397]
Evaluating [6320/8634 (73%)] Loss: 0.000000 Metrics: [0.043676053487030776, 0.16249465763601878, 2.0688178754426074, 0.07481702857900367, 0.9800610587175267, 0.9949861928580193, 0.9979887198172427]
Evaluating [6360/8634 (74%)] Loss: 0.000000 Metrics: [0.04382473358422452, 0.1629781881918213, 2.0719254379450844, 0.07491963607115927, 0.9799086635159981, 0.9949908284449944, 0.9979982403643062]
Evaluating [6400/8634 (74%)] Loss: 0.000000 Metrics: [0.04377227986126123, 0.16281366968217287, 2.0690378024219833, 0.07487479139378912, 0.9799573540464114, 0.9949923618180199, 0.9980002860656495]
Evaluating [6440/8634 (75%)] Loss: 0.000000 Metrics: [0.043682002788886676, 0.16222976997971203, 2.0650384884116, 0.07474255831076543, 0.9800364641216518, 0.9950092561087301, 0.9980071959788663]
Evaluating [6480/8634 (75%)] Loss: 0.000000 Metrics: [0.043641156632773896, 0.1622167434491647, 2.063273116472657, 0.07471470278364499, 0.9800854461938281, 0.9950131420109309, 0.9980065288899748]
Evaluating [6520/8634 (76%)] Loss: 0.000000 Metrics: [0.04358590465085852, 0.16183649946994594, 2.06102079365086, 0.07463478168477788, 0.9801415937767918, 0.9950263597818285, 0.9980122322540493]
Evaluating [6560/8634 (76%)] Loss: 0.000000 Metrics: [0.043560738655142726, 0.16153669829063938, 2.0597539182226674, 0.07460663830637206, 0.9801545762608815, 0.9950332282629365, 0.9980166847115063]
Evaluating [6600/8634 (76%)] Loss: 0.000000 Metrics: [0.043657997889984236, 0.16139306549081042, 2.0557066377853848, 0.0746384965076984, 0.9801469260871284, 0.9950442010798045, 0.9980225777380468]
Evaluating [6640/8634 (77%)] Loss: 0.000000 Metrics: [0.04384748904537595, 0.1620751546437268, 2.0582393370669707, 0.07481470497903318, 0.9799928040039249, 0.9950374067816811, 0.9980262573567258]
Evaluating [6680/8634 (77%)] Loss: 0.000000 Metrics: [0.0437767558241365, 0.16159735963225758, 2.056038204958538, 0.07470887656881402, 0.9800552451763992, 0.9950528273287023, 0.9980327520353357]
Evaluating [6720/8634 (78%)] Loss: 0.000000 Metrics: [0.043696229375191686, 0.16103535524035487, 2.0528776063091945, 0.07456526604038517, 0.9801338531721989, 0.9950741822611041, 0.998041789133043]
Evaluating [6760/8634 (78%)] Loss: 0.000000 Metrics: [0.04362751014367588, 0.16094563528454645, 2.0524216317559305, 0.0745626900403484, 0.9801828253025912, 0.9950697420617244, 0.9980348943429951]
Evaluating [6800/8634 (79%)] Loss: 0.000000 Metrics: [0.043542720017622265, 0.16070136661189194, 2.0501264530187213, 0.07445626095007427, 0.9802573970471085, 0.9950876872667808, 0.9980418861560771]
Evaluating [6840/8634 (79%)] Loss: 0.000000 Metrics: [0.043498499327124435, 0.1608426823027299, 2.0517838922413913, 0.07443517373885128, 0.9802957345295705, 0.9950943712392282, 0.9980404399285293]
Evaluating [6880/8634 (80%)] Loss: 0.000000 Metrics: [0.04362697212095352, 0.1626200861849954, 2.0627625923765085, 0.0746388472530063, 0.9801538055180741, 0.9950545326425253, 0.9980275836908273]
Evaluating [6920/8634 (80%)] Loss: 0.000000 Metrics: [0.043737763561022075, 0.16537610568887615, 2.077861674480857, 0.07486613715760145, 0.9799774054914704, 0.9950134452627215, 0.9980160516832853]
Evaluating [6960/8634 (81%)] Loss: 0.000000 Metrics: [0.043700173114076216, 0.16544483697506862, 2.079668193790153, 0.07480564061525531, 0.9800220390942822, 0.9950280075133514, 0.9980223536731114]
Evaluating [7000/8634 (81%)] Loss: 0.000000 Metrics: [0.04366221927719282, 0.16529171589322514, 2.0796509324499826, 0.07477566200608834, 0.9800594797205223, 0.9950315754810628, 0.9980225106608149]
Evaluating [7040/8634 (82%)] Loss: 0.000000 Metrics: [0.04360605617924293, 0.1651495784834277, 2.0805869747278063, 0.07470605664486338, 0.9800906467254918, 0.9950410886706023, 0.9980287620101047]
Evaluating [7080/8634 (82%)] Loss: 0.000000 Metrics: [0.04358540873251807, 0.1653304047792081, 2.083480913216231, 0.07469655142555766, 0.9801220280475719, 0.9950500211955396, 0.9980300145949121]
Evaluating [7120/8634 (82%)] Loss: 0.000000 Metrics: [0.04402052148197414, 0.17217118767783468, 2.095252671743236, 0.07513901268568009, 0.9796352639555831, 0.9948139076184704, 0.9979096103069388]
Evaluating [7160/8634 (83%)] Loss: 0.000000 Metrics: [0.04603003111195378, 0.2167004242277252, 2.1483447581521293, 0.07725060702980613, 0.9774523534627642, 0.9934194246772546, 0.9970766353027942]
Evaluating [7200/8634 (83%)] Loss: 0.000000 Metrics: [0.0473412108871133, 0.24138487957809013, 2.181597699443025, 0.07865928747515087, 0.9759267486571869, 0.992551015370821, 0.9966035777580338]
Evaluating [7240/8634 (84%)] Loss: 0.000000 Metrics: [0.04734010655547136, 0.24068847743373029, 2.1792782436942106, 0.07859031927047333, 0.9759648415354923, 0.9925833012609316, 0.9966204452501479]
Evaluating [7280/8634 (84%)] Loss: 0.000000 Metrics: [0.047279295757355615, 0.24005526795766408, 2.1775198229817496, 0.07850819890584569, 0.9760455191626519, 0.9926072581395183, 0.9966319054569788]
Evaluating [7320/8634 (85%)] Loss: 0.000000 Metrics: [0.04722152030509096, 0.23957003074667876, 2.1767391188976317, 0.07847504180593264, 0.9761062589817704, 0.9926130870299663, 0.9966361228151746]
Evaluating [7360/8634 (85%)] Loss: 0.000000 Metrics: [0.0471840158466192, 0.23899161551300516, 2.1762053596199418, 0.0784546215895789, 0.9761460526696821, 0.9926167754691698, 0.996638561454489]
Evaluating [7400/8634 (86%)] Loss: 0.000000 Metrics: [0.04711538562296984, 0.23831764232368283, 2.175046114028064, 0.07840181872554645, 0.9762006051329077, 0.9926157659274506, 0.9966446237124742]
Evaluating [7440/8634 (86%)] Loss: 0.000000 Metrics: [0.047096448351529714, 0.23804542858096517, 2.173854724161409, 0.07850521112306885, 0.976134143579522, 0.9925362037830665, 0.9966437351764514]
Evaluating [7480/8634 (87%)] Loss: 0.000000 Metrics: [0.047051780037850786, 0.2375053540549314, 2.171472603020519, 0.07843936312783373, 0.9761921563383666, 0.9925431429750299, 0.9966463665693274]
Evaluating [7520/8634 (87%)] Loss: 0.000000 Metrics: [0.04695487112634932, 0.23656658615696086, 2.1680248800315494, 0.07826176010581351, 0.976295885468252, 0.9925785795135468, 0.9966628770503678]
Evaluating [7560/8634 (88%)] Loss: 0.000000 Metrics: [0.04706639829206265, 0.23703772512197305, 2.1700393220193486, 0.07836041997263937, 0.9761313473585578, 0.9925520153658568, 0.9966708691748826]
Evaluating [7600/8634 (88%)] Loss: 0.000000 Metrics: [0.047222064646297586, 0.23711435383803686, 2.1710194067182242, 0.07848360006063118, 0.9759694807107936, 0.9925524486237154, 0.9966792721429708]
Evaluating [7640/8634 (88%)] Loss: 0.000000 Metrics: [0.04714021343397583, 0.2363360914236212, 2.169084788164335, 0.07840999232610461, 0.9760380006763081, 0.9925691224741892, 0.9966840973483178]
Evaluating [7680/8634 (89%)] Loss: 0.000000 Metrics: [0.04720525212238711, 0.2361501517883349, 2.169902461715987, 0.07847548445526184, 0.9760206406686307, 0.9925792167441853, 0.9966886229454245]
Evaluating [7720/8634 (89%)] Loss: 0.000000 Metrics: [0.04721901690641916, 0.235673179154807, 2.1689769105724768, 0.07845717629263883, 0.9760406990259368, 0.9926010985443611, 0.9967026065680932]
Evaluating [7760/8634 (90%)] Loss: 0.000000 Metrics: [0.04715485591801347, 0.23493152727243122, 2.167225811491305, 0.0783847272108124, 0.9761070298322295, 0.9926216493129607, 0.9967116773727474]
Evaluating [7800/8634 (90%)] Loss: 0.000000 Metrics: [0.04708191103539965, 0.23417800180642642, 2.1651440946085887, 0.07832303519655087, 0.9761773028440336, 0.9926439889178341, 0.9967198222455413]
Evaluating [7840/8634 (91%)] Loss: 0.000000 Metrics: [0.047036351817043666, 0.23336087922106985, 2.1609200357296308, 0.07821848559758852, 0.9762681288109297, 0.9926766138452561, 0.9967341073706758]
Evaluating [7880/8634 (91%)] Loss: 0.000000 Metrics: [0.047163405121733955, 0.23320349620925052, 2.1600009907293187, 0.07833089496192669, 0.9761537871167794, 0.9927033823294495, 0.9967481345429816]
Evaluating [7920/8634 (92%)] Loss: 0.000000 Metrics: [0.04727814096569022, 0.23387075424555484, 2.1613015805763056, 0.07843597275241401, 0.9758665526238273, 0.9926387423880321, 0.9967427925460418]
Evaluating [7960/8634 (92%)] Loss: 0.000000 Metrics: [0.047197718206485204, 0.23342126513008077, 2.160651621837108, 0.0783523679101506, 0.9759388977380531, 0.9926586640716587, 0.9967501438972609]
Evaluating [8000/8634 (93%)] Loss: 0.000000 Metrics: [0.0471718911802216, 0.2328544221567053, 2.15951429541723, 0.07831018320216115, 0.9759892632486938, 0.9926774928314154, 0.9967601574739495]
Evaluating [8040/8634 (93%)] Loss: 0.000000 Metrics: [0.04718491293997632, 0.23260796149033156, 2.1614897822865324, 0.07829125464292375, 0.976017353524915, 0.9927006982891337, 0.9967711241320573]
Evaluating [8080/8634 (94%)] Loss: 0.000000 Metrics: [0.0471478350491873, 0.23242211373570354, 2.161959165675426, 0.07824514544366375, 0.9760622128685699, 0.992724369773473, 0.9967812311675872]
Evaluating [8120/8634 (94%)] Loss: 0.000000 Metrics: [0.047059217910058716, 0.2316014027937135, 2.1581488457755835, 0.07813564744475028, 0.9761365808764868, 0.9927467085316509, 0.9967907016954232]
Evaluating [8160/8634 (95%)] Loss: 0.000000 Metrics: [0.047007689229218406, 0.23100096649583635, 2.156785067449274, 0.0781178619375754, 0.9761811910862142, 0.9927507378370907, 0.9967881712776807]
Evaluating [8200/8634 (95%)] Loss: 0.000000 Metrics: [0.04741179335374595, 0.23589013992442906, 2.1685139747021753, 0.07866556770323806, 0.9755531056963272, 0.9925185131956908, 0.9966966994824394]
Evaluating [8240/8634 (95%)] Loss: 0.000000 Metrics: [0.04843687517417053, 0.2548499945953427, 2.203898780657174, 0.07994938460539135, 0.9741385607819509, 0.9916427855611976, 0.9962213001937376]
Evaluating [8280/8634 (96%)] Loss: 0.000000 Metrics: [0.049381742170685534, 0.2716762872520304, 2.233037093114058, 0.08103322669813323, 0.972888947130545, 0.99094206240119, 0.9958361008552444]
Evaluating [8320/8634 (96%)] Loss: 0.000000 Metrics: [0.04936449159974377, 0.2710842149870681, 2.2326200649704506, 0.08097767880568278, 0.9729306368021632, 0.9909741308247347, 0.9958539248744732]
Evaluating [8360/8634 (97%)] Loss: 0.000000 Metrics: [0.04928891156942429, 0.27024949427281547, 2.230671875344541, 0.08085936382573292, 0.973010154531436, 0.9910073986580371, 0.9958724886057794]
Evaluating [8400/8634 (97%)] Loss: 0.000000 Metrics: [0.04922711096927471, 0.26985717749179217, 2.2316697587770555, 0.08079947282575023, 0.9730606777941548, 0.9910281283093247, 0.9958847540243839]
Evaluating [8440/8634 (98%)] Loss: 0.000000 Metrics: [0.04927125389276664, 0.2697925016758807, 2.2329820318121505, 0.08085365992439242, 0.973019478071699, 0.9910407005201384, 0.9958930549901742]
Evaluating [8480/8634 (98%)] Loss: 0.000000 Metrics: [0.04930429176889242, 0.269836988352789, 2.2341865073872804, 0.0809265517727844, 0.9730105872242497, 0.9910495499624618, 0.9958970909184989]
Evaluating [8520/8634 (99%)] Loss: 0.000000 Metrics: [0.0494478642822494, 0.2751275325660427, 2.2424730837582927, 0.08130567460619237, 0.9729499942160805, 0.9909993744993176, 0.9958526143247716]
Evaluating [8560/8634 (99%)] Loss: 0.000000 Metrics: [0.04968592528313837, 0.28416663695007555, 2.252476110034556, 0.08174103503070877, 0.9728684652091131, 0.9909579846046428, 0.9958218917811037]
Evaluating [8600/8634 (100%)] Loss: 0.000000 Metrics: [0.049701329603465565, 0.283856158427785, 2.2531346360269575, 0.0817808011558424, 0.9728534780624605, 0.9909610692857725, 0.9958281887738933]
{'loss': 0.0, 'metrics': [0.050102306426327375, 0.29000417841479736, 2.2656219501116497, 0.08224765892285756, 0.9723125736912782, 0.9907187367026828, 0.9957081838727743], 'metrics_correct': [0.0501023064263273, 0.29000417841479575, 2.2656219501116426, 0.08224765892285742, 0.9723125736912774, 0.9907187367026803, 0.9957081838727698], 'valid_batches': 4317.0, 'loss_loss': 0.0, 'metrics_info': ['abs_rel_sparse_metric', 'sq_rel_sparse_metric', 'rmse_sparse_metric', 'rmse_log_sparse_metric', 'a1_sparse_metric', 'a2_sparse_metric', 'a3_sparse_metric']}
Finished