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+{ + "version": "0.2.0", + "configurations": [ + { + "name": "Train VBR Codec", + "type": "debugpy", + "request": "launch", + "program": "${workspaceFolder}/fine_tune_vq_w_mask.py", + "console": "integratedTerminal", + "args": [ + "-c", "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/fine_tune_vq_w_mask.yaml", + "--world_size", "2" + ], + "env": { + "CUDA_VISIBLE_DEVICES": "2,3", + "MASTER_ADDR": "localhost", + "MASTER_PORT": "12345", + "NODE_RANK": "0", + "NPROC_PER_NODE": "2", + "NNODES": "1" + }, + "preLaunchTask": "" + } + ] +} diff --git a/Fine_Tune_VQ_w_P/20240604_030821.log b/Fine_Tune_VQ_w_P/20240604_030821.log new file mode 100644 index 0000000000000000000000000000000000000000..8c89ae289b9f06487b4400b16a3742b7cc27dd7b --- /dev/null +++ b/Fine_Tune_VQ_w_P/20240604_030821.log @@ -0,0 +1,174 @@ +2024-06-04 03:08:21,626 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/Fine_Tune_VQ_w_P//20240604_030821.log +2024-06-04 03:08:21,626 [INFO ] ======================= exp ======================= +2024-06-04 03:08:21,626 [INFO ] config:config/vpt_default.yaml +2024-06-04 03:08:21,626 [INFO ] name:exp +2024-06-04 03:08:21,626 [INFO ] lr:None +2024-06-04 03:08:21,626 [INFO ] world_size:4 +2024-06-04 03:08:21,626 [INFO ] local_rank:-1 +2024-06-04 03:08:21,627 [INFO ] dist_on_itp:False +2024-06-04 03:08:21,627 [INFO ] dist_url:env:// +2024-06-04 03:08:21,627 [INFO ] TEST:False +2024-06-04 03:08:21,627 [INFO ] model:mage_vit_base_patch16 +2024-06-04 03:08:21,627 [INFO ] dataset:coco +2024-06-04 03:08:21,627 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/COCO +2024-06-04 03:08:21,627 [INFO ] kodak_path:/home/t2vg-a100-G4-10/project/qyp/datasets/test/ +2024-06-04 03:08:21,627 [INFO ] eval_path: +2024-06-04 03:08:21,627 [INFO ] batch_size:64 +2024-06-04 03:08:21,627 [INFO ] learning_rate:0.0001 +2024-06-04 03:08:21,627 [INFO ] test_batch_size:1 +2024-06-04 03:08:21,627 [INFO ] num_workers:4 +2024-06-04 03:08:21,627 [INFO ] epochs:100 +2024-06-04 03:08:21,627 [INFO ] blr:0.0001 +2024-06-04 03:08:21,627 [INFO ] min_lr:5e-06 +2024-06-04 03:08:21,627 [INFO ] accum_iter:1 +2024-06-04 03:08:21,627 [INFO ] input_size:256 +2024-06-04 03:08:21,627 [INFO ] weight_decay:0.05 +2024-06-04 03:08:21,627 [INFO ] warmup_epochs:10 +2024-06-04 03:08:21,627 [INFO ] mask_ratio_min:0.25 +2024-06-04 03:08:21,627 [INFO ] mask_ratio_max:0.75 +2024-06-04 03:08:21,627 [INFO ] grad_clip:3.0 +2024-06-04 03:08:21,628 [INFO ] device:cuda +2024-06-04 03:08:21,628 [INFO ] distributed:True +2024-06-04 03:08:21,628 [INFO ] seed:0 +2024-06-04 03:08:21,628 [INFO ] gpu_id:2 +2024-06-04 03:08:21,628 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth +2024-06-04 03:08:21,628 [INFO ] maskrcnn_path:/home/t2vg-a100-G4-10/project/qyp/ckpt/detectron2/mask_rcnn_r50_fpn_3x.pkl +2024-06-04 03:08:21,628 [INFO ] start_epoch:0 +2024-06-04 03:08:21,628 [INFO ] pin_mem:True +2024-06-04 03:08:21,628 [INFO ] save:True +2024-06-04 03:08:21,628 [INFO ] exp_name:Fine_Tune_VQ_w_P +2024-06-04 03:08:21,628 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-06-04 03:08:21,628 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/Fine_Tune_VQ_w_P +2024-06-04 03:08:21,628 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/Fine_Tune_VQ_w_P +2024-06-04 03:08:21,628 [INFO ] strart_epoch:0 +2024-06-04 03:08:21,628 [INFO ] cuda:True +2024-06-04 03:08:21,628 [INFO ] rank:0 +2024-06-04 03:08:21,628 [INFO ] gpu:0 +2024-06-04 03:08:21,628 [INFO ] dist_backend:nccl +2024-06-04 03:08:21,628 [INFO ] =================================================== +2024-06-04 03:10:20,905 [INFO ] -1|taskloss:2.49146|mask ratio:0.75||bpp:0.01396|mask:0.00003|psnr:14.06076|lpips:0.46229|dists:0.19295 +2024-06-04 03:10:48,101 [INFO ] -1|taskloss:1.68947|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.15691|lpips:0.41068|dists:0.16344 +2024-06-04 03:11:14,835 [INFO ] -1|taskloss:1.33339|mask ratio:0.55||bpp:0.02512|mask:0.00003|psnr:15.94234|lpips:0.37990|dists:0.14601 +2024-06-04 03:11:41,295 [INFO ] -1|taskloss:1.03034|mask ratio:0.45||bpp:0.03068|mask:0.00003|psnr:16.56941|lpips:0.35252|dists:0.13884 +2024-06-04 03:11:41,296 [INFO ] Test loss=1.636163592338562 +2024-06-04 03:28:26,377 [INFO ] 0|taskloss:2.33836|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.29709|lpips:0.46480|dists:0.21399 +2024-06-04 03:28:53,053 [INFO ] 0|taskloss:1.62612|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:15.42644|lpips:0.42104|dists:0.19210 +2024-06-04 03:29:20,005 [INFO ] 0|taskloss:1.29490|mask ratio:0.55||bpp:0.02512|mask:0.00003|psnr:15.93270|lpips:0.38919|dists:0.17758 +2024-06-04 03:29:47,006 [INFO ] 0|taskloss:0.99774|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.65782|lpips:0.36090|dists:0.16477 +2024-06-04 03:29:47,006 [INFO ] Test loss=1.973321333527565 +2024-06-04 03:29:47,960 [INFO ] Saving BEST checkpoint! +2024-06-04 03:46:15,723 [INFO ] 1|taskloss:2.27418|mask ratio:0.75||bpp:0.01398|mask:0.00003|psnr:14.28722|lpips:0.47362|dists:0.22182 +2024-06-04 03:46:42,375 [INFO ] 1|taskloss:1.65933|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.20894|lpips:0.41849|dists:0.19428 +2024-06-04 03:47:09,078 [INFO ] 1|taskloss:1.28544|mask ratio:0.55||bpp:0.02511|mask:0.00003|psnr:15.95363|lpips:0.39304|dists:0.18215 +2024-06-04 03:47:35,953 [INFO ] 1|taskloss:1.02331|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.67013|lpips:0.35942|dists:0.17008 +2024-06-04 03:47:35,954 [INFO ] Test loss=2.0538930036127567 +2024-06-04 04:04:02,717 [INFO ] 2|taskloss:2.25254|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:13.97699|lpips:0.47056|dists:0.22627 +2024-06-04 04:04:29,767 [INFO ] 2|taskloss:1.66452|mask ratio:0.65||bpp:0.01954|mask:0.00003|psnr:15.12406|lpips:0.42705|dists:0.20062 +2024-06-04 04:04:56,853 [INFO ] 2|taskloss:1.26301|mask ratio:0.55||bpp:0.02512|mask:0.00003|psnr:15.65730|lpips:0.39007|dists:0.18716 +2024-06-04 04:05:23,967 [INFO ] 2|taskloss:1.03645|mask ratio:0.45||bpp:0.03068|mask:0.00003|psnr:16.02926|lpips:0.36933|dists:0.18181 +2024-06-04 04:05:23,968 [INFO ] Test loss=2.067601718939841 +2024-06-04 04:21:49,690 [INFO ] 3|taskloss:2.28804|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.03534|lpips:0.48001|dists:0.22667 +2024-06-04 04:22:16,508 [INFO ] 3|taskloss:1.66096|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.18743|lpips:0.42347|dists:0.20229 +2024-06-04 04:22:43,283 [INFO ] 3|taskloss:1.28439|mask ratio:0.55||bpp:0.02512|mask:0.00003|psnr:15.80286|lpips:0.39074|dists:0.18586 +2024-06-04 04:23:09,940 [INFO ] 3|taskloss:1.01509|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.39696|lpips:0.37266|dists:0.18077 +2024-06-04 04:23:09,941 [INFO ] Test loss=2.0790173208806664 +2024-06-04 04:39:36,248 [INFO ] 4|taskloss:2.17187|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.04568|lpips:0.46708|dists:0.21667 +2024-06-04 04:40:03,220 [INFO ] 4|taskloss:1.68821|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.43250|lpips:0.42161|dists:0.19500 +2024-06-04 04:40:29,994 [INFO ] 4|taskloss:1.23894|mask ratio:0.55||bpp:0.02511|mask:0.00003|psnr:15.90158|lpips:0.38726|dists:0.18233 +2024-06-04 04:40:56,799 [INFO ] 4|taskloss:1.01278|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.31897|lpips:0.36575|dists:0.17080 +2024-06-04 04:40:56,799 [INFO ] Test loss=2.047704319062177 +2024-06-04 04:57:23,679 [INFO ] 5|taskloss:2.24287|mask ratio:0.75||bpp:0.01395|mask:0.00003|psnr:14.29049|lpips:0.46545|dists:0.21852 +2024-06-04 04:57:50,801 [INFO ] 5|taskloss:1.62839|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.02618|lpips:0.41894|dists:0.19633 +2024-06-04 04:58:17,569 [INFO ] 5|taskloss:1.29102|mask ratio:0.55||bpp:0.02511|mask:0.00003|psnr:15.74514|lpips:0.39395|dists:0.18185 +2024-06-04 04:58:44,510 [INFO ] 5|taskloss:1.01440|mask ratio:0.45||bpp:0.03066|mask:0.00003|psnr:16.38981|lpips:0.36329|dists:0.17676 +2024-06-04 04:58:44,511 [INFO ] Test loss=2.0560930917417863 +2024-06-04 05:15:10,098 [INFO ] 6|taskloss:2.32205|mask ratio:0.75||bpp:0.01398|mask:0.00003|psnr:14.04229|lpips:0.46922|dists:0.21845 +2024-06-04 05:15:36,966 [INFO ] 6|taskloss:1.60729|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:14.98244|lpips:0.42790|dists:0.20636 +2024-06-04 05:16:03,975 [INFO ] 6|taskloss:1.26320|mask ratio:0.55||bpp:0.02509|mask:0.00003|psnr:15.94796|lpips:0.38764|dists:0.18432 +2024-06-04 05:16:30,771 [INFO ] 6|taskloss:1.03816|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.29310|lpips:0.37118|dists:0.17678 +2024-06-04 05:16:30,771 [INFO ] Test loss=2.0717015551963414 +2024-06-04 05:32:57,502 [INFO ] 7|taskloss:2.36936|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.30189|lpips:0.46484|dists:0.21939 +2024-06-04 05:33:24,434 [INFO ] 7|taskloss:1.63491|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.08945|lpips:0.42373|dists:0.19809 +2024-06-04 05:33:51,433 [INFO ] 7|taskloss:1.23911|mask ratio:0.55||bpp:0.02511|mask:0.00003|psnr:15.95378|lpips:0.38986|dists:0.18503 +2024-06-04 05:34:18,373 [INFO ] 7|taskloss:0.99703|mask ratio:0.45||bpp:0.03068|mask:0.00003|psnr:16.33615|lpips:0.36766|dists:0.17350 +2024-06-04 05:34:18,373 [INFO ] Test loss=2.0780272555211923 +2024-06-04 05:50:45,587 [INFO ] 8|taskloss:2.23844|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.26759|lpips:0.46414|dists:0.22619 +2024-06-04 05:51:12,540 [INFO ] 8|taskloss:1.66498|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:14.91135|lpips:0.43537|dists:0.21694 +2024-06-04 05:51:39,386 [INFO ] 8|taskloss:1.27593|mask ratio:0.55||bpp:0.02510|mask:0.00003|psnr:15.52610|lpips:0.40321|dists:0.20067 +2024-06-04 05:52:05,972 [INFO ] 8|taskloss:1.03960|mask ratio:0.45||bpp:0.03066|mask:0.00003|psnr:15.80372|lpips:0.38153|dists:0.19366 +2024-06-04 05:52:05,973 [INFO ] Test loss=2.0742442923749422 +2024-06-04 06:08:33,064 [INFO ] 9|taskloss:2.32985|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.33968|lpips:0.47202|dists:0.22093 +2024-06-04 06:08:59,961 [INFO ] 9|taskloss:1.62963|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.15994|lpips:0.42402|dists:0.19959 +2024-06-04 06:09:26,718 [INFO ] 9|taskloss:1.25288|mask ratio:0.55||bpp:0.02510|mask:0.00003|psnr:15.62853|lpips:0.38951|dists:0.17977 +2024-06-04 06:09:53,638 [INFO ] 9|taskloss:1.00600|mask ratio:0.45||bpp:0.03066|mask:0.00003|psnr:16.26209|lpips:0.36578|dists:0.17169 +2024-06-04 06:09:53,639 [INFO ] Test loss=2.0731508512786263 +2024-06-04 06:26:19,204 [INFO ] 10|taskloss:2.34149|mask ratio:0.75||bpp:0.01398|mask:0.00003|psnr:14.26575|lpips:0.47004|dists:0.22524 +2024-06-04 06:26:45,967 [INFO ] 10|taskloss:1.62147|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:15.09978|lpips:0.42139|dists:0.19693 +2024-06-04 06:27:12,796 [INFO ] 10|taskloss:1.27020|mask ratio:0.55||bpp:0.02510|mask:0.00003|psnr:15.77150|lpips:0.38540|dists:0.17910 +2024-06-04 06:27:39,548 [INFO ] 10|taskloss:1.02932|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.26428|lpips:0.36994|dists:0.17073 +2024-06-04 06:27:39,549 [INFO ] Test loss=2.0839089696479647 +2024-06-04 06:44:06,036 [INFO ] 11|taskloss:2.24896|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.07473|lpips:0.47022|dists:0.21116 +2024-06-04 06:44:32,940 [INFO ] 11|taskloss:1.69199|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.18235|lpips:0.42282|dists:0.18916 +2024-06-04 06:44:59,784 [INFO ] 11|taskloss:1.26253|mask ratio:0.55||bpp:0.02512|mask:0.00003|psnr:16.03878|lpips:0.38759|dists:0.17844 +2024-06-04 06:45:26,464 [INFO ] 11|taskloss:1.01063|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.42851|lpips:0.35960|dists:0.16572 +2024-06-04 06:45:26,465 [INFO ] Test loss=2.0745057896989785 +2024-06-04 07:01:51,689 [INFO ] 12|taskloss:2.26208|mask ratio:0.75||bpp:0.01398|mask:0.00003|psnr:14.23028|lpips:0.46908|dists:0.22384 +2024-06-04 07:02:18,690 [INFO ] 12|taskloss:1.60826|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:15.05205|lpips:0.42262|dists:0.19667 +2024-06-04 07:02:45,653 [INFO ] 12|taskloss:1.24774|mask ratio:0.55||bpp:0.02510|mask:0.00003|psnr:15.88546|lpips:0.38749|dists:0.18423 +2024-06-04 07:03:12,706 [INFO ] 12|taskloss:1.00849|mask ratio:0.45||bpp:0.03068|mask:0.00003|psnr:16.33344|lpips:0.36366|dists:0.17469 +2024-06-04 07:03:12,707 [INFO ] Test loss=2.0502657253597727 +2024-06-04 07:19:36,537 [INFO ] 13|taskloss:2.27210|mask ratio:0.75||bpp:0.01396|mask:0.00003|psnr:14.07016|lpips:0.47514|dists:0.21749 +2024-06-04 07:20:03,327 [INFO ] 13|taskloss:1.62562|mask ratio:0.65||bpp:0.01954|mask:0.00003|psnr:15.32130|lpips:0.41837|dists:0.19381 +2024-06-04 07:20:30,312 [INFO ] 13|taskloss:1.20922|mask ratio:0.55||bpp:0.02510|mask:0.00003|psnr:16.01362|lpips:0.38459|dists:0.17793 +2024-06-04 07:20:57,097 [INFO ] 13|taskloss:1.02460|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.43850|lpips:0.36615|dists:0.17285 +2024-06-04 07:20:57,097 [INFO ] Test loss=2.045453323897087 +2024-06-04 07:37:23,230 [INFO ] 14|taskloss:2.26558|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.31213|lpips:0.47121|dists:0.22154 +2024-06-04 07:37:50,002 [INFO ] 14|taskloss:1.65192|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:15.25740|lpips:0.43203|dists:0.20555 +2024-06-04 07:38:16,697 [INFO ] 14|taskloss:1.28469|mask ratio:0.55||bpp:0.02511|mask:0.00003|psnr:15.82785|lpips:0.39241|dists:0.18424 +2024-06-04 07:38:43,483 [INFO ] 14|taskloss:0.98623|mask ratio:0.45||bpp:0.03068|mask:0.00003|psnr:16.52078|lpips:0.36753|dists:0.17625 +2024-06-04 07:38:43,484 [INFO ] Test loss=2.058469656600645 +2024-06-04 07:55:08,283 [INFO ] 15|taskloss:2.34680|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.18895|lpips:0.46331|dists:0.22039 +2024-06-04 07:55:34,925 [INFO ] 15|taskloss:1.61486|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:15.15459|lpips:0.42977|dists:0.20644 +2024-06-04 07:56:01,738 [INFO ] 15|taskloss:1.21967|mask 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18|taskloss:1.00574|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.33763|lpips:0.36889|dists:0.17756 +2024-06-04 08:50:01,284 [INFO ] Test loss=2.039618709603845 +2024-06-04 09:06:34,348 [INFO ] 19|taskloss:2.28699|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.06225|lpips:0.47773|dists:0.22275 +2024-06-04 09:07:01,971 [INFO ] 19|taskloss:1.62656|mask ratio:0.65||bpp:0.01953|mask:0.00003|psnr:15.01703|lpips:0.42262|dists:0.19507 +2024-06-04 09:07:29,380 [INFO ] 19|taskloss:1.26701|mask ratio:0.55||bpp:0.02511|mask:0.00003|psnr:16.04263|lpips:0.38800|dists:0.18259 +2024-06-04 09:07:56,604 [INFO ] 19|taskloss:1.02889|mask ratio:0.45||bpp:0.03066|mask:0.00003|psnr:16.22125|lpips:0.36636|dists:0.17257 +2024-06-04 09:07:56,605 [INFO ] Test loss=2.0622663147548583 +2024-06-04 09:24:25,377 [INFO ] 20|taskloss:2.24449|mask ratio:0.75||bpp:0.01396|mask:0.00003|psnr:14.10004|lpips:0.46875|dists:0.22301 +2024-06-04 09:24:52,350 [INFO ] 20|taskloss:1.67153|mask 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loss=2.0479125060594314 +2024-06-04 10:00:03,966 [INFO ] 22|taskloss:2.29455|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.29998|lpips:0.46570|dists:0.21669 +2024-06-04 10:00:32,019 [INFO ] 22|taskloss:1.68339|mask ratio:0.65||bpp:0.01951|mask:0.00003|psnr:15.14034|lpips:0.42580|dists:0.20284 +2024-06-04 10:00:59,949 [INFO ] 22|taskloss:1.26892|mask ratio:0.55||bpp:0.02510|mask:0.00003|psnr:16.01256|lpips:0.39080|dists:0.18718 +2024-06-04 10:01:27,782 [INFO ] 22|taskloss:1.02441|mask ratio:0.45||bpp:0.03067|mask:0.00003|psnr:16.42309|lpips:0.36846|dists:0.17773 +2024-06-04 10:01:27,783 [INFO ] Test loss=2.0797956952938494 +2024-06-04 10:17:48,799 [INFO ] 23|taskloss:2.28370|mask ratio:0.75||bpp:0.01397|mask:0.00003|psnr:14.41319|lpips:0.47015|dists:0.21995 +2024-06-04 10:18:16,236 [INFO ] 23|taskloss:1.61646|mask ratio:0.65||bpp:0.01952|mask:0.00003|psnr:15.18517|lpips:0.42225|dists:0.20285 +2024-06-04 10:18:43,717 [INFO ] 23|taskloss:1.26966|mask 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0000000000000000000000000000000000000000..65d6cdd8ec58c648805eaf7da5f03ab4ab038d72 --- /dev/null +++ b/MIM_vbr/20240516_010714.log @@ -0,0 +1,53 @@ +2024-05-16 01:07:14,223 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr//20240516_010714.log +2024-05-16 01:07:14,223 [INFO ] ======================= exp ======================= +2024-05-16 01:07:14,223 [INFO ] config:config/vpt_default.yaml +2024-05-16 01:07:14,223 [INFO ] name:exp +2024-05-16 01:07:14,223 [INFO ] lr:None +2024-05-16 01:07:14,223 [INFO ] world_size:1 +2024-05-16 01:07:14,223 [INFO ] local_rank:-1 +2024-05-16 01:07:14,223 [INFO ] dist_on_itp:False +2024-05-16 01:07:14,223 [INFO ] dist_url:env:// +2024-05-16 01:07:14,223 [INFO ] TEST:False +2024-05-16 01:07:14,224 [INFO ] model:mage_vit_base_patch16 +2024-05-16 01:07:14,224 [INFO ] dataset:coco +2024-05-16 01:07:14,224 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/COCO +2024-05-16 01:07:14,224 [INFO ] kodak_path:/home/t2vg-a100-G4-10/project/qyp/datasets/COCO/val2017/ +2024-05-16 01:07:14,224 [INFO ] eval_path: +2024-05-16 01:07:14,224 [INFO ] batch_size:32 +2024-05-16 01:07:14,224 [INFO ] test_batch_size:1 +2024-05-16 01:07:14,224 [INFO ] num_workers:4 +2024-05-16 01:07:14,224 [INFO ] epochs:400 +2024-05-16 01:07:14,224 [INFO ] blr:0.00015 +2024-05-16 01:07:14,224 [INFO ] min_lr:5e-06 +2024-05-16 01:07:14,224 [INFO ] accum_iter:1 +2024-05-16 01:07:14,224 [INFO ] input_size:256 +2024-05-16 01:07:14,224 [INFO ] weight_decay:0.05 +2024-05-16 01:07:14,224 [INFO ] warmup_epochs:20 +2024-05-16 01:07:14,224 [INFO ] mask_ratio_min:0.5 +2024-05-16 01:07:14,224 [INFO ] mask_ratio_max:0.9 +2024-05-16 01:07:14,224 [INFO ] mask_ratio_mu:0.55 +2024-05-16 01:07:14,224 [INFO ] mask_ratio_std:0.25 +2024-05-16 01:07:14,225 [INFO ] grad_clip:3.0 +2024-05-16 01:07:14,225 [INFO ] device:cuda +2024-05-16 01:07:14,225 [INFO ] distributed:True +2024-05-16 01:07:14,225 [INFO ] seed:0 +2024-05-16 01:07:14,225 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/random_cls_ori_pos_embed.pth +2024-05-16 01:07:14,225 [INFO ] start_epoch:0 +2024-05-16 01:07:14,225 [INFO ] pin_mem:True +2024-05-16 01:07:14,225 [INFO ] save:True +2024-05-16 01:07:14,225 [INFO ] exp_name:MIM_vbr +2024-05-16 01:07:14,225 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-16 01:07:14,225 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_high_resolu_eval +2024-05-16 01:07:14,225 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_high_resolu_eval +2024-05-16 01:07:14,225 [INFO ] strart_epoch:0 +2024-05-16 01:07:14,225 [INFO ] cuda:True +2024-05-16 01:07:14,225 [INFO ] rank:0 +2024-05-16 01:07:14,225 [INFO ] gpu:0 +2024-05-16 01:07:14,225 [INFO ] dist_backend:nccl +2024-05-16 01:07:14,225 [INFO ] =================================================== +2024-05-16 04:50:34,512 [INFO ] -1|bpp:0.06320|mask:0.01002|mask_ratio:0.05|psnr:19.27399|lpips:0.29888|dists:0.10206|Test loss:3932.46558 +2024-05-16 08:29:19,640 [INFO ] -1|bpp:0.05797|mask:0.01040|mask_ratio:0.15|psnr:18.67500|lpips:0.31491|dists:0.10963|Test loss:1802.33093 +2024-05-16 12:06:32,090 [INFO ] -1|bpp:0.05017|mask:0.01099|mask_ratio:0.3|psnr:17.72943|lpips:0.34331|dists:0.12283|Test loss:563.63690 +2024-05-16 15:45:18,037 [INFO ] -1|bpp:0.04235|mask:0.01158|mask_ratio:0.45|psnr:16.67805|lpips:0.37921|dists:0.13982|Test loss:177.07100 +2024-05-16 19:29:09,057 [INFO ] -1|bpp:0.03453|mask:0.01215|mask_ratio:0.6|psnr:15.40944|lpips:0.42819|dists:0.16429|Test loss:56.36422 +2024-05-16 22:59:34,921 [INFO ] -1|bpp:0.02675|mask:0.01275|mask_ratio:0.75|psnr:13.72992|lpips:0.49992|dists:0.20360|Test loss:18.22452 diff --git a/MIM_vbr/checkpoint-399.pth b/MIM_vbr/checkpoint-399.pth new file mode 100644 index 0000000000000000000000000000000000000000..0c1f9a90c519c9a9d635a88e01fa688f0ea4ccef --- /dev/null +++ b/MIM_vbr/checkpoint-399.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4435ef340ca3965a9c47c956fea2bb48e8613162260e443c4a37d428e6890569 +size 1960373936 diff --git a/MIM_vbr/continue_tune_rope.log b/MIM_vbr/continue_tune_rope.log new file mode 100644 index 0000000000000000000000000000000000000000..6304e422645cb93949e3b53c6e69346a0492593e --- /dev/null +++ b/MIM_vbr/continue_tune_rope.log @@ -0,0 +1,514 @@ +2024-05-03 13:24:36,083 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr//20240503_132436.log +2024-05-03 13:24:36,083 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr//20240503_132436.log +2024-05-03 13:24:36,083 [INFO ] ======================= exp ======================= +2024-05-03 13:24:36,083 [INFO ] ======================= exp ======================= +2024-05-03 13:24:36,083 [INFO ] config:config/vpt_default.yaml +2024-05-03 13:24:36,083 [INFO ] config:config/vpt_default.yaml +2024-05-03 13:24:36,083 [INFO ] name:exp +2024-05-03 13:24:36,083 [INFO ] name:exp +2024-05-03 13:24:36,083 [INFO ] lr:None +2024-05-03 13:24:36,083 [INFO ] lr:None +2024-05-03 13:24:36,083 [INFO ] world_size:4 +2024-05-03 13:24:36,083 [INFO ] world_size:4 +2024-05-03 13:24:36,084 [INFO ] local_rank:-1 +2024-05-03 13:24:36,084 [INFO ] local_rank:-1 +2024-05-03 13:24:36,084 [INFO ] dist_on_itp:False +2024-05-03 13:24:36,084 [INFO ] dist_on_itp:False +2024-05-03 13:24:36,084 [INFO ] dist_url:env:// +2024-05-03 13:24:36,084 [INFO ] dist_url:env:// +2024-05-03 13:24:36,084 [INFO ] TEST:False +2024-05-03 13:24:36,084 [INFO ] TEST:False +2024-05-03 13:24:36,084 [INFO ] model:mage_vit_base_patch16 +2024-05-03 13:24:36,084 [INFO ] model:mage_vit_base_patch16 +2024-05-03 13:24:36,084 [INFO ] dataset:imagenet +2024-05-03 13:24:36,084 [INFO ] dataset:imagenet +2024-05-03 13:24:36,084 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr//20240503_132436.log +2024-05-03 13:24:36,084 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-03 13:24:36,084 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-03 13:24:36,084 [INFO ] eval_path: +2024-05-03 13:24:36,084 [INFO ] eval_path: +2024-05-03 13:24:36,084 [INFO ] batch_size:64 +2024-05-03 13:24:36,084 [INFO ] batch_size:64 +2024-05-03 13:24:36,084 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr//20240503_132436.log +2024-05-03 13:24:36,084 [INFO ] ======================= exp ======================= +2024-05-03 13:24:36,084 [INFO ] test_batch_size:64 +2024-05-03 13:24:36,084 [INFO ] test_batch_size:64 +2024-05-03 13:24:36,084 [INFO ] config:config/vpt_default.yaml +2024-05-03 13:24:36,084 [INFO ] num_workers:4 +2024-05-03 13:24:36,084 [INFO ] num_workers:4 +2024-05-03 13:24:36,084 [INFO ] ======================= exp ======================= +2024-05-03 13:24:36,084 [INFO ] name:exp +2024-05-03 13:24:36,084 [INFO ] epochs:400 +2024-05-03 13:24:36,084 [INFO ] epochs:400 +2024-05-03 13:24:36,084 [INFO ] lr:None +2024-05-03 13:24:36,084 [INFO ] blr:0.00015 +2024-05-03 13:24:36,084 [INFO ] config:config/vpt_default.yaml +2024-05-03 13:24:36,084 [INFO ] blr:0.00015 +2024-05-03 13:24:36,084 [INFO ] world_size:4 +2024-05-03 13:24:36,084 [INFO ] min_lr:5e-06 +2024-05-03 13:24:36,084 [INFO ] name:exp +2024-05-03 13:24:36,084 [INFO ] min_lr:5e-06 +2024-05-03 13:24:36,084 [INFO ] accum_iter:1 +2024-05-03 13:24:36,084 [INFO ] local_rank:-1 +2024-05-03 13:24:36,084 [INFO ] lr:None +2024-05-03 13:24:36,084 [INFO ] accum_iter:1 +2024-05-03 13:24:36,084 [INFO ] world_size:4 +2024-05-03 13:24:36,084 [INFO ] dist_on_itp:False +2024-05-03 13:24:36,084 [INFO ] input_size:256 +2024-05-03 13:24:36,084 [INFO ] input_size:256 +2024-05-03 13:24:36,084 [INFO ] weight_decay:0.05 +2024-05-03 13:24:36,084 [INFO ] dist_url:env:// +2024-05-03 13:24:36,084 [INFO ] local_rank:-1 +2024-05-03 13:24:36,084 [INFO ] weight_decay:0.05 +2024-05-03 13:24:36,084 [INFO ] warmup_epochs:20 +2024-05-03 13:24:36,084 [INFO ] warmup_epochs:20 +2024-05-03 13:24:36,084 [INFO ] TEST:False +2024-05-03 13:24:36,084 [INFO ] dist_on_itp:False +2024-05-03 13:24:36,084 [INFO ] mask_ratio_min:0.5 +2024-05-03 13:24:36,084 [INFO ] mask_ratio_min:0.5 +2024-05-03 13:24:36,084 [INFO ] model:mage_vit_base_patch16 +2024-05-03 13:24:36,084 [INFO ] dist_url:env:// +2024-05-03 13:24:36,084 [INFO ] mask_ratio_max:0.9 +2024-05-03 13:24:36,084 [INFO ] mask_ratio_max:0.9 +2024-05-03 13:24:36,084 [INFO ] TEST:False +2024-05-03 13:24:36,084 [INFO ] dataset:imagenet +2024-05-03 13:24:36,084 [INFO ] mask_ratio_mu:0.55 +2024-05-03 13:24:36,084 [INFO ] mask_ratio_mu:0.55 +2024-05-03 13:24:36,084 [INFO ] model:mage_vit_base_patch16 +2024-05-03 13:24:36,084 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-03 13:24:36,084 [INFO ] mask_ratio_std:0.25 +2024-05-03 13:24:36,084 [INFO ] mask_ratio_std:0.25 +2024-05-03 13:24:36,084 [INFO ] dataset:imagenet +2024-05-03 13:24:36,084 [INFO ] eval_path: +2024-05-03 13:24:36,084 [INFO ] grad_clip:3.0 +2024-05-03 13:24:36,084 [INFO ] grad_clip:3.0 +2024-05-03 13:24:36,085 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-03 13:24:36,085 [INFO ] batch_size:64 +2024-05-03 13:24:36,085 [INFO ] device:cuda +2024-05-03 13:24:36,085 [INFO ] device:cuda +2024-05-03 13:24:36,085 [INFO ] test_batch_size:64 +2024-05-03 13:24:36,085 [INFO ] eval_path: +2024-05-03 13:24:36,085 [INFO ] distributed:True +2024-05-03 13:24:36,085 [INFO ] distributed:True +2024-05-03 13:24:36,085 [INFO ] num_workers:4 +2024-05-03 13:24:36,085 [INFO ] batch_size:64 +2024-05-03 13:24:36,085 [INFO ] seed:0 +2024-05-03 13:24:36,085 [INFO ] seed:0 +2024-05-03 13:24:36,085 [INFO ] test_batch_size:64 +2024-05-03 13:24:36,085 [INFO ] epochs:400 +2024-05-03 13:24:36,085 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/random_with_rope.pth +2024-05-03 13:24:36,085 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/random_with_rope.pth +2024-05-03 13:24:36,085 [INFO ] blr:0.00015 +2024-05-03 13:24:36,085 [INFO ] num_workers:4 +2024-05-03 13:24:36,085 [INFO ] start_epoch:0 +2024-05-03 13:24:36,085 [INFO ] start_epoch:0 +2024-05-03 13:24:36,085 [INFO ] epochs:400 +2024-05-03 13:24:36,085 [INFO ] min_lr:5e-06 +2024-05-03 13:24:36,085 [INFO ] pin_mem:True +2024-05-03 13:24:36,085 [INFO ] pin_mem:True +2024-05-03 13:24:36,085 [INFO ] blr:0.00015 +2024-05-03 13:24:36,085 [INFO ] accum_iter:1 +2024-05-03 13:24:36,085 [INFO ] save:True +2024-05-03 13:24:36,085 [INFO ] save:True +2024-05-03 13:24:36,085 [INFO ] input_size:256 +2024-05-03 13:24:36,085 [INFO ] min_lr:5e-06 +2024-05-03 13:24:36,085 [INFO ] exp_name:MIM_vbr +2024-05-03 13:24:36,085 [INFO ] exp_name:MIM_vbr +2024-05-03 13:24:36,085 [INFO ] weight_decay:0.05 +2024-05-03 13:24:36,085 [INFO ] accum_iter:1 +2024-05-03 13:24:36,085 [INFO ] warmup_epochs:20 +2024-05-03 13:24:36,085 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-03 13:24:36,085 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-03 13:24:36,085 [INFO ] input_size:256 +2024-05-03 13:24:36,085 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,085 [INFO ] mask_ratio_min:0.5 +2024-05-03 13:24:36,085 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,085 [INFO ] weight_decay:0.05 +2024-05-03 13:24:36,085 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,085 [INFO ] mask_ratio_max:0.9 +2024-05-03 13:24:36,085 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,085 [INFO ] warmup_epochs:20 +2024-05-03 13:24:36,085 [INFO ] strart_epoch:0 +2024-05-03 13:24:36,085 [INFO ] mask_ratio_mu:0.55 +2024-05-03 13:24:36,085 [INFO ] strart_epoch:0 +2024-05-03 13:24:36,085 [INFO ] mask_ratio_min:0.5 +2024-05-03 13:24:36,085 [INFO ] cuda:True +2024-05-03 13:24:36,085 [INFO ] mask_ratio_std:0.25 +2024-05-03 13:24:36,085 [INFO ] cuda:True +2024-05-03 13:24:36,085 [INFO ] mask_ratio_max:0.9 +2024-05-03 13:24:36,085 [INFO ] rank:0 +2024-05-03 13:24:36,085 [INFO ] grad_clip:3.0 +2024-05-03 13:24:36,085 [INFO ] rank:1 +2024-05-03 13:24:36,085 [INFO ] gpu:0 +2024-05-03 13:24:36,085 [INFO ] mask_ratio_mu:0.55 +2024-05-03 13:24:36,085 [INFO ] device:cuda +2024-05-03 13:24:36,085 [INFO ] gpu:1 +2024-05-03 13:24:36,085 [INFO ] dist_backend:nccl +2024-05-03 13:24:36,085 [INFO ] distributed:True +2024-05-03 13:24:36,085 [INFO ] mask_ratio_std:0.25 +2024-05-03 13:24:36,085 [INFO ] dist_backend:nccl +2024-05-03 13:24:36,085 [INFO ] =================================================== +2024-05-03 13:24:36,085 [INFO ] grad_clip:3.0 +2024-05-03 13:24:36,085 [INFO ] seed:0 +2024-05-03 13:24:36,085 [INFO ] =================================================== +2024-05-03 13:24:36,085 [INFO ] device:cuda +2024-05-03 13:24:36,085 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/random_with_rope.pth +2024-05-03 13:24:36,085 [INFO ] start_epoch:0 +2024-05-03 13:24:36,085 [INFO ] distributed:True +2024-05-03 13:24:36,085 [INFO ] seed:0 +2024-05-03 13:24:36,085 [INFO ] pin_mem:True +2024-05-03 13:24:36,085 [INFO ] save:True +2024-05-03 13:24:36,085 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/random_with_rope.pth +2024-05-03 13:24:36,085 [INFO ] exp_name:MIM_vbr +2024-05-03 13:24:36,085 [INFO ] start_epoch:0 +2024-05-03 13:24:36,086 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-03 13:24:36,086 [INFO ] pin_mem:True +2024-05-03 13:24:36,086 [INFO ] save:True +2024-05-03 13:24:36,086 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,086 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,086 [INFO ] exp_name:MIM_vbr +2024-05-03 13:24:36,086 [INFO ] strart_epoch:0 +2024-05-03 13:24:36,086 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-03 13:24:36,086 [INFO ] cuda:True +2024-05-03 13:24:36,086 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,086 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr +2024-05-03 13:24:36,086 [INFO ] rank:3 +2024-05-03 13:24:36,086 [INFO ] gpu:3 +2024-05-03 13:24:36,086 [INFO ] strart_epoch:0 +2024-05-03 13:24:36,086 [INFO ] cuda:True +2024-05-03 13:24:36,086 [INFO ] dist_backend:nccl +2024-05-03 13:24:36,086 [INFO ] =================================================== +2024-05-03 13:24:36,086 [INFO ] rank:2 +2024-05-03 13:24:36,086 [INFO ] gpu:2 +2024-05-03 13:24:36,086 [INFO ] dist_backend:nccl +2024-05-03 13:24:36,086 [INFO ] =================================================== +2024-05-03 13:25:40,076 [INFO ] -1|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28939|msssim:0.33530|lpips:0.50428|dists:0.27922|Test loss:22.31097 +2024-05-03 13:45:27,931 [INFO ] 71|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27695|msssim:0.33996|lpips:0.50599|dists:0.28065|Test loss:22.37941 +2024-05-03 14:05:20,381 [INFO ] 72|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18272|msssim:0.33581|lpips:0.50830|dists:0.28347|Test loss:22.39342 +2024-05-03 14:25:08,798 [INFO ] 73|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.07158|msssim:0.33378|lpips:0.50512|dists:0.28206|Test loss:22.34897 +2024-05-03 14:45:13,282 [INFO ] 74|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25096|msssim:0.33580|lpips:0.50984|dists:0.28295|Test loss:22.41223 +2024-05-03 15:05:00,304 [INFO ] 75|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34433|msssim:0.34183|lpips:0.50830|dists:0.28667|Test loss:22.41598 +2024-05-03 15:24:49,830 [INFO ] 76|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18746|msssim:0.33532|lpips:0.50817|dists:0.28051|Test loss:22.38807 +2024-05-03 15:44:36,569 [INFO ] 77|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.24094|msssim:0.33337|lpips:0.50695|dists:0.28093|Test loss:22.35640 +2024-05-03 16:04:23,158 [INFO ] 78|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25324|msssim:0.33446|lpips:0.51007|dists:0.28490|Test loss:22.40084 +2024-05-03 16:24:10,702 [INFO ] 79|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22266|msssim:0.33541|lpips:0.50979|dists:0.28361|Test loss:22.35502 +2024-05-03 16:43:58,827 [INFO ] 80|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.10645|msssim:0.32806|lpips:0.51042|dists:0.28348|Test loss:22.37403 +2024-05-03 17:03:49,009 [INFO ] 81|bpp:0.01873|mask:0.00888|mask_ratio:0.75|psnr:12.16949|msssim:0.33372|lpips:0.50971|dists:0.28431|Test loss:22.38677 +2024-05-03 17:23:36,872 [INFO ] 82|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.19599|msssim:0.33479|lpips:0.50738|dists:0.27982|Test loss:22.39563 +2024-05-03 17:43:24,129 [INFO ] 83|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.13946|msssim:0.33657|lpips:0.50608|dists:0.28026|Test loss:22.37709 +2024-05-03 18:03:09,632 [INFO ] 84|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.19059|msssim:0.32992|lpips:0.51449|dists:0.28200|Test loss:22.39230 +2024-05-03 18:22:55,813 [INFO ] 85|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18655|msssim:0.33290|lpips:0.50831|dists:0.28350|Test loss:22.38299 +2024-05-03 18:42:41,651 [INFO ] 86|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.21415|msssim:0.33196|lpips:0.50763|dists:0.28348|Test loss:22.34028 +2024-05-03 19:02:43,711 [INFO ] 87|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.12306|msssim:0.33658|lpips:0.50986|dists:0.28280|Test loss:22.35450 +2024-05-03 19:22:30,211 [INFO ] 88|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27434|msssim:0.33857|lpips:0.50953|dists:0.28144|Test loss:22.32993 +2024-05-03 19:42:31,870 [INFO ] 89|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29707|msssim:0.33239|lpips:0.51159|dists:0.28729|Test loss:22.39535 +2024-05-03 20:02:18,523 [INFO ] 90|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.93105|msssim:0.32874|lpips:0.51374|dists:0.28500|Test loss:22.37451 +2024-05-03 20:22:40,704 [INFO ] 91|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.15259|msssim:0.33332|lpips:0.51196|dists:0.28565|Test loss:22.39588 +2024-05-03 20:42:50,402 [INFO ] 92|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22207|msssim:0.33683|lpips:0.50868|dists:0.28256|Test loss:22.34765 +2024-05-03 21:03:00,830 [INFO ] 93|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.11886|msssim:0.33659|lpips:0.50814|dists:0.28405|Test loss:22.34739 +2024-05-03 21:23:11,053 [INFO ] 94|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.13203|msssim:0.33091|lpips:0.50892|dists:0.28377|Test loss:22.36003 +2024-05-03 21:43:19,380 [INFO ] 95|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.13799|msssim:0.33715|lpips:0.51032|dists:0.28500|Test loss:22.35577 +2024-05-03 22:03:29,276 [INFO ] 96|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29663|msssim:0.33942|lpips:0.50849|dists:0.28102|Test loss:22.39275 +2024-05-03 22:23:40,310 [INFO ] 97|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31019|msssim:0.34155|lpips:0.50632|dists:0.28538|Test loss:22.33029 +2024-05-03 22:25:02,985 [INFO ] 98|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28031|msssim:0.33755|lpips:0.50549|dists:0.28245|Test loss:22.33734 +2024-05-03 22:45:13,341 [INFO ] 99|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.17199|msssim:0.33258|lpips:0.50680|dists:0.28107|Test loss:22.37611 +2024-05-03 23:05:22,081 [INFO ] 100|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.12885|msssim:0.33378|lpips:0.50697|dists:0.27960|Test loss:22.35676 +2024-05-03 23:25:59,251 [INFO ] 101|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23955|msssim:0.33759|lpips:0.50643|dists:0.28164|Test loss:22.33918 +2024-05-03 23:46:28,120 [INFO ] 102|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28548|msssim:0.33363|lpips:0.50599|dists:0.28383|Test loss:22.36715 +2024-05-04 00:07:06,785 [INFO ] 103|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.38201|msssim:0.33652|lpips:0.50561|dists:0.28420|Test loss:22.34940 +2024-05-04 00:27:18,585 [INFO ] 104|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23971|msssim:0.33526|lpips:0.50535|dists:0.28196|Test loss:22.32486 +2024-05-04 00:47:46,770 [INFO ] 105|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.16863|msssim:0.33425|lpips:0.50665|dists:0.28272|Test loss:22.35667 +2024-05-04 01:07:48,995 [INFO ] 106|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33955|msssim:0.34124|lpips:0.50687|dists:0.28063|Test loss:22.33323 +2024-05-04 01:27:57,424 [INFO ] 107|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32910|msssim:0.33785|lpips:0.50615|dists:0.28166|Test loss:22.33379 +2024-05-04 01:48:04,206 [INFO ] 108|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.17313|msssim:0.33479|lpips:0.50718|dists:0.28483|Test loss:22.34892 +2024-05-04 02:08:13,034 [INFO ] 109|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25400|msssim:0.33878|lpips:0.50396|dists:0.27973|Test loss:22.27535 +2024-05-04 02:28:39,568 [INFO ] 110|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18919|msssim:0.33519|lpips:0.50766|dists:0.28370|Test loss:22.34272 +2024-05-04 02:48:31,198 [INFO ] 111|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25287|msssim:0.34151|lpips:0.50405|dists:0.28154|Test loss:22.29224 +2024-05-04 03:08:18,193 [INFO ] 112|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25592|msssim:0.33658|lpips:0.50613|dists:0.27908|Test loss:22.31837 +2024-05-04 03:28:06,312 [INFO ] 113|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.19013|msssim:0.33855|lpips:0.50963|dists:0.28313|Test loss:22.35398 +2024-05-04 03:47:53,623 [INFO ] 114|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22372|msssim:0.33530|lpips:0.50483|dists:0.27558|Test loss:22.27828 +2024-05-04 04:07:39,992 [INFO ] 115|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.35171|msssim:0.34350|lpips:0.50526|dists:0.28044|Test loss:22.30720 +2024-05-04 04:27:27,792 [INFO ] 116|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.14400|msssim:0.33273|lpips:0.51011|dists:0.28111|Test loss:22.33026 +2024-05-04 04:47:15,548 [INFO ] 117|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22451|msssim:0.33556|lpips:0.50722|dists:0.28211|Test loss:22.30748 +2024-05-04 05:07:04,750 [INFO ] 118|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39794|msssim:0.33993|lpips:0.50137|dists:0.28058|Test loss:22.32248 +2024-05-04 05:26:52,003 [INFO ] 119|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35083|msssim:0.34228|lpips:0.50405|dists:0.28113|Test loss:22.29005 +2024-05-04 05:46:54,175 [INFO ] 120|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.19306|msssim:0.33767|lpips:0.50508|dists:0.28069|Test loss:22.30016 +2024-05-04 06:07:56,985 [INFO ] 121|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.30696|msssim:0.34229|lpips:0.50263|dists:0.27623|Test loss:22.32207 +2024-05-04 06:28:36,777 [INFO ] 122|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32276|msssim:0.34709|lpips:0.50338|dists:0.27890|Test loss:22.27271 +2024-05-04 06:49:34,456 [INFO ] 123|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.15545|msssim:0.33990|lpips:0.50738|dists:0.28200|Test loss:22.31753 +2024-05-04 07:10:10,082 [INFO ] 124|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26117|msssim:0.33900|lpips:0.50466|dists:0.28039|Test loss:22.31486 +2024-05-04 07:30:52,259 [INFO ] 125|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22142|msssim:0.33723|lpips:0.50712|dists:0.28167|Test loss:22.32770 +2024-05-04 07:51:29,029 [INFO ] 126|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29230|msssim:0.33539|lpips:0.50329|dists:0.27957|Test loss:22.30125 +2024-05-04 08:11:57,295 [INFO ] 127|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.10057|msssim:0.33604|lpips:0.50390|dists:0.27900|Test loss:22.32180 +2024-05-04 08:32:29,651 [INFO ] 128|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35982|msssim:0.34019|lpips:0.50363|dists:0.27863|Test loss:22.30414 +2024-05-04 08:53:13,202 [INFO ] 129|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.15058|msssim:0.33351|lpips:0.50549|dists:0.27943|Test loss:22.26283 +2024-05-04 09:13:57,329 [INFO ] 130|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.27512|msssim:0.33742|lpips:0.50353|dists:0.27905|Test loss:22.30725 +2024-05-04 09:15:24,858 [INFO ] 131|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.20872|msssim:0.34373|lpips:0.50210|dists:0.28035|Test loss:22.27275 +2024-05-04 09:36:10,653 [INFO ] 132|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20383|msssim:0.33974|lpips:0.50339|dists:0.27824|Test loss:22.29464 +2024-05-04 09:56:51,249 [INFO ] 133|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.12968|msssim:0.33882|lpips:0.50371|dists:0.27564|Test loss:22.30516 +2024-05-04 10:17:09,926 [INFO ] 134|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.15733|msssim:0.33909|lpips:0.50420|dists:0.27941|Test loss:22.28371 +2024-05-04 10:37:22,751 [INFO ] 135|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.14190|msssim:0.33220|lpips:0.50398|dists:0.27657|Test loss:22.25691 +2024-05-04 10:58:32,045 [INFO ] 136|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.21952|msssim:0.34333|lpips:0.50654|dists:0.28236|Test loss:22.29695 +2024-05-04 11:19:04,383 [INFO ] 137|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.12107|msssim:0.33130|lpips:0.50644|dists:0.27993|Test loss:22.30494 +2024-05-04 11:39:23,509 [INFO ] 138|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28914|msssim:0.34730|lpips:0.50144|dists:0.27736|Test loss:22.26708 +2024-05-04 11:59:44,396 [INFO ] 139|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.12318|msssim:0.33862|lpips:0.50449|dists:0.27968|Test loss:22.28541 +2024-05-04 12:20:03,670 [INFO ] 140|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36024|msssim:0.33926|lpips:0.50315|dists:0.27705|Test loss:22.24402 +2024-05-04 12:40:47,447 [INFO ] 141|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37666|msssim:0.33869|lpips:0.50093|dists:0.27720|Test loss:22.27834 +2024-05-04 13:01:04,137 [INFO ] 142|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.21903|msssim:0.34257|lpips:0.50445|dists:0.27656|Test loss:22.27222 +2024-05-04 13:21:33,832 [INFO ] 143|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23086|msssim:0.33986|lpips:0.50459|dists:0.28040|Test loss:22.30935 +2024-05-04 13:42:00,911 [INFO ] 144|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18145|msssim:0.34159|lpips:0.50526|dists:0.27986|Test loss:22.29733 +2024-05-04 14:02:21,466 [INFO ] 145|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.41649|msssim:0.34486|lpips:0.50061|dists:0.27830|Test loss:22.27924 +2024-05-04 14:23:01,203 [INFO ] 146|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.40444|msssim:0.33984|lpips:0.50394|dists:0.27939|Test loss:22.27717 +2024-05-04 14:44:11,569 [INFO ] 147|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.10670|msssim:0.33935|lpips:0.50644|dists:0.28129|Test loss:22.28859 +2024-05-04 15:05:12,252 [INFO ] 148|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.31263|msssim:0.34371|lpips:0.50163|dists:0.27719|Test loss:22.28988 +2024-05-04 15:26:22,270 [INFO ] 149|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.11357|msssim:0.34112|lpips:0.50444|dists:0.28143|Test loss:22.27471 +2024-05-04 15:47:18,685 [INFO ] 150|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37583|msssim:0.34108|lpips:0.50440|dists:0.27920|Test loss:22.28879 +2024-05-04 16:08:26,498 [INFO ] 151|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.23233|msssim:0.33481|lpips:0.50793|dists:0.28015|Test loss:22.30692 +2024-05-04 16:29:29,985 [INFO ] 152|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.38831|msssim:0.34042|lpips:0.50221|dists:0.27659|Test loss:22.24423 +2024-05-04 16:50:31,249 [INFO ] 153|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.19962|msssim:0.34101|lpips:0.50407|dists:0.28065|Test loss:22.29826 +2024-05-04 17:11:31,226 [INFO ] 154|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.11960|msssim:0.34173|lpips:0.50215|dists:0.27693|Test loss:22.27951 +2024-05-04 17:32:28,025 [INFO ] 155|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.17072|msssim:0.33614|lpips:0.50420|dists:0.27642|Test loss:22.25476 +2024-05-04 17:53:03,788 [INFO ] 156|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18252|msssim:0.33534|lpips:0.50560|dists:0.27726|Test loss:22.23966 +2024-05-04 18:13:53,631 [INFO ] 157|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.30066|msssim:0.34024|lpips:0.50406|dists:0.27919|Test loss:22.27285 +2024-05-04 18:34:02,548 [INFO ] 158|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.16105|msssim:0.33724|lpips:0.50539|dists:0.27802|Test loss:22.28563 +2024-05-04 18:54:17,391 [INFO ] 159|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.26286|msssim:0.33958|lpips:0.50246|dists:0.27786|Test loss:22.25693 +2024-05-04 19:14:45,980 [INFO ] 160|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.10013|msssim:0.33552|lpips:0.50203|dists:0.27745|Test loss:22.27180 +2024-05-04 19:35:55,093 [INFO ] 161|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.19999|msssim:0.34075|lpips:0.50399|dists:0.28136|Test loss:22.28090 +2024-05-04 19:57:04,549 [INFO ] 162|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35397|msssim:0.34255|lpips:0.50396|dists:0.28067|Test loss:22.30754 +2024-05-04 20:18:09,435 [INFO ] 163|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.07471|msssim:0.33934|lpips:0.50488|dists:0.27944|Test loss:22.29782 +2024-05-04 20:19:36,675 [INFO ] 164|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.15191|msssim:0.33803|lpips:0.50363|dists:0.28080|Test loss:22.30785 +2024-05-04 20:40:45,907 [INFO ] 165|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35093|msssim:0.34222|lpips:0.50133|dists:0.27822|Test loss:22.23812 +2024-05-04 21:02:06,997 [INFO ] 166|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.14147|msssim:0.34612|lpips:0.50051|dists:0.27732|Test loss:22.26769 +2024-05-04 21:23:13,250 [INFO ] 167|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28212|msssim:0.34412|lpips:0.50155|dists:0.27944|Test loss:22.26505 +2024-05-04 21:44:09,446 [INFO ] 168|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.23860|msssim:0.34012|lpips:0.50238|dists:0.27605|Test loss:22.24193 +2024-05-04 22:04:20,528 [INFO ] 169|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18714|msssim:0.34326|lpips:0.50174|dists:0.27552|Test loss:22.19141 +2024-05-04 22:24:52,856 [INFO ] 170|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23269|msssim:0.34707|lpips:0.50277|dists:0.28087|Test loss:22.25782 +2024-05-04 22:46:02,786 [INFO ] 171|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34741|msssim:0.34084|lpips:0.50177|dists:0.27559|Test loss:22.23672 +2024-05-04 23:06:52,141 [INFO ] 172|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.28709|msssim:0.34182|lpips:0.50183|dists:0.27629|Test loss:22.25575 +2024-05-04 23:27:45,873 [INFO ] 173|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32183|msssim:0.34279|lpips:0.50424|dists:0.27647|Test loss:22.26211 +2024-05-04 23:49:09,385 [INFO ] 174|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27799|msssim:0.34067|lpips:0.50149|dists:0.27606|Test loss:22.25085 +2024-05-05 00:09:54,865 [INFO ] 175|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.23614|msssim:0.34026|lpips:0.50595|dists:0.27867|Test loss:22.26061 +2024-05-05 00:30:37,643 [INFO ] 176|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.26992|msssim:0.34440|lpips:0.50466|dists:0.27647|Test loss:22.26275 +2024-05-05 00:51:58,336 [INFO ] 177|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.27768|msssim:0.34192|lpips:0.50217|dists:0.27718|Test loss:22.24697 +2024-05-05 01:12:41,638 [INFO ] 178|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.22162|msssim:0.34181|lpips:0.50407|dists:0.28091|Test loss:22.28326 +2024-05-05 01:33:33,913 [INFO ] 179|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33287|msssim:0.34137|lpips:0.50010|dists:0.27889|Test loss:22.25626 +2024-05-05 01:54:24,919 [INFO ] 180|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26020|msssim:0.33738|lpips:0.50198|dists:0.27898|Test loss:22.27012 +2024-05-05 02:15:11,781 [INFO ] 181|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.24128|msssim:0.34296|lpips:0.50206|dists:0.27911|Test loss:22.27168 +2024-05-05 02:36:00,258 [INFO ] 182|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.29608|msssim:0.34143|lpips:0.50258|dists:0.27963|Test loss:22.23007 +2024-05-05 02:56:51,997 [INFO ] 183|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.14427|msssim:0.33923|lpips:0.50353|dists:0.27847|Test loss:22.26153 +2024-05-05 03:17:42,729 [INFO ] 184|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.24628|msssim:0.33477|lpips:0.50120|dists:0.27460|Test loss:22.22222 +2024-05-05 03:38:55,556 [INFO ] 185|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22856|msssim:0.33995|lpips:0.49928|dists:0.27506|Test loss:22.21741 +2024-05-05 03:59:54,038 [INFO ] 186|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.25829|msssim:0.34396|lpips:0.50041|dists:0.27953|Test loss:22.21000 +2024-05-05 04:20:57,361 [INFO ] 187|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33900|msssim:0.34563|lpips:0.50119|dists:0.27801|Test loss:22.24254 +2024-05-05 04:41:56,894 [INFO ] 188|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25541|msssim:0.34364|lpips:0.50117|dists:0.27644|Test loss:22.22793 +2024-05-05 05:02:57,947 [INFO ] 189|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.29414|msssim:0.34397|lpips:0.50046|dists:0.27675|Test loss:22.22504 +2024-05-05 05:24:00,200 [INFO ] 190|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42643|msssim:0.34251|lpips:0.50034|dists:0.27756|Test loss:22.20704 +2024-05-05 05:45:06,616 [INFO ] 191|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.24620|msssim:0.33765|lpips:0.50179|dists:0.27763|Test loss:22.24989 +2024-05-05 06:05:48,232 [INFO ] 192|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20766|msssim:0.34222|lpips:0.50102|dists:0.27429|Test loss:22.20998 +2024-05-05 06:26:41,032 [INFO ] 193|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28995|msssim:0.34232|lpips:0.50011|dists:0.27672|Test loss:22.19870 +2024-05-05 06:47:28,088 [INFO ] 194|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.31111|msssim:0.34537|lpips:0.50108|dists:0.27792|Test loss:22.22558 +2024-05-05 07:08:15,894 [INFO ] 195|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.19613|msssim:0.34356|lpips:0.50329|dists:0.27837|Test loss:22.27292 +2024-05-05 07:29:07,588 [INFO ] 196|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34946|msssim:0.34073|lpips:0.50324|dists:0.27687|Test loss:22.25968 +2024-05-05 07:30:34,517 [INFO ] 197|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28973|msssim:0.34913|lpips:0.49958|dists:0.27724|Test loss:22.21089 +2024-05-05 07:51:24,205 [INFO ] 198|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.22106|msssim:0.33796|lpips:0.50167|dists:0.27715|Test loss:22.27205 +2024-05-05 08:12:37,156 [INFO ] 199|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.26782|msssim:0.34472|lpips:0.50100|dists:0.27782|Test loss:22.21741 +2024-05-05 08:33:49,412 [INFO ] 200|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28700|msssim:0.34200|lpips:0.50071|dists:0.27705|Test loss:22.24541 +2024-05-05 08:55:00,482 [INFO ] 201|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26943|msssim:0.34567|lpips:0.50009|dists:0.27623|Test loss:22.23442 +2024-05-05 09:15:55,407 [INFO ] 202|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.28424|msssim:0.33845|lpips:0.50023|dists:0.27742|Test loss:22.21661 +2024-05-05 09:37:07,037 [INFO ] 203|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36869|msssim:0.34921|lpips:0.49663|dists:0.27317|Test loss:22.18738 +2024-05-05 09:58:22,992 [INFO ] 204|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29465|msssim:0.34732|lpips:0.49851|dists:0.27560|Test loss:22.21542 +2024-05-05 10:19:30,345 [INFO ] 205|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34941|msssim:0.34522|lpips:0.49991|dists:0.27673|Test loss:22.23310 +2024-05-05 10:40:34,778 [INFO ] 206|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29729|msssim:0.34074|lpips:0.49963|dists:0.27439|Test loss:22.18471 +2024-05-05 11:01:52,830 [INFO ] 207|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20647|msssim:0.34440|lpips:0.50193|dists:0.27544|Test loss:22.16906 +2024-05-05 11:23:10,869 [INFO ] 208|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33049|msssim:0.34167|lpips:0.49881|dists:0.27573|Test loss:22.22115 +2024-05-05 11:44:29,730 [INFO ] 209|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.36687|msssim:0.34201|lpips:0.50120|dists:0.27688|Test loss:22.21192 +2024-05-05 12:05:44,940 [INFO ] 210|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.19353|msssim:0.33601|lpips:0.50266|dists:0.27510|Test loss:22.24716 +2024-05-05 12:27:06,882 [INFO ] 211|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31839|msssim:0.34303|lpips:0.49930|dists:0.27422|Test loss:22.19657 +2024-05-05 12:48:21,046 [INFO ] 212|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42858|msssim:0.34387|lpips:0.49946|dists:0.27734|Test loss:22.23478 +2024-05-05 13:09:31,493 [INFO ] 213|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31374|msssim:0.34684|lpips:0.49643|dists:0.27400|Test loss:22.22582 +2024-05-05 13:30:34,522 [INFO ] 214|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.21293|msssim:0.34112|lpips:0.49883|dists:0.27535|Test loss:22.21174 +2024-05-05 13:51:47,865 [INFO ] 215|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.41841|msssim:0.34328|lpips:0.49828|dists:0.27544|Test loss:22.16456 +2024-05-05 14:13:13,905 [INFO ] 216|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25790|msssim:0.34093|lpips:0.49955|dists:0.27657|Test loss:22.21031 +2024-05-05 14:34:27,772 [INFO ] 217|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.25331|msssim:0.34462|lpips:0.50060|dists:0.27798|Test loss:22.19541 +2024-05-05 14:55:44,043 [INFO ] 218|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34120|msssim:0.34909|lpips:0.49698|dists:0.27423|Test loss:22.18064 +2024-05-05 15:17:02,181 [INFO ] 219|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37122|msssim:0.34335|lpips:0.49592|dists:0.27407|Test loss:22.21357 +2024-05-05 15:38:13,952 [INFO ] 220|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33670|msssim:0.34260|lpips:0.50183|dists:0.27681|Test loss:22.21649 +2024-05-05 15:59:11,744 [INFO ] 221|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.28591|msssim:0.34584|lpips:0.50050|dists:0.27571|Test loss:22.23926 +2024-05-05 16:20:07,029 [INFO ] 222|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26039|msssim:0.34677|lpips:0.49812|dists:0.27303|Test loss:22.18227 +2024-05-05 16:41:01,438 [INFO ] 223|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35407|msssim:0.34511|lpips:0.49698|dists:0.27452|Test loss:22.20431 +2024-05-05 17:01:56,573 [INFO ] 224|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.36067|msssim:0.34552|lpips:0.49578|dists:0.27214|Test loss:22.16769 +2024-05-05 17:23:09,208 [INFO ] 225|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34319|msssim:0.34962|lpips:0.49547|dists:0.27391|Test loss:22.15918 +2024-05-05 17:44:29,823 [INFO ] 226|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.21200|msssim:0.34131|lpips:0.49971|dists:0.27562|Test loss:22.19288 +2024-05-05 18:05:38,805 [INFO ] 227|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34673|msssim:0.34305|lpips:0.49692|dists:0.27394|Test loss:22.18381 +2024-05-05 18:26:44,515 [INFO ] 228|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26413|msssim:0.33844|lpips:0.50093|dists:0.27558|Test loss:22.21518 +2024-05-05 18:47:55,570 [INFO ] 229|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20001|msssim:0.34085|lpips:0.50149|dists:0.27539|Test loss:22.16549 +2024-05-05 18:49:21,297 [INFO ] 230|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.24847|msssim:0.34171|lpips:0.50002|dists:0.27554|Test loss:22.18368 +2024-05-05 19:10:31,373 [INFO ] 231|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.26719|msssim:0.34548|lpips:0.50115|dists:0.27678|Test loss:22.23358 +2024-05-05 19:31:27,088 [INFO ] 232|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29815|msssim:0.34711|lpips:0.49873|dists:0.27557|Test loss:22.20006 +2024-05-05 19:52:35,830 [INFO ] 233|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.45504|msssim:0.35044|lpips:0.50058|dists:0.27448|Test loss:22.19251 +2024-05-05 20:13:42,913 [INFO ] 234|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.36103|msssim:0.34949|lpips:0.49969|dists:0.27417|Test loss:22.19380 +2024-05-05 20:34:48,991 [INFO ] 235|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.43152|msssim:0.34284|lpips:0.49837|dists:0.27674|Test loss:22.17983 +2024-05-05 20:55:57,801 [INFO ] 236|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31431|msssim:0.34494|lpips:0.49805|dists:0.27595|Test loss:22.18135 +2024-05-05 21:17:01,921 [INFO ] 237|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39373|msssim:0.34481|lpips:0.49903|dists:0.27500|Test loss:22.19592 +2024-05-05 21:38:15,187 [INFO ] 238|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32557|msssim:0.34869|lpips:0.50099|dists:0.27830|Test loss:22.18304 +2024-05-05 21:59:20,693 [INFO ] 239|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35618|msssim:0.34089|lpips:0.49914|dists:0.27589|Test loss:22.22733 +2024-05-05 22:20:28,843 [INFO ] 240|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.38320|msssim:0.34359|lpips:0.49845|dists:0.27558|Test loss:22.20330 +2024-05-05 22:41:33,103 [INFO ] 241|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.41832|msssim:0.34829|lpips:0.49624|dists:0.27300|Test loss:22.20170 +2024-05-05 23:02:36,039 [INFO ] 242|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26759|msssim:0.34710|lpips:0.49868|dists:0.27406|Test loss:22.18713 +2024-05-05 23:23:41,292 [INFO ] 243|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.25480|msssim:0.33822|lpips:0.49764|dists:0.27286|Test loss:22.20413 +2024-05-05 23:44:51,601 [INFO ] 244|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.24137|msssim:0.34107|lpips:0.49667|dists:0.27336|Test loss:22.19491 +2024-05-06 00:05:59,368 [INFO ] 245|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.44126|msssim:0.34903|lpips:0.49630|dists:0.27284|Test loss:22.21318 +2024-05-06 00:26:57,417 [INFO ] 246|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35589|msssim:0.34458|lpips:0.49846|dists:0.27602|Test loss:22.18793 +2024-05-06 00:48:01,606 [INFO ] 247|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29865|msssim:0.34271|lpips:0.49826|dists:0.27014|Test loss:22.17141 +2024-05-06 01:09:07,370 [INFO ] 248|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.31535|msssim:0.34943|lpips:0.49795|dists:0.27377|Test loss:22.17951 +2024-05-06 01:30:20,686 [INFO ] 249|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39809|msssim:0.34563|lpips:0.50040|dists:0.27597|Test loss:22.16323 +2024-05-06 01:51:22,543 [INFO ] 250|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.28734|msssim:0.35247|lpips:0.49776|dists:0.27438|Test loss:22.14602 +2024-05-06 02:12:51,537 [INFO ] 251|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.28380|msssim:0.34202|lpips:0.49683|dists:0.27599|Test loss:22.20221 +2024-05-06 02:33:50,217 [INFO ] 252|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33769|msssim:0.35137|lpips:0.49936|dists:0.27664|Test loss:22.18598 +2024-05-06 02:55:05,177 [INFO ] 253|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.30681|msssim:0.34182|lpips:0.50133|dists:0.27604|Test loss:22.19516 +2024-05-06 03:16:12,944 [INFO ] 254|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39329|msssim:0.34256|lpips:0.49706|dists:0.27318|Test loss:22.16968 +2024-05-06 03:37:19,768 [INFO ] 255|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.44825|msssim:0.34998|lpips:0.49653|dists:0.27555|Test loss:22.16102 +2024-05-06 03:58:30,008 [INFO ] 256|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.30121|msssim:0.34926|lpips:0.49515|dists:0.27333|Test loss:22.14309 +2024-05-06 04:19:52,471 [INFO ] 257|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35113|msssim:0.34655|lpips:0.49584|dists:0.27406|Test loss:22.19664 +2024-05-06 04:40:58,643 [INFO ] 258|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34788|msssim:0.34555|lpips:0.49748|dists:0.27429|Test loss:22.15390 +2024-05-06 05:02:09,625 [INFO ] 259|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37536|msssim:0.34993|lpips:0.49926|dists:0.27615|Test loss:22.16938 +2024-05-06 05:23:15,111 [INFO ] 260|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.30780|msssim:0.34366|lpips:0.49741|dists:0.27334|Test loss:22.14079 +2024-05-06 05:44:58,198 [INFO ] 261|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32658|msssim:0.34584|lpips:0.49735|dists:0.27514|Test loss:22.15270 +2024-05-06 06:06:38,107 [INFO ] 262|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34172|msssim:0.34937|lpips:0.49673|dists:0.27184|Test loss:22.16732 +2024-05-06 06:08:06,494 [INFO ] 263|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34207|msssim:0.34853|lpips:0.49675|dists:0.27406|Test loss:22.15906 +2024-05-06 06:29:36,637 [INFO ] 264|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.46720|msssim:0.34606|lpips:0.49734|dists:0.27330|Test loss:22.16713 +2024-05-06 06:50:49,014 [INFO ] 265|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.39163|msssim:0.34577|lpips:0.49680|dists:0.27300|Test loss:22.15004 +2024-05-06 07:11:43,798 [INFO ] 266|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34610|msssim:0.34627|lpips:0.49790|dists:0.27368|Test loss:22.18821 +2024-05-06 07:32:32,314 [INFO ] 267|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.35793|msssim:0.34650|lpips:0.49353|dists:0.27379|Test loss:22.15183 +2024-05-06 07:53:23,679 [INFO ] 268|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35679|msssim:0.34936|lpips:0.49728|dists:0.27346|Test loss:22.14839 +2024-05-06 08:14:16,352 [INFO ] 269|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33630|msssim:0.34671|lpips:0.49758|dists:0.27488|Test loss:22.11851 +2024-05-06 08:35:41,030 [INFO ] 270|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.50002|msssim:0.35202|lpips:0.49321|dists:0.27390|Test loss:22.13897 +2024-05-06 08:55:44,311 [INFO ] 271|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.26539|msssim:0.33970|lpips:0.49583|dists:0.27329|Test loss:22.18124 +2024-05-06 09:15:39,615 [INFO ] 272|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35656|msssim:0.34859|lpips:0.49631|dists:0.27369|Test loss:22.16513 +2024-05-06 09:35:34,569 [INFO ] 273|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37810|msssim:0.34932|lpips:0.49532|dists:0.27358|Test loss:22.14227 +2024-05-06 09:55:25,915 [INFO ] 274|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33323|msssim:0.34434|lpips:0.49772|dists:0.27299|Test loss:22.13977 +2024-05-06 10:15:09,984 [INFO ] 275|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29836|msssim:0.34814|lpips:0.49551|dists:0.27238|Test loss:22.17381 +2024-05-06 10:34:55,762 [INFO ] 276|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.41804|msssim:0.34762|lpips:0.49558|dists:0.27152|Test loss:22.13700 +2024-05-06 10:54:41,271 [INFO ] 277|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34791|msssim:0.34759|lpips:0.49562|dists:0.27405|Test loss:22.12424 +2024-05-06 11:14:24,991 [INFO ] 278|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.43791|msssim:0.34807|lpips:0.49478|dists:0.27218|Test loss:22.15800 +2024-05-06 11:34:06,688 [INFO ] 279|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37573|msssim:0.34698|lpips:0.49517|dists:0.27491|Test loss:22.17768 +2024-05-06 11:53:52,546 [INFO ] 280|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.32452|msssim:0.34915|lpips:0.49737|dists:0.27415|Test loss:22.14754 +2024-05-06 12:13:42,463 [INFO ] 281|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23457|msssim:0.34536|lpips:0.49823|dists:0.27429|Test loss:22.18300 +2024-05-06 12:33:26,800 [INFO ] 282|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32926|msssim:0.34179|lpips:0.49565|dists:0.27257|Test loss:22.12766 +2024-05-06 12:53:12,784 [INFO ] 283|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37897|msssim:0.34765|lpips:0.49597|dists:0.27514|Test loss:22.13906 +2024-05-06 13:13:01,155 [INFO ] 284|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.35453|msssim:0.34555|lpips:0.49322|dists:0.27190|Test loss:22.15379 +2024-05-06 13:32:55,503 [INFO ] 285|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.25997|msssim:0.34366|lpips:0.49811|dists:0.27320|Test loss:22.14294 +2024-05-06 13:53:05,337 [INFO ] 286|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33252|msssim:0.34884|lpips:0.49568|dists:0.27505|Test loss:22.14334 +2024-05-06 14:12:57,225 [INFO ] 287|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37169|msssim:0.34353|lpips:0.49783|dists:0.27417|Test loss:22.14881 +2024-05-06 14:32:49,867 [INFO ] 288|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28825|msssim:0.34587|lpips:0.49870|dists:0.27497|Test loss:22.16187 +2024-05-06 14:52:38,558 [INFO ] 289|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37071|msssim:0.34675|lpips:0.49692|dists:0.27600|Test loss:22.16026 +2024-05-06 15:12:23,303 [INFO ] 290|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37343|msssim:0.35109|lpips:0.49530|dists:0.27273|Test loss:22.14378 +2024-05-06 15:32:11,252 [INFO ] 291|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37243|msssim:0.34936|lpips:0.49572|dists:0.27396|Test loss:22.17331 +2024-05-06 15:51:58,068 [INFO ] 292|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.55883|msssim:0.35747|lpips:0.49342|dists:0.27048|Test loss:22.13242 +2024-05-06 16:11:44,645 [INFO ] 293|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33236|msssim:0.34500|lpips:0.49713|dists:0.27188|Test loss:22.13761 +2024-05-06 16:31:32,164 [INFO ] 294|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37576|msssim:0.35218|lpips:0.49406|dists:0.27047|Test loss:22.14124 +2024-05-06 16:51:17,715 [INFO ] 295|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.38965|msssim:0.34790|lpips:0.49487|dists:0.27294|Test loss:22.12060 +2024-05-06 16:52:39,431 [INFO ] 296|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32517|msssim:0.35096|lpips:0.49530|dists:0.27325|Test loss:22.13534 +2024-05-06 17:12:41,901 [INFO ] 297|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.38625|msssim:0.35519|lpips:0.49261|dists:0.27088|Test loss:22.13709 +2024-05-06 17:32:30,223 [INFO ] 298|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.45197|msssim:0.35502|lpips:0.49372|dists:0.27574|Test loss:22.10319 +2024-05-06 17:53:02,560 [INFO ] 299|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27800|msssim:0.34538|lpips:0.49640|dists:0.27273|Test loss:22.16596 +2024-05-06 18:13:17,675 [INFO ] 300|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.43854|msssim:0.35064|lpips:0.49498|dists:0.27317|Test loss:22.13541 +2024-05-06 18:33:40,420 [INFO ] 301|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42806|msssim:0.35004|lpips:0.49591|dists:0.27262|Test loss:22.13021 +2024-05-06 18:54:35,721 [INFO ] 302|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.40629|msssim:0.35309|lpips:0.49433|dists:0.27251|Test loss:22.10585 +2024-05-06 19:15:23,438 [INFO ] 303|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.43635|msssim:0.35212|lpips:0.49461|dists:0.27331|Test loss:22.09783 +2024-05-06 19:36:11,827 [INFO ] 304|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.41165|msssim:0.34843|lpips:0.49495|dists:0.27378|Test loss:22.13802 +2024-05-06 19:56:38,743 [INFO ] 305|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36958|msssim:0.35154|lpips:0.49564|dists:0.27358|Test loss:22.18889 +2024-05-06 20:17:02,992 [INFO ] 306|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.36006|msssim:0.34681|lpips:0.49732|dists:0.27467|Test loss:22.16472 +2024-05-06 20:37:27,146 [INFO ] 307|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33619|msssim:0.34884|lpips:0.49251|dists:0.27202|Test loss:22.10112 +2024-05-06 20:57:51,479 [INFO ] 308|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.38734|msssim:0.34704|lpips:0.49590|dists:0.27333|Test loss:22.16504 +2024-05-06 21:17:40,325 [INFO ] 309|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.47638|msssim:0.34979|lpips:0.49419|dists:0.27257|Test loss:22.09890 +2024-05-06 21:38:20,823 [INFO ] 310|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.45121|msssim:0.35461|lpips:0.49393|dists:0.27205|Test loss:22.10250 +2024-05-06 21:58:54,290 [INFO ] 311|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42236|msssim:0.34856|lpips:0.49429|dists:0.27074|Test loss:22.12690 +2024-05-06 22:19:28,569 [INFO ] 312|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42022|msssim:0.34901|lpips:0.49516|dists:0.27319|Test loss:22.14768 +2024-05-06 22:40:03,891 [INFO ] 313|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35907|msssim:0.34569|lpips:0.49461|dists:0.27076|Test loss:22.15050 +2024-05-06 23:00:39,129 [INFO ] 314|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28936|msssim:0.34797|lpips:0.49777|dists:0.27248|Test loss:22.13753 +2024-05-06 23:21:22,649 [INFO ] 315|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29058|msssim:0.35014|lpips:0.49607|dists:0.27623|Test loss:22.14135 +2024-05-06 23:42:05,520 [INFO ] 316|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37230|msssim:0.34671|lpips:0.49491|dists:0.27353|Test loss:22.08937 +2024-05-07 00:02:59,358 [INFO ] 317|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33447|msssim:0.35346|lpips:0.49475|dists:0.27089|Test loss:22.10210 +2024-05-07 00:23:07,579 [INFO ] 318|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.49132|msssim:0.35204|lpips:0.49550|dists:0.27231|Test loss:22.11998 +2024-05-07 00:42:57,724 [INFO ] 319|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37582|msssim:0.35174|lpips:0.49482|dists:0.27242|Test loss:22.16880 +2024-05-07 01:02:46,713 [INFO ] 320|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35898|msssim:0.35062|lpips:0.49379|dists:0.27271|Test loss:22.11210 +2024-05-07 01:22:57,462 [INFO ] 321|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.32817|msssim:0.35568|lpips:0.49295|dists:0.26980|Test loss:22.14307 +2024-05-07 01:42:55,818 [INFO ] 322|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.40482|msssim:0.34941|lpips:0.49731|dists:0.27440|Test loss:22.12967 +2024-05-07 02:03:35,260 [INFO ] 323|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.48860|msssim:0.35103|lpips:0.49498|dists:0.27326|Test loss:22.12212 +2024-05-07 02:23:46,368 [INFO ] 324|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.41354|msssim:0.35028|lpips:0.49496|dists:0.27339|Test loss:22.11742 +2024-05-07 02:43:38,446 [INFO ] 325|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37060|msssim:0.34866|lpips:0.49335|dists:0.27179|Test loss:22.13050 +2024-05-07 03:03:35,008 [INFO ] 326|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.52411|msssim:0.35067|lpips:0.49280|dists:0.27236|Test loss:22.11754 +2024-05-07 03:23:27,535 [INFO ] 327|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39696|msssim:0.35174|lpips:0.49652|dists:0.27232|Test loss:22.12615 +2024-05-07 03:43:44,363 [INFO ] 328|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.39724|msssim:0.34908|lpips:0.49480|dists:0.27135|Test loss:22.14223 +2024-05-07 03:45:05,759 [INFO ] 329|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.40106|msssim:0.35324|lpips:0.49468|dists:0.27436|Test loss:22.10712 +2024-05-07 04:04:47,920 [INFO ] 330|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32596|msssim:0.34426|lpips:0.49445|dists:0.26987|Test loss:22.13806 +2024-05-07 04:24:31,360 [INFO ] 331|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26787|msssim:0.34595|lpips:0.49652|dists:0.27052|Test loss:22.12575 +2024-05-07 04:44:11,953 [INFO ] 332|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.43838|msssim:0.34961|lpips:0.49307|dists:0.27184|Test loss:22.11797 +2024-05-07 05:03:51,738 [INFO ] 333|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42003|msssim:0.35362|lpips:0.49367|dists:0.27208|Test loss:22.11651 +2024-05-07 05:23:34,532 [INFO ] 334|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.37144|msssim:0.35248|lpips:0.49673|dists:0.27496|Test loss:22.14811 +2024-05-07 05:43:17,906 [INFO ] 335|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.41272|msssim:0.35291|lpips:0.49492|dists:0.27193|Test loss:22.12853 +2024-05-07 06:03:26,128 [INFO ] 336|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31984|msssim:0.34795|lpips:0.49517|dists:0.27108|Test loss:22.11144 +2024-05-07 06:23:14,028 [INFO ] 337|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35224|msssim:0.35102|lpips:0.49385|dists:0.27123|Test loss:22.12615 +2024-05-07 06:43:29,005 [INFO ] 338|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34989|msssim:0.34449|lpips:0.49436|dists:0.27227|Test loss:22.14807 +2024-05-07 07:03:22,585 [INFO ] 339|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35900|msssim:0.35273|lpips:0.49441|dists:0.27247|Test loss:22.11002 +2024-05-07 07:23:32,865 [INFO ] 340|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.43373|msssim:0.35449|lpips:0.49218|dists:0.27258|Test loss:22.09663 +2024-05-07 07:44:08,078 [INFO ] 341|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.49582|msssim:0.35435|lpips:0.49275|dists:0.27220|Test loss:22.09673 +2024-05-07 08:05:12,924 [INFO ] 342|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27418|msssim:0.35315|lpips:0.49452|dists:0.27191|Test loss:22.11007 +2024-05-07 08:26:23,259 [INFO ] 343|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.35699|msssim:0.34731|lpips:0.49411|dists:0.27074|Test loss:22.13691 +2024-05-07 08:47:40,259 [INFO ] 344|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.43843|msssim:0.35445|lpips:0.49354|dists:0.27134|Test loss:22.09072 +2024-05-07 09:08:57,546 [INFO ] 345|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.40076|msssim:0.35221|lpips:0.49355|dists:0.27157|Test loss:22.12100 +2024-05-07 09:30:12,427 [INFO ] 346|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36718|msssim:0.35203|lpips:0.49355|dists:0.27277|Test loss:22.08085 +2024-05-07 09:50:36,931 [INFO ] 347|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34232|msssim:0.34843|lpips:0.49354|dists:0.27177|Test loss:22.12479 +2024-05-07 10:10:45,355 [INFO ] 348|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42333|msssim:0.34844|lpips:0.49300|dists:0.27182|Test loss:22.11209 +2024-05-07 10:30:50,802 [INFO ] 349|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42717|msssim:0.35086|lpips:0.49458|dists:0.27253|Test loss:22.11386 +2024-05-07 10:50:55,389 [INFO ] 350|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.35927|msssim:0.34962|lpips:0.49381|dists:0.26980|Test loss:22.10434 +2024-05-07 11:11:33,800 [INFO ] 351|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29633|msssim:0.34823|lpips:0.49724|dists:0.27674|Test loss:22.14577 +2024-05-07 11:32:25,539 [INFO ] 352|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36162|msssim:0.35235|lpips:0.49426|dists:0.26995|Test loss:22.10663 +2024-05-07 11:53:24,658 [INFO ] 353|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37880|msssim:0.35225|lpips:0.49382|dists:0.26963|Test loss:22.11384 +2024-05-07 12:14:36,359 [INFO ] 354|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.40548|msssim:0.34668|lpips:0.49391|dists:0.27245|Test loss:22.14980 +2024-05-07 12:35:38,612 [INFO ] 355|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42286|msssim:0.35354|lpips:0.49382|dists:0.27242|Test loss:22.12479 +2024-05-07 12:56:38,507 [INFO ] 356|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33143|msssim:0.35170|lpips:0.49645|dists:0.27390|Test loss:22.13347 +2024-05-07 13:17:39,596 [INFO ] 357|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36672|msssim:0.34573|lpips:0.49592|dists:0.27143|Test loss:22.13349 +2024-05-07 13:38:44,893 [INFO ] 358|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42944|msssim:0.35298|lpips:0.49284|dists:0.27126|Test loss:22.12769 +2024-05-07 13:59:43,389 [INFO ] 359|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.38038|msssim:0.35037|lpips:0.49457|dists:0.27234|Test loss:22.09782 +2024-05-07 14:20:17,253 [INFO ] 360|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.38950|msssim:0.35237|lpips:0.49389|dists:0.27129|Test loss:22.09906 +2024-05-07 14:40:43,289 [INFO ] 361|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.43936|msssim:0.34640|lpips:0.49196|dists:0.27161|Test loss:22.11428 +2024-05-07 14:42:07,243 [INFO ] 362|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.40003|msssim:0.35458|lpips:0.49361|dists:0.27126|Test loss:22.08922 +2024-05-07 15:02:21,429 [INFO ] 363|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.48445|msssim:0.35553|lpips:0.49189|dists:0.27160|Test loss:22.08326 +2024-05-07 15:22:34,155 [INFO ] 364|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.35317|msssim:0.35344|lpips:0.49614|dists:0.27294|Test loss:22.15190 +2024-05-07 15:42:52,603 [INFO ] 365|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42529|msssim:0.35113|lpips:0.49577|dists:0.27466|Test loss:22.11909 +2024-05-07 16:03:38,749 [INFO ] 366|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.33904|msssim:0.35202|lpips:0.49448|dists:0.27241|Test loss:22.12104 +2024-05-07 16:24:24,975 [INFO ] 367|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42592|msssim:0.35292|lpips:0.49367|dists:0.27379|Test loss:22.11291 +2024-05-07 16:44:56,650 [INFO ] 368|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39894|msssim:0.35064|lpips:0.49435|dists:0.27276|Test loss:22.12427 +2024-05-07 17:05:25,010 [INFO ] 369|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.48808|msssim:0.35531|lpips:0.49202|dists:0.27039|Test loss:22.11523 +2024-05-07 17:25:58,021 [INFO ] 370|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39756|msssim:0.35016|lpips:0.49330|dists:0.27168|Test loss:22.10594 +2024-05-07 17:46:18,607 [INFO ] 371|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.49306|msssim:0.35298|lpips:0.49468|dists:0.27251|Test loss:22.11654 +2024-05-07 18:06:49,127 [INFO ] 372|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34533|msssim:0.35142|lpips:0.49453|dists:0.27251|Test loss:22.12963 +2024-05-07 18:27:08,641 [INFO ] 373|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.34550|msssim:0.35208|lpips:0.49310|dists:0.27301|Test loss:22.11551 +2024-05-07 18:47:50,057 [INFO ] 374|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37816|msssim:0.35055|lpips:0.49417|dists:0.27248|Test loss:22.10346 +2024-05-07 19:08:22,282 [INFO ] 375|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36707|msssim:0.34627|lpips:0.49279|dists:0.27090|Test loss:22.07935 +2024-05-07 19:29:07,038 [INFO ] 376|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42175|msssim:0.35269|lpips:0.49278|dists:0.27011|Test loss:22.07009 +2024-05-07 19:49:50,578 [INFO ] 377|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.40540|msssim:0.35338|lpips:0.49272|dists:0.27135|Test loss:22.12318 +2024-05-07 20:10:17,385 [INFO ] 378|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39138|msssim:0.34611|lpips:0.49605|dists:0.27153|Test loss:22.13597 +2024-05-07 20:30:46,458 [INFO ] 379|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.39260|msssim:0.34934|lpips:0.49563|dists:0.27480|Test loss:22.14463 +2024-05-07 20:51:11,028 [INFO ] 380|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.42045|msssim:0.34795|lpips:0.49372|dists:0.26907|Test loss:22.10082 +2024-05-07 21:11:40,325 [INFO ] 381|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27697|msssim:0.34880|lpips:0.49537|dists:0.27470|Test loss:22.09123 +2024-05-07 21:32:25,704 [INFO ] 382|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35365|msssim:0.34741|lpips:0.49410|dists:0.27204|Test loss:22.12331 +2024-05-07 21:53:04,306 [INFO ] 383|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28105|msssim:0.35066|lpips:0.49763|dists:0.27276|Test loss:22.13340 +2024-05-07 22:13:43,291 [INFO ] 384|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31601|msssim:0.34885|lpips:0.49536|dists:0.27251|Test loss:22.12415 +2024-05-07 22:34:19,100 [INFO ] 385|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.34422|msssim:0.35122|lpips:0.49521|dists:0.27288|Test loss:22.14277 +2024-05-07 22:54:56,647 [INFO ] 386|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.41440|msssim:0.34890|lpips:0.49383|dists:0.27097|Test loss:22.10782 +2024-05-07 23:15:36,868 [INFO ] 387|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.45614|msssim:0.35336|lpips:0.49236|dists:0.27192|Test loss:22.09378 +2024-05-07 23:36:07,945 [INFO ] 388|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.38647|msssim:0.34605|lpips:0.49631|dists:0.27383|Test loss:22.12287 +2024-05-07 23:56:49,417 [INFO ] 389|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.38224|msssim:0.35091|lpips:0.49457|dists:0.27112|Test loss:22.07871 +2024-05-08 00:17:12,737 [INFO ] 390|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.43890|msssim:0.35296|lpips:0.49275|dists:0.27223|Test loss:22.13008 +2024-05-08 00:37:32,168 [INFO ] 391|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35077|msssim:0.34966|lpips:0.49267|dists:0.27072|Test loss:22.13726 +2024-05-08 00:58:00,221 [INFO ] 392|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.43491|msssim:0.34659|lpips:0.49620|dists:0.27359|Test loss:22.16497 +2024-05-08 01:18:28,620 [INFO ] 393|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.46330|msssim:0.35150|lpips:0.49221|dists:0.27154|Test loss:22.10838 +2024-05-08 01:39:01,279 [INFO ] 394|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.48316|msssim:0.35406|lpips:0.49092|dists:0.26870|Test loss:22.06746 +2024-05-08 01:40:42,930 [INFO ] 395|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.39606|msssim:0.35099|lpips:0.49426|dists:0.27250|Test loss:22.11529 +2024-05-08 02:00:57,924 [INFO ] 396|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.37678|msssim:0.35195|lpips:0.49500|dists:0.27399|Test loss:22.10022 +2024-05-08 02:21:26,695 [INFO ] 397|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36464|msssim:0.35303|lpips:0.49387|dists:0.27059|Test loss:22.10485 +2024-05-08 02:42:04,184 [INFO ] 398|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.42602|msssim:0.34833|lpips:0.49350|dists:0.27095|Test loss:22.11267 +2024-05-08 03:02:23,282 [INFO ] 399|bpp:0.01875|mask:0.00888|mask_ratio:0.75|psnr:12.46092|msssim:0.34956|lpips:0.49528|dists:0.27270|Test loss:22.10216 diff --git a/MIM_vbr/events.out.tfevents.1714742678.t2vg-a100-G4-10.4118013.0 b/MIM_vbr/events.out.tfevents.1714742678.t2vg-a100-G4-10.4118013.0 new file mode 100644 index 0000000000000000000000000000000000000000..96cc95bb307ec6e4cc017afb3c33232a8cbb4bf4 --- 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--- /dev/null +++ b/MIM_vbr_eval_lossless/20240518_044257.log @@ -0,0 +1,53 @@ +2024-05-18 04:42:57,952 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless//20240518_044257.log +2024-05-18 04:42:57,952 [INFO ] ======================= exp ======================= +2024-05-18 04:42:57,952 [INFO ] config:config/vpt_default.yaml +2024-05-18 04:42:57,952 [INFO ] name:exp +2024-05-18 04:42:57,952 [INFO ] lr:None +2024-05-18 04:42:57,952 [INFO ] world_size:1 +2024-05-18 04:42:57,952 [INFO ] local_rank:-1 +2024-05-18 04:42:57,952 [INFO ] dist_on_itp:False +2024-05-18 04:42:57,952 [INFO ] dist_url:env:// +2024-05-18 04:42:57,952 [INFO ] TEST:False +2024-05-18 04:42:57,952 [INFO ] model:mage_vit_base_patch16 +2024-05-18 04:42:57,953 [INFO ] dataset:imagenet +2024-05-18 04:42:57,953 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-18 04:42:57,953 [INFO ] eval_path: +2024-05-18 04:42:57,953 [INFO ] batch_size:64 +2024-05-18 04:42:57,953 [INFO ] test_batch_size:200 +2024-05-18 04:42:57,953 [INFO ] num_workers:4 +2024-05-18 04:42:57,953 [INFO ] epochs:400 +2024-05-18 04:42:57,953 [INFO ] blr:0.0002 +2024-05-18 04:42:57,953 [INFO ] min_lr:5e-06 +2024-05-18 04:42:57,953 [INFO ] accum_iter:1 +2024-05-18 04:42:57,953 [INFO ] input_size:256 +2024-05-18 04:42:57,953 [INFO ] weight_decay:0.05 +2024-05-18 04:42:57,953 [INFO ] warmup_epochs:20 +2024-05-18 04:42:57,953 [INFO ] mask_ratio_min:0.5 +2024-05-18 04:42:57,953 [INFO ] mask_ratio_max:0.9 +2024-05-18 04:42:57,953 [INFO ] mask_ratio_mu:0.55 +2024-05-18 04:42:57,953 [INFO ] mask_ratio_std:0.25 +2024-05-18 04:42:57,953 [INFO ] grad_clip:3.0 +2024-05-18 04:42:57,953 [INFO ] device:cuda +2024-05-18 04:42:57,953 [INFO ] distributed:True +2024-05-18 04:42:57,953 [INFO ] seed:0 +2024-05-18 04:42:57,953 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth +2024-05-18 04:42:57,953 [INFO ] start_epoch:0 +2024-05-18 04:42:57,953 [INFO ] pin_mem:True +2024-05-18 04:42:57,953 [INFO ] save:True +2024-05-18 04:42:57,953 [INFO ] exp_name:MIM_vbr_eval_lossless +2024-05-18 04:42:57,954 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-18 04:42:57,954 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless +2024-05-18 04:42:57,954 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless +2024-05-18 04:42:57,954 [INFO ] strart_epoch:0 +2024-05-18 04:42:57,954 [INFO ] cuda:True +2024-05-18 04:42:57,954 [INFO ] rank:0 +2024-05-18 04:42:57,954 [INFO ] gpu:0 +2024-05-18 04:42:57,954 [INFO ] dist_backend:nccl +2024-05-18 04:42:57,954 [INFO ] =================================================== +2024-05-18 06:20:16,116 [INFO ] -1|bpp:0.02418|mask:0.00847|mask_ratio:0.6|psnr:14.80285|msssim:0.51750|lpips:0.27254|dists:0.19907|accu:0.63626|Test loss:64.62691 +2024-05-18 07:56:33,739 [INFO ] -1|bpp:0.02791|mask:0.00819|mask_ratio:0.5|psnr:15.67397|msssim:0.56751|lpips:0.23728|dists:0.18073|accu:0.67258|Test loss:139.71758 +2024-05-18 09:30:08,858 [INFO ] -1|bpp:0.02046|mask:0.00875|mask_ratio:0.7|psnr:13.74718|msssim:0.45169|lpips:0.32138|dists:0.22512|accu:0.56262|Test loss:30.11541 +2024-05-18 11:05:40,441 [INFO ] -1|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:13.17649|msssim:0.41227|lpips:0.35134|dists:0.24154|accu:0.50616|Test loss:21.23853 +2024-05-18 12:36:08,830 [INFO ] -1|bpp:0.03521|mask:0.00764|mask_ratio:0.3|psnr:17.07054|msssim:0.63998|lpips:0.18754|dists:0.15498|accu:0.70446|Test loss:643.00879 +2024-05-18 14:00:50,675 [INFO ] -1|bpp:0.04252|mask:0.00709|mask_ratio:0.1|psnr:18.25655|msssim:0.69397|lpips:0.15150|dists:0.13635|accu:0.72014|Test loss:2993.12524 +2024-05-18 15:25:33,426 [INFO ] -1|bpp:0.04581|mask:0.00684|mask_ratio:0.01|psnr:18.75086|msssim:0.71406|lpips:0.13808|dists:0.12934|accu:0.72174|Test loss:6007.86475 diff --git a/MIM_vbr_eval_lossless/20240521_120210.log b/MIM_vbr_eval_lossless/20240521_120210.log new file mode 100644 index 0000000000000000000000000000000000000000..7f780b07868b8619777f6f4d52e3661dc687fb4a --- /dev/null +++ b/MIM_vbr_eval_lossless/20240521_120210.log @@ -0,0 +1,52 @@ +2024-05-21 12:02:10,417 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless//20240521_120210.log +2024-05-21 12:02:10,418 [INFO ] ======================= exp ======================= +2024-05-21 12:02:10,418 [INFO ] config:config/vpt_default.yaml +2024-05-21 12:02:10,418 [INFO ] name:exp +2024-05-21 12:02:10,418 [INFO ] lr:None +2024-05-21 12:02:10,418 [INFO ] world_size:1 +2024-05-21 12:02:10,418 [INFO ] local_rank:-1 +2024-05-21 12:02:10,418 [INFO ] dist_on_itp:False +2024-05-21 12:02:10,418 [INFO ] dist_url:env:// +2024-05-21 12:02:10,418 [INFO ] TEST:False +2024-05-21 12:02:10,418 [INFO ] model:mage_vit_base_patch16 +2024-05-21 12:02:10,418 [INFO ] dataset:imagenet +2024-05-21 12:02:10,418 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-21 12:02:10,418 [INFO ] kodak_path:/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/ +2024-05-21 12:02:10,418 [INFO ] eval_path: +2024-05-21 12:02:10,418 [INFO ] batch_size:64 +2024-05-21 12:02:10,418 [INFO ] test_batch_size:200 +2024-05-21 12:02:10,418 [INFO ] num_workers:4 +2024-05-21 12:02:10,418 [INFO ] epochs:400 +2024-05-21 12:02:10,418 [INFO ] blr:0.0002 +2024-05-21 12:02:10,419 [INFO ] min_lr:5e-06 +2024-05-21 12:02:10,419 [INFO ] accum_iter:1 +2024-05-21 12:02:10,419 [INFO ] input_size:256 +2024-05-21 12:02:10,419 [INFO ] weight_decay:0.05 +2024-05-21 12:02:10,419 [INFO ] warmup_epochs:20 +2024-05-21 12:02:10,419 [INFO ] mask_ratio_min:0.5 +2024-05-21 12:02:10,419 [INFO ] mask_ratio_max:0.9 +2024-05-21 12:02:10,419 [INFO ] mask_ratio_mu:0.55 +2024-05-21 12:02:10,419 [INFO ] mask_ratio_std:0.25 +2024-05-21 12:02:10,419 [INFO ] grad_clip:3.0 +2024-05-21 12:02:10,419 [INFO ] device:cuda +2024-05-21 12:02:10,419 [INFO ] distributed:True +2024-05-21 12:02:10,419 [INFO ] seed:0 +2024-05-21 12:02:10,419 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth +2024-05-21 12:02:10,419 [INFO ] start_epoch:0 +2024-05-21 12:02:10,419 [INFO ] pin_mem:True +2024-05-21 12:02:10,419 [INFO ] save:True +2024-05-21 12:02:10,419 [INFO ] exp_name:MIM_vbr_eval_lossless +2024-05-21 12:02:10,419 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-21 12:02:10,419 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless +2024-05-21 12:02:10,419 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless +2024-05-21 12:02:10,419 [INFO ] strart_epoch:0 +2024-05-21 12:02:10,420 [INFO ] cuda:True +2024-05-21 12:02:10,420 [INFO ] rank:0 +2024-05-21 12:02:10,420 [INFO ] gpu:0 +2024-05-21 12:02:10,420 [INFO ] dist_backend:nccl +2024-05-21 12:02:10,420 [INFO ] =================================================== +2024-05-21 12:03:00,851 [INFO ] -1|bpp:0.00784|mask:0.00908|mask_ratio:0.8|psnr:13.82288|msssim:0.34806|lpips:0.42462|dists:0.27493|Test loss:15.10523 +2024-05-21 12:03:41,020 [INFO ] -1|bpp:0.00987|mask:0.00891|mask_ratio:0.75|psnr:14.37770|msssim:0.39104|lpips:0.38284|dists:0.26121|Test loss:22.23518 +2024-05-21 12:04:19,453 [INFO ] -1|bpp:0.01170|mask:0.00879|mask_ratio:0.7|psnr:15.33662|msssim:0.45445|lpips:0.34781|dists:0.23473|Test loss:31.04415 +2024-05-21 12:05:01,263 [INFO ] -1|bpp:0.01369|mask:0.00867|mask_ratio:0.65|psnr:15.06704|msssim:0.46401|lpips:0.32283|dists:0.22039|Test loss:45.53056 +2024-05-21 12:05:46,943 [INFO ] -1|bpp:0.01568|mask:0.00854|mask_ratio:0.6|psnr:16.10357|msssim:0.51571|lpips:0.28952|dists:0.20649|Test loss:66.32143 diff --git a/MIM_vbr_eval_lossless/20240521_121146.log b/MIM_vbr_eval_lossless/20240521_121146.log new file mode 100644 index 0000000000000000000000000000000000000000..2e39cb8252d358d721caa067b72af3af0a80ff20 --- /dev/null +++ b/MIM_vbr_eval_lossless/20240521_121146.log @@ -0,0 +1,63 @@ +2024-05-21 12:11:46,727 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless//20240521_121146.log +2024-05-21 12:11:46,727 [INFO ] ======================= exp ======================= +2024-05-21 12:11:46,727 [INFO ] config:config/vpt_default.yaml +2024-05-21 12:11:46,727 [INFO ] name:exp +2024-05-21 12:11:46,727 [INFO ] lr:None +2024-05-21 12:11:46,727 [INFO ] world_size:1 +2024-05-21 12:11:46,727 [INFO ] local_rank:-1 +2024-05-21 12:11:46,727 [INFO ] dist_on_itp:False +2024-05-21 12:11:46,727 [INFO ] dist_url:env:// +2024-05-21 12:11:46,727 [INFO ] TEST:False +2024-05-21 12:11:46,727 [INFO ] model:mage_vit_base_patch16 +2024-05-21 12:11:46,728 [INFO ] dataset:imagenet +2024-05-21 12:11:46,728 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet +2024-05-21 12:11:46,728 [INFO ] kodak_path:/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/ +2024-05-21 12:11:46,728 [INFO ] eval_path: +2024-05-21 12:11:46,728 [INFO ] batch_size:64 +2024-05-21 12:11:46,728 [INFO ] test_batch_size:200 +2024-05-21 12:11:46,728 [INFO ] num_workers:4 +2024-05-21 12:11:46,728 [INFO ] epochs:400 +2024-05-21 12:11:46,728 [INFO ] blr:0.0002 +2024-05-21 12:11:46,728 [INFO ] min_lr:5e-06 +2024-05-21 12:11:46,728 [INFO ] accum_iter:1 +2024-05-21 12:11:46,728 [INFO ] input_size:256 +2024-05-21 12:11:46,728 [INFO ] weight_decay:0.05 +2024-05-21 12:11:46,728 [INFO ] warmup_epochs:20 +2024-05-21 12:11:46,728 [INFO ] mask_ratio_min:0.5 +2024-05-21 12:11:46,728 [INFO ] mask_ratio_max:0.9 +2024-05-21 12:11:46,728 [INFO ] mask_ratio_mu:0.55 +2024-05-21 12:11:46,728 [INFO ] mask_ratio_std:0.25 +2024-05-21 12:11:46,728 [INFO ] grad_clip:3.0 +2024-05-21 12:11:46,728 [INFO ] device:cuda +2024-05-21 12:11:46,729 [INFO ] distributed:True +2024-05-21 12:11:46,729 [INFO ] seed:0 +2024-05-21 12:11:46,729 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth +2024-05-21 12:11:46,729 [INFO ] start_epoch:0 +2024-05-21 12:11:46,729 [INFO ] pin_mem:True +2024-05-21 12:11:46,729 [INFO ] save:True +2024-05-21 12:11:46,729 [INFO ] exp_name:MIM_vbr_eval_lossless +2024-05-21 12:11:46,729 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-05-21 12:11:46,729 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless +2024-05-21 12:11:46,729 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless +2024-05-21 12:11:46,729 [INFO ] strart_epoch:0 +2024-05-21 12:11:46,729 [INFO ] cuda:True +2024-05-21 12:11:46,729 [INFO ] rank:0 +2024-05-21 12:11:46,729 [INFO ] gpu:0 +2024-05-21 12:11:46,729 [INFO ] dist_backend:nccl +2024-05-21 12:11:46,729 [INFO ] =================================================== +2024-05-21 12:12:38,993 [INFO ] -1|bpp:0.00784|mask:0.00908|mask_ratio:0.8|psnr:13.82276|msssim:0.34805|lpips:0.42462|dists:0.27493|Test loss:15.10523 +2024-05-21 12:13:18,373 [INFO ] -1|bpp:0.00987|mask:0.00891|mask_ratio:0.75|psnr:14.37755|msssim:0.39104|lpips:0.38284|dists:0.26121|Test loss:22.23518 +2024-05-21 12:13:58,355 [INFO ] -1|bpp:0.01170|mask:0.00879|mask_ratio:0.7|psnr:15.33651|msssim:0.45445|lpips:0.34781|dists:0.23473|Test loss:31.04415 +2024-05-21 12:14:38,346 [INFO ] -1|bpp:0.01369|mask:0.00867|mask_ratio:0.65|psnr:15.06691|msssim:0.46401|lpips:0.32283|dists:0.22039|Test loss:45.53056 +2024-05-21 12:15:17,341 [INFO ] -1|bpp:0.01568|mask:0.00854|mask_ratio:0.6|psnr:16.10348|msssim:0.51571|lpips:0.28952|dists:0.20649|Test loss:66.32143 +2024-05-21 12:15:59,518 [INFO ] -1|bpp:0.01769|mask:0.00842|mask_ratio:0.55|psnr:16.66886|msssim:0.54068|lpips:0.27387|dists:0.19902|Test loss:97.59130 +2024-05-21 12:16:43,259 [INFO ] -1|bpp:0.01969|mask:0.00830|mask_ratio:0.5|psnr:17.05398|msssim:0.55989|lpips:0.25349|dists:0.18627|Test loss:143.87851 +2024-05-21 12:17:21,314 [INFO ] -1|bpp:0.02154|mask:0.00818|mask_ratio:0.45|psnr:17.42430|msssim:0.58354|lpips:0.24210|dists:0.17977|Test loss:203.66936 +2024-05-21 12:18:04,417 [INFO ] -1|bpp:0.02357|mask:0.00794|mask_ratio:0.4|psnr:17.89758|msssim:0.60007|lpips:0.22977|dists:0.17287|Test loss:304.47241 +2024-05-21 12:18:46,874 [INFO ] -1|bpp:0.02555|mask:0.00781|mask_ratio:0.35|psnr:18.47392|msssim:0.62136|lpips:0.21420|dists:0.16763|Test loss:447.87213 +2024-05-21 12:19:30,648 [INFO ] -1|bpp:0.02756|mask:0.00769|mask_ratio:0.3|psnr:18.70894|msssim:0.64106|lpips:0.19699|dists:0.16134|Test loss:662.32788 +2024-05-21 12:20:10,918 [INFO ] -1|bpp:0.02956|mask:0.00757|mask_ratio:0.25|psnr:18.98025|msssim:0.65567|lpips:0.18680|dists:0.15801|Test loss:983.28394 +2024-05-21 12:20:50,854 [INFO ] -1|bpp:0.03138|mask:0.00745|mask_ratio:0.2|psnr:19.34285|msssim:0.66431|lpips:0.18044|dists:0.15229|Test loss:1385.45557 +2024-05-21 12:21:32,470 [INFO ] -1|bpp:0.03339|mask:0.00732|mask_ratio:0.15|psnr:19.71813|msssim:0.68537|lpips:0.16575|dists:0.14260|Test loss:2053.51758 +2024-05-21 12:22:12,619 [INFO ] -1|bpp:0.03539|mask:0.00720|mask_ratio:0.1|psnr:20.06234|msssim:0.69720|lpips:0.15856|dists:0.14339|Test loss:3017.49292 +2024-05-21 12:22:51,448 [INFO ] -1|bpp:0.03739|mask:0.00703|mask_ratio:0.05|psnr:20.41125|msssim:0.71023|lpips:0.15008|dists:0.14040|Test loss:4599.72754 diff --git a/VQ_w_Prompt/20240602_123240.log b/VQ_w_Prompt/20240602_123240.log new file mode 100644 index 0000000000000000000000000000000000000000..d291f813660379a251c1254b5ec17e2c0000b072 --- /dev/null +++ b/VQ_w_Prompt/20240602_123240.log @@ -0,0 +1,196 @@ +2024-06-02 12:32:40,143 [INFO ] Logging file is /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt//20240602_123240.log +2024-06-02 12:32:40,143 [INFO ] ======================= exp ======================= +2024-06-02 12:32:40,143 [INFO ] config:config/vpt_default.yaml +2024-06-02 12:32:40,143 [INFO ] name:exp +2024-06-02 12:32:40,144 [INFO ] lr:None +2024-06-02 12:32:40,144 [INFO ] world_size:1 +2024-06-02 12:32:40,144 [INFO ] local_rank:-1 +2024-06-02 12:32:40,144 [INFO ] dist_on_itp:False +2024-06-02 12:32:40,144 [INFO ] dist_url:env:// +2024-06-02 12:32:40,144 [INFO ] TEST:False +2024-06-02 12:32:40,144 [INFO ] model:mage_vit_base_patch16 +2024-06-02 12:32:40,144 [INFO ] dataset:coco +2024-06-02 12:32:40,144 [INFO ] dataset_path:/home/t2vg-a100-G4-10/project/qyp/datasets/COCO +2024-06-02 12:32:40,144 [INFO ] kodak_path:/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/ +2024-06-02 12:32:40,144 [INFO ] eval_path: +2024-06-02 12:32:40,144 [INFO ] batch_size:128 +2024-06-02 12:32:40,144 [INFO ] learning_rate:0.0001 +2024-06-02 12:32:40,144 [INFO ] test_batch_size:1 +2024-06-02 12:32:40,144 [INFO ] num_workers:4 +2024-06-02 12:32:40,144 [INFO ] epochs:100 +2024-06-02 12:32:40,144 [INFO ] blr:0.00015 +2024-06-02 12:32:40,144 [INFO ] min_lr:5e-06 +2024-06-02 12:32:40,144 [INFO ] accum_iter:1 +2024-06-02 12:32:40,144 [INFO ] input_size:256 +2024-06-02 12:32:40,144 [INFO ] weight_decay:0.05 +2024-06-02 12:32:40,144 [INFO ] warmup_epochs:10 +2024-06-02 12:32:40,144 [INFO ] mask_ratio_min:0.5 +2024-06-02 12:32:40,144 [INFO ] mask_ratio_max:0.9 +2024-06-02 12:32:40,144 [INFO ] mask_ratio_mu:0.55 +2024-06-02 12:32:40,145 [INFO ] mask_ratio_std:0.25 +2024-06-02 12:32:40,145 [INFO ] grad_clip:3.0 +2024-06-02 12:32:40,145 [INFO ] device:cuda +2024-06-02 12:32:40,145 [INFO ] distributed:False +2024-06-02 12:32:40,145 [INFO ] seed:0 +2024-06-02 12:32:40,145 [INFO ] gpu_id:0,1 +2024-06-02 12:32:40,145 [INFO ] resume:/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth +2024-06-02 12:32:40,145 [INFO ] maskrcnn_path:/home/t2vg-a100-G4-10/project/qyp/ckpt/detectron2/mask_rcnn_r50_fpn_3x.pkl +2024-06-02 12:32:40,145 [INFO ] start_epoch:0 +2024-06-02 12:32:40,145 [INFO ] pin_mem:True +2024-06-02 12:32:40,145 [INFO ] save:True +2024-06-02 12:32:40,145 [INFO ] exp_name:VQ_w_Prompt +2024-06-02 12:32:40,145 [INFO ] root:/home/t2vg-a100-G4-10/project/qyp/mimc_rope +2024-06-02 12:32:40,145 [INFO ] output_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt +2024-06-02 12:32:40,145 [INFO ] log_dir:/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt +2024-06-02 12:32:40,145 [INFO ] strart_epoch:0 +2024-06-02 12:32:40,145 [INFO ] cuda:True +2024-06-02 12:32:40,145 [INFO ] =================================================== +2024-06-02 12:36:03,409 [INFO ] Epoch: -1 | perc loss: 0.33139 | psnr: 21.90954 | lpips: 0.22209 | dists: 0.09723 +2024-06-02 12:55:03,529 [INFO ] Epoch: 0 | perc loss: 0.33168 | psnr: 21.83200 | lpips: 0.22776 | dists: 0.11052 +2024-06-02 12:55:03,533 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 12:55:03,812 [INFO ] Saving BEST checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 13:13:51,301 [INFO ] Epoch: 1 | perc loss: 0.33369 | psnr: 21.56722 | lpips: 0.22748 | dists: 0.10901 +2024-06-02 13:13:51,305 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 13:32:37,056 [INFO ] Epoch: 2 | perc loss: 0.32811 | psnr: 21.75561 | lpips: 0.22586 | dists: 0.10543 +2024-06-02 13:32:37,060 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 13:32:37,942 [INFO ] Saving BEST checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 13:51:19,223 [INFO ] Epoch: 3 | perc loss: 0.33057 | psnr: 21.71532 | lpips: 0.22620 | dists: 0.10827 +2024-06-02 13:51:19,227 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 14:10:08,103 [INFO ] Epoch: 4 | perc loss: 0.33012 | psnr: 21.70657 | lpips: 0.22693 | dists: 0.10772 +2024-06-02 14:10:08,108 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 14:28:55,372 [INFO ] Epoch: 5 | perc loss: 0.33078 | psnr: 21.71807 | lpips: 0.22735 | dists: 0.10827 +2024-06-02 14:28:55,376 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 14:47:49,287 [INFO ] Epoch: 6 | perc loss: 0.33090 | psnr: 21.79235 | lpips: 0.22534 | dists: 0.10498 +2024-06-02 14:47:49,291 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 15:06:38,406 [INFO ] Epoch: 7 | perc loss: 0.33033 | psnr: 21.74950 | lpips: 0.22632 | dists: 0.10654 +2024-06-02 15:06:38,410 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 15:25:29,010 [INFO ] Epoch: 8 | perc loss: 0.32939 | psnr: 21.71603 | lpips: 0.22598 | dists: 0.10593 +2024-06-02 15:25:29,014 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 15:44:21,498 [INFO ] Epoch: 9 | perc loss: 0.33421 | psnr: 21.67705 | lpips: 0.22861 | dists: 0.10923 +2024-06-02 15:44:21,502 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 16:03:15,138 [INFO ] Epoch: 10 | perc loss: 0.33003 | psnr: 21.75746 | lpips: 0.22706 | dists: 0.10813 +2024-06-02 16:03:15,141 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 16:21:55,573 [INFO ] Epoch: 11 | perc loss: 0.33178 | psnr: 21.70939 | lpips: 0.22774 | dists: 0.10836 +2024-06-02 16:21:55,577 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 16:40:34,144 [INFO ] Epoch: 12 | perc loss: 0.33171 | psnr: 21.89004 | lpips: 0.22690 | dists: 0.10719 +2024-06-02 16:40:34,147 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 16:59:27,863 [INFO ] Epoch: 13 | perc loss: 0.32893 | psnr: 21.72392 | lpips: 0.22659 | dists: 0.10690 +2024-06-02 16:59:27,866 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 17:18:09,724 [INFO ] Epoch: 14 | perc loss: 0.33351 | psnr: 21.66716 | lpips: 0.22815 | dists: 0.10899 +2024-06-02 17:18:09,727 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 17:36:50,797 [INFO ] Epoch: 15 | perc loss: 0.33180 | psnr: 21.96293 | lpips: 0.22651 | dists: 0.10622 +2024-06-02 17:36:50,801 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 17:55:29,103 [INFO ] Epoch: 16 | perc loss: 0.33189 | psnr: 21.86891 | lpips: 0.22776 | dists: 0.10837 +2024-06-02 17:55:29,107 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 18:14:11,530 [INFO ] Epoch: 17 | perc loss: 0.33210 | psnr: 21.85856 | lpips: 0.22759 | dists: 0.10800 +2024-06-02 18:14:11,534 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 18:32:51,595 [INFO ] Epoch: 18 | perc loss: 0.33330 | psnr: 21.89861 | lpips: 0.22754 | dists: 0.10816 +2024-06-02 18:32:51,598 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 18:51:33,222 [INFO ] Epoch: 19 | perc loss: 0.33425 | psnr: 21.91737 | lpips: 0.22715 | dists: 0.10672 +2024-06-02 18:51:33,226 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 19:10:20,889 [INFO ] Epoch: 20 | perc loss: 0.33207 | psnr: 21.79711 | lpips: 0.22724 | dists: 0.10738 +2024-06-02 19:10:20,892 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 19:29:09,240 [INFO ] Epoch: 21 | perc loss: 0.33146 | psnr: 21.83736 | lpips: 0.22665 | dists: 0.10620 +2024-06-02 19:29:09,244 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 19:47:50,856 [INFO ] Epoch: 22 | perc loss: 0.33375 | psnr: 21.78199 | lpips: 0.22757 | dists: 0.10758 +2024-06-02 19:47:50,860 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 20:06:27,901 [INFO ] Epoch: 23 | perc loss: 0.33233 | psnr: 21.79922 | lpips: 0.22696 | dists: 0.10653 +2024-06-02 20:06:27,905 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 20:25:12,269 [INFO ] Epoch: 24 | perc loss: 0.33376 | psnr: 21.88994 | lpips: 0.22836 | dists: 0.10865 +2024-06-02 20:25:12,272 [INFO ] Saving checkpoint: 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/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 23:13:45,201 [INFO ] Epoch: 33 | perc loss: 0.33209 | psnr: 21.90117 | lpips: 0.22692 | dists: 0.10679 +2024-06-02 23:13:45,206 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 23:32:29,814 [INFO ] Epoch: 34 | perc loss: 0.33378 | psnr: 21.78412 | lpips: 0.22783 | dists: 0.10720 +2024-06-02 23:32:29,818 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-02 23:51:14,307 [INFO ] Epoch: 35 | perc loss: 0.33245 | psnr: 21.80858 | lpips: 0.22757 | dists: 0.10710 +2024-06-02 23:51:14,311 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 00:09:57,617 [INFO ] Epoch: 36 | perc loss: 0.33471 | psnr: 21.78929 | lpips: 0.22853 | dists: 0.10903 +2024-06-03 00:09:57,620 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 00:28:36,627 [INFO ] Epoch: 37 | perc loss: 0.33349 | psnr: 21.98594 | lpips: 0.22731 | dists: 0.10736 +2024-06-03 00:28:36,630 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 00:47:20,972 [INFO ] Epoch: 38 | perc loss: 0.33425 | psnr: 21.97987 | lpips: 0.22731 | dists: 0.10701 +2024-06-03 00:47:20,976 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 01:05:59,529 [INFO ] Epoch: 39 | perc loss: 0.33325 | psnr: 21.69044 | lpips: 0.22760 | dists: 0.10743 +2024-06-03 01:05:59,534 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 01:24:45,951 [INFO ] Epoch: 40 | perc loss: 0.33306 | psnr: 21.84931 | lpips: 0.22768 | dists: 0.10715 +2024-06-03 01:24:45,955 [INFO ] Saving checkpoint: 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/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 02:58:26,226 [INFO ] Epoch: 45 | perc loss: 0.33221 | psnr: 21.75377 | lpips: 0.22706 | dists: 0.10657 +2024-06-03 02:58:26,230 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 03:17:13,245 [INFO ] Epoch: 46 | perc loss: 0.33469 | psnr: 21.82597 | lpips: 0.22778 | dists: 0.10752 +2024-06-03 03:17:13,248 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 03:35:52,662 [INFO ] Epoch: 47 | perc loss: 0.33427 | psnr: 21.91075 | lpips: 0.22704 | dists: 0.10703 +2024-06-03 03:35:52,666 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 03:54:34,629 [INFO ] Epoch: 48 | perc loss: 0.33344 | psnr: 21.73634 | lpips: 0.22771 | dists: 0.10786 +2024-06-03 03:54:34,634 [INFO ] Saving checkpoint: 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/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 07:58:06,507 [INFO ] Epoch: 61 | perc loss: 0.33431 | psnr: 21.97837 | lpips: 0.22842 | dists: 0.10904 +2024-06-03 07:58:06,512 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 08:16:49,835 [INFO ] Epoch: 62 | perc loss: 0.33514 | psnr: 21.99198 | lpips: 0.22782 | dists: 0.10722 +2024-06-03 08:16:49,839 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 08:35:33,361 [INFO ] Epoch: 63 | perc loss: 0.33423 | psnr: 21.85692 | lpips: 0.22768 | dists: 0.10727 +2024-06-03 08:35:33,366 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 08:54:14,939 [INFO ] Epoch: 64 | perc loss: 0.33443 | psnr: 21.82958 | lpips: 0.22788 | dists: 0.10829 +2024-06-03 08:54:14,942 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 09:12:53,969 [INFO ] Epoch: 65 | perc loss: 0.33494 | psnr: 21.82883 | lpips: 0.22806 | dists: 0.10793 +2024-06-03 09:12:53,973 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 09:31:37,870 [INFO ] Epoch: 66 | perc loss: 0.33452 | psnr: 21.88860 | lpips: 0.22774 | dists: 0.10777 +2024-06-03 09:31:37,874 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 09:50:15,999 [INFO ] Epoch: 67 | perc loss: 0.33397 | psnr: 21.87563 | lpips: 0.22739 | dists: 0.10670 +2024-06-03 09:50:16,002 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 10:09:03,337 [INFO ] Epoch: 68 | perc loss: 0.33403 | psnr: 21.84018 | lpips: 0.22757 | dists: 0.10754 +2024-06-03 10:09:03,340 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 10:27:57,411 [INFO ] Epoch: 69 | perc loss: 0.33503 | psnr: 21.81171 | lpips: 0.22727 | dists: 0.10667 +2024-06-03 10:27:57,415 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 10:46:47,458 [INFO ] Epoch: 70 | perc loss: 0.33324 | psnr: 21.89323 | lpips: 0.22725 | dists: 0.10659 +2024-06-03 10:46:47,462 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 11:05:28,932 [INFO ] Epoch: 71 | perc loss: 0.33461 | psnr: 21.83094 | lpips: 0.22864 | dists: 0.10749 +2024-06-03 11:05:28,935 [INFO ] Saving checkpoint: /home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/checkpoint.pth.tar +2024-06-03 11:24:26,070 [INFO ] Epoch: 72 | perc loss: 0.33356 | psnr: 21.72290 | lpips: 0.22737 | dists: 0.10574 +2024-06-03 11:24:26,074 [INFO ] Saving checkpoint: 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b/cal_upper_bound.py new file mode 100644 index 0000000000000000000000000000000000000000..513264003b38bb994bda4e84d460d0271acc4597 --- /dev/null +++ b/cal_upper_bound.py @@ -0,0 +1,287 @@ +import argparse +import math +import sys +import os +import time +import logging +from datetime import datetime +from model_vq import Model_VQ +import torch +import torch.nn as nn +from omegaconf import OmegaConf + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +import lpips +from torch.nn import functional as F +from torchvision import utils as vutils +import numpy as np +import glob + +import util.misc as misc +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import os + +os.environ['CUDA_VISIBLE_DEVICES'] = '3' + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips_vgg(self, rec, ori): + loss_fn_vgg = lpips.LPIPS(net='vgg').cuda() + lipis_vgg = loss_fn_vgg(rec, ori) + return lipis_vgg + + def lpips_alex(self, rec, ori): + loss_fn_alex = lpips.LPIPS(net='alex').cuda() + lipis_alex = loss_fn_alex(rec, ori) + return lipis_alex + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value + + def forward(self, ori, rec): + out = {} + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["lpips_vgg"] = self.lpips_vgg(torch.clamp(rec, 0, 1), ori) + out["lpips_alex"] = self.lpips_alex(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1, 1, 3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + # 均匀添加padding + dh_half = dh // 2 + dw_half = dw // 2 + dh_extra = dh % 2 + dw_extra = dw % 2 + x = F.pad(x, (dw_half, dw_half + dw_extra, dh_half, dh_half + dh_extra)) + return x, h, w + + + +def save_img(img: torch.Tensor, vis_path, input_p, rec=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if rec: + vis_path = vis_path + '/rec' + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + img_name = vis_path + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(img_name), nrow=8) + + +def inference(epoch, eval_path, model, metrics_criterion, device, stage='test'): + model.eval() + psnr = AverageMeter() + lpips_vgg = AverageMeter() + lpips_alex = AverageMeter() + dists = AverageMeter() + + vis_path = os.path.join("./VQGAN/", stage) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + for input_p in eval_path: + x, hx, wx = load_img(input_p, padding=True, factor=64) + x = x.to(device) + rec = model(x) + + x = x[:, :, :hx, :wx] + rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(x, rec) + + psnr.update(out_criterion['psnr']) + lpips_vgg.update(out_criterion['lpips_vgg']) + lpips_alex.update(out_criterion['lpips_alex']) + dists.update(out_criterion['dists']) + + ## ======================= update progress bar & visualization ======================= ## + # save_img(x, vis_path, input_p) + save_img(rec, vis_path, input_p, rec=True) + + model.train() + log_txt = f"{epoch}|psnr:{psnr.avg:.5f}|lpips_vgg:{lpips_vgg.avg.mean().item():.5f}|lpips_alex:{lpips_alex.avg.mean().item():.5f}|dists:{dists.avg.mean().item():.5f}" + logging.info(log_txt) + return psnr + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="/home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/cal_upper_bound.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument( + '--eval_path', + default='/home/t2vg-a100-G4-10/project/qyp/datasets/COCO/val2017', + type=str, + help='path to the evaluation dataset', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + action='store_true', + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.jpg'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + np.random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.jpg'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + config = OmegaConf.load('config/vqgan.yaml').model + model = Model_VQ(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + + model.to(device) + + metrics_criterion = CalMetrics() + ## ======================= pre validation ======================= ## + test_loss = inference(-1, eval_path, model, metrics_criterion, device, 'val') + + + +if __name__ == "__main__": + main(sys.argv[1:]) \ No newline at end of file diff --git a/ckpt_pretrained/vgg.pth b/ckpt_pretrained/vgg.pth new file mode 100644 index 0000000000000000000000000000000000000000..f57dcf5cc764d61c8a460365847fb2137ff0a62d --- /dev/null +++ b/ckpt_pretrained/vgg.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a78928a0af1e5f0fcb1f3b9e8f8c3a2a5a3de244d830ad5c1feddc79b8432868 +size 7289 diff --git a/coco_eval.py b/coco_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/config/cal_upper_bound.yaml b/config/cal_upper_bound.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e4b8d1d62a9b479184b22464ce31ae0beffde63e --- /dev/null +++ b/config/cal_upper_bound.yaml @@ -0,0 +1,13 @@ +# eval_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak' +eval_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO/val2017' +# eval_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/shark_new/' +device: "cuda" +seed: 0 +save: True +name: "VQ" +exp_name: "VQ" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True \ No newline at end of file diff --git a/config/eval copy.yaml b/config/eval copy.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df77961678d2940c0560b668cb58e1b0b66328ca --- /dev/null +++ b/config/eval copy.yaml @@ -0,0 +1,41 @@ +model: mage_vit_base_patch16 +dataset: "coco" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO' +# kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/test/' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/' +eval_path: '' +batch_size: 128 # Batch size per GPU +learning_rate: 1.e-4 +test_batch_size: 1 +num_workers: 4 +epochs: 100 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 10 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +gpu_id: 0,1 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +# resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/checkpoint-399.pth' +# resume: '/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_rope/random_with_rope.pth' +maskrcnn_path: '/home/t2vg-a100-G4-10/project/qyp/ckpt/detectron2/mask_rcnn_r50_fpn_3x.pkl' +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "VQ_w_Prompt" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True +TEST: True diff --git a/config/eval.yaml b/config/eval.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df77961678d2940c0560b668cb58e1b0b66328ca --- /dev/null +++ b/config/eval.yaml @@ -0,0 +1,41 @@ +model: mage_vit_base_patch16 +dataset: "coco" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO' +# kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/test/' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/' +eval_path: '' +batch_size: 128 # Batch size per GPU +learning_rate: 1.e-4 +test_batch_size: 1 +num_workers: 4 +epochs: 100 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 10 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +gpu_id: 0,1 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +# resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/checkpoint-399.pth' +# resume: '/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_rope/random_with_rope.pth' +maskrcnn_path: '/home/t2vg-a100-G4-10/project/qyp/ckpt/detectron2/mask_rcnn_r50_fpn_3x.pkl' +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "VQ_w_Prompt" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True +TEST: True diff --git a/config/eval_config.yaml b/config/eval_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5e662cfa5738d302a4a219fadbbc08471966c746 --- /dev/null +++ b/config/eval_config.yaml @@ -0,0 +1,34 @@ +model: mage_vit_base_patch16 +dataset: "imagenet" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/' +eval_path: '' +batch_size: 64 # Batch size per GPU +test_batch_size: 200 +num_workers: 4 +epochs: 400 +blr: 2.e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 20 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "MIM_vbr_eval_lossless" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_eval_lossless/logs_eval" # path where to tensorboard log +strart_epoch: 0 +cuda: True diff --git a/config/eval_high_res.yaml b/config/eval_high_res.yaml new file mode 100644 index 0000000000000000000000000000000000000000..48dc57a5419d24fef241f3eeb05634ec31d14cf1 --- /dev/null +++ b/config/eval_high_res.yaml @@ -0,0 +1,41 @@ +model: mage_vit_base_patch16 +dataset: "coco" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/test/' +# kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/' +eval_path: '' +batch_size: 128 # Batch size per GPU +learning_rate: 1.e-4 +test_batch_size: 1 +num_workers: 4 +epochs: 100 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 10 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +gpu_id: 0,1 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +# resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/checkpoint-399.pth' +# resume: '/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_rope/random_with_rope.pth' +maskrcnn_path: '/home/t2vg-a100-G4-10/project/qyp/ckpt/detectron2/mask_rcnn_r50_fpn_3x.pkl' +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "Test_High_Res" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/Test_High_Res" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/Test_High_Res/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True +TEST: True diff --git a/config/eval_vq.yaml b/config/eval_vq.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f887b03bf4929261592f20e31d3e3ab1bb6c29eb --- /dev/null +++ b/config/eval_vq.yaml @@ -0,0 +1,38 @@ +model: mage_vit_base_patch16 +dataset: "coco" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO' +# kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/test/' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO/val2017/' +eval_path: '' +batch_size: 32 # Batch size per GPU +test_batch_size: 1 +num_workers: 4 +epochs: 400 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 20 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +# resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/checkpoint-399.pth' +# resume: '/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_rope/random_with_rope.pth' +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "VQGAN" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQGAN" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQGAN/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True +TEST: True diff --git a/config/fine_tune_vq_w_mask.yaml b/config/fine_tune_vq_w_mask.yaml new file mode 100644 index 0000000000000000000000000000000000000000..97d43369aa2e19f48e36c1ffc7438bf422c82791 --- /dev/null +++ b/config/fine_tune_vq_w_mask.yaml @@ -0,0 +1,39 @@ +model: mage_vit_base_patch16 +dataset: "coco" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/test/' +# kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/' +eval_path: '' +batch_size: 64 # Batch size per GPU +learning_rate: 1.e-4 +test_batch_size: 1 +num_workers: 4 +epochs: 100 +blr: 1.e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 10 +mask_ratio_min: 0.25 +mask_ratio_max: 0.75 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +gpu_id: 2 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +# resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/checkpoint-399.pth' +# resume: '/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_rope/random_with_rope.pth' +maskrcnn_path: '/home/t2vg-a100-G4-10/project/qyp/ckpt/detectron2/mask_rcnn_r50_fpn_3x.pkl' +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "Fine_Tune_VQ_w_P" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/Fine_Tune_VQ_w_P" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/Fine_Tune_VQ_w_P/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True +TEST: True diff --git a/config/train_det.yaml b/config/train_det.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa23481235c89cb9cdaa3eb436c02cfbd41614aa --- /dev/null +++ b/config/train_det.yaml @@ -0,0 +1,38 @@ +model: mage_vit_base_patch16 +dataset: "coco" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/COCO' +kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/kodak/' +# kodak_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/test/' +eval_path: '' +batch_size: 32 # Batch size per GPU +test_batch_size: 1 +num_workers: 4 +epochs: 400 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 20 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +resume: '/home/t2vg-a100-G4-10/project/qyp/lossless/checkpoint-330.pth' # Path to the checkpoint to resume from +# resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/checkpoint-399.pth' +# resume: '/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_rope/random_with_rope.pth' +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "MIM_vbr_kodak" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_kodak" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr_kodak/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True +TEST: True diff --git a/config/train_im_cls.yaml b/config/train_im_cls.yaml new file mode 100644 index 0000000000000000000000000000000000000000..11c9d1f82cce864845f19bdf34652943376718da --- /dev/null +++ b/config/train_im_cls.yaml @@ -0,0 +1,32 @@ +model: mage_vit_base_patch16 +dataset: "imagenet" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet' +eval_path: '' +batch_size: 256 # Batch size per GPU +test_batch_size: 64 +num_workers: 4 +epochs: 100 +learning_rate: 1.e-4 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 10 +mask_ratio_min: 0.3 +mask_ratio_max: 0.75 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/train_im_cls/checkpoint-lastbest.pth' # Path to the checkpoint to resume from +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "train_im_cls" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/train_im_cls" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/train_im_cls/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True diff --git a/config/train_mim_config.yaml b/config/train_mim_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8f0c5027ac9d7b88c331a6ccbb8b8543c69d2a19 --- /dev/null +++ b/config/train_mim_config.yaml @@ -0,0 +1,33 @@ +model: mage_vit_base_patch16 +dataset: "imagenet" +dataset_path: '/home/t2vg-a100-G4-10/project/qyp/datasets/imagenet' +eval_path: '' +batch_size: 64 # Batch size per GPU +test_batch_size: 64 +num_workers: 4 +epochs: 400 +blr: 1.5e-4 # 1.5e-4 # base learning rate: absolute_lr = base_lr * total_batch_size / 256 +min_lr: 5.e-6 +accum_iter: 1 # Accumulate gradient iterations (for increasing the effective batch size under memory constraints) +input_size: 256 +weight_decay: 0.05 +warmup_epochs: 20 +mask_ratio_min: 0.5 +mask_ratio_max: 0.9 +mask_ratio_mu: 0.55 +mask_ratio_std: 0.25 +grad_clip: 3.0 +device: "cuda" +distributed: True +seed: 0 +resume: '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/random_with_rope.pth' # Path to the checkpoint to resume from +start_epoch: 0 +pin_mem: True +save: True +name: "exp" +exp_name: "MIM_vbr" +root: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope" +output_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr" # path where to save, empty for no saving +log_dir: "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/MIM_vbr/logs" # path where to tensorboard log +strart_epoch: 0 +cuda: True diff --git a/config/vqgan.yaml b/config/vqgan.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9109df9b044f163fb56efed73cc5f2e986754f03 --- /dev/null +++ b/config/vqgan.yaml @@ -0,0 +1,16 @@ +model: + target: taming.models.vqgan.VQModel + params: + embed_dim: 256 + n_embed: 1024 + ddconfig: + double_z: False + z_channels: 256 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: [ 1,1,2,2,4] # num_down = len(ch_mult)-1 + num_res_blocks: 2 + attn_resolutions: [16] + dropout: 0.0 diff --git a/deploited/models_mage_codec_pos_linear.py b/deploited/models_mage_codec_pos_linear.py new file mode 100644 index 0000000000000000000000000000000000000000..8b18c23f706fce94c8e5a2e183615c7ad6adb216 --- /dev/null +++ b/deploited/models_mage_codec_pos_linear.py @@ -0,0 +1,767 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union + +SCALES_MIN = 0.11 +SCALES_MAX = 256 +SCALES_LEVELS = 64 +def get_scale_table(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS): + return torch.exp(torch.linspace(math.log(min), math.log(max), levels)) + +def ste_round(x: Tensor) -> Tensor: + return torch.round(x) - x.detach() + x + +def conv(in_channels, out_channels, kernel_size=5, stride=2): + return nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + ) + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() + # 使用Gumbel分布进行采样，增加随机性 + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + +def adjust_mask_and_drop_embeddings(token_keep_mask): + """ + Adjusts the token_keep_mask to the nearest square number of True values by randomly setting + some of them to False, and then applies this adjusted mask to input_embeddings. + + Parameters: + - input_embeddings: Tensor, The embeddings tensor. + - token_keep_mask: BoolTensor, The mask tensor indicating which tokens to keep. + + Returns: + - Tensor, Adjusted input embeddings after applying the modified token_keep_mask. + """ + # 获取非零（即值为True）元素的索引 + non_zero_indices = token_keep_mask.nonzero(as_tuple=True) + # 计算非零元素的数量 + non_zero_count = non_zero_indices[0].size(0) + # 计算最近的整数平方倍 + next_square = math.floor(math.sqrt(non_zero_count))**2 + # 计算需要移除的元素数量 + remove_count = non_zero_count - next_square + if remove_count > 0: + # 如果需要移除元素以达到整数平方倍 + permuted_indices = torch.randperm(non_zero_count)[:remove_count] + for idx in permuted_indices: + token_keep_mask[non_zero_indices[0][idx], non_zero_indices[1][idx]] = False + # 使用更新后的token_keep_mask + # input_embeddings_after_drop = input_embeddings[token_keep_mask] + + return token_keep_mask + + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + # outputs = self.quantize( + # x, "noise" if training else "dequantize", means.long() + # ) + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids, z_H, z_W + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,1 + latent_h * latent_w) + seq_length = input_shape[1] + position_ids = self.position_ids[:, :seq_length] + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + + # if seq_length != (1 + z_H * z_W): + # Exclude the first embedding (position 0) before interpolation + position_embeddings_to_interp = position_embeddings[:, 1:, :] # (b, 256, embed_dim) + # Perform bilinear interpolation + interpolated_embeddings = F.interpolate( + position_embeddings_to_interp.permute(0, 2, 1).reshape(1, -1, 16, 16), + size=(z_H, z_W), mode="bicubic", align_corners=False + ).reshape(1, -1, z_H * z_W).permute(0, 2, 1) # (b, seq_len-1, embed_dim) + # Concatenate the first embedding back + position_embeddings = torch.cat([position_embeddings[:, :1, :], interpolated_embeddings], dim=1) + + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, mask_ratio_mu=0.55, mask_ratio_std=0.25, + vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=256 +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + # self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - mask_ratio_mu) / mask_ratio_std, + # (mask_ratio_max - mask_ratio_mu) / mask_ratio_std, + # loc=mask_ratio_mu, scale=mask_ratio_std) + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches # 256 + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + def get_dec_pos_embed(self, z_H, z_W): + # Exclude the first embedding (position 0) before interpolation + position_embeddings_to_interp = self.decoder_pos_embed_learned[:, 1:, :] # (b, 256, embed_dim) + # Perform bilinear interpolation + interpolated_embeddings = F.interpolate( + position_embeddings_to_interp.permute(0, 2, 1).reshape(1, -1, 16, 16), + size=(z_H, z_W), mode="bicubic", align_corners=False + ).reshape(1, -1, z_H * z_W).permute(0, 2, 1) # (b, seq_len -1 , embed_dim) + # Concatenate the first embedding back + position_embeddings = torch.cat([self.decoder_pos_embed_learned[:, :1, :], interpolated_embeddings], dim=1) + + return position_embeddings + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, z_H, z_W = z_q.size() + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + mask_ratio_min = self.mask_ratio_min # 0.5 + + if is_training: + # mask_rate = self.mask_ratio_generator.rvs(1)[0] + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + token_all_mask = (noise <= cutoff_mask).float() # 逻辑上标记那些token是被mask掉的 + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置 + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, z_H, z_W): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices, z_H, z_W) # get embeddings [B, 257, 768] + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, token_indices, token_all_mask, token_drop_mask + + def forward_decoding(self, x, token_drop_mask, token_all_mask, z_H, z_W): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + dec_pos_embed_learned = self.get_dec_pos_embed(z_H, z_W) + x = x_after_pad + dec_pos_embed_learned # add learnable pos embedding + # x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + # print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + # 判断latent_hat和latent是否相等 + # print((latent_hat == latent).all()) + cdf_mask_token = self.get_cdf_token_mask(token_all_mask).cpu() + sym = (token_all_mask.short() + 1).cpu() + bs_mask_token = torchac.encode_float_cdf(cdf_mask_token, sym, check_input_bounds=True) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=z_H, w=z_W).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_sym = torchac.decode_float_cdf(cdf_mask_token, bs_mask_token) + decoded_mask = (decoded_sym - 1).to(device=imgs.device) + latent_hat = latent_hat.squeeze(1) + x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask, z_H, z_W) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask, z_H, z_W) + ## calculate loss + task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + 'bs_mask_token': bs_mask_token, + 'mask_ratio': mask_ratio, + 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'z_H': z_H, + 'z_W': z_W, + } + return return_dict + + # def update(self, scale_table=None, force=False): + # if scale_table is None: + # scale_table = get_scale_table() + # updated = self.gaussian_conditional.update_scale_table(scale_table, force=force) + # updated |= super().update(force=force) + # return updated + + def gen_img(self, logits, token_all_mask, token_indices, z_H, z_W, num_iter=12, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices, z_H, z_W) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask, z_H, z_W) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, z_H, z_W, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/deploited/models_mage_codec_rm_pos.py b/deploited/models_mage_codec_rm_pos.py new file mode 100644 index 0000000000000000000000000000000000000000..7f02850f59c56d7d474f4c0004fffd76d7aea677 --- /dev/null +++ b/deploited/models_mage_codec_rm_pos.py @@ -0,0 +1,759 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union + +SCALES_MIN = 0.11 +SCALES_MAX = 256 +SCALES_LEVELS = 64 +def get_scale_table(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS): + return torch.exp(torch.linspace(math.log(min), math.log(max), levels)) + +def ste_round(x: Tensor) -> Tensor: + return torch.round(x) - x.detach() + x + +def conv(in_channels, out_channels, kernel_size=5, stride=2): + return nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + ) + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() + # 使用Gumbel分布进行采样，增加随机性 + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + +def adjust_mask_and_drop_embeddings(token_keep_mask): + """ + Adjusts the token_keep_mask to the nearest square number of True values by randomly setting + some of them to False, and then applies this adjusted mask to input_embeddings. + + Parameters: + - input_embeddings: Tensor, The embeddings tensor. + - token_keep_mask: BoolTensor, The mask tensor indicating which tokens to keep. + + Returns: + - Tensor, Adjusted input embeddings after applying the modified token_keep_mask. + """ + # 获取非零（即值为True）元素的索引 + non_zero_indices = token_keep_mask.nonzero(as_tuple=True) + # 计算非零元素的数量 + non_zero_count = non_zero_indices[0].size(0) + # 计算最近的整数平方倍 + next_square = math.floor(math.sqrt(non_zero_count))**2 + # 计算需要移除的元素数量 + remove_count = non_zero_count - next_square + if remove_count > 0: + # 如果需要移除元素以达到整数平方倍 + permuted_indices = torch.randperm(non_zero_count)[:remove_count] + for idx in permuted_indices: + token_keep_mask[non_zero_indices[0][idx], non_zero_indices[1][idx]] = False + # 使用更新后的token_keep_mask + # input_embeddings_after_drop = input_embeddings[token_keep_mask] + + return token_keep_mask + + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + # outputs = self.quantize( + # x, "noise" if training else "dequantize", means.long() + # ) + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + # self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + # self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + # torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,257) + + seq_length = input_shape[1] + + # position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + + # position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + # embeddings = inputs_embeds + position_embeddings + embeddings = inputs_embeds + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, mask_ratio_mu=0.55, mask_ratio_std=0.25, + vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=256 +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + # self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - mask_ratio_mu) / mask_ratio_std, + # (mask_ratio_max - mask_ratio_mu) / mask_ratio_std, + # loc=mask_ratio_mu, scale=mask_ratio_std) + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + # self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + # self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + # self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + # pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + # self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + # self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + # torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + # def get_cdf_token_mask(self, token_all_mask): + # bsz, seq_len = token_all_mask.size() + # # 直接生成一个0到1之间的线性空间 + # linear_space = torch.linspace(0, 1, steps=seq_len+1) + # # 无需映射到-1到1 + # cdf_mask_token = linear_space + # # 调整形状以匹配token_all_mask，并扩展到每个batch + # cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + # b=bsz, s=seq_len) + # # cdf_mask_token = cdf_mask_token.unsqueeze(0).unsqueeze(-1).repeat(bsz, 1, seq_len) + # # 添加填充以匹配原始代码的操作 + # cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + # return cdf_mask_token + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, z_H, z_W = z_q.size() + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + mask_ratio_min = self.mask_ratio_min # 0.5 + + if is_training: + # mask_rate = self.mask_ratio_generator.rvs(1)[0] + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + token_all_mask = (noise <= cutoff_mask).float() # 逻辑上标记那些token是被mask掉的 + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置 + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, token_indices, token_all_mask, token_drop_mask + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + # x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + x = x_after_pad + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + # print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + # 判断latent_hat和latent是否相等 + # print((latent_hat == latent).all()) + cdf_mask_token = self.get_cdf_token_mask(token_all_mask).cpu() + sym = (token_all_mask.short() + 1).cpu() + bs_mask_token = torchac.encode_float_cdf(cdf_mask_token, sym, check_input_bounds=True) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=z_H, w=z_W).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_sym = torchac.decode_float_cdf(cdf_mask_token, bs_mask_token) + decoded_mask = (decoded_sym - 1).to(device=imgs.device) + latent_hat = latent_hat.squeeze(1) + x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + 'bs_mask_token': bs_mask_token, + 'mask_ratio': mask_ratio, + 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'z_H': z_H, + 'z_W': z_W, + } + return return_dict + + # def update(self, scale_table=None, force=False): + # if scale_table is None: + # scale_table = get_scale_table() + # updated = self.gaussian_conditional.update_scale_table(scale_table, force=force) + # updated |= super().update(force=force) + # return updated + + def gen_img(self, logits, token_all_mask, token_indices, z_H, z_W, num_iter=12, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, z_H, z_W, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/deploited/models_mage_codec_rope.py b/deploited/models_mage_codec_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..3e9a3e90d47484a18b37d85006fd3737b303830a --- /dev/null +++ b/deploited/models_mage_codec_rope.py @@ -0,0 +1,767 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union +from util.RoPE import LlamaDynamicNTKScalingRotaryEmbedding, apply_rotary_pos_emb + +SCALES_MIN = 0.11 +SCALES_MAX = 256 +SCALES_LEVELS = 64 +def get_scale_table(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS): + return torch.exp(torch.linspace(math.log(min), math.log(max), levels)) + +def ste_round(x: Tensor) -> Tensor: + return torch.round(x) - x.detach() + x + +def conv(in_channels, out_channels, kernel_size=5, stride=2): + return nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + ) + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() + # 使用Gumbel分布进行采样，增加随机性 + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + +def adjust_mask_and_drop_embeddings(token_keep_mask): + """ + Adjusts the token_keep_mask to the nearest square number of True values by randomly setting + some of them to False, and then applies this adjusted mask to input_embeddings. + + Parameters: + - input_embeddings: Tensor, The embeddings tensor. + - token_keep_mask: BoolTensor, The mask tensor indicating which tokens to keep. + + Returns: + - Tensor, Adjusted input embeddings after applying the modified token_keep_mask. + """ + # 获取非零（即值为True）元素的索引 + non_zero_indices = token_keep_mask.nonzero(as_tuple=True) + # 计算非零元素的数量 + non_zero_count = non_zero_indices[0].size(0) + # 计算最近的整数平方倍 + next_square = math.floor(math.sqrt(non_zero_count))**2 + # 计算需要移除的元素数量 + remove_count = non_zero_count - next_square + if remove_count > 0: + # 如果需要移除元素以达到整数平方倍 + permuted_indices = torch.randperm(non_zero_count)[:remove_count] + for idx in permuted_indices: + token_keep_mask[non_zero_indices[0][idx], non_zero_indices[1][idx]] = False + # 使用更新后的token_keep_mask + # input_embeddings_after_drop = input_embeddings[token_keep_mask] + + return token_keep_mask + + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + # outputs = self.quantize( + # x, "noise" if training else "dequantize", means.long() + # ) + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class Attention(nn.Module): + def __init__(self, dim, max_position_embeddings, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.max_position_embeddings = max_position_embeddings + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( + head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=8 + ) + # self.register_buffer("position_ids", torch.arange(self.max_position_embeddings).expand((1, -1))) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + cos, sin = self.rotary_emb(v, seq_len=N) + position_ids = torch.arange(N).expand((1, -1)) + q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, max_position_embeddings, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, max_position_embeddings, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,1 + latent_h * latent_w) + seq_length = input_shape[1] + # position_ids = self.position_ids[:, :seq_length] + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + # position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + + # embeddings = inputs_embeds + position_embeddings + embeddings = inputs_embeds + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, mask_ratio_mu=0.55, mask_ratio_std=0.25, + vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('/home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.max_position_embeddings = 256 + 1 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=256 +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + # self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - mask_ratio_mu) / mask_ratio_std, + # (mask_ratio_max - mask_ratio_mu) / mask_ratio_std, + # loc=mask_ratio_mu, scale=mask_ratio_std) + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches # 256 + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, self.max_position_embeddings, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + # def get_dec_pos_embed(self, z_H, z_W): + # # Exclude the first embedding (position 0) before interpolation + # position_embeddings = self.decoder_pos_embed_learned # (b, 256, embed_dim) + # # Perform bilinear interpolation + # interpolated_embeddings = F.interpolate( + # position_embeddings.permute(0, 2, 1), + # size=(1+z_H*z_W), mode="linear", align_corners=False + # ).permute(0, 2, 1) # (b, seq_len -1 , embed_dim) + # # Concatenate the first embedding back + # # position_embeddings = torch.cat([self.decoder_pos_embed_learned[:, :1, :], interpolated_embeddings], dim=1) + # position_embeddings = interpolated_embeddings + + # return position_embeddings + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, z_H, z_W = z_q.size() + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + mask_ratio_min = self.mask_ratio_min # 0.5 + + if is_training: + # mask_rate = self.mask_ratio_generator.rvs(1)[0] + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + token_all_mask = (noise <= cutoff_mask).float() # 逻辑上标记那些token是被mask掉的 + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置 + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, token_indices, token_all_mask, token_drop_mask + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + # x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + x = x_after_pad + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + # print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + # 判断latent_hat和latent是否相等 + # print((latent_hat == latent).all()) + cdf_mask_token = self.get_cdf_token_mask(token_all_mask).cpu() + sym = (token_all_mask.short() + 1).cpu() + bs_mask_token = torchac.encode_float_cdf(cdf_mask_token, sym, check_input_bounds=True) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=z_H, w=z_W).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_sym = torchac.decode_float_cdf(cdf_mask_token, bs_mask_token) + decoded_mask = (decoded_sym - 1).to(device=imgs.device) + latent_hat = latent_hat.squeeze(1) + x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + 'bs_mask_token': bs_mask_token, + 'mask_ratio': mask_ratio, + 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'z_H': z_H, + 'z_W': z_W, + } + return return_dict + + # def update(self, scale_table=None, force=False): + # if scale_table is None: + # scale_table = get_scale_table() + # updated = self.gaussian_conditional.update_scale_table(scale_table, force=force) + # updated |= super().update(force=force) + # return updated + + def gen_img(self, logits, token_all_mask, token_indices, z_H, z_W, num_iter=12, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, z_H, z_W, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/deploited/random_with_rope.log b/deploited/random_with_rope.log new file mode 100644 index 0000000000000000000000000000000000000000..52f7d983174502f10ba885e9b8f372a77f8c06ee --- /dev/null +++ b/deploited/random_with_rope.log @@ -0,0 +1,118 @@ +2024-04-26 09:40:33,300 [INFO ] Logging file is /home/v-ruoyufeng/v-ruoyufeng/qyp/mage/MIM_vbr//20240426_094033.log +2024-04-26 09:40:33,301 [INFO ] ======================= exp ======================= +2024-04-26 09:40:33,301 [INFO ] config:config/vpt_default.yaml +2024-04-26 09:40:33,301 [INFO ] name:exp +2024-04-26 09:40:33,301 [INFO ] lr:None +2024-04-26 09:40:33,301 [INFO ] world_size:1 +2024-04-26 09:40:33,301 [INFO ] local_rank:-1 +2024-04-26 09:40:33,301 [INFO ] dist_on_itp:False +2024-04-26 09:40:33,301 [INFO ] dist_url:env:// +2024-04-26 09:40:33,301 [INFO ] TEST:False +2024-04-26 09:40:33,301 [INFO ] model:mage_vit_base_patch16 +2024-04-26 09:40:33,301 [INFO ] dataset:imagenet +2024-04-26 09:40:33,301 [INFO ] dataset_path:/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets/imagenet +2024-04-26 09:40:33,301 [INFO ] eval_path: +2024-04-26 09:40:33,302 [INFO ] batch_size:64 +2024-04-26 09:40:33,302 [INFO ] test_batch_size:96 +2024-04-26 09:40:33,302 [INFO ] num_workers:4 +2024-04-26 09:40:33,302 [INFO ] epochs:400 +2024-04-26 09:40:33,302 [INFO ] blr:0.0002 +2024-04-26 09:40:33,302 [INFO ] min_lr:5e-06 +2024-04-26 09:40:33,302 [INFO ] accum_iter:1 +2024-04-26 09:40:33,302 [INFO ] input_size:256 +2024-04-26 09:40:33,302 [INFO ] weight_decay:0.05 +2024-04-26 09:40:33,302 [INFO ] warmup_epochs:20 +2024-04-26 09:40:33,302 [INFO ] mask_ratio_min:0.5 +2024-04-26 09:40:33,302 [INFO ] mask_ratio_max:0.9 +2024-04-26 09:40:33,302 [INFO ] mask_ratio_mu:0.55 +2024-04-26 09:40:33,302 [INFO ] mask_ratio_std:0.25 +2024-04-26 09:40:33,303 [INFO ] grad_clip:3.0 +2024-04-26 09:40:33,303 [INFO ] device:cuda +2024-04-26 09:40:33,303 [INFO ] distributed:True +2024-04-26 09:40:33,303 [INFO ] seed:0 +2024-04-26 09:40:33,303 [INFO ] resume:/home/v-ruoyufeng/v-ruoyufeng/qyp/mimc_cls/checkpoint-220.pth +2024-04-26 09:40:33,303 [INFO ] start_epoch:0 +2024-04-26 09:40:33,303 [INFO ] pin_mem:True +2024-04-26 09:40:33,303 [INFO ] save:True +2024-04-26 09:40:33,303 [INFO ] exp_name:MIM_vbr +2024-04-26 09:40:33,303 [INFO ] root:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage +2024-04-26 09:40:33,303 [INFO ] output_dir:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/MIM_vbr +2024-04-26 09:40:33,303 [INFO ] log_dir:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/MIM_vbr +2024-04-26 09:40:33,303 [INFO ] strart_epoch:0 +2024-04-26 09:40:33,303 [INFO ] cuda:True +2024-04-26 09:40:33,304 [INFO ] rank:0 +2024-04-26 09:40:33,304 [INFO ] gpu:0 +2024-04-26 09:40:33,304 [INFO ] dist_backend:nccl +2024-04-26 09:40:33,304 [INFO ] =================================================== +2024-04-26 09:43:59,448 [INFO ] -1|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.19228|msssim:0.19877|lpips:0.65322|dists:0.41351|Test loss:26.43571 +2024-04-26 11:03:41,816 [INFO ] 0|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.59349|msssim:0.26058|lpips:0.58663|dists:0.36222|Test loss:24.07364 +2024-04-26 12:23:19,122 [INFO ] 1|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.76288|msssim:0.28263|lpips:0.56872|dists:0.33883|Test loss:23.62683 +2024-04-26 13:43:34,495 [INFO ] 2|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.85301|msssim:0.28844|lpips:0.55959|dists:0.32906|Test loss:23.47551 +2024-04-26 15:03:52,749 [INFO ] 3|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.92868|msssim:0.29865|lpips:0.55056|dists:0.32090|Test loss:23.33249 +2024-04-26 16:23:59,470 [INFO ] 4|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.89536|msssim:0.30246|lpips:0.54562|dists:0.31920|Test loss:23.25514 +2024-04-26 17:44:34,630 [INFO ] 5|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.77614|msssim:0.30227|lpips:0.54257|dists:0.30956|Test loss:23.14211 +2024-04-26 19:05:03,170 [INFO ] 6|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.99645|msssim:0.30600|lpips:0.53496|dists:0.30253|Test loss:23.07454 +2024-04-26 20:25:38,328 [INFO ] 7|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.01199|msssim:0.31352|lpips:0.53121|dists:0.29895|Test loss:23.00766 +2024-04-26 21:46:25,922 [INFO ] 8|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.95318|msssim:0.31135|lpips:0.53315|dists:0.30050|Test loss:22.96596 +2024-04-26 23:07:15,459 [INFO ] 9|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.86282|msssim:0.31913|lpips:0.52871|dists:0.30449|Test loss:22.93078 +2024-04-27 00:27:59,646 [INFO ] 10|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.02011|msssim:0.32396|lpips:0.52660|dists:0.29969|Test loss:22.88336 +2024-04-27 01:48:56,864 [INFO ] 11|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.07232|msssim:0.32188|lpips:0.52460|dists:0.29983|Test loss:22.86315 +2024-04-27 03:09:11,839 [INFO ] 12|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.01043|msssim:0.32597|lpips:0.52504|dists:0.30010|Test loss:22.79985 +2024-04-27 04:29:49,673 [INFO ] 13|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.10261|msssim:0.32433|lpips:0.52505|dists:0.29726|Test loss:22.78871 +2024-04-27 05:50:35,081 [INFO ] 14|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.11125|msssim:0.32866|lpips:0.51948|dists:0.29619|Test loss:22.71350 +2024-04-27 07:10:26,373 [INFO ] 15|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.06473|msssim:0.32313|lpips:0.51909|dists:0.29284|Test loss:22.72064 +2024-04-27 08:29:03,434 [INFO ] 16|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.93886|msssim:0.32386|lpips:0.51924|dists:0.29350|Test loss:22.67830 +2024-04-27 09:48:42,090 [INFO ] 17|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.91387|msssim:0.32367|lpips:0.51942|dists:0.29057|Test loss:22.68104 +2024-04-27 11:07:05,282 [INFO ] 18|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.04036|msssim:0.32969|lpips:0.51792|dists:0.29092|Test loss:22.64099 +2024-04-27 12:26:41,635 [INFO ] 19|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:11.97499|msssim:0.32280|lpips:0.51902|dists:0.28883|Test loss:22.61567 +2024-04-27 13:46:27,048 [INFO ] 20|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.04421|msssim:0.32968|lpips:0.51996|dists:0.28981|Test loss:22.59735 +2024-04-27 15:07:26,901 [INFO ] 21|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.09963|msssim:0.32759|lpips:0.51643|dists:0.29144|Test loss:22.58986 +2024-04-27 16:27:18,924 [INFO ] 22|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25422|msssim:0.33293|lpips:0.51218|dists:0.28637|Test loss:22.55061 +2024-04-27 17:47:28,605 [INFO ] 23|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.03031|msssim:0.32350|lpips:0.51621|dists:0.28594|Test loss:22.55859 +2024-04-27 19:06:30,307 [INFO ] 24|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.01311|msssim:0.33033|lpips:0.51452|dists:0.28868|Test loss:22.51194 +2024-04-27 20:26:25,446 [INFO ] 25|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.12269|msssim:0.33470|lpips:0.51502|dists:0.28773|Test loss:22.53424 +2024-04-27 21:45:53,181 [INFO ] 26|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22099|msssim:0.33310|lpips:0.51191|dists:0.28884|Test loss:22.50715 +2024-04-27 23:06:53,646 [INFO ] 27|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.08395|msssim:0.33514|lpips:0.51467|dists:0.28832|Test loss:22.50186 +2024-04-28 00:26:46,986 [INFO ] 28|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23953|msssim:0.33534|lpips:0.51394|dists:0.28696|Test loss:22.49509 +2024-04-28 01:47:49,741 [INFO ] 29|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.11600|msssim:0.33224|lpips:0.51388|dists:0.28858|Test loss:22.48610 +2024-04-28 03:08:05,651 [INFO ] 30|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.04495|msssim:0.33490|lpips:0.51515|dists:0.28526|Test loss:22.47889 +2024-04-28 04:29:31,374 [INFO ] 31|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.13572|msssim:0.33166|lpips:0.51150|dists:0.28472|Test loss:22.44420 +2024-04-28 04:36:00,208 [INFO ] 32|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29226|msssim:0.33557|lpips:0.51159|dists:0.28618|Test loss:22.46866 +2024-04-28 05:55:42,942 [INFO ] 33|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.24471|msssim:0.33982|lpips:0.50918|dists:0.28688|Test loss:22.45550 +2024-04-28 07:14:35,318 [INFO ] 34|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26866|msssim:0.34018|lpips:0.50957|dists:0.28656|Test loss:22.46537 +2024-04-28 08:33:33,967 [INFO ] 35|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.17031|msssim:0.33502|lpips:0.51055|dists:0.28262|Test loss:22.42946 +2024-04-28 09:54:29,978 [INFO ] 36|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25919|msssim:0.33783|lpips:0.51071|dists:0.28604|Test loss:22.45442 +2024-04-28 11:15:39,983 [INFO ] 37|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20635|msssim:0.33632|lpips:0.50824|dists:0.28454|Test loss:22.45101 +2024-04-28 12:35:52,799 [INFO ] 38|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32195|msssim:0.34213|lpips:0.50806|dists:0.28493|Test loss:22.43027 +2024-04-28 13:55:26,853 [INFO ] 39|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.36062|msssim:0.33969|lpips:0.50819|dists:0.28332|Test loss:22.41071 +2024-04-28 15:15:39,162 [INFO ] 40|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22780|msssim:0.33774|lpips:0.50971|dists:0.28230|Test loss:22.41198 +2024-04-28 16:36:09,180 [INFO ] 41|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29715|msssim:0.34114|lpips:0.50735|dists:0.28232|Test loss:22.39130 +2024-04-28 17:57:27,808 [INFO ] 42|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.09102|msssim:0.33483|lpips:0.50913|dists:0.28347|Test loss:22.41543 +2024-04-28 19:17:26,206 [INFO ] 43|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20306|msssim:0.34007|lpips:0.51021|dists:0.28400|Test loss:22.40152 +2024-04-28 20:35:40,022 [INFO ] 44|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.22824|msssim:0.33591|lpips:0.50983|dists:0.28228|Test loss:22.42137 +2024-04-28 21:54:40,195 [INFO ] 45|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.38086|msssim:0.34395|lpips:0.50842|dists:0.28619|Test loss:22.39918 +2024-04-28 23:13:35,391 [INFO ] 46|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.16532|msssim:0.33896|lpips:0.50933|dists:0.28314|Test loss:22.41701 +2024-04-29 00:32:11,881 [INFO ] 47|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26819|msssim:0.33832|lpips:0.50565|dists:0.28173|Test loss:22.37620 +2024-04-29 01:52:28,969 [INFO ] 48|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29298|msssim:0.34225|lpips:0.50837|dists:0.28685|Test loss:22.38713 +2024-04-29 03:11:36,092 [INFO ] 49|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.10050|msssim:0.33825|lpips:0.50617|dists:0.28070|Test loss:22.36497 +2024-04-29 04:31:26,489 [INFO ] 50|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.33884|msssim:0.34209|lpips:0.50693|dists:0.28271|Test loss:22.37217 +2024-04-29 05:52:26,051 [INFO ] 51|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32687|msssim:0.34543|lpips:0.50534|dists:0.28436|Test loss:22.36113 +2024-04-29 07:12:41,027 [INFO ] 52|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.23419|msssim:0.34313|lpips:0.50683|dists:0.28346|Test loss:22.34546 +2024-04-29 08:32:37,029 [INFO ] 53|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.19908|msssim:0.34212|lpips:0.51034|dists:0.28401|Test loss:22.35217 +2024-04-29 09:53:24,004 [INFO ] 54|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.20177|msssim:0.34039|lpips:0.50786|dists:0.28354|Test loss:22.35897 +2024-04-29 11:13:36,377 [INFO ] 55|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26382|msssim:0.34287|lpips:0.50529|dists:0.28344|Test loss:22.36005 +2024-04-29 12:34:01,726 [INFO ] 56|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.17537|msssim:0.34354|lpips:0.50563|dists:0.28147|Test loss:22.34528 +2024-04-29 13:55:09,515 [INFO ] 57|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.12880|msssim:0.34119|lpips:0.50656|dists:0.28078|Test loss:22.33371 +2024-04-29 15:16:53,478 [INFO ] 58|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.24665|msssim:0.34374|lpips:0.50576|dists:0.28350|Test loss:22.32770 +2024-04-29 16:37:56,041 [INFO ] 59|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.21803|msssim:0.34118|lpips:0.50617|dists:0.28309|Test loss:22.35576 +2024-04-29 17:57:24,559 [INFO ] 60|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.27436|msssim:0.34233|lpips:0.50439|dists:0.28176|Test loss:22.33442 +2024-04-29 19:16:29,152 [INFO ] 61|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.18612|msssim:0.33822|lpips:0.50917|dists:0.28183|Test loss:22.35925 +2024-04-29 20:35:53,546 [INFO ] 62|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28599|msssim:0.34549|lpips:0.50387|dists:0.28009|Test loss:22.33322 +2024-04-29 21:55:16,529 [INFO ] 63|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.21795|msssim:0.34211|lpips:0.50608|dists:0.27972|Test loss:22.33488 +2024-04-29 23:14:30,846 [INFO ] 64|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.29407|msssim:0.34144|lpips:0.50596|dists:0.28011|Test loss:22.35140 +2024-04-29 23:19:58,151 [INFO ] 65|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.28235|msssim:0.34247|lpips:0.50468|dists:0.27957|Test loss:22.33808 +2024-04-30 00:39:16,656 [INFO ] 66|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.25703|msssim:0.34225|lpips:0.50430|dists:0.28089|Test loss:22.31090 +2024-04-30 01:59:45,043 [INFO ] 67|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.26808|msssim:0.34043|lpips:0.50586|dists:0.28125|Test loss:22.32784 +2024-04-30 03:21:24,464 [INFO ] 68|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.31984|msssim:0.34534|lpips:0.50445|dists:0.28171|Test loss:22.30779 +2024-04-30 04:42:24,830 [INFO ] 69|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.35958|msssim:0.34179|lpips:0.50447|dists:0.28130|Test loss:22.31575 +2024-04-30 06:03:32,581 [INFO ] 70|bpp:0.01874|mask:0.00888|mask_ratio:0.75|psnr:12.32468|msssim:0.34391|lpips:0.50387|dists:0.28060|Test loss:22.32640 diff --git a/deploited/random_with_rope.pth b/deploited/random_with_rope.pth new file mode 100644 index 0000000000000000000000000000000000000000..a3d346d8720de168ce815faaa12d8f9459e556c5 --- /dev/null +++ b/deploited/random_with_rope.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b81f2e9114c831f49bf4926d5a40d8655a9d21376f3e71fcc66ecfb1c5db1cb7 +size 1960373872 diff --git a/deploited/test_crop.py b/deploited/test_crop.py new file mode 100644 index 0000000000000000000000000000000000000000..19053942d4048ae30af509a935b1cbd609b1a30b --- /dev/null +++ b/deploited/test_crop.py @@ -0,0 +1,68 @@ +from util.utils import pad_to_multiple_of_256, split_into_blocks, merge_blocks, crop_to_original_shape +import glob +import os +import torch +import numpy as np +import math +from torch.nn import functional as F +import PIL.Image as Image +from torchvision import utils as vutils + + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1, 1, 3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + # 均匀添加padding + dh_half = dh // 2 + dw_half = dw // 2 + dh_extra = dh % 2 + dw_extra = dw % 2 + x = F.pad(x, (dw_half, dw_half + dw_extra, dh_half, dh_half + dh_extra)) + return x, h, w + +def save_img(img: torch.Tensor, vis_path, input_p, rec=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if rec: + vis_path = vis_path + '/rec' + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + img_name = vis_path + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(img_name), nrow=8) + + +eval_path = sorted(glob.glob(os.path.join('/home/t2vg-a100-G4-10/project/qyp/datasets/test', '*.jpg'))) +vis_path = os.path.join("./test_crop/") +os.makedirs(vis_path, exist_ok=True) + +for input_p in eval_path: + x, hx, wx = load_img(input_p, padding=True, factor=64) + print("ori height", hx, "ori width", wx) + ori_shape = x.shape + print("input shape", ori_shape) + x = pad_to_multiple_of_256(x, 0) + save_img(x, vis_path, input_p, rec=False) + print("shape after padding", x.shape) + _, _, new_h, new_w = x.shape + x = split_into_blocks(x) + print("new shape", x.shape) + new_bsz = x.size(0) + new_shape = [ori_shape[0], 3, new_h, new_w] + x = merge_blocks(x, new_shape) + print("shape after merge", x.shape) + x = crop_to_original_shape(x, ori_shape) + print("shape after crop", x.shape) + save_img(x, vis_path, input_p, rec=True) \ No newline at end of file diff --git a/deploited/train_vbr_codec_rope.py b/deploited/train_vbr_codec_rope.py new file mode 100644 index 0000000000000000000000000000000000000000..093afb4ac3cbe2ecaf596fdb98c14fe6a282ee99 --- /dev/null +++ b/deploited/train_vbr_codec_rope.py @@ -0,0 +1,673 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import models_mage_codec_rope +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + # bpp = sum( + # (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)) + # for likelihoods in out_net["likelihoods"].values() + # ) + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1,1,3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + x = F.pad(x, (0, dw, 0, dh)) + return x, h, w + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W']) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 500 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/t2vg-a100-G4-10/project/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + # _, _, hx, wx = d.shape + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W'], num_iter=12) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + _, accu, _ = cls_criterion(d, rec, l) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + accuracy.update(accu) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset, sampler=sampler_test, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + # model = models_mage_codec.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + # vqgan_ckpt_path=vqgan_ckpt_path) + model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + if args.TEST: # test only + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.3]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.75 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + data_loader_train = DataLoader( + current_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/detection.py b/detection.py new file mode 100644 index 0000000000000000000000000000000000000000..eeff724f7c52ce5e8ba906117a93e8a28a8d00d8 --- /dev/null +++ b/detection.py @@ -0,0 +1,635 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import numpy as np + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec_high_resolu +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +from collections import OrderedDict +import pickle +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding +from util.utils import adaptively_split_and_pad, crop_and_reconstruct +from util.alignment import Alignment + +## General +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone + +## Test +from detectron2.evaluation import COCOEvaluator +from detectron2.data.datasets import register_coco_instances +from detectron2.data import build_detection_test_loader +from detectron2.data.detection_utils import read_image + +from contextlib import ExitStack, contextmanager + + +## Function for model to eval 用于临时将模型切换到评估模式并在操作完成后恢复模型的原始模式 +@contextmanager +def inference_context(model): + training_mode = model.training + model.eval() + yield + model.train(training_mode) + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) # 初始化对齐模块，用于图像大小调整。 + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) # imagenet mean + + def forward(self, output, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) # RGB to BGR, [0,1] to [0,255] + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(output["x_hat"], 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def train_one_epoch(train_dataloader, optimizer, model, criterion_rd, criterion_task, lmbda): + model.train() + device = next(model.parameters()).device + tqdm_emu = tqdm.tqdm(enumerate(train_dataloader), total=len(train_dataloader), leave=False) + for i, d in tqdm_emu: + d = d.to(device) + + optimizer.zero_grad() + + out_net = model(d) + + out_criterion = criterion_rd(out_net, d) + perc_loss = criterion_task(out_net, d) + total_loss = perc_loss + lmbda * out_criterion['bpp_loss'] + total_loss.backward() + optimizer.step() + + update_txt=f'[{i*len(d)}/{len(train_dataloader.dataset)}] | Loss: {total_loss.item():.3f} | Distortion loss: {perc_loss.item():.5f} | Bpp loss: {out_criterion["bpp_loss"].item():.4f}' + tqdm_emu.set_postfix_str(update_txt, refresh=True) + + +def validation_epoch(epoch, val_dataloader, model, criterion_rd, criterion_task, lmbda): + model.eval() + device = next(model.parameters()).device + + bpp_loss = AverageMeter() + mse_loss = AverageMeter() + psnr = AverageMeter() + percloss = AverageMeter() + totalloss = AverageMeter() + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(val_dataloader),leave=False, total=len(val_dataloader)) + for i, d in tqdm_meter: + align = Alignment(divisor=256, mode='resize').to(device) + + d = d.to(device) + align_d = align.align(d) + + out_net = model(align_d) + out_net['x_hat'] = align.resume(out_net['x_hat']).clamp_(0, 1) + out_criterion = criterion_rd(out_net, d) + perc_loss = criterion_task(out_net, d) + total_loss = perc_loss + lmbda * out_criterion['bpp_loss'] + + bpp_loss.update(out_criterion["bpp_loss"]) + mse_loss.update(out_criterion["mse_loss"]) + psnr.update(out_criterion['psnr']) + percloss.update(perc_loss) + totalloss.update(total_loss) + + txt = f"Loss: {totalloss.avg:.3f} | MSE loss: {mse_loss.avg:.5f} | Perception loss: {percloss.avg:.4f} | Bpp loss: {bpp_loss.avg:.4f}" + tqdm_meter.set_postfix_str(txt) + + model.train() + print(f"Epoch: {epoch} | bpp loss: {bpp_loss.avg:.5f} | psnr: {psnr.avg:.5f}") + return totalloss.avg + + +def test_epoch(test_dataloader, model, criterion_rd, predictor, evaluator): + model.eval() + device = next(model.parameters()).device + pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) + + bpp_loss = AverageMeter() + psnr = AverageMeter() + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_dataloader),leave=False, total=len(test_dataloader)) + for i, batch in tqdm_meter: + with ExitStack() as stack: + ## model to eval() + if isinstance(predictor.model, nn.Module): + stack.enter_context(inference_context(predictor.model)) # inference_context：将预测器的模型设为评估模式 + stack.enter_context(torch.no_grad()) + + align = Alignment(divisor=256, mode='resize').to(device) + rcnn_align = Alignment(divisor=32).to(device) + + img = read_image(batch[0]["file_name"], format="BGR") + d = torch.stack([batch[0]['image'].float().div(255)]).flip(1).to(device) + align_d = align.align(d) + + out_net = model(align_d) + out_net['x_hat'] = align.resume(out_net['x_hat']).clamp_(0, 1) + out_criterion = criterion_rd(out_net, d) + + trand_y_tilde = out_net['x_hat'].flip(1).mul(255) + trand_y_tilde = rcnn_align.align(trand_y_tilde - pixel_mean) + + bpp_loss.update(out_criterion["bpp_loss"]) + psnr.update(out_criterion['psnr']) + + ## MaskRCNN + predictions = predictor(img, trand_y_tilde) + evaluator.process(batch, [predictions]) + txt = f"Bpp loss: {bpp_loss.avg:.4f} | PSNR loss: {psnr.avg:.4f}" + tqdm_meter.set_postfix_str(txt) + + results = evaluator.evaluate() + model.train() + print(f"bpp loss: {bpp_loss.avg:.5f} | psnr: {psnr.avg:.5f}") + return + + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_test_high_resolu/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, d in tqdm_meter: + d = d.to(device) + d0 = d + b_ori, _, h_ori, w_ori = d.shape + d, patch_sizes, num_blocks_h, num_blocks_w = adaptively_split_and_pad(d) + # d, h_ori, w_ori = prepadding(d) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['new_shape']) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], num_iter=20) + rec = crop_and_reconstruct(rec, patch_sizes, num_blocks_h, num_blocks_w) + rec = rec.unsqueeze(0) + rec = rec.to(device) + print('d0:', d0.shape) + print('rec:', rec.shape) + + # d = d[:, :, :h_ori, :w_ori] + # rec = rec[:, :, :h_ori, :w_ori] + # rec = rec[:, :, :hx, :wx] + out_criterion = metrics_criterion(d0, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + # if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d0, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg")) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_det = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_val = transforms.Compose([ + # transforms.Resize(224), + # transforms.CenterCrop(224), + transforms.ToTensor() + ]) + + + if args.dataset=='coco': + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_det, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + # val_dataset = Kodak(args.kodak_path, transform_val) + val_dataset = MSCOCO(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_high_resolu.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + # cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.5 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + + +if __name__ == "__main__": + main(sys.argv[1:]) \ No newline at end of file diff --git a/engine_finetune.py b/engine_finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..a36c8d3bfe8535d4b46faa6a4278cdd36ccc6438 --- /dev/null +++ b/engine_finetune.py @@ -0,0 +1,128 @@ +import math +import sys +from typing import Iterable, Optional + +import torch + +from timm.data import Mixup +from timm.utils import accuracy + +import util.misc as misc +import util.lr_sched as lr_sched +from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD + + +def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module, + data_loader: Iterable, optimizer: torch.optim.Optimizer, + device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, + mixup_fn: Optional[Mixup] = None, log_writer=None, + args=None, denormalize=False): + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + + accum_iter = args.accum_iter + + optimizer.zero_grad() + + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + samples = samples.to(device, non_blocking=True) + targets = targets.to(device, non_blocking=True) + + if mixup_fn is not None: + samples, targets = mixup_fn(samples, targets) + + if denormalize: + for c_id, (mean, std) in enumerate(zip(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)): + samples[:, c_id] = samples[:, c_id] * std + mean + + with torch.cuda.amp.autocast(): + outputs = model(samples) + loss = criterion(outputs, targets) + + loss_value = loss.item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + loss /= accum_iter + loss_scaler(loss, optimizer, clip_grad=max_norm, + parameters=model.parameters(), create_graph=False, + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + min_lr = 10. + max_lr = 0. + for group in optimizer.param_groups: + min_lr = min(min_lr, group["lr"]) + max_lr = max(max_lr, group["lr"]) + + metric_logger.update(lr=max_lr) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', max_lr, epoch_1000x) + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +@torch.no_grad() +def evaluate(data_loader, model, device, denormalize=False): + criterion = torch.nn.CrossEntropyLoss() + + metric_logger = misc.MetricLogger(delimiter=" ") + header = 'Test:' + + # switch to evaluation mode + model.eval() + + for batch in metric_logger.log_every(data_loader, 10, header): + images = batch[0] + target = batch[-1] + images = images.to(device, non_blocking=True) + target = target.to(device, non_blocking=True) + + if denormalize: + for c_id, (mean, std) in enumerate(zip(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)): + images[:, c_id] = images[:, c_id] * std + mean + + # compute output + with torch.cuda.amp.autocast(): + output = model(images) + loss = criterion(output, target) + + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + + batch_size = images.shape[0] + metric_logger.update(loss=loss.item()) + metric_logger.meters['acc1'].update(acc1.item(), n=batch_size) + metric_logger.meters['acc5'].update(acc5.item(), n=batch_size) + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}' + .format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss)) + + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} \ No newline at end of file diff --git a/engine_pretrain.py b/engine_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..9afc84f3d4cee9a1024d10eeb67713b482b02397 --- /dev/null +++ b/engine_pretrain.py @@ -0,0 +1,72 @@ +import math +import sys +from typing import Iterable + +import torch + +import util.misc as misc +import util.lr_sched as lr_sched + + +def train_one_epoch(model: torch.nn.Module, + data_loader: Iterable, optimizer: torch.optim.Optimizer, + device: torch.device, epoch: int, loss_scaler, + log_writer=None, + args=None): + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + + accum_iter = args.accum_iter + + optimizer.zero_grad() + + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + samples = samples.to(device, non_blocking=True) + + with torch.cuda.amp.autocast(): + loss, _, _ = model(samples) + + loss_value = loss.item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + loss /= accum_iter + loss_scaler(loss, optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} \ No newline at end of file diff --git a/environment.yaml b/environment.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32dab056d604067da50d8bfb4b0966eb2a5fcadb --- /dev/null +++ b/environment.yaml @@ -0,0 +1,26 @@ +name: mage +channels: + - pytorch + - defaults +dependencies: + - python=3.8.5 + - pip=20.3 + - cudatoolkit=10.2 + - pytorch=1.7.1 + - torchvision=0.8.2 + - numpy=1.19.2 + - pip: + - albumentations==0.4.3 + - opencv-python==4.1.2.30 + - pudb==2019.2 + - imageio==2.9.0 + - imageio-ffmpeg==0.4.2 + - pytorch-lightning==1.0.8 + - omegaconf==2.0.0 + - test-tube>=0.7.5 + - streamlit>=0.73.1 + - einops==0.3.0 + - more-itertools>=8.0.0 + - transformers==4.3.1 + - timm==0.3.2 + - -e . diff --git a/eval.py b/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..b28ab1cf076f160f59e6b1a71cb775bbf01c0ca7 --- /dev/null +++ b/eval.py @@ -0,0 +1,601 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +import lpips +from util.dataloader import MSCOCO, Kodak, prepadding + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + loss_fn_alex = lpips.LPIPS(net='alex').cuda() + lipis_alex = loss_fn_alex(rec, ori) + return lipis_alex.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + # out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + out["bpp_loss"] = out["bpp"] + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + self.perceptual_loss = False + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1,1,3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + x = F.pad(x, (0, dw, 0, dh)) + return x, h, w + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr_eval_lossless/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/t2vg-a100-G4-10/project/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_images_by_batch(rec, vis_path, batch_idx): + # 保存每个图像 + for i in range(rec.size(0)): # 假设 rec 是 (batch_size, C, H, W) + image_filename = os.path.join(vis_path, f"batch_{batch_idx}_{i}.jpg") + # rec[i] 选择单个图像，unsqueeze(0) 添加一个批次维度 + vutils.save_image(rec[i], image_filename) + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + test_vis_path = os.path.join("/home/t2vg-a100-G4-10/project/qyp/rec_fid", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + _, accu, _ = cls_criterion(d, rec, l) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + accuracy.update(accu) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + save_images_by_batch(rec, test_vis_path, i) + # vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_kodak(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + test_vis_path = os.path.join("/home/t2vg-a100-G4-10/project/qyp/mimc_rope/eval_kodak_256", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, d in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + d = d[:, :, :h_ori, :w_ori] + rec = rec[:, :, :h_ori, :w_ori] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + # _, accu, _ = cls_criterion(d, rec, l) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + # accuracy.update(accu) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + save_images_by_batch(rec, test_vis_path, i) + # vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + action='store_true', + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + transform_val = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(256), + transforms.ToTensor() + ]) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + # val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + val_dataset = Kodak(args.kodak_path, transform_val) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + + test_dataloader = DataLoader(test_dataset, sampler=sampler_test, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + if True: # test only + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05]: + loss = inference_with_kodak(-1, val_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/eval_kodak_256/0.05/batch_0_0.jpg b/eval_kodak_256/0.05/batch_0_0.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0ff12aabfbdd34195e261dd138fb4d7f3c200dfd Binary files /dev/null and b/eval_kodak_256/0.05/batch_0_0.jpg differ diff --git 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+++ b/eval_only copy.py @@ -0,0 +1,381 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import models_mage_codec_full +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape, MSCOCO_inference + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + # out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + # out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='val'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + # msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_high_resolu_eval/", stage) + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, (d, filename) in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + # rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape']) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + # rec = rec[:, :, :hx, :wx] + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + # msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + # test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + with torch.no_grad(): + filename = filename[0] + # real_fake_images = torch.cat((d, rec), dim=0) + # vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec, os.path.join(vis_path, f"{base_filename}.jpg")) + # vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{base_filename}_mask.jpg")) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + # vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg")) + + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + # log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}" + logging.info(log_txt) + return bpp_loss.avg + + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_det = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + + if args.dataset=='coco': + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_det, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + # val_dataset = Kodak(args.kodak_path, transform_val) + val_dataset = MSCOCO_inference(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_full.__dict__[args.model](mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + for manual_mask_ratio in [0.5]: + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, manual_mask_ratio, args, 'val') + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/eval_only.py b/eval_only.py new file mode 100644 index 0000000000000000000000000000000000000000..07c278be0da47ed58e388a3cf3d932cf51dd38d8 --- /dev/null +++ b/eval_only.py @@ -0,0 +1,381 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import models_mage_codec_full +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape, MSCOCO_inference + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + # out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + # out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='val'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + # msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_high_resolu_eval/", stage) + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, (d, filename) in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + # rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape']) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + # rec = rec[:, :, :hx, :wx] + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + # msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + # test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + with torch.no_grad(): + filename = filename[0] + # real_fake_images = torch.cat((d, rec), dim=0) + # vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec, os.path.join(vis_path, f"{base_filename}.jpg")) + # vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{base_filename}_mask.jpg")) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + # vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg")) + + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + # log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}" + logging.info(log_txt) + return bpp_loss.avg + + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_det = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + + if args.dataset=='coco': + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_det, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + # val_dataset = Kodak(args.kodak_path, transform_val) + val_dataset = MSCOCO_inference(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_full.__dict__[args.model](mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + for manual_mask_ratio in [0.5]: + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, manual_mask_ratio, args, 'val') + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/eval_vq.py b/eval_vq.py new file mode 100644 index 0000000000000000000000000000000000000000..eea13d9b1ecad231db53533b0731acec92b717ea --- /dev/null +++ b/eval_vq.py @@ -0,0 +1,332 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +from model_vq import Model_VQ +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape +from omegaconf import OmegaConf + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, rec): + out = {} + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + + return out + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def inference(epoch, test_loader, model, metrics_criterion, device, stage='val'): + model.eval() + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + + vis_path = os.path.join("./VQGAN/", stage) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, (d, filename) in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=64) + rec = model(d) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + # rec = rec[:, :, :hx, :wx] + out_criterion = metrics_criterion(d, rec) + + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + with torch.no_grad(): + filename = filename[0] + # real_fake_images = torch.cat((d, rec), dim=0) + # vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec, os.path.join(vis_path, f"{base_filename}.jpg")) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + # log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + log_txt = f"{epoch}|psnr:{psnr.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}" + logging.info(log_txt) + return + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_det = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + if args.dataset=='coco': + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_det, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + # val_dataset = Kodak(args.kodak_path, transform_val) + val_dataset = MSCOCO(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + config = OmegaConf.load('config/vqgan.yaml').model + model = Model_VQ(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + + model.to(device) + # 打印所有可学习的参数 + print("List of all learnable parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.size()}") + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + metrics_criterion = CalMetrics() + + print("############## pre validation ##############") + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, 'val') + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/fine_tune_vq.py b/fine_tune_vq.py new file mode 100644 index 0000000000000000000000000000000000000000..3c92c41d5339d0a9b9800642b960bf4559e155b2 --- /dev/null +++ b/fine_tune_vq.py @@ -0,0 +1,537 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +from model_vq import Model_VQ, Model_VQ_former +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape, MSCOCO_inference +from util.alignment import Alignment +from omegaconf import OmegaConf +import pickle +from collections import OrderedDict + +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone +import shutil + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def forward(self, ori, rec): + out = {} + out["mse"] = self.mse(ori, rec) + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) + + def forward(self, x_hat, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(x_hat, 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def train_one_epoch(train_dataloader, optimizer, model, criterion_task, criterion_rd): + model.train() + loss_cri = nn.MSELoss().cuda() + device = next(model.parameters()).device + tqdm_emu = tqdm.tqdm(enumerate(train_dataloader), total=len(train_dataloader), leave=False) + for i, d in tqdm_emu: + d = d.to(device) + + optimizer.zero_grad() + + out_net = model(d) + + perc_loss = criterion_task(out_net, d) + # out_criterion = criterion_rd(d, out_net) + total_loss = perc_loss + # total_loss = out_criterion["mse"] + # total_loss = loss_cri(out_net, d) + + # print("total_loss=", total_loss) + total_loss.backward() + for name, param in model.named_parameters(): + if "prompt" in name: + if param.grad is not None: + print(f'Gradient for {name}:') + print(param.grad.max()) + print("grad_min=", param.grad.min()) + else: + print(f'No gradient for {name}') + optimizer.step() + + update_txt=f'[{i*len(d)}/{len(train_dataloader.dataset)}] | Percloss: {total_loss.item():.4f}' + tqdm_emu.set_postfix_str(update_txt, refresh=True) + + +def validation_epoch(epoch, val_dataloader, model, criterion_rd, criterion_task): + model.eval() + device = next(model.parameters()).device + + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + percloss = AverageMeter() + totalloss = AverageMeter() + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(val_dataloader),leave=False, total=len(val_dataloader)) + for i, d in tqdm_meter: + align = Alignment(divisor=256, mode='resize').to(device) + + d = d.to(device) + align_d = align.align(d) + + x_hat = model(align_d) + x_hat = align.resume(x_hat).clamp_(0, 1) + out_criterion = criterion_rd(d, x_hat) + perc_loss = criterion_task(x_hat, d) + total_loss = perc_loss + + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + percloss.update(perc_loss) + totalloss.update(total_loss) + + txt = f"Loss: {totalloss.avg:.3f} | psnr: {psnr.avg:.5f} | Perception loss: {percloss.avg:.4f}" + tqdm_meter.set_postfix_str(txt) + + model.train() + log_txt = f"Epoch: {epoch} | perc loss: {percloss.avg:.5f} | psnr: {psnr.avg:.5f} | lpips: {lpips.avg:.5f} | dists: {dists.avg:.5f}" + logging.info(log_txt) + return totalloss.avg + +def inference(epoch, inference_loader, model, criterion_rd, criterion_task): + model.eval() + device = next(model.parameters()).device + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + percloss = AverageMeter() + totalloss = AverageMeter() + + vis_path = os.path.join("./VQGAN/best_loss") + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(inference_loader), leave=False, total=len(inference_loader)) + for i, (d, filenames) in tqdm_meter: + align = Alignment(divisor=256, mode='resize').to(device) + d = d.to(device) + align_d = align.align(d) + + x_hat = model(align_d) + x_hat = align.resume(x_hat).clamp_(0, 1) + out_criterion = criterion_rd(d, x_hat) + perc_loss = criterion_task(x_hat, d) + total_loss = perc_loss + + rec = model(d) + rec = rec.to(device) + + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + percloss.update(perc_loss) + totalloss.update(total_loss) + + ## ======================= update progress bar & visualization ======================= ## + with torch.no_grad(): + for j, filename in enumerate(filenames): + filename = filename[0] + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec[j], os.path.join(vis_path, f"{base_filename}.jpg")) + + txt = f"Loss: {totalloss.avg:.3f} | psnr: {psnr.avg:.5f} | Perception loss: {percloss.avg:.4f}" + tqdm_meter.set_postfix_str(txt) + + model.train() + return totalloss.avg + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def configure_optimizers(net, args): + """Set optimizer for only the parameters for propmts""" + + parameters = {k for k, p in net.named_parameters() + if "prompt" in k + } + + params_dict = dict(net.named_parameters()) + + + optimizer = optim.Adam( + (params_dict[n] for n in sorted(parameters)), + lr=args.learning_rate, + ) + + return optimizer + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + logging.info(f"Saving checkpoint: {base_dir+filename}") + torch.save(state, base_dir+filename) + if is_best: + logging.info(f"Saving BEST checkpoint: {base_dir+filename}") + shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id) + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomCrop((256, 256)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor() + ]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + if args.dataset=='coco': + cfg = get_cfg() # get default cfg + cfg.merge_from_file("/home/t2vg-a100-G4-10/project/qyp/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") + cfg.MODEL.WEIGHTS = args.maskrcnn_path + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_train, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + test_dataset = MSCOCO(args.dataset_path + "/val2017/", transform=transform_val) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [50, 4950]) + # val_dataset = Kodak(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + if args.distributed: + # if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_train = torch.utils.data.DistributedSampler(train_dataset, shuffle=True) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + + train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=True, pin_memory=(device=="cuda")) + val_dataloader = DataLoader(val_dataset, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + # vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt_old/checkpoint_9.pth.tar' + config = OmegaConf.load('config/vqgan.yaml').model + model = Model_VQ(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + # model = Model_VQ_former(ddconfig=config.params.ddconfig, + # n_embed=config.params.n_embed, # 1024 + # embed_dim=config.params.embed_dim, # 256 + # ckpt_path=vqgan_ckpt_path) + + model.to(device) + + # if args.resume: # load from previous checkpoint + # logging.info("Loading "+str(args.resume)) + # checkpoint = torch.load(args.resume, map_location=device) + # if list(checkpoint["state_dict"].keys())[0][:7]=='module.': + # from collections import OrderedDict + # new_state_dict = OrderedDict() + # for k, v in checkpoint["state_dict"].items(): + # name = k[7:] # remove `module.` + # new_state_dict[name] = v + # else: + # new_state_dict = checkpoint['state_dict'] + # net.load_state_dict(new_state_dict, strict=True) + + use_parallel=False + if args.cuda and torch.cuda.device_count() > 1: + model = CustomDataParallel(model) + use_parallel = True + + # 打印所有可学习的参数 + print("List of all learnable parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.size()}") + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + optimizer = configure_optimizers(model, args) + lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[], gamma=0.1) + + metrics_criterion = CalMetrics() + task_criterion = TaskLoss(cfg, device) + + if args.TEST: + inference_dataset = MSCOCO_inference(args.dataset_path + "/val2017/", transform=transform_val) + inference_dataloader = DataLoader(inference_dataset, batch_size=8, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_loss = inference(0, inference_dataloader, model, metrics_criterion, task_criterion) + + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + test_loss = validation_epoch(-1, val_dataloader, model, metrics_criterion, task_criterion) + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + train_one_epoch(train_dataloader, optimizer, model, task_criterion, metrics_criterion) + test_loss = validation_epoch(epoch, val_dataloader, model, metrics_criterion, task_criterion) + lr_scheduler.step() + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.save: + if use_parallel: + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + save_checkpoint( + { + "epoch": epoch, + "state_dict": model.state_dict(), + "loss": test_loss, + "optimizer": optimizer.state_dict(), + "lr_scheduler": lr_scheduler.state_dict(), + }, + is_best, + base_dir, + filename='checkpoint.pth.tar' + ) + if epoch%10==9: + shutil.copyfile(base_dir+'checkpoint.pth.tar', base_dir+ f"checkpoint_{epoch}.pth.tar" ) + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/fine_tune_vq_w_mask copy.py b/fine_tune_vq_w_mask copy.py new file mode 100644 index 0000000000000000000000000000000000000000..292333cda28c07224d1cae247d675586cac5fee1 --- /dev/null +++ b/fine_tune_vq_w_mask copy.py @@ -0,0 +1,511 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +from model_vq import Model_VQ, Model_VQ_former +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import models_mage_codec_hs_w_p +import models_mage_codec_full +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape, MSCOCO_inference +from util.alignment import Alignment +from omegaconf import OmegaConf +import pickle +from collections import OrderedDict + +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone +import shutil + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def forward(self, ori, rec, out_net): + out = {} + out["mse"] = self.mse(ori, rec) + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out['bpp'], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + return out + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) + + def forward(self, x_hat, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(x_hat, 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def train_one_epoch(train_dataloader, optimizer, model, criterion_task): + model.train() + device = next(model.parameters()).device + tqdm_emu = tqdm.tqdm(enumerate(train_dataloader), total=len(train_dataloader), leave=False) + for i, d in tqdm_emu: + d = d.to(device) + optimizer.zero_grad() + + out_net = model(d, is_training=True) + rec = model.gen_img_train(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + + perc_loss = criterion_task(rec, d) + total_loss = perc_loss + + total_loss.backward() + for name, param in model.named_parameters(): + if "prompt" in name: + if param.grad is not None: + print(f'Gradient for {name}:') + print(param.grad.max()) + print("grad_min=", param.grad.min()) + else: + print(f'No gradient for {name}') + optimizer.step() + mask_ratio = out_net['mask_ratio'] + update_txt=f'[{i*len(d)}/{len(train_dataloader.dataset)}] | Mask ratio: {mask_ratio:.4f} | Percloss: {total_loss.item():.4f}' + tqdm_emu.set_postfix_str(update_txt, refresh=True) + +def validation_epoch(epoch, val_dataloader, model, criterion_rd, criterion_task, manual_mask_ratio): + model.eval() + device = next(model.parameters()).device + + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + percloss = AverageMeter() + + vis_path = os.path.join("./Fine_Tune_VQ_w_P/val/") + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(val_dataloader),leave=False, total=len(val_dataloader)) + for i, d in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + + out_criterion = criterion_rd(d, rec, out_net) + perc_loss = criterion_task(rec, d) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + percloss.update(perc_loss) + + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg")) + + txt = f" | Perception loss: {percloss.avg:.4f} | psnr: {psnr.avg:.5f}" + tqdm_meter.set_postfix_str(txt) + + model.train() + log_txt = f"Epoch: {epoch}|bpp loss: {bpp_loss.avg:.5f}|bpp mask: {bpp_mask.avg:.5f}|perc loss: {percloss.avg:.5f}|psnr: {psnr.avg:.5f}|lpips: {lpips.avg:.5f}|dists: {dists.avg:.5f}" + logging.info(log_txt) + return percloss.avg + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + + vis_path = os.path.join("./Fine_Tune_VQ_w_P/") + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, (d, filename) in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + # rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape']) + rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + out_criterion = metrics_criterion(d, rec, out_net) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + ## ======================= update progress bar & visualization ======================= ## + with torch.no_grad(): + filename = filename[0] + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec, os.path.join(vis_path, f"{base_filename}.jpg")) + + model.train() + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}" + logging.info(log_txt) + return bpp_loss.avg + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def configure_optimizers(net, args): + """Set optimizer for only the parameters for propmts""" + parameters = {k for k, p in net.named_parameters() + if "prompt" in k + } + params_dict = dict(net.named_parameters()) + optimizer = optim.Adam( + (params_dict[n] for n in sorted(parameters)), + lr=args.learning_rate, + ) + + return optimizer + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + logging.info(f"Saving checkpoint: {base_dir+filename}") + torch.save(state, base_dir+filename) + if is_best: + logging.info(f"Saving BEST checkpoint: {base_dir+filename}") + shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id) + print("CUDA_VISIBLE_DEVICES=", str(args.gpu_id)) + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomCrop((256, 256)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor() + ]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + if args.dataset=='coco': + cfg = get_cfg() # get default cfg + cfg.merge_from_file("/home/t2vg-a100-G4-10/project/qyp/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") + cfg.MODEL.WEIGHTS = args.maskrcnn_path + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_train, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + test_dataset = MSCOCO(args.dataset_path + "/val2017/", transform=transform_val) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [50, 4950]) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=True, pin_memory=(device=="cuda")) + val_dataloader = DataLoader(val_dataset, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + # vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt_old/checkpoint_9.pth.tar' + # config = OmegaConf.load('config/vqgan.yaml').model + # model = Model_VQ(ddconfig=config.params.ddconfig, + # n_embed=config.params.n_embed, # 1024 + # embed_dim=config.params.embed_dim, # 256 + # ckpt_path=vqgan_ckpt_path) + model = models_mage_codec_hs_w_p.__dict__[args.model](mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + model.to(device) + + use_parallel=False + if args.cuda and torch.cuda.device_count() > 1: + model = CustomDataParallel(model) + use_parallel = True + + # 打印所有可学习的参数 + print("List of all learnable parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.size()}") + + # following timm: set wd as 0 for bias and norm layers + optimizer = configure_optimizers(model, args) + lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[], gamma=0.1) + + metrics_criterion = CalMetrics() + task_criterion = TaskLoss(cfg, device) + + if args.TEST: + inference_dataset = MSCOCO_inference(args.dataset_path + "/val2017/", transform=transform_val) + inference_dataloader = DataLoader(inference_dataset, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_loss = inference(0, inference_dataloader, model, metrics_criterion, task_criterion) + return + + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + test_loss = validation_epoch(-1, val_dataloader, model, metrics_criterion, task_criterion, manual_mask_ratio=0.5) + # test_loss = inference(-1, inference_dataloader, model, metrics_criterion, device, manual_mask_ratio=0.3) + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + for epoch in tqrange: + train_one_epoch(train_dataloader, optimizer, model, task_criterion) + test_loss = validation_epoch(epoch, val_dataloader, model, metrics_criterion, task_criterion, manual_mask_ratio=0.7) + lr_scheduler.step() + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.save: + if use_parallel: + state_dict = model.module.state_dict() + else: + state_dict = model.state_dict() + save_checkpoint( + { + "epoch": epoch, + "state_dict": state_dict, + "loss": test_loss, + "optimizer": optimizer.state_dict(), + "lr_scheduler": lr_scheduler.state_dict(), + }, + is_best, + base_dir, + filename='checkpoint.pth.tar' + ) + if epoch%10==9: + shutil.copyfile(base_dir+'checkpoint.pth.tar', base_dir+ f"checkpoint_{epoch}.pth.tar" ) + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/fine_tune_vq_w_mask.py b/fine_tune_vq_w_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..d96ebbc51f507fd4bb95ca838b394b754cb46ef7 --- /dev/null +++ b/fine_tune_vq_w_mask.py @@ -0,0 +1,615 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec_hs_w_p +import models_mage_codec_full +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape, MSCOCO_inference +import shutil + +# Task loss +import pickle +from collections import OrderedDict + +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone +from util.alignment import Alignment + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) + + def forward(self, x_hat, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(x_hat, 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def train_one_epoch(dataloader, optimizer, model, criterion_task, device, + epoch, loss_scaler, log_writer, args, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./Fine_Tune_VQ_w_P/", stage) + os.makedirs(vis_path, exist_ok=True) + + for i, d in enumerate(metric_logger.log_every(dataloader, print_freq, header)): + d = d.to(device, non_blocking=True) # d = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if i % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, i / len(dataloader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(d, is_training=True, manual_mask_rate=None) + + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = out_net['rec'] + # rec = rec.to(device) + + task_loss = criterion_task(rec, d) + loss_value = task_loss.item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + task_loss /= accum_iter + loss_scaler(task_loss, optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(i + 1) % accum_iter == 0) + if (i + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(mask_ratio=out_net['mask_ratio']) + metric_logger.update(loss=task_loss.item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (i + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((i / len(d) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if i % 400 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + + +def val_epoch(epoch, test_loader, model, metrics_criterion, task_criterion, device, manual_mask_ratio, stage='val'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + taskloss = AverageMeter() + + vis_path = os.path.join("./Fine_Tune_VQ_w_P/", stage) + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, d in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + out_criterion = metrics_criterion(d, out_net, rec) + perc_loss = task_criterion(rec, d) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + taskloss.update(perc_loss) + ## ======================= update progress bar & visualization ======================= ## + if i % 10 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg")) + + model.train() + + # Synchronize taskloss.avg across all processes + if dist.is_initialized(): + taskloss_tensor = torch.tensor(taskloss.avg, device=device) + dist.all_reduce(taskloss_tensor, op=dist.ReduceOp.SUM) + taskloss_avg = taskloss_tensor.item() / dist.get_world_size() + else: + taskloss_avg = taskloss.avg + + # Only log from rank 0 + if dist.get_rank() == 0: + log_txt = f"{epoch}|taskloss:{taskloss_avg:.5f}|mask ratio:{manual_mask_ratio}||bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|psnr:{psnr.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}" + logging.info(log_txt) + + return taskloss_avg + + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, stage='val'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + + vis_path = os.path.join("./Fine_Tune_VQ_w_P/", stage) + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, (d, filename) in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + with torch.no_grad(): + filename = filename[0] + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec, os.path.join(vis_path, f"{base_filename}.jpg")) + + model.train() + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + # log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}" + logging.info(log_txt) + return bpp_loss.avg + + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + logging.info(f"Saving checkpoint: {base_dir+filename}") + torch.save(state, base_dir+filename) + if is_best: + logging.info(f"Saving BEST checkpoint: {base_dir+filename}") + shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + if dist.get_rank() == 0: + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + if dist.get_rank() == 0: + logging.info(msg) + for k in args.__dict__: + if dist.get_rank() == 0: + logging.info(k + ':' + str(args.__dict__[k])) + if dist.get_rank() == 0: + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomCrop((256, 256)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + if args.dataset=='coco': + cfg = get_cfg() # get default cfg + cfg.merge_from_file("/home/t2vg-a100-G4-10/project/qyp/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") + cfg.MODEL.WEIGHTS = args.maskrcnn_path + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_train, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + # val_dataset = Kodak(args.kodak_path, transform_val) + test_dataset = MSCOCO(args.dataset_path + "/val2017/", transform=transform_val) + # test_dataset = MSCOCO_inference(args.dataset_path + "/val2017/", transform=transform_val) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [50, 4950]) + # inference_dataset = MSCOCO_inference(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=False + ) + # sampler_inf = torch.utils.data.DistributedSampler( + # inference_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=False + # ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + # inference_dataloader = DataLoader(inference_dataset, sampler=sampler_inf, batch_size=1, + # num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_hs_w_p.__dict__[args.model](mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + # 打印所有可学习的参数 + print("List of all learnable parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.size()}") + + # print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + # print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + task_criterion = TaskLoss(cfg, device) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.7 + test_loss = 0 + for val_mask_ratio in [0.75, 0.65, 0.55, 0.45]: + task_loss = val_epoch(-1, val_dataloader, model, metrics_criterion, task_criterion, device, val_mask_ratio, 'val') + test_loss += task_loss + test_loss = test_loss / 4 + + if dist.get_rank() == 0: + logging.info(f"Test loss={test_loss}") + # test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, manual_mask_ratio, 'val') + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + sampler_train = torch.utils.data.DistributedSampler(train_dataset, shuffle=True) + data_loader_train = DataLoader( + train_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(data_loader_train, optimizer, model, task_criterion, device, + epoch, loss_scaler, log_writer=log_writer, args=args, stage='train') + + for val_mask_ratio in [0.75, 0.65, 0.55, 0.45]: + task_loss = val_epoch(epoch, val_dataloader, model, metrics_criterion, task_criterion, device, val_mask_ratio, 'val') + test_loss += task_loss + test_loss = test_loss / 4 + if dist.get_rank() == 0: + logging.info(f"Test loss={test_loss}") + # test_loss = val_epoch(epoch, val_dataloader, model, metrics_criterion, task_criterion, device, val_mask_ratio, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + if dist.get_rank() == 0: + logging.info(f"Saving BEST checkpoint!") + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) + diff --git a/gen_img_uncond.py b/gen_img_uncond.py new file mode 100644 index 0000000000000000000000000000000000000000..c05884e4426d6943bf4c8e414701f6ba227b7d7c --- /dev/null +++ b/gen_img_uncond.py @@ -0,0 +1,149 @@ +import torch +import os +import math +import argparse +import models_mage +import numpy as np +from tqdm import tqdm +import cv2 + + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() # num of tokens to be mask + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + + +def gen_image(model, bsz, seed, num_iter=12, choice_temperature=4.5): + torch.manual_seed(seed) + np.random.seed(seed) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = model.mask_token_label + unknown_number_in_the_beginning = 256 + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + + initial_token_indices = mask_token_id * torch.ones(bsz, unknown_number_in_the_beginning) # init token_indices with mask token label + + token_indices = initial_token_indices.cuda() + + for step in range(num_iter): + cur_ids = token_indices.clone().long() # 当前已知id + + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = model.fake_class_label # add cls token + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id # set mask token + + token_drop_mask = torch.zeros_like(token_indices) # no drop at inference + + # token embedding + input_embeddings = model.token_emb(token_indices) + + # encoder + x = input_embeddings + for blk in model.blocks: + x = blk(x) + x = model.norm(x) + + # decoder + logits = model.forward_decoder(x, token_drop_mask, token_all_mask) # generate logits + logits = logits[:, 1:, :codebook_size] + + # get token prediction + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # 从logits的第三维采出概率最大的 + + # get ids for next step + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # unknown位置取sampled_ids，已知位置取cur_ids + # torch.where(condition, x, y) + # condition: 一个布尔张量，其元素决定从 x 还是 y 中选取对应位置的元素。 + # x: 当 condition 的对应位置为 True 时，将从这个张量中选取元素。 + # y: 当 condition 的对应位置为 False 时，将从这个张量中选取元素。 + + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + # 已知位置设置非常高的置信度使其无法被更改 + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + + mask_len = torch.Tensor([np.floor(unknown_number_in_the_beginning * mask_ratio)]).cuda() + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) # 未被mask的位置由sampled_ids代替 + + # vqgan visualization + z_q = model.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, 16, 16, codebook_emb_dim)) + gen_images = model.vqgan.decode(z_q) + return gen_images + + +parser = argparse.ArgumentParser('MAGE generation', add_help=False) +parser.add_argument('--temp', default=4.5, type=float, + help='sampling temperature') +parser.add_argument('--num_iter', default=12, type=int, + help='number of iterations for generation') +parser.add_argument('--batch_size', default=32, type=int, + help='batch size for generation') +parser.add_argument('--num_images', default=50000, type=int, + help='number of images to generate') +parser.add_argument('--ckpt', type=str, + help='checkpoint') +parser.add_argument('--model', default='mage_vit_base_patch16', type=str, + help='model') +parser.add_argument('--output_dir', default='output_dir/fid/gen/mage-vitb', type=str, + help='name') + +args = parser.parse_args() + +vqgan_ckpt_path = 'vqgan_jax_strongaug.ckpt' + +model = models_mage.__dict__[args.model](norm_pix_loss=False, + mask_ratio_mu=0.55, mask_ratio_std=0.25, + mask_ratio_min=0.0, mask_ratio_max=1.0, + vqgan_ckpt_path=vqgan_ckpt_path) +model.to(0) + +checkpoint = torch.load(args.ckpt, map_location='cpu') +model.load_state_dict(checkpoint['model']) +model.eval() + +num_steps = args.num_images // args.batch_size + 1 +gen_img_list = [] +save_folder = os.path.join(args.output_dir, "temp{}-iter{}".format(args.temp, args.num_iter)) +if not os.path.exists(save_folder): + os.makedirs(save_folder) +for i in tqdm(range(num_steps)): + with torch.no_grad(): + gen_images_batch = gen_image(model, bsz=args.batch_size, seed=i, choice_temperature=args.temp, num_iter=args.num_iter) + gen_images_batch = gen_images_batch.detach().cpu() + gen_img_list.append(gen_images_batch) + + # save img + for b_id in range(args.batch_size): + if i*args.batch_size+b_id >= args.num_images: + break + gen_img = np.clip(gen_images_batch[b_id].numpy().transpose([1, 2, 0]) * 255, 0, 255) + gen_img = gen_img.astype(np.uint8)[:, :, ::-1] + cv2.imwrite(os.path.join(save_folder, '{}.png'.format(str(i*args.batch_size+b_id).zfill(5))), gen_img) diff --git a/inference_fine_tune_vq.py b/inference_fine_tune_vq.py new file mode 100644 index 0000000000000000000000000000000000000000..238333361a6e7a1befdaedcfe8c3ca6ed29702b7 --- /dev/null +++ b/inference_fine_tune_vq.py @@ -0,0 +1,534 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +from model_vq import Model_VQ, Model_VQ_former +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape, MSCOCO_inference +from util.alignment import Alignment +from omegaconf import OmegaConf +import pickle +from collections import OrderedDict + +from detectron2.config import get_cfg +from detectron2.layers import ShapeSpec +from detectron2.modeling.backbone.fpn import build_resnet_fpn_backbone +import shutil + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def forward(self, ori, rec): + out = {} + out["mse"] = self.mse(ori, rec) + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) + + def forward(self, x_hat, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(x_hat, 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def train_one_epoch(train_dataloader, optimizer, model, criterion_task, criterion_rd): + model.train() + loss_cri = nn.MSELoss().cuda() + device = next(model.parameters()).device + tqdm_emu = tqdm.tqdm(enumerate(train_dataloader), total=len(train_dataloader), leave=False) + for i, d in tqdm_emu: + d = d.to(device) + + optimizer.zero_grad() + + out_net = model(d) + + perc_loss = criterion_task(out_net, d) + # out_criterion = criterion_rd(d, out_net) + total_loss = perc_loss + # total_loss = out_criterion["mse"] + # total_loss = loss_cri(out_net, d) + + # print("total_loss=", total_loss) + total_loss.backward() + for name, param in model.named_parameters(): + if "prompt" in name: + if param.grad is not None: + print(f'Gradient for {name}:') + print(param.grad.max()) + print("grad_min=", param.grad.min()) + else: + print(f'No gradient for {name}') + optimizer.step() + + update_txt=f'[{i*len(d)}/{len(train_dataloader.dataset)}] | Percloss: {total_loss.item():.4f}' + tqdm_emu.set_postfix_str(update_txt, refresh=True) + + +def validation_epoch(epoch, val_dataloader, model, criterion_rd, criterion_task): + model.eval() + device = next(model.parameters()).device + + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + percloss = AverageMeter() + totalloss = AverageMeter() + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(val_dataloader),leave=False, total=len(val_dataloader)) + for i, d in tqdm_meter: + align = Alignment(divisor=256, mode='resize').to(device) + + d = d.to(device) + align_d = align.align(d) + + x_hat = model(align_d) + x_hat = align.resume(x_hat).clamp_(0, 1) + out_criterion = criterion_rd(d, x_hat) + perc_loss = criterion_task(x_hat, d) + total_loss = perc_loss + + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + percloss.update(perc_loss) + totalloss.update(total_loss) + + txt = f"Loss: {totalloss.avg:.3f} | psnr: {psnr.avg:.5f} | Perception loss: {percloss.avg:.4f}" + tqdm_meter.set_postfix_str(txt) + + model.train() + log_txt = f"Epoch: {epoch} | perc loss: {percloss.avg:.5f} | psnr: {psnr.avg:.5f} | lpips: {lpips.avg:.5f} | dists: {dists.avg:.5f}" + logging.info(log_txt) + return totalloss.avg + +def inference(epoch, inference_loader, model, criterion_rd, criterion_task): + model.eval() + device = next(model.parameters()).device + psnr = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + percloss = AverageMeter() + totalloss = AverageMeter() + + vis_path = os.path.join("./VQGAN/best_loss") + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(inference_loader), leave=False, total=len(inference_loader)) + for i, (d, filenames) in tqdm_meter: + align = Alignment(divisor=256, mode='resize').to(device) + d = d.to(device) + align_d = align.align(d) + + x_hat = model(align_d) + x_hat = align.resume(x_hat).clamp_(0, 1) + out_criterion = criterion_rd(d, x_hat) + perc_loss = criterion_task(x_hat, d) + total_loss = perc_loss + + rec = model(d) + rec = rec.to(device) + + psnr.update(out_criterion['psnr']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + percloss.update(perc_loss) + totalloss.update(total_loss) + + ## ======================= update progress bar & visualization ======================= ## + with torch.no_grad(): + for j, filename in enumerate(filenames): + filename = filename[0] + base_filename = os.path.splitext(filename)[0] # 获取文件名，不含扩展名 + vutils.save_image(rec[j], os.path.join(vis_path, f"{base_filename}.jpg")) + + txt = f"Loss: {totalloss.avg:.3f} | psnr: {psnr.avg:.5f} | Perception loss: {percloss.avg:.4f}" + tqdm_meter.set_postfix_str(txt) + + model.train() + return totalloss.avg + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def configure_optimizers(net, args): + """Set optimizer for only the parameters for propmts""" + + parameters = {k for k, p in net.named_parameters() + if "prompt" in k + } + + params_dict = dict(net.named_parameters()) + + + optimizer = optim.Adam( + (params_dict[n] for n in sorted(parameters)), + lr=args.learning_rate, + ) + + return optimizer + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + logging.info(f"Saving checkpoint: {base_dir+filename}") + torch.save(state, base_dir+filename) + if is_best: + logging.info(f"Saving BEST checkpoint: {base_dir+filename}") + shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id) + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomCrop((256, 256)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor() + ]) + transform_val = transforms.Compose([ + transforms.ToTensor() + ]) + + if args.dataset=='coco': + cfg = get_cfg() # get default cfg + cfg.merge_from_file("/home/t2vg-a100-G4-10/project/qyp/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") + cfg.MODEL.WEIGHTS = args.maskrcnn_path + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_train, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + test_dataset = MSCOCO(args.dataset_path + "/val2017/", transform=transform_val) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [50, 4950]) + # val_dataset = Kodak(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + if args.distributed: + # if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_train = torch.utils.data.DistributedSampler(train_dataset, shuffle=True) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + + train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, + num_workers=args.num_workers, shuffle=True, pin_memory=(device=="cuda")) + val_dataloader = DataLoader(val_dataset, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + # vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mimc_rope/VQ_w_Prompt_old/checkpoint_9.pth.tar' + config = OmegaConf.load('config/vqgan.yaml').model + model = Model_VQ(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + # model = Model_VQ_former(ddconfig=config.params.ddconfig, + # n_embed=config.params.n_embed, # 1024 + # embed_dim=config.params.embed_dim, # 256 + # ckpt_path=vqgan_ckpt_path) + + model.to(device) + + if args.resume: # load from previous checkpoint + logging.info("Loading "+str(args.resume)) + checkpoint = torch.load(args.resume, map_location=device) + if list(checkpoint["state_dict"].keys())[0][:7]=='module.': + from collections import OrderedDict + new_state_dict = OrderedDict() + for k, v in checkpoint["state_dict"].items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + else: + new_state_dict = checkpoint['state_dict'] + net.load_state_dict(new_state_dict, strict=True) + + use_parallel=False + if args.cuda and torch.cuda.device_count() > 1: + net = CustomDataParallel(net) + use_parallel = True + + # 打印所有可学习的参数 + print("List of all learnable parameters:") + for name, param in model.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.size()}") + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + optimizer = configure_optimizers(model, args) + lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[], gamma=0.1) + + metrics_criterion = CalMetrics() + task_criterion = TaskLoss(cfg, device) + + if True: + inference_dataset = MSCOCO_inference(args.dataset_path + "/val2017/", transform=transform_val) + inference_dataloader = DataLoader(inference_dataset, batch_size=8, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_loss = inference(0, inference_dataloader, model, metrics_criterion, task_criterion) + return + + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + test_loss = validation_epoch(-1, val_dataloader, model, metrics_criterion, task_criterion) + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + train_one_epoch(train_dataloader, optimizer, model, task_criterion, metrics_criterion) + test_loss = validation_epoch(epoch, val_dataloader, model, metrics_criterion, task_criterion) + lr_scheduler.step() + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.save: + save_checkpoint( + { + "epoch": epoch, + "state_dict": model.state_dict(), + "loss": test_loss, + "optimizer": optimizer.state_dict(), + "lr_scheduler": lr_scheduler.state_dict(), + }, + is_best, + base_dir, + filename='checkpoint.pth.tar' + ) + if epoch%10==9: + shutil.copyfile(base_dir+'checkpoint.pth.tar', base_dir+ f"checkpoint_{epoch}.pth.tar" ) + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/install_pkg.py b/install_pkg.py new file mode 100644 index 0000000000000000000000000000000000000000..ebb8db8536f10a776a7ba6fd05b27dcc29f141f3 --- /dev/null +++ b/install_pkg.py @@ -0,0 +1,12 @@ +import subprocess + +def install_packages_from_file(file_path): + with open(file_path, 'r') as file: + packages = file.readlines() + for package in packages: + package = package.strip() + if package: + subprocess.call(['pip', 'install', package]) + +if __name__ == "__main__": + install_packages_from_file('requirements.txt') diff --git a/model_vq.py b/model_vq.py new file mode 100644 index 0000000000000000000000000000000000000000..214a8dd85535987325ab1551e8a0c1e86b7b9c17 --- /dev/null +++ b/model_vq.py @@ -0,0 +1,121 @@ +import torch.nn as nn +from taming.models.vqgan import VQModel, VQModel_w_Prompt +import torch.nn.functional as F + + +def pad_to_multiple_of_16(latent, pad_value, patch_size=16): + h, w = latent.size(2), latent.size(3) + target_h = ((h - 1) // patch_size + 1) * patch_size + target_w = ((w - 1) // patch_size + 1) * patch_size + pad_h = (target_h - h) // 2 + pad_w = (target_w - w) // 2 + # 额外处理奇数padding的情况 + pad_h_extra = (target_h - h) % 2 + pad_w_extra = (target_w - w) % 2 + padded_latent = F.pad(latent, (pad_w, pad_w + pad_w_extra, pad_h, pad_h + pad_h_extra), mode='constant', value=pad_value) # 指定左、右、上、下的填充宽度 + print("After padding: ", padded_latent.shape) + return padded_latent + + +def split_into_blocks(latent, patch_size=16): + b, c, h, w = latent.size() + blocks = latent.view(b, c, h // patch_size, patch_size, w // patch_size, patch_size) + blocks = blocks.permute(0, 2, 4, 1, 3, 5).contiguous().view(-1, c, patch_size, patch_size) + print("After splitting into blocks: ", blocks.shape) + return blocks + +# def merge_blocks(blocks, original_shape): +# b, c, h, w = original_shape +# num_blocks_per_row = w // 16 +# num_blocks_per_col = h // 16 + +# # 恢复到原始形状的顺序 +# blocks = blocks.view(b, num_blocks_per_col, num_blocks_per_row, c, 16, 16) +# blocks = blocks.permute(0, 3, 1, 4, 2, 5).contiguous() +# blocks = blocks.view(b, c, h, w) +# print("After merging blocks: ", blocks.shape) +# return blocks + +def merge_blocks(blocks, original_shape, patch_size=16): + b, c, h, w = original_shape + num_blocks_per_row = w // patch_size + num_blocks_per_col = h // patch_size + + # 恢复到原始形状的顺序 + blocks = blocks.view(b, num_blocks_per_col, num_blocks_per_row, c, patch_size, patch_size) + blocks = blocks.permute(0, 3, 1, 4, 2, 5).contiguous() + blocks = blocks.view(b, c, h, w) + print("After merging blocks: ", blocks.shape) + return blocks + +def crop_to_original_shape(blocks, original_shape): + _, _, padded_height, padded_width = blocks.shape + original_height, original_width = original_shape[2], original_shape[3] + start_h = (padded_height - original_height) // 2 + end_h = start_h + original_height + start_w = (padded_width - original_width) // 2 + end_w = start_w + original_width + cropped_blocks = blocks[:, :, start_h:end_h, start_w:end_w] + print("After cropping to original shape: ", cropped_blocks.shape) + return cropped_blocks + +class Model_VQ(nn.Module): + def __init__(self, ddconfig, n_embed, embed_dim, ckpt_path): + super(Model_VQ, self).__init__() + self.vqgan = VQModel_w_Prompt(ddconfig=ddconfig, n_embed=n_embed, embed_dim=embed_dim, ckpt_path=ckpt_path) + # self.mask_token_label = 2024 + + # for param in self.vqgan.parameters(): + # param.requires_grad = False + for name, param in self.vqgan.named_parameters(): + if 'prompt' not in name: + param.requires_grad = False + + def forward(self, input): + # codebook_emb_dim = 256 + z_q, _, token_tuple = self.vqgan.encode(input) # z_q: (b0, 256, h0, w0), token_tuple: (B, 256, h0, w0) + gen_images =self.vqgan.decode(z_q) + return gen_images + + +class Model_VQ_former(nn.Module): + def __init__(self, ddconfig, n_embed, embed_dim, ckpt_path): + super(Model_VQ_former, self).__init__() + # self.vqgan = VQModel_w_Prompt(ddconfig=ddconfig, n_embed=n_embed, embed_dim=embed_dim, ckpt_path=ckpt_path) + self.vqgan = VQModel(ddconfig=ddconfig, n_embed=n_embed, embed_dim=embed_dim, ckpt_path=ckpt_path) + # self.mask_token_label = 2024 + + # for param in self.vqgan.parameters(): + # param.requires_grad = False + for name, param in self.vqgan.named_parameters(): + if 'prompt' not in name: + param.requires_grad = False + + def forward(self, input): + # codebook_emb_dim = 256 + z_q, _, token_tuple = self.vqgan.encode(input) # z_q: (b0, 256, h0, w0), token_tuple: (B, 256, h0, w0) + gen_images =self.vqgan.decode(z_q) + return gen_images + + +if __name__ == "__main__": + import torchvision.transforms as transforms + from PIL import Image + import matplotlib.pyplot as plt + + # 加载和处理图像 + img = Image.open('/home/t2vg-a100-G4-10/project/qyp/mimc_rope/shark/val/rec/000000001000.jpg').convert('RGB') # 修改为你的图像路径 + transform = transforms.Compose([ + transforms.ToTensor(), + ]) + img_tensor = transform(img).unsqueeze(0) # 添加批次维度 + + # 应用函数 + padded_img = pad_to_multiple_of_16(img_tensor, pad_value=0, patch_size=256) + blocks = split_into_blocks(padded_img, patch_size=256) + + # 可视化和保存块 + for i, block in enumerate(blocks): + plt.imshow(block.permute(1, 2, 0).numpy()) + plt.title(f'Block {i}') + plt.savefig(f'block_{i}.png') # 保存每个块的图片 \ No newline at end of file diff --git a/models_mage.py b/models_mage.py new file mode 100644 index 0000000000000000000000000000000000000000..4f0af5c6ca01716e14eab2472de87072a570094a --- /dev/null +++ b/models_mage.py @@ -0,0 +1,375 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() + + seq_length = input_shape[1] + + position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) + + position_embeddings = self.position_embeddings(position_ids) + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=1.0, mask_ratio_mu=0.55, mask_ratio_std=0.25, + vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, + embed_dim=config.params.embed_dim, + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 + self.mask_token_label = vocab_size - 1 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, + hidden_size=embed_dim, + max_position_embeddings=256+1, + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - mask_ratio_mu) / mask_ratio_std, + (mask_ratio_max - mask_ratio_mu) / mask_ratio_std, + loc=mask_ratio_mu, scale=mask_ratio_std) + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.blocks = nn.ModuleList([ + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + + self.initialize_weights() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward_encoder(self, x): + # tokenization + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) + + _, _, token_indices = token_tuple + token_indices = token_indices.reshape(z_q.size(0), -1) + gt_indices = token_indices.clone().detach().long() + + # masking + bsz, seq_len = token_indices.size() + mask_ratio_min = self.mask_ratio_min + mask_rate = self.mask_ratio_generator.rvs(1)[0] + + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() + token_all_mask = (noise <= cutoff_mask).float() + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + # print(mask_rate, num_dropped_tokens, num_masked_tokens, token_drop_mask.sum(dim=1), token_all_mask.sum(dim=1)) + token_indices[token_all_mask.nonzero(as_tuple=True)] = self.mask_token_label + # print("Masekd num token:", torch.sum(token_indices == self.mask_token_label, dim=1)) + + # concate class token + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + # bert embedding + input_embeddings = self.token_emb(token_indices) + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # apply Transformer blocks + x = input_embeddings_after_drop + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, gt_indices, token_drop_mask, token_all_mask + + def forward_decoder(self, x, token_drop_mask, token_all_mask): + # embed tokens + x = self.decoder_embed(x) + + # append mask tokens to sequence + if self.pad_with_cls_token: + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) + + # add pos embed + x = x_after_pad + self.decoder_pos_embed_learned + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + x = self.mlm_layer(x, word_embeddings) + # print("Logits shape:", x.shape) + + return x + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def forward(self, imgs): + latent, gt_indices, token_drop_mask, token_all_mask = self.forward_encoder(imgs) + logits = self.forward_decoder(latent, token_drop_mask, token_all_mask) + loss = self.forward_loss(gt_indices, logits, token_all_mask) + return loss, imgs, token_all_mask + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/models_mage_codec.py b/models_mage_codec.py new file mode 100644 index 0000000000000000000000000000000000000000..d8d22c9e9149b26c3fc898546ac9a4fe692f077b --- /dev/null +++ b/models_mage_codec.py @@ -0,0 +1,755 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union + +SCALES_MIN = 0.11 +SCALES_MAX = 256 +SCALES_LEVELS = 64 +def get_scale_table(min=SCALES_MIN, max=SCALES_MAX, levels=SCALES_LEVELS): + return torch.exp(torch.linspace(math.log(min), math.log(max), levels)) + +def ste_round(x: Tensor) -> Tensor: + return torch.round(x) - x.detach() + x + +def conv(in_channels, out_channels, kernel_size=5, stride=2): + return nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + ) + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() + # 使用Gumbel分布进行采样，增加随机性 + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + +def adjust_mask_and_drop_embeddings(token_keep_mask): + """ + Adjusts the token_keep_mask to the nearest square number of True values by randomly setting + some of them to False, and then applies this adjusted mask to input_embeddings. + + Parameters: + - input_embeddings: Tensor, The embeddings tensor. + - token_keep_mask: BoolTensor, The mask tensor indicating which tokens to keep. + + Returns: + - Tensor, Adjusted input embeddings after applying the modified token_keep_mask. + """ + # 获取非零（即值为True）元素的索引 + non_zero_indices = token_keep_mask.nonzero(as_tuple=True) + # 计算非零元素的数量 + non_zero_count = non_zero_indices[0].size(0) + # 计算最近的整数平方倍 + next_square = math.floor(math.sqrt(non_zero_count))**2 + # 计算需要移除的元素数量 + remove_count = non_zero_count - next_square + if remove_count > 0: + # 如果需要移除元素以达到整数平方倍 + permuted_indices = torch.randperm(non_zero_count)[:remove_count] + for idx in permuted_indices: + token_keep_mask[non_zero_indices[0][idx], non_zero_indices[1][idx]] = False + # 使用更新后的token_keep_mask + # input_embeddings_after_drop = input_embeddings[token_keep_mask] + + return token_keep_mask + + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + # outputs = self.quantize( + # x, "noise" if training else "dequantize", means.long() + # ) + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,257) + + seq_length = input_shape[1] + + position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + + position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, mask_ratio_mu=0.55, mask_ratio_std=0.25, + vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=img_size +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + # self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - mask_ratio_mu) / mask_ratio_std, + # (mask_ratio_max - mask_ratio_mu) / mask_ratio_std, + # loc=mask_ratio_mu, scale=mask_ratio_std) + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + # def get_cdf_token_mask(self, token_all_mask): + # bsz, seq_len = token_all_mask.size() + # # 直接生成一个0到1之间的线性空间 + # linear_space = torch.linspace(0, 1, steps=seq_len+1) + # # 无需映射到-1到1 + # cdf_mask_token = linear_space + # # 调整形状以匹配token_all_mask，并扩展到每个batch + # cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + # b=bsz, s=seq_len) + # # cdf_mask_token = cdf_mask_token.unsqueeze(0).unsqueeze(-1).repeat(bsz, 1, seq_len) + # # 添加填充以匹配原始代码的操作 + # cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + # return cdf_mask_token + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + mask_ratio_min = self.mask_ratio_min # 0.5 + + if is_training: + # mask_rate = self.mask_ratio_generator.rvs(1)[0] + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + token_all_mask = (noise <= cutoff_mask).float() # 逻辑上标记那些token是被mask掉的 + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置 + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, token_indices, token_all_mask, token_drop_mask + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + print("Logits shape:", logits.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + # 判断latent_hat和latent是否相等 + # print((latent_hat == latent).all()) + cdf_mask_token = self.get_cdf_token_mask(token_all_mask).cpu() + sym = (token_all_mask.short() + 1).cpu() + bs_mask_token = torchac.encode_float_cdf(cdf_mask_token, sym, check_input_bounds=True) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=16, w=16).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_sym = torchac.decode_float_cdf(cdf_mask_token, bs_mask_token) + decoded_mask = (decoded_sym - 1).to(device=imgs.device) + latent_hat = latent_hat.squeeze(1) + x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + 'bs_mask_token': bs_mask_token, + 'mask_ratio': mask_ratio, + 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + } + return return_dict + + # def update(self, scale_table=None, force=False): + # if scale_table is None: + # scale_table = get_scale_table() + # updated = self.gaussian_conditional.update_scale_table(scale_table, force=force) + # updated |= super().update(force=force) + # return updated + + def gen_img(self, logits, token_all_mask, token_indices, num_iter=12, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, 16, 16, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/models_mage_codec_cls.py b/models_mage_codec_cls.py new file mode 100644 index 0000000000000000000000000000000000000000..075623f30d3b716dae61bc98f49b40e5ada79742 --- /dev/null +++ b/models_mage_codec_cls.py @@ -0,0 +1,742 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union +from timm.models.layers import trunc_normal_ +from util.rle import rle_encode, rle_decode + + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() + # 使用Gumbel分布进行采样，增加随机性 + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + + +class ImportancePredictor(nn.Module): + """ + Input: z_q: [b, (h*w), c] + Output: importance_score: [b, N] + """ + def __init__(self, embed_dim=768): # 768 + super().__init__() + self.in_conv = nn.Sequential( + FusedLayerNorm(embed_dim, eps=1e-5), + nn.Linear(embed_dim, embed_dim), + nn.GELU() + ) + + self.out_conv = nn.Sequential( + nn.Linear(embed_dim, embed_dim // 2), + nn.GELU(), + nn.Linear(embed_dim // 2, embed_dim // 4), + nn.GELU(), + nn.Linear(embed_dim // 4, 1) + ) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x, policy): + x = self.in_conv(x) + B, N, C = x.size() + local_x = x[:, :, :C // 2] + global_x = (x[:, :, C // 2:] * policy).sum(dim=1, keepdim=True) / torch.sum(policy, dim=1, keepdim=True) + x = torch.cat([local_x, global_x.expand(B, N, C // 2)], dim=-1) + x = self.out_conv(x) + return x.squeeze(-1) # 将形状从 [b, N, 1] 转换为 [b, N] + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,257) + + seq_length = input_shape[1] + + position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + + position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=img_size +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.token_predictor = ImportancePredictor(config.params.embed_dim) # predict importance tokens + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + self.token_all_mask_param = nn.Parameter(torch.zeros((1, 256))) # 假设最大序列长度为8192 + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + self.freeze_parameters() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + output: gt_indices, + token_indices, decoding要用 + unmaksed_token_indices, 算码率要用 + token_all_mask, decoding要用 + token_drop_mask, decoding要用 + mask_ratio, 可视化 + importance_scores 可视化· + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + + if is_training: + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * 0.2)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + + z_q = rearrange(z_q, 'b c h w -> b (h w) c') + B, num_patches, _ = z_q.shape + mask = torch.ones(B, num_patches, 1, dtype=z_q.dtype, device=z_q.device) + importance_scores = self.token_predictor(z_q, mask) + + topk_scores, topk_indices = torch.topk(importance_scores, num_masked_tokens, dim=1, largest=False) + + # 创建 token_all_mask + token_all_mask = torch.zeros_like(importance_scores, requires_grad=True) + token_all_mask = token_all_mask.scatter(1, topk_indices, 1.0) + + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置 + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, importance_scores + + def pre_decoding(self, gt_indices, token_indices, token_all_mask, token_drop_mask): + bsz, seq_len = token_indices.size() + padded_token_indices = torch.full_like(token_indices, fill_value=self.mask_token_label) + + token_indices = token_indices * (1 - token_all_mask) + padded_token_indices * token_all_mask + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + # x = input_embeddings + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, token_indices, token_all_mask, token_drop_mask + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + # print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def freeze_parameters(self): + for name, param in self.named_parameters(): + if 'token_predictor' not in name: + param.requires_grad = False + # self.token_all_mask_param.requires_grad = True + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, im_scores = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + _, latent_likelihoods = self.entropy_bottleneck(latent) + mask_stream, mask_len = rle_encode(token_all_mask) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=16, w=16).unsqueeze(1) + im_scores_vis = rearrange(im_scores, 'b (h w) -> b h w', h=16, w=16).unsqueeze(1) + + ## ---------- decoding process ---------- ## + # decoded_mask = rle_decode(mask_stream, token_all_mask.shape).float() + # decoded_mask = decoded_mask.to(device=imgs.device) + # latent_hat = latent_hat.squeeze(1) + x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, token_indices, token_all_mask, token_drop_mask) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + 'mask_len': mask_len, + # 'bs_mask_token': bs_mask_token, + 'mask_ratio': mask_ratio, + # 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'im_score_vis': im_scores_vis, + } + return return_dict + + def gen_img(self, logits, token_all_mask, token_indices, num_iter=6, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, 16, 16, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/models_mage_codec_cls_old.py b/models_mage_codec_cls_old.py new file mode 100644 index 0000000000000000000000000000000000000000..9694e6ebffd719820269b9969a4d426a887c81ce --- /dev/null +++ b/models_mage_codec_cls_old.py @@ -0,0 +1,747 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union +from timm.models.layers import trunc_normal_ +from util.rle import rle_encode, rle_decode + + +def mask_by_random_topk(mask_len, probs, temperature=1.0): + mask_len = mask_len.squeeze() + # 使用Gumbel分布进行采样，增加随机性 + confidence = torch.log(probs) + torch.Tensor(temperature * np.random.gumbel(size=probs.shape)).cuda() + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, mask_len.long()-1:mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + + +class ImportancePredictor(nn.Module): + """ + Input: z_q: [b, (h*w), c] + Output: importance_score: [b, N] + """ + def __init__(self, embed_dim=768): # 768 + super().__init__() + self.in_conv = nn.Sequential( + FusedLayerNorm(embed_dim, eps=1e-5), + nn.Linear(embed_dim, embed_dim), + nn.GELU() + ) + + self.out_conv = nn.Sequential( + nn.Linear(embed_dim, embed_dim // 2), + nn.GELU(), + nn.Linear(embed_dim // 2, embed_dim // 4), + nn.GELU(), + nn.Linear(embed_dim // 4, 1) + ) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x, policy): + x = self.in_conv(x) + B, N, C = x.size() + local_x = x[:, :, :C // 2] + global_x = (x[:, :, C // 2:] * policy).sum(dim=1, keepdim=True) / torch.sum(policy, dim=1, keepdim=True) + x = torch.cat([local_x, global_x.expand(B, N, C // 2)], dim=-1) + x = self.out_conv(x) + return x.squeeze(-1) # 将形状从 [b, N, 1] 转换为 [b, N] + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,257) + + seq_length = input_shape[1] + + position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + + position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + self.vqgan = VQModel(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=img_size +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.token_predictor = ImportancePredictor(config.params.embed_dim) # predict importance tokens + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + + self.norm_pix_loss = norm_pix_loss + + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + self.freeze_parameters() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + + if is_training: + mask_rate = self.random_sample_mask_rate() + else: + # num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + + z_q = rearrange(z_q, 'b c h w -> b (h w) c') + B, num_patches, _ = z_q.shape + mask = torch.ones(B, num_patches, 1, dtype=z_q.dtype, device=z_q.device) + importance_scores = self.token_predictor(z_q, mask) + + if is_training: + # 使用 Gumbel-Softmax 进行可微排序 + gumbel_noise = torch.distributions.Gumbel(0, 1).sample(importance_scores.shape).to(importance_scores.device) + gumbel_scores = importance_scores + gumbel_noise + topk_scores, topk_indices = torch.topk(gumbel_scores, num_masked_tokens, dim=1, largest=False) + else: + # 推理时直接使用 importance_scores + topk_scores, topk_indices = torch.topk(importance_scores, num_masked_tokens, dim=1, largest=False) + + # 创建 token_all_mask + token_all_mask = torch.zeros_like(importance_scores) + token_all_mask.scatter_(1, topk_indices, 1.0) + + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置 + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, importance_scores + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + # print("Input embedding shape:", input_embeddings.shape) + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + # print("Input embedding after drop shape:", input_embeddings_after_drop.shape) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + # print("Encoder representation shape:", x.shape) + + return x, token_indices, token_all_mask, token_drop_mask + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def freeze_parameters(self): + for name, param in self.named_parameters(): + if 'token_predictor' not in name: + param.requires_grad = False + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, im_scores = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + mask_stream, mask_len = rle_encode(token_all_mask) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=16, w=16).unsqueeze(1) + im_scores_vis = rearrange(im_scores, 'b (h w) -> b h w', h=16, w=16).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_mask = rle_decode(mask_stream, token_all_mask.shape).float() + decoded_mask = decoded_mask.to(device=imgs.device) + latent_hat = latent_hat.squeeze(1) + x, token_indices, token_all_mask, token_drop_mask = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + # task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + # 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + 'mask_len': mask_len, + # 'bs_mask_token': bs_mask_token, + 'mask_ratio': mask_ratio, + # 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'im_score_vis': im_scores_vis, + } + return return_dict + + def gen_img(self, logits, token_all_mask, token_indices, num_iter=6, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + # mask_len = torch.tensor([np.floor(unknown_number_in_the_beginning.numpy() * mask_ratio.numpy())]).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + # 根据unknown_map的值，在相应位置上选择sampled_ids或cur_ids中的值，并将其存储到sampled_ids张量中。 + # 换句话说，它将模型预测的类别（在未知位置）与之前已知的类别（在已知位置）合并到一个张量中。 + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, 16, 16, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/models_mage_codec_full.py b/models_mage_codec_full.py new file mode 100644 index 0000000000000000000000000000000000000000..40f7ec5a09ce04a9afe193b127b8c2ec290a4618 --- /dev/null +++ b/models_mage_codec_full.py @@ -0,0 +1,793 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel, VQModel_w_Prompt +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union +from util.utils import adaptively_split_and_pad, crop_and_reconstruct +from timm.models.layers import trunc_normal_ +from util.rle import rle_encode, rle_decode + + +def mask_by_random_topk(batch_mask_len, batch_probs, temperature, token_all_mask=None): + """ Mask by random top-k operation for a single batch """ + gumbel_noise = torch.Tensor(temperature * np.random.gumbel(size=batch_probs.shape)).to(batch_probs.device) + confidence = torch.log(batch_probs) + gumbel_noise + + if token_all_mask is not None: + high_confidence = torch.max(confidence) + 1 # Set a very high confidence for known tokens + confidence = torch.where(token_all_mask, high_confidence, confidence) + + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, batch_mask_len.long()-1:batch_mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + # outputs = self.quantize( + # x, "noise" if training else "dequantize", means.long() + # ) + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + + +class ImportancePredictor(nn.Module): + """ + Input: z_q: [b, (h*w), c] + Output: importance_score: [b, N] + """ + def __init__(self, embed_dim=768): # 768 + super().__init__() + self.in_conv = nn.Sequential( + FusedLayerNorm(embed_dim, eps=1e-5), + nn.Linear(embed_dim, embed_dim), + nn.GELU() + ) + + self.out_conv = nn.Sequential( + nn.Linear(embed_dim, embed_dim // 2), + nn.GELU(), + nn.Linear(embed_dim // 2, embed_dim // 4), + nn.GELU(), + nn.Linear(embed_dim // 4, 1) + ) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x, policy): + x = self.in_conv(x) + B, N, C = x.size() + local_x = x[:, :, :C // 2] + global_x = (x[:, :, C // 2:] * policy).sum(dim=1, keepdim=True) / torch.sum(policy, dim=1, keepdim=True) + x = torch.cat([local_x, global_x.expand(B, N, C // 2)], dim=-1) + x = self.out_conv(x) + return x.squeeze(-1) # 将形状从 [b, N, 1] 转换为 [b, N] + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,257) + + seq_length = input_shape[1] + + position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + + position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + # self.vqgan = VQModel(ddconfig=config.params.ddconfig, + # n_embed=config.params.n_embed, # 1024 + # embed_dim=config.params.embed_dim, # 256 + # ckpt_path=vqgan_ckpt_path) + self.vqgan = VQModel_w_Prompt(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + for param in self.vqgan.parameters(): + param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=img_size +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding + + self.token_predictor = ImportancePredictor(config.params.embed_dim) # predict importance tokens + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + self.norm_pix_loss = norm_pix_loss + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, z_H, z_W = z_q.size() + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() # gt_indcies: [b, seq_len] + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + mask_ratio_min = self.mask_ratio_min # 0.5 + + if is_training: + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + token_all_mask = (noise <= cutoff_mask).float() # 逻辑上标记那些token是被mask掉的 + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置, [bsz, seq_len]元素为true和false + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token [b, unmaksed_seq_len] + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, z_H, z_W): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices # [b0, z_H*z_W] + # ============ 3. Adding class token ============ # + ## ============ padding to mutiple of 16 by mask_token_label ============ ## + b0 = token_indices.size(0) + token_indices = token_indices.reshape(b0, z_H, z_W).unsqueeze(1) # reshape 成图像 + ori_shape = token_indices.shape + # split成小patch: [new_bsz, c, 16, 16] + token_indices, patch_sizes, num_blocks_h, num_blocks_w = adaptively_split_and_pad(token_indices, self.mask_token_label) + # ori_shape = token_indices.shape + # token_indices = pad_to_multiple_of_16(token_indices, self.mask_token_label) + # _, _, new_h, new_w = token_indices.shape # 得到padding后的图像宽高 + # token_indices = split_into_blocks(token_indices) # 拆成小patch，batch size变大了 + new_bsz = token_indices.size(0) # 获得拆分后的batch size + token_indices = token_indices.squeeze(1).reshape(new_bsz, -1) # reshape到我要处理的形式: [new_bsz, 256] + # new_shape = [new_bsz, 1, new_h, new_w] + + ## ========== padding gt_indices for cal loss + gt_indices = gt_indices.reshape(b0, z_H, z_W).unsqueeze(1) + gt_indices, patch_sizes, num_blocks_h, num_blocks_w = adaptively_split_and_pad(gt_indices, self.mask_token_label) + gt_indices = gt_indices.squeeze(1).reshape(new_bsz, -1) + # padding token_all_mask + token_all_mask = token_all_mask.reshape(b0, z_H, z_W).unsqueeze(1) + token_all_mask, _, _, _ = adaptively_split_and_pad(token_all_mask, self.mask_token_label) + # token_all_mask = pad_to_multiple_of_16(token_all_mask, 1) + # token_all_mask = split_into_blocks(token_all_mask) + token_all_mask = token_all_mask.squeeze(1).reshape(new_bsz, -1) + + # padding token_drop_mask + token_drop_mask = token_drop_mask.reshape(b0, z_H, z_W).unsqueeze(1) + token_drop_mask, _, _, _ = adaptively_split_and_pad(token_drop_mask, self.mask_token_label) + # token_drop_mask = pad_to_multiple_of_16(token_drop_mask, 1) + # token_drop_mask = split_into_blocks(token_drop_mask) + token_drop_mask = token_drop_mask.squeeze(1).reshape(new_bsz, -1) + + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + + # return x, token_indices, token_all_mask, token_drop_mask, ori_shape, new_shape + return x, token_indices, token_all_mask, token_drop_mask, patch_sizes, num_blocks_h, num_blocks_w, ori_shape, gt_indices + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + # print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + # 判断latent_hat和latent是否相等 + # print((latent_hat == latent).all()) + mask_stream, mask_len = rle_encode(token_all_mask) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=z_H, w=z_W).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_mask = rle_decode(mask_stream, token_all_mask.shape).float() + decoded_mask = decoded_mask.cuda() + # print((decoded_mask == token_all_mask).all()) + latent_hat = latent_hat.squeeze(1) + # x, token_indices, token_all_mask, token_drop_mask, ori_shape, new_shape = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask, z_H, z_W) + x, token_indices, token_all_mask, token_drop_mask, patch_sizes, num_blocks_h, num_blocks_w, ori_shape, gt_indices = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask, z_H, z_W) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + # task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + # 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + # 'bs_mask_token': bs_mask_token, + 'mask_len': mask_len, + 'mask_ratio': mask_ratio, + # 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'ori_shape': ori_shape, + 'patch_sizes': patch_sizes, + 'num_blocks_h': num_blocks_h, + 'num_blocks_w': num_blocks_w, + } + return return_dict + + def gen_img(self, logits, token_all_mask, token_indices, ori_shape, patch_sizes, num_blocks_h, num_blocks_w, num_iter=6, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + bsz = logits.size(0) # new_bsz + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + + for step in range(num_iter): + if step == 0: + # print("enter in step==0") + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + unknown_map = (cur_ids == mask_token_id) # unknown_map表示padding或被mask掉的位置 + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + unknown_number_in_the_beginning = torch.sum(unknown_map, dim=-1, keepdims=True).float() + # print('begin unknown:', unknown_number_in_the_beginning) + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + # print('len=', mask_len) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(torch.min(mask_len), selected_probs, choice_temperature * (1 - ratio)) + + is_subset = (masking <= unknown_map).all() + # print("token_all_mask是masking的子集:", is_subset.item()) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) # 已知位置用sampled_ids代替 + else: + # print("enter in step > 0") + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = (token_indices == mask_token_id) + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # 填充sampled_ids + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + unknown_number_in_the_beginning = torch.sum(unknown_map, dim=1, keepdims=True).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(torch.min(mask_len), selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # new_bsz, _, new_h, new_w = new_shape + # b0, _, h0, w0 = ori_shape + # print("sampled_ids.shape=", sampled_ids.shape) + sampled_ids = sampled_ids.reshape(bsz, 16, 16).unsqueeze(1) # 变回拆成的小patch形式 + # print('sampled_ids.shape:', sampled_ids.shape) + sampled_ids = crop_and_reconstruct(sampled_ids, patch_sizes, num_blocks_h, num_blocks_w) + # print("sampled_ids.shape=", sampled_ids.shape) + sampled_ids = sampled_ids.reshape(ori_shape[0], -1) + # print("sampled_ids.shape=", sampled_ids.shape) + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(ori_shape[0], ori_shape[2], ori_shape[3], codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/models_mage_codec_hs_w_p.py b/models_mage_codec_hs_w_p.py new file mode 100644 index 0000000000000000000000000000000000000000..2cc38ad7a9febc59de6bc5a284fb0f505efd679d --- /dev/null +++ b/models_mage_codec_hs_w_p.py @@ -0,0 +1,898 @@ +from functools import partial + +import torch +import torch.nn as nn + +from timm.models.vision_transformer import PatchEmbed, DropPath, Mlp + +from util.pos_embed import get_2d_sincos_pos_embed + +from taming.models.vqgan import VQModel_w_Prompt +from omegaconf import OmegaConf +import numpy as np +import scipy.stats as stats +from compressai.entropy_models import EntropyBottleneck +from compressai.layers import conv3x3, subpel_conv3x3 +import math +from torch import Tensor +from einops import rearrange, repeat +import torch.nn.functional as F +import torchac +from typing import Any, Callable, List, Optional, Tuple, Union +from util.utils import adaptively_split_and_pad, crop_and_reconstruct +from timm.models.layers import trunc_normal_ +from util.rle import rle_encode, rle_decode + + +def mask_by_random_topk(batch_mask_len, batch_probs, temperature, token_all_mask=None): + """ Mask by random top-k operation for a single batch """ + gumbel_noise = torch.Tensor(temperature * np.random.gumbel(size=batch_probs.shape)).to(batch_probs.device) + confidence = torch.log(batch_probs) + gumbel_noise + + if token_all_mask is not None: + high_confidence = torch.max(confidence) + 1 # Set a very high confidence for known tokens + confidence = torch.where(token_all_mask, high_confidence, confidence) + + sorted_confidence, _ = torch.sort(confidence, axis=-1) + # Obtains cut off threshold given the mask lengths. + cut_off = sorted_confidence[:, batch_mask_len.long()-1:batch_mask_len.long()] + # Masks tokens with lower confidence. + masking = (confidence <= cut_off) + return masking + + +class FactorizedEntropyModel(EntropyBottleneck): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, x: Tensor, training: Optional[bool] = None) -> Tuple[Tensor, Tensor]: + if training is None: + training = self.training + + # 输入形状已经是 [b, c, seq_len]，无需转置 + shape = x.size() + + # Add noise or quantize + means = self._get_medians() + # outputs = self.quantize( + # x, "noise" if training else "dequantize", means.long() + # ) + outputs = self.quantize( + x, "dequantize", means.long() + ) + + if not torch.jit.is_scripting(): + likelihood = self._likelihood(outputs) + if self.use_likelihood_bound: + likelihood = self.likelihood_lower_bound(likelihood) + else: + raise NotImplementedError("TorchScript is not yet supported") + + return outputs, likelihood + + def compress(self, x): + # 构建索引，适用于单通道序列数据 + indexes = self._build_indexes(x.size()) + # 获取中位数，已经适配为单通道 + medians = self._get_medians().detach() + # 调整 medians 的形状以匹配 x 的形状 + medians = medians.expand_as(x) + # 调用基类的 compress 方法进行压缩 + return super().compress(x, indexes, medians) + + def decompress(self, strings, size): + # 预期的输出大小应包括单个通道 + output_size = (len(strings), 1, *size) # 这里 size 应该是 seq_len + # 构建索引 + indexes = self._build_indexes(output_size).to(self._quantized_cdf.device) + # 获取中位数并调整其形状以匹配预期输出的形状 + medians = self._extend_ndims(self._get_medians().detach(), len(size)) + medians = medians.expand(len(strings), 1, *([-1] * len(size))) + # 调用基类的 decompress 方法进行解压缩 + return super().decompress(strings, indexes, medians.dtype, medians) + + def _preprocess(self, x): + x = x.permute(0, 2, 3, 1).contiguous() + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +try: + from apex.normalization import FusedLayerNorm +except: + FusedLayerNorm = LayerNorm + + +class ImportancePredictor(nn.Module): + """ + Input: z_q: [b, (h*w), c] + Output: importance_score: [b, N] + """ + def __init__(self, embed_dim=768): # 768 + super().__init__() + self.in_conv = nn.Sequential( + FusedLayerNorm(embed_dim, eps=1e-5), + nn.Linear(embed_dim, embed_dim), + nn.GELU() + ) + + self.out_conv = nn.Sequential( + nn.Linear(embed_dim, embed_dim // 2), + nn.GELU(), + nn.Linear(embed_dim // 2, embed_dim // 4), + nn.GELU(), + nn.Linear(embed_dim // 4, 1) + ) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward(self, x, policy): + x = self.in_conv(x) + B, N, C = x.size() + local_x = x[:, :, :C // 2] + global_x = (x[:, :, C // 2:] * policy).sum(dim=1, keepdim=True) / torch.sum(policy, dim=1, keepdim=True) + x = torch.cat([local_x, global_x.expand(B, N, C // 2)], dim=-1) + x = self.out_conv(x) + return x.squeeze(-1) # 将形状从 [b, N, 1] 转换为 [b, N] + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) # （3,B,num_heads,N,head_dim） + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + with torch.cuda.amp.autocast(enabled=False): + attn = (q.float() @ k.float().transpose(-2, -1)) * self.scale + + attn = attn - torch.max(attn, dim=-1, keepdim=True)[0] + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + # x是经过self-attn后的feature，attn是注意力权重矩阵，描述输入序列中各个元素之间的相关性 + return x, attn + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() # drop_path=0 + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) # mlp_ratio=4 + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) # drop=0.1 + + def forward(self, x, return_attention=False): + if return_attention: + _, attn = self.attn(self.norm1(x)) + return attn + else: + y, _ = self.attn(self.norm1(x)) + x = x + self.drop_path(y) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class LabelSmoothingCrossEntropy(nn.Module): + """ NLL loss with label smoothing. + """ + def __init__(self, smoothing=0.1): + super(LabelSmoothingCrossEntropy, self).__init__() + assert smoothing < 1.0 + self.smoothing = smoothing + self.confidence = 1. - smoothing + + def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + logprobs = torch.nn.functional.log_softmax(x, dim=-1) + nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1)) + nll_loss = nll_loss.squeeze(1) + smooth_loss = -logprobs.mean(dim=-1) + loss = self.confidence * nll_loss + self.smoothing * smooth_loss + return loss + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, vocab_size, hidden_size, max_position_embeddings, dropout=0.1): + super().__init__() + self.word_embeddings = nn.Embedding(vocab_size, hidden_size) + self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(hidden_size, eps=1e-6) + self.dropout = nn.Dropout(dropout) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(max_position_embeddings).expand((1, -1))) # (1, 257) + # 创建一个形状为(1, max_position_embeddings)的缓冲张量position_ids，其包含了从0到max_position_embeddings-1的整数。 + # 这个缓冲张量将被用于获取position_embeddings的位置信息，以便在前向传播过程中使用 + + torch.nn.init.normal_(self.word_embeddings.weight, std=.02) + torch.nn.init.normal_(self.position_embeddings.weight, std=.02) + + def forward( + self, input_ids + ): + input_shape = input_ids.size() # input_ids: (B, N)（32,257) + + seq_length = input_shape[1] + + position_ids = self.position_ids[:, :seq_length] + + inputs_embeds = self.word_embeddings(input_ids) # (B, seq_len, embed_dim) + + position_embeddings = self.position_embeddings(position_ids) # (1, seq_len, embed_dim) + embeddings = inputs_embeds + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class MlmLayer(nn.Module): + + def __init__(self, feat_emb_dim, word_emb_dim, vocab_size): + super().__init__() + self.fc = nn.Linear(feat_emb_dim, word_emb_dim) + self.gelu = nn.GELU() + self.ln = nn.LayerNorm(word_emb_dim) + self.bias = nn.Parameter(torch.zeros(1, 1, vocab_size)) + + def forward(self, x, word_embeddings): # x: (b, seq_len, embed_dim) + mlm_hidden = self.fc(x) + mlm_hidden = self.gelu(mlm_hidden) + mlm_hidden = self.ln(mlm_hidden) + word_embeddings = word_embeddings.transpose(0, 1) + logits = torch.matmul(mlm_hidden, word_embeddings) + logits = logits + self.bias + return logits # (b, seq_len, vocab_size) 表示对于输入序列中的每个位置，模型预测它对应词汇表中每个单词的原始单词的未归一化概率 + + +class MaskedGenerativeEncoderViT(nn.Module): + """ Masked Autoencoder with VisionTransformer backbone + """ + def __init__(self, img_size=256, patch_size=16, in_chans=3, # need to change the default value of img_size + embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=512, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4., norm_layer=nn.LayerNorm, norm_pix_loss=False, + mask_ratio_min=0.5, mask_ratio_max=0.8, vqgan_ckpt_path='vqgan_jax_strongaug.ckpt'): + super().__init__() + # -------------------------------------------------------------------------- + # VQGAN specifics + config = OmegaConf.load('config/vqgan.yaml').model + self.vqgan = VQModel_w_Prompt(ddconfig=config.params.ddconfig, + n_embed=config.params.n_embed, # 1024 + embed_dim=config.params.embed_dim, # 256 + ckpt_path=vqgan_ckpt_path) + # for param in self.vqgan.parameters(): + # param.requires_grad = False + + self.codebook_size = config.params.n_embed # 1024 + vocab_size = self.codebook_size + 1000 + 1 # 1024 codebook size, 1000 classes, 1 for mask token. + self.fake_class_label = self.codebook_size + 1100 - 1024 # 1100 + self.mask_token_label = vocab_size - 1 # 2024 + self.token_emb = BertEmbeddings(vocab_size=vocab_size, # 向量空间大小，1024个embedding + 1000 class + 1 mask token + hidden_size=embed_dim, + max_position_embeddings=img_size +1, + # max_position_embeddings=256+1, # 256个patch + 1 class token + dropout=0.1) + + # MAGE variant masking ratio + self.mask_ratio_min = mask_ratio_min + self.mask_ratio_max = mask_ratio_max + + # -------------------------------------------------------------------------- + # MAGE encoder specifics + dropout_rate = 0.1 + self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dim) # 256, 16, 3, 1024, (B,N,C) n: 256/16*256/16=256, c=1024 + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False) # fixed sin-cos embedding # predict importance tokens + + self.blocks = nn.ModuleList([ # encoder + Block(embed_dim, num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(depth)]) # depth=12 for mage-vitb, embed_dim=768 + self.norm = norm_layer(embed_dim) # layer norm + + # -------------------------------------------------------------------------- + # MAGE decoder specifics + self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) # decoder_embed_dim=512 + self.pad_with_cls_token = True + + self.decoder_pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim), requires_grad=False) # fixed sin-cos embedding + self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, num_patches + 1, decoder_embed_dim)) # learnable pos embedding + + self.decoder_blocks = nn.ModuleList([ + Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, qk_scale=None, norm_layer=norm_layer, + drop=dropout_rate, attn_drop=dropout_rate) + for i in range(decoder_depth)]) # decoder_depth=8 for mage-vitb + + self.decoder_norm = norm_layer(decoder_embed_dim) + self.decoder_pred = nn.Linear(decoder_embed_dim, patch_size**2 * in_chans, bias=True) # decoder to patch + # -------------------------------------------------------------------------- + + # -------------------------------------------------------------------------- + # MlmLayer + self.mlm_layer = MlmLayer(feat_emb_dim=decoder_embed_dim, word_emb_dim=embed_dim, vocab_size=vocab_size) + self.norm_pix_loss = norm_pix_loss + self.criterion = LabelSmoothingCrossEntropy(smoothing=0.1) + # -------------------------------------------------------------------------- + self.entropy_bottleneck = FactorizedEntropyModel(1) + + self.initialize_weights() + self.freeze() + + def initialize_weights(self): + # initialization + # initialize (and freeze) pos_embed by sin-cos embedding + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + decoder_pos_embed = get_2d_sincos_pos_embed(self.decoder_pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=True) + self.decoder_pos_embed.data.copy_(torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) + + # initialize patch_embed like nn.Linear (instead of nn.Conv2d) + w = self.patch_embed.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + # timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.) + torch.nn.init.normal_(self.cls_token, std=.02) + torch.nn.init.normal_(self.mask_token, std=.02) + torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02) + + # initialize nn.Linear and nn.LayerNorm + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + # we use xavier_uniform following official JAX ViT: + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def random_sample_mask_rate(self): + # 生成一个 （0, 1] 范围内的随机数 + random_sample = 1 - torch.rand(1) + # 映射到 mask_ratio_min 到 mask_ratio_max 的范围 + mask_rate = self.mask_ratio_min + random_sample * (self.mask_ratio_max - self.mask_ratio_min) + return mask_rate.item() # 转换为Python的标量值 + + def get_cdf_token_mask(self, token_all_mask): + bsz, seq_len = token_all_mask.size() + # --- use Normal distribution. + dist_normal = torch.distributions.Normal(0, 2) + cdf_mask_token = dist_normal.cdf(torch.arange(1, seq_len + 1)) + cdf_mask_token = (cdf_mask_token - .5) * 2 + cdf_mask_token = repeat(cdf_mask_token, 'Lp -> b s Lp', + b=bsz, s=seq_len) + + cdf_mask_token = F.pad(cdf_mask_token, (1, 0)) + return cdf_mask_token + + def freeze(self): + for name, param in self.named_parameters(): + if 'prompt' not in name: + param.requires_grad = False + + def pre_encoding(self, x, is_training=False, manual_mask_rate=None): + """ + input: x: (B, 3, H, W) + """ + # ============ 1. tokenization ============ # + with torch.no_grad(): + z_q, _, token_tuple = self.vqgan.encode(x) # z_q: (B, 256, 16, 16), token_tuple: (B, 256, 16, 16) + + _, _, z_H, z_W = z_q.size() + _, _, token_indices = token_tuple # token_indices: (B*H*W,)(8192) + token_indices = token_indices.reshape(z_q.size(0), -1) # token_indices: (B, H*W) + gt_indices = token_indices.clone().detach().long() # gt_indcies: [b, seq_len] + + # ============ 2. masking process ============ # + bsz, seq_len = token_indices.size() # seq_len=h*w + mask_ratio_min = self.mask_ratio_min # 0.5 + + if is_training: + mask_rate = self.random_sample_mask_rate() + num_dropped_tokens = int(np.ceil(seq_len * mask_ratio_min)) + else: + num_dropped_tokens = 0 + if manual_mask_rate is not None: + mask_rate = manual_mask_rate + else: + raise ValueError("mask_rate should be provided for inference!") + + num_masked_tokens = int(np.ceil(seq_len * mask_rate)) + mask_ratio = num_masked_tokens / seq_len # for calculate vbr lambda + + # it is possible that two elements of the noise is the same, so do a while loop to avoid it + while True: + noise = torch.rand(bsz, seq_len, device=x.device) # noise in [0, 1] + sorted_noise, _ = torch.sort(noise, dim=1) # ascend: small is remove, large is keep + if num_dropped_tokens > 0: + cutoff_drop = sorted_noise[:, num_dropped_tokens-1:num_dropped_tokens] + else: + cutoff_drop = torch.zeros((bsz, 1), device=x.device) + cutoff_mask = sorted_noise[:, num_masked_tokens-1:num_masked_tokens] + token_drop_mask = (noise <= cutoff_drop).float() # 逻辑上标记那些token是被drop掉的 + token_all_mask = (noise <= cutoff_mask).float() # 逻辑上标记那些token是被mask掉的 + if token_drop_mask.sum() == bsz*num_dropped_tokens and token_all_mask.sum() == bsz*num_masked_tokens: + break + else: + print("Rerandom the noise!") + + # 获取unmasked token及其位置 + unmasked_pos = token_all_mask == 0 # 未被mask的位置, [bsz, seq_len]元素为true和false + unmaksed_token_indices = token_indices[unmasked_pos].reshape(bsz, -1) # 未被mask的token [b, unmaksed_seq_len] + + return gt_indices, token_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W + + def pre_decoding(self, gt_indices, unmaksed_token_indices, token_all_mask, token_drop_mask, z_H, z_W): + bsz, seq_len = gt_indices.size() + padded_token_indices = torch.full_like(gt_indices, fill_value=self.mask_token_label) + # 将未被mask的token填充回去 + # 我们需要一个计数器来追踪每个batch中已经填充了多少个unmaksed_token_indices + unmasked_token_counter = [0 for _ in range(bsz)] + + for b in range(bsz): + for idx in range(seq_len): + # 如果当前位置未被mask，则从unmaksed_token_indices填充；否则，保留mask_token_label + if (token_all_mask[b, idx] == 0): # 检查是否未被mask + # 替换相应的unmaksed token + padded_token_indices[b, idx] = unmaksed_token_indices[b, unmasked_token_counter[b]] + # 更新计数器 + unmasked_token_counter[b] += 1 + + token_indices = padded_token_indices # [b0, z_H*z_W] + # ============ 3. Adding class token ============ # + ## ============ padding to mutiple of 16 by mask_token_label ============ ## + b0 = token_indices.size(0) + token_indices = token_indices.reshape(b0, z_H, z_W).unsqueeze(1) # reshape 成图像 + ori_shape = token_indices.shape + # split成小patch: [new_bsz, c, 16, 16] + token_indices, patch_sizes, num_blocks_h, num_blocks_w = adaptively_split_and_pad(token_indices, self.mask_token_label) + # ori_shape = token_indices.shape + # token_indices = pad_to_multiple_of_16(token_indices, self.mask_token_label) + # _, _, new_h, new_w = token_indices.shape # 得到padding后的图像宽高 + # token_indices = split_into_blocks(token_indices) # 拆成小patch，batch size变大了 + new_bsz = token_indices.size(0) # 获得拆分后的batch size + token_indices = token_indices.squeeze(1).reshape(new_bsz, -1) # reshape到我要处理的形式: [new_bsz, 256] + # new_shape = [new_bsz, 1, new_h, new_w] + + ## ========== padding gt_indices for cal loss + gt_indices = gt_indices.reshape(b0, z_H, z_W).unsqueeze(1) + gt_indices, patch_sizes, num_blocks_h, num_blocks_w = adaptively_split_and_pad(gt_indices, self.mask_token_label) + gt_indices = gt_indices.squeeze(1).reshape(new_bsz, -1) + # padding token_all_mask + token_all_mask = token_all_mask.reshape(b0, z_H, z_W).unsqueeze(1) + token_all_mask, _, _, _ = adaptively_split_and_pad(token_all_mask, self.mask_token_label) + # token_all_mask = pad_to_multiple_of_16(token_all_mask, 1) + # token_all_mask = split_into_blocks(token_all_mask) + token_all_mask = token_all_mask.squeeze(1).reshape(new_bsz, -1) + + # padding token_drop_mask + token_drop_mask = token_drop_mask.reshape(b0, z_H, z_W).unsqueeze(1) + token_drop_mask, _, _, _ = adaptively_split_and_pad(token_drop_mask, self.mask_token_label) + # token_drop_mask = pad_to_multiple_of_16(token_drop_mask, 1) + # token_drop_mask = split_into_blocks(token_drop_mask) + token_drop_mask = token_drop_mask.squeeze(1).reshape(new_bsz, -1) + + # ============ 3. Adding class token ============ # + # concate class token, add [CLS] token to aggregate sequence-level representations + token_indices = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label # [B, 257] + # Masks (token_drop_mask and token_all_mask) are updated to account for the added class token, + # ensuring the first position is always kept by setting it to 0 (indicating "do not mask/drop") + # 添加0向量，和token_indices，表示[CLS] token不会被mask/drop + token_drop_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_drop_mask], dim=1) + token_all_mask = torch.cat([torch.zeros(token_indices.size(0), 1).cuda(), token_all_mask], dim=1) + token_indices = token_indices.long() + + # ============ 4. Embedding and Dropout ============ # + # bert embedding + input_embeddings = self.token_emb(token_indices) # get embeddings [B, 257, 768] + bsz, seq_len, emb_dim = input_embeddings.shape + + # dropping + token_keep_mask = 1 - token_drop_mask + input_embeddings_after_drop = input_embeddings[token_keep_mask.nonzero(as_tuple=True)].reshape(bsz, -1, emb_dim) + + # ============ 5. Transformer encoding ============ # + x = input_embeddings_after_drop # (B, seq_len_after_drop, embed_dim) # 32, 129, 768 + for blk in self.blocks: + x = blk(x) # each block has a multi-head self-attention and a mlp + x = self.norm(x) + + # return x, token_indices, token_all_mask, token_drop_mask, ori_shape, new_shape + return x, token_indices, token_all_mask, token_drop_mask, patch_sizes, num_blocks_h, num_blocks_w, ori_shape, gt_indices + + def forward_decoding(self, x, token_drop_mask, token_all_mask): + """ + x: output x of forward_encoder() + token_drop_mask: positions for dropped tokens + token_all_mask: positions for masked tokens + """ + # ============ 1. Prepare Embedding and padding tokens ============ # + # embed tokens + x = self.decoder_embed(x) # input_embedding_after_padding + + # append mask tokens to sequence + # replicates the [CLS] token embedding across the sequence length where masking is to be applied + if self.pad_with_cls_token: # True + mask_tokens = x[:, 0:1].repeat(1, token_all_mask.shape[1], 1) + else: + mask_tokens = self.mask_token.repeat(token_all_mask.shape[0], token_all_mask.shape[1], 1) + + # ============ 2. Prepare positional embedding ============ # + # put undropped tokens into original sequence + x_after_pad = mask_tokens.clone() # 未被drop的tokens被填充回去 + x_after_pad[(1 - token_drop_mask).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2]) + # set undropped but masked positions with mask + x_after_pad = torch.where(token_all_mask.unsqueeze(-1).bool(), mask_tokens, x_after_pad) # 被drop的也padding + + # add pos embed + x = x_after_pad + self.decoder_pos_embed_learned # add learnable pos embedding + + # apply Transformer blocks + for blk in self.decoder_blocks: + x = blk(x) + + x = self.decoder_norm(x) + + word_embeddings = self.token_emb.word_embeddings.weight.data.detach() + logits = self.mlm_layer(x, word_embeddings) # produce predictions for masked tokens + # print("Logits shape:", x.shape) + + return logits + + def forward_loss(self, gt_indices, logits, mask): + bsz, seq_len = gt_indices.size() + # logits and mask are with seq_len+1 but gt_indices is with seq_len + loss = self.criterion(logits[:, 1:, :self.codebook_size].reshape(bsz*seq_len, -1), gt_indices.reshape(bsz*seq_len)) + loss = loss.reshape(bsz, seq_len) + loss = (loss * mask[:, 1:]).sum() / mask[:, 1:].sum() # mean loss on removed patches + return loss + + def cal_lmbda(self, mask_ratio, A=5e-1, B=8): + lmbda = A * torch.exp(B * (1 - mask_ratio)) + return lmbda + + def cal_loss(self, logits, gt_indices, mask, mask_ratio): + mask_ratio = torch.tensor(mask_ratio) + ## cal cross entropy loss + task_loss = self.forward_loss(gt_indices, logits, mask) + lmbda = self.cal_lmbda(mask_ratio) + ## cal total loss for codec optimization + return task_loss, lmbda + + def forward(self, imgs, is_training=False, manual_mask_rate=None): + ## ---------- encoding process ---------- ## + gt_indices, token_indices, latent, token_all_mask, token_drop_mask, mask_ratio, z_H, z_W = self.pre_encoding(imgs, is_training, manual_mask_rate) + latent = latent.unsqueeze(1) + + latent_hat, latent_likelihoods = self.entropy_bottleneck(latent) + # 判断latent_hat和latent是否相等 + # print((latent_hat == latent).all()) + mask_stream, mask_len = rle_encode(token_all_mask) + mask_vis = rearrange(token_all_mask, 'b (h w) -> b h w', h=z_H, w=z_W).unsqueeze(1) + + ## ---------- decoding process ---------- ## + decoded_mask = rle_decode(mask_stream, token_all_mask.shape).float() + decoded_mask = decoded_mask.cuda() + # print((decoded_mask == token_all_mask).all()) + latent_hat = latent_hat.squeeze(1) + # x, token_indices, token_all_mask, token_drop_mask, ori_shape, new_shape = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask, z_H, z_W) + x, token_indices, token_all_mask, token_drop_mask, patch_sizes, num_blocks_h, num_blocks_w, ori_shape, gt_indices = self.pre_decoding(gt_indices, latent_hat, decoded_mask, token_drop_mask, z_H, z_W) + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + ## calculate loss + # task_loss, lmbda = self.cal_loss(logits, gt_indices, token_all_mask, mask_ratio) + if is_training: + rec = self.gen_img_train(logits, token_all_mask, token_indices) + else: + rec = None + return_dict = { + 'logits': logits, + 'likelihoods': latent_likelihoods, + # 'task_loss': task_loss, + 'token_indices': token_indices, + 'token_all_mask': token_all_mask, + # 'bs_mask_token': bs_mask_token, + 'mask_len': mask_len, + 'mask_ratio': mask_ratio, + # 'lambda': lmbda, + 'mask_vis': 1 - mask_vis, + 'ori_shape': ori_shape, + 'patch_sizes': patch_sizes, + 'num_blocks_h': num_blocks_h, + 'num_blocks_w': num_blocks_w, + 'rec': rec + } + return return_dict + + def gen_img(self, logits, token_all_mask, token_indices, ori_shape, patch_sizes, num_blocks_h, num_blocks_w, num_iter=6, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + bsz = logits.size(0) # new_bsz + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + + for step in range(num_iter): + if step == 0: + # print("enter in step==0") + cur_ids = token_indices.clone().long() # token_indices represent the current state of the sequence(unmasked tokens) + cur_ids = cur_ids[:, 1:] # 从第二列开始到最后一列 + logits = logits[:, 1:, :codebook_size] + + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + # get ids for next step + unknown_map = (cur_ids == mask_token_id) # unknown_map表示padding或被mask掉的位置 + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + unknown_number_in_the_beginning = torch.sum(unknown_map, dim=-1, keepdims=True).float() + # print('begin unknown:', unknown_number_in_the_beginning) + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + # print('len=', mask_len) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(torch.min(mask_len), selected_probs, choice_temperature * (1 - ratio)) + + is_subset = (masking <= unknown_map).all() + # print("token_all_mask是masking的子集:", is_subset.item()) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) # 已知位置用sampled_ids代替 + else: + # print("enter in step > 0") + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = (token_indices == mask_token_id) + + token_drop_mask = torch.zeros_like(token_indices) + + # token embedding + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + # get token prediction + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + # get ids for next step + # unknown_map: type bool，shape is same as cur_ids and sampled_ids, indicate where the value will be replace + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) # 填充sampled_ids + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + unknown_number_in_the_beginning = torch.sum(unknown_map, dim=1, keepdims=True).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(torch.min(mask_len), selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # new_bsz, _, new_h, new_w = new_shape + # b0, _, h0, w0 = ori_shape + # print("sampled_ids.shape=", sampled_ids.shape) + sampled_ids = sampled_ids.reshape(bsz, 16, 16).unsqueeze(1) # 变回拆成的小patch形式 + # print('sampled_ids.shape:', sampled_ids.shape) + sampled_ids = crop_and_reconstruct(sampled_ids, patch_sizes, num_blocks_h, num_blocks_w) + # print("sampled_ids.shape=", sampled_ids.shape) + sampled_ids = sampled_ids.reshape(ori_shape[0], -1) + # print("sampled_ids.shape=", sampled_ids.shape) + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(ori_shape[0], ori_shape[2], ori_shape[3], codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + + def gen_img_train(self, logits, token_all_mask, token_indices, num_iter=6, choice_temperature=4.5): + """ + generated image at inference + seed: random seed + logits: predicted logits by model decoder + token_all_mask: mask token indices + token_indices: token indices of the input image after the vq tokenizer + num_iter: number of iterations for sampling + choice_temperature: temperature for sampling + """ + # torch.manual_seed(seed) + # np.random.seed(seed) + bsz = logits.size(0) + codebook_emb_dim = 256 + codebook_size = 1024 + mask_token_id = self.mask_token_label + _CONFIDENCE_OF_KNOWN_TOKENS = +np.inf + unknown_number_in_the_beginning = torch.sum(token_all_mask, dim=-1, keepdims=True).float() + for step in range(num_iter): + if step == 0: + cur_ids = token_indices.clone().long() + cur_ids = cur_ids[:, 1:] + logits = logits[:, 1:, :codebook_size] + # the author said a little tricky here, "For iter=1, they use argmax and temp=0.0. + # For iter=6, we use categorical sampling and temp=4.5." + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + # Defines the mask ratio for the next round. The number to mask out is + # determined by mask_ratio * unknown_number_in_the_beginning. + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + token_indices = torch.where(masking, mask_token_id, sampled_ids) + else: + cur_ids = token_indices.clone().long() # .long(): to int64 + token_indices = torch.cat( + [torch.zeros(token_indices.size(0), 1).cuda(device=token_indices.device), token_indices], dim=1) + token_indices[:, 0] = self.fake_class_label + token_indices = token_indices.long() + token_all_mask = token_indices == mask_token_id + + token_drop_mask = torch.zeros_like(token_indices) + input_embeddings = self.token_emb(token_indices) # get input embeddings + + # encoder + x = input_embeddings + for blk in self.blocks: + x = blk(x) + x = self.norm(x) + + # decoder + logits = self.forward_decoding(x, token_drop_mask, token_all_mask) + logits = logits[:, 1:, :codebook_size] # remove the cls token and dims > codebook_size + + sample_dist = torch.distributions.categorical.Categorical(logits=logits) + sampled_ids = sample_dist.sample() # sampled_ids = torch.argmax(logits, dim=-1) + + unknown_map = (cur_ids == mask_token_id) + sampled_ids = torch.where(unknown_map, sampled_ids, cur_ids) + + ratio = 1. * (step + 1) / num_iter + mask_ratio = np.cos(math.pi / 2. * ratio) # ratio = cosine(Π/2 * i/num_iter) + # sample ids according to prediction confidence + probs = torch.nn.functional.softmax(logits, dim=-1) + selected_probs = torch.squeeze( + torch.gather(probs, dim=-1, index=torch.unsqueeze(sampled_ids, -1)), -1) + + selected_probs = torch.where(unknown_map, selected_probs.double(), _CONFIDENCE_OF_KNOWN_TOKENS).float() + unknown_number_in_the_beginning = unknown_number_in_the_beginning.clone().detach().cuda() + mask_ratio = torch.tensor(mask_ratio).cuda() + mask_len = torch.floor(unknown_number_in_the_beginning * mask_ratio).long() # 每个iter剩余的mask token数 + # Keeps at least one of prediction in this round and also masks out at least + # one and for the next iteration + mask_len = torch.maximum(torch.Tensor([1]).cuda(), + torch.minimum(torch.sum(unknown_map, dim=-1, keepdims=True) - 1, mask_len)) + + # Sample masking tokens for next iteration + masking = mask_by_random_topk(mask_len[0], selected_probs, choice_temperature * (1 - ratio)) + # Masks tokens with lower confidence. + token_indices = torch.where(masking, mask_token_id, sampled_ids) + + # vqgan visualization + z_q = self.vqgan.quantize.get_codebook_entry(sampled_ids, shape=(bsz, 16, 16, codebook_emb_dim)) + gen_images = self.vqgan.decode(z_q) + return gen_images + +def mage_vit_base_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=768, depth=12, num_heads=12, + decoder_embed_dim=768, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model + + +def mage_vit_large_patch16(**kwargs): + model = MaskedGenerativeEncoderViT( + patch_size=16, embed_dim=1024, depth=24, num_heads=16, + decoder_embed_dim=1024, decoder_depth=8, decoder_num_heads=16, + mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs) + return model diff --git a/pip_list.txt b/pip_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..0526fd5346861d320458b2a2fcc385c5ddf04326 --- /dev/null +++ b/pip_list.txt @@ -0,0 +1,152 @@ +Package Version +------------------------- ------------ +absl-py 2.1.0 +aiohttp 3.9.3 +aiosignal 1.3.1 +albumentations 0.4.3 +altair 5.3.0 +anykeystore 0.2 +apex 0.9.10.dev0 +async-timeout 4.0.3 +attrs 23.2.0 +blinker 1.7.0 +cachetools 5.3.3 +certifi 2024.2.2 +charset-normalizer 3.3.2 +click 8.1.7 +compressai 1.2.4 +contourpy 1.1.1 +cryptacular 1.6.2 +cycler 0.12.1 +datasets 2.18.0 +defusedxml 0.7.1 +dill 0.3.8 +DISTS-pytorch 0.1 +einops 0.3.0 +filelock 3.13.3 +fonttools 4.50.0 +frozenlist 1.4.1 +fsspec 2024.2.0 +future 1.0.0 +gitdb 4.0.11 +GitPython 3.1.43 +google-auth 2.29.0 +google-auth-oauthlib 1.0.0 +greenlet 3.0.3 +grpcio 1.62.1 +huggingface-hub 0.22.1 +hupper 1.12.1 +idna 3.6 +imageio 2.9.0 +imageio-ffmpeg 0.4.2 +imgaug 0.2.6 +importlib_metadata 7.1.0 +importlib_resources 6.4.0 +Jinja2 3.1.3 +joblib 1.3.2 +jsonschema 4.21.1 +jsonschema-specifications 2023.12.1 +kiwisolver 1.4.5 +lazy_loader 0.3 +lpips 0.1.4 +Markdown 3.6 +markdown-it-py 3.0.0 +MarkupSafe 2.1.5 +matplotlib 3.7.5 +mdurl 0.1.2 +more-itertools 10.2.0 +multidict 6.0.5 +multiprocess 0.70.16 +networkx 3.1 +ninja 1.11.1.1 +numpy 1.24.4 +oauthlib 3.2.2 +omegaconf 2.0.0 +opencv-python 4.1.2.30 +opencv-python-headless 4.9.0.80 +packaging 23.2 +pandas 2.0.3 +PasteDeploy 3.1.0 +pbkdf2 1.3 +pillow 10.2.0 +pip 23.3.1 +pkgutil_resolve_name 1.3.10 +plaster 1.1.2 +plaster-pastedeploy 1.0.1 +protobuf 4.25.3 +psutil 5.9.8 +pudb 2019.2 +pyarrow 15.0.2 +pyarrow-hotfix 0.6 +pyasn1 0.6.0 +pyasn1_modules 0.4.0 +pydeck 0.8.1b0 +Pygments 2.17.2 +pyparsing 3.1.2 +pyramid 2.0.2 +pyramid-mailer 0.15.1 +python-dateutil 2.9.0.post0 +python3-openid 3.2.0 +pytorch-lightning 1.0.8 +pytorch-msssim 1.0.0 +pytz 2024.1 +PyWavelets 1.4.1 +PyYAML 6.0.1 +referencing 0.34.0 +regex 2023.12.25 +repoze.sendmail 4.4.1 +requests 2.31.0 +requests-oauthlib 2.0.0 +rich 13.7.1 +rpds-py 0.18.0 +rsa 4.9 +sacremoses 0.1.1 +safetensors 0.4.2 +scikit-image 0.20.0 +scikit-learn 1.3.2 +scipy 1.9.1 +setuptools 68.2.2 +six 1.16.0 +smmap 5.0.1 +SQLAlchemy 2.0.29 +streamlit 1.32.2 +tenacity 8.2.3 +tensorboard 2.14.0 +tensorboard-data-server 0.7.2 +test-tube 0.7.5 +threadpoolctl 3.4.0 +tifffile 2023.7.10 +timm 0.3.2 +tokenizers 0.10.3 +toml 0.10.2 +toolz 0.12.1 +torch 1.12.1+cu113 +torch-fidelity 0.3.0 +torch_geometric 2.5.2 +torchac 0.9.3 +torchaudio 0.12.1+cu113 +torchvision 0.13.1+cu113 +tornado 6.4 +tqdm 4.66.2 +transaction 4.0 +transformers 4.3.1 +translationstring 1.4 +typing_extensions 4.10.0 +tzdata 2024.1 +urllib3 2.2.1 +urwid 2.6.10 +velruse 1.1.1 +venusian 3.1.0 +watchdog 4.0.0 +wcwidth 0.2.13 +WebOb 1.8.7 +Werkzeug 3.0.2 +wheel 0.41.2 +WTForms 3.1.2 +wtforms-recaptcha 0.3.2 +xxhash 3.4.1 +yarl 1.9.4 +zipp 3.18.1 +zope.deprecation 5.0 +zope.interface 6.3 +zope.sqlalchemy 3.1 \ No newline at end of file diff --git a/prepare_imgnet_val.py b/prepare_imgnet_val.py new file mode 100644 index 0000000000000000000000000000000000000000..59bbd4d47f87f7b33d4614d220c77386aa949e7f --- /dev/null +++ b/prepare_imgnet_val.py @@ -0,0 +1,32 @@ +import torchvision.transforms as transforms +import torchvision.datasets as datasets +from PIL import Image +import os +import argparse +from tqdm import tqdm + + +parser = argparse.ArgumentParser('Get ImageNet validation set for FID/IS evaluation', add_help=False) +parser.add_argument('--data_path', default='/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets/imagenet', type=str, + help='imagenet dataset path') +parser.add_argument('--output_dir', default='/home/v-ruoyufeng/v-ruoyufeng/qyp/fid/imagenet-val', type=str, + help='output directory') + +args = parser.parse_args() + +transform_val = transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(256)]) +dataset_val = datasets.ImageFolder(os.path.join(args.data_path, 'val'), transform=transform_val) + +if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + +nsamples = len(dataset_val) +indices = range(nsamples) +for i in tqdm(indices): + sample = dataset_val[i] + img = sample[0] + sample_name = os.path.join(args.output_dir, '{}.png'.format(str(i).zfill(5))) + img.save(sample_name) + diff --git a/random_cls_ori_pos_embed.log b/random_cls_ori_pos_embed.log new file mode 100644 index 0000000000000000000000000000000000000000..dc0f9c93bf818b1b69ecc670b5bae4e29a697cd6 --- /dev/null +++ b/random_cls_ori_pos_embed.log @@ -0,0 +1,270 @@ +2024-04-04 10:10:35,559 [INFO ] Logging file is /home/v-ruoyufeng/v-ruoyufeng/qyp/mage/MIM_vbr//20240404_101035.log +2024-04-04 10:10:35,559 [INFO ] ======================= exp ======================= +2024-04-04 10:10:35,559 [INFO ] config:config/vpt_default.yaml +2024-04-04 10:10:35,559 [INFO ] name:exp +2024-04-04 10:10:35,559 [INFO ] lr:None +2024-04-04 10:10:35,559 [INFO ] world_size:1 +2024-04-04 10:10:35,559 [INFO ] local_rank:-1 +2024-04-04 10:10:35,559 [INFO ] dist_on_itp:False +2024-04-04 10:10:35,559 [INFO ] dist_url:env:// +2024-04-04 10:10:35,560 [INFO ] TEST:False +2024-04-04 10:10:35,560 [INFO ] model:mage_vit_base_patch16 +2024-04-04 10:10:35,560 [INFO ] dataset:imagenet +2024-04-04 10:10:35,560 [INFO ] dataset_path:/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets/imagenet +2024-04-04 10:10:35,560 [INFO ] eval_path: +2024-04-04 10:10:35,560 [INFO ] batch_size:64 +2024-04-04 10:10:35,560 [INFO ] test_batch_size:64 +2024-04-04 10:10:35,560 [INFO ] num_workers:4 +2024-04-04 10:10:35,560 [INFO ] epochs:400 +2024-04-04 10:10:35,560 [INFO ] blr:0.0002 +2024-04-04 10:10:35,560 [INFO ] min_lr:5e-06 +2024-04-04 10:10:35,560 [INFO ] accum_iter:1 +2024-04-04 10:10:35,560 [INFO ] input_size:256 +2024-04-04 10:10:35,560 [INFO ] weight_decay:0.05 +2024-04-04 10:10:35,560 [INFO ] warmup_epochs:20 +2024-04-04 10:10:35,560 [INFO ] mask_ratio_min:0.5 +2024-04-04 10:10:35,560 [INFO ] mask_ratio_max:0.9 +2024-04-04 10:10:35,560 [INFO ] mask_ratio_mu:0.55 +2024-04-04 10:10:35,560 [INFO ] mask_ratio_std:0.25 +2024-04-04 10:10:35,560 [INFO ] grad_clip:3.0 +2024-04-04 10:10:35,560 [INFO ] device:cuda +2024-04-04 10:10:35,560 [INFO ] distributed:True +2024-04-04 10:10:35,560 [INFO ] seed:0 +2024-04-04 10:10:35,560 [INFO ] resume:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/ckpt_pretrained/models--Qiyp--mage/snapshots/b0692a453d4725bd80c37c2362549a46b4ff5c33/mage-vitb-1600.pth +2024-04-04 10:10:35,560 [INFO ] start_epoch:0 +2024-04-04 10:10:35,560 [INFO ] pin_mem:True +2024-04-04 10:10:35,561 [INFO ] save:True +2024-04-04 10:10:35,561 [INFO ] exp_name:MIM_vbr +2024-04-04 10:10:35,561 [INFO ] root:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage +2024-04-04 10:10:35,561 [INFO ] output_dir:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/MIM_vbr +2024-04-04 10:10:35,561 [INFO ] log_dir:/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/MIM_vbr +2024-04-04 10:10:35,561 [INFO ] strart_epoch:0 +2024-04-04 10:10:35,561 [INFO ] cuda:True +2024-04-04 10:10:35,561 [INFO ] rank:0 +2024-04-04 10:10:35,561 [INFO ] gpu:0 +2024-04-04 10:10:35,561 [INFO ] dist_backend:nccl +2024-04-04 10:10:35,561 [INFO ] =================================================== +2024-04-04 10:13:13,690 [INFO ] -1|bpp:0.03588|mask:0.00888|psnr:13.21569|msssim:0.40911|lpips:0.45404|dists:0.23838|Test loss:21.23764 +2024-04-04 11:06:46,026 [INFO ] 0|bpp:0.03584|mask:0.00888|psnr:13.25991|msssim:0.40834|lpips:0.45306|dists:0.23826|Test loss:21.15657 +2024-04-04 12:00:15,346 [INFO ] 1|bpp:0.03581|mask:0.00888|psnr:13.30248|msssim:0.40643|lpips:0.45406|dists:0.23907|Test loss:21.17125 +2024-04-04 12:53:33,489 [INFO ] 2|bpp:0.03582|mask:0.00888|psnr:13.24826|msssim:0.40624|lpips:0.45338|dists:0.23765|Test loss:21.15188 +2024-04-04 13:47:41,896 [INFO ] 3|bpp:0.03575|mask:0.00888|psnr:13.28249|msssim:0.40607|lpips:0.45297|dists:0.23768|Test loss:21.12303 +2024-04-04 14:41:56,862 [INFO ] 4|bpp:0.03564|mask:0.00888|psnr:13.25143|msssim:0.40685|lpips:0.45330|dists:0.23795|Test loss:21.14644 +2024-04-04 15:35:31,770 [INFO ] 5|bpp:0.03557|mask:0.00888|psnr:13.26131|msssim:0.40685|lpips:0.45363|dists:0.23887|Test loss:21.19393 +2024-04-04 16:29:11,532 [INFO ] 6|bpp:0.03546|mask:0.00888|psnr:13.21842|msssim:0.40426|lpips:0.45401|dists:0.23786|Test loss:21.16948 +2024-04-04 17:22:59,963 [INFO ] 7|bpp:0.03532|mask:0.00888|psnr:13.21817|msssim:0.40414|lpips:0.45494|dists:0.23901|Test loss:21.18227 +2024-04-04 18:16:50,244 [INFO ] 8|bpp:0.03517|mask:0.00888|psnr:13.25219|msssim:0.40479|lpips:0.45472|dists:0.23937|Test loss:21.17950 +2024-04-04 19:10:37,297 [INFO ] 9|bpp:0.03497|mask:0.00888|psnr:13.22418|msssim:0.40449|lpips:0.45591|dists:0.23957|Test loss:21.20218 +2024-04-04 20:04:08,083 [INFO ] 10|bpp:0.03474|mask:0.00888|psnr:13.21343|msssim:0.40339|lpips:0.45610|dists:0.23996|Test loss:21.18849 +2024-04-04 20:57:43,693 [INFO ] 11|bpp:0.03451|mask:0.00888|psnr:13.21845|msssim:0.40688|lpips:0.45485|dists:0.23974|Test loss:21.18549 +2024-04-04 21:51:29,642 [INFO ] 12|bpp:0.03418|mask:0.00888|psnr:13.24243|msssim:0.40650|lpips:0.45584|dists:0.23969|Test loss:21.21256 +2024-04-04 22:45:16,493 [INFO ] 13|bpp:0.03385|mask:0.00888|psnr:13.22611|msssim:0.40215|lpips:0.45613|dists:0.23979|Test loss:21.21400 +2024-04-04 23:38:52,657 [INFO ] 14|bpp:0.03342|mask:0.00888|psnr:13.21293|msssim:0.40289|lpips:0.45580|dists:0.24071|Test loss:21.21564 +2024-04-05 00:32:26,334 [INFO ] 15|bpp:0.03296|mask:0.00888|psnr:13.23067|msssim:0.40598|lpips:0.45579|dists:0.24009|Test loss:21.23161 +2024-04-05 01:26:06,922 [INFO ] 16|bpp:0.03242|mask:0.00888|psnr:13.24654|msssim:0.40281|lpips:0.45715|dists:0.24212|Test loss:21.23940 +2024-04-05 02:19:46,733 [INFO ] 17|bpp:0.03180|mask:0.00888|psnr:13.16186|msssim:0.40173|lpips:0.45739|dists:0.24126|Test loss:21.25885 +2024-04-05 03:13:24,186 [INFO ] 18|bpp:0.03103|mask:0.00888|psnr:13.19183|msssim:0.40148|lpips:0.45707|dists:0.24031|Test loss:21.28280 +2024-04-05 04:07:14,276 [INFO ] 19|bpp:0.03014|mask:0.00888|psnr:13.15489|msssim:0.40153|lpips:0.45676|dists:0.24086|Test loss:21.23785 +2024-04-05 05:00:55,073 [INFO ] 20|bpp:0.02920|mask:0.00888|psnr:13.15852|msssim:0.40212|lpips:0.45757|dists:0.24113|Test loss:21.25983 +2024-04-05 05:54:45,989 [INFO ] 21|bpp:0.02800|mask:0.00888|psnr:13.13521|msssim:0.39955|lpips:0.45804|dists:0.24114|Test loss:21.25788 +2024-04-05 06:48:24,886 [INFO ] 22|bpp:0.02674|mask:0.00888|psnr:13.17988|msssim:0.40214|lpips:0.45873|dists:0.24214|Test loss:21.24751 +2024-04-05 07:42:11,734 [INFO ] 23|bpp:0.02536|mask:0.00888|psnr:13.19882|msssim:0.40117|lpips:0.45813|dists:0.24128|Test loss:21.26404 +2024-04-05 08:35:46,337 [INFO ] 24|bpp:0.02404|mask:0.00888|psnr:13.21194|msssim:0.40371|lpips:0.45747|dists:0.24135|Test loss:21.26660 +2024-04-05 09:29:27,153 [INFO ] 25|bpp:0.02291|mask:0.00888|psnr:13.20188|msssim:0.40062|lpips:0.45807|dists:0.24188|Test loss:21.23427 +2024-04-05 10:22:52,381 [INFO ] 26|bpp:0.02197|mask:0.00888|psnr:13.14814|msssim:0.40075|lpips:0.45877|dists:0.24235|Test loss:21.29180 +2024-04-05 11:16:15,547 [INFO ] 27|bpp:0.02118|mask:0.00888|psnr:13.15129|msssim:0.40000|lpips:0.45883|dists:0.24192|Test loss:21.27330 +2024-04-05 12:09:31,666 [INFO ] 28|bpp:0.02057|mask:0.00888|psnr:13.17894|msssim:0.40016|lpips:0.45954|dists:0.24152|Test loss:21.26936 +2024-04-05 13:03:04,398 [INFO ] 29|bpp:0.02008|mask:0.00888|psnr:13.20106|msssim:0.39910|lpips:0.45833|dists:0.24140|Test loss:21.25571 +2024-04-05 13:56:56,606 [INFO ] 30|bpp:0.01973|mask:0.00888|psnr:13.17408|msssim:0.40113|lpips:0.46004|dists:0.24257|Test loss:21.27850 +2024-04-05 14:50:37,352 [INFO ] 31|bpp:0.01949|mask:0.00888|psnr:13.15342|msssim:0.40157|lpips:0.45898|dists:0.24402|Test loss:21.27100 +2024-04-05 14:54:35,775 [INFO ] 32|bpp:0.01948|mask:0.00888|psnr:13.20013|msssim:0.40031|lpips:0.45797|dists:0.24194|Test loss:21.25621 +2024-04-05 15:48:10,563 [INFO ] 33|bpp:0.01935|mask:0.00888|psnr:13.12793|msssim:0.39763|lpips:0.45879|dists:0.24217|Test loss:21.25178 +2024-04-05 16:41:55,235 [INFO ] 34|bpp:0.01928|mask:0.00888|psnr:13.16231|msssim:0.40116|lpips:0.45893|dists:0.24117|Test loss:21.24767 +2024-04-05 17:35:26,265 [INFO ] 35|bpp:0.01924|mask:0.00888|psnr:13.24866|msssim:0.39974|lpips:0.45919|dists:0.24213|Test loss:21.27820 +2024-04-05 18:29:01,394 [INFO ] 36|bpp:0.01920|mask:0.00888|psnr:13.11560|msssim:0.39778|lpips:0.45926|dists:0.24169|Test loss:21.28531 +2024-04-05 19:22:38,342 [INFO ] 37|bpp:0.01916|mask:0.00888|psnr:13.14812|msssim:0.39992|lpips:0.45861|dists:0.24273|Test loss:21.27174 +2024-04-05 20:16:19,290 [INFO ] 38|bpp:0.01913|mask:0.00888|psnr:13.19050|msssim:0.40133|lpips:0.45827|dists:0.24164|Test loss:21.26350 +2024-04-05 21:09:52,435 [INFO ] 39|bpp:0.01910|mask:0.00888|psnr:13.10390|msssim:0.39911|lpips:0.45989|dists:0.24030|Test loss:21.27172 +2024-04-05 22:03:49,137 [INFO ] 40|bpp:0.01907|mask:0.00888|psnr:13.13918|msssim:0.40074|lpips:0.45902|dists:0.24253|Test loss:21.28191 +2024-04-05 22:57:31,089 [INFO ] 41|bpp:0.01904|mask:0.00888|psnr:13.14114|msssim:0.39898|lpips:0.46019|dists:0.24178|Test loss:21.25347 +2024-04-05 23:51:10,633 [INFO ] 42|bpp:0.01902|mask:0.00888|psnr:13.06371|msssim:0.39825|lpips:0.46138|dists:0.24287|Test loss:21.28773 +2024-04-06 00:44:44,476 [INFO ] 43|bpp:0.01900|mask:0.00888|psnr:13.15455|msssim:0.40027|lpips:0.45891|dists:0.24140|Test loss:21.29425 +2024-04-06 01:38:31,125 [INFO ] 44|bpp:0.01898|mask:0.00888|psnr:13.19394|msssim:0.40077|lpips:0.45903|dists:0.24406|Test loss:21.28284 +2024-04-06 02:32:04,482 [INFO ] 45|bpp:0.01896|mask:0.00888|psnr:13.08619|msssim:0.39907|lpips:0.45993|dists:0.24123|Test loss:21.25745 +2024-04-06 03:25:44,093 [INFO ] 46|bpp:0.01895|mask:0.00888|psnr:13.11747|msssim:0.40111|lpips:0.45994|dists:0.24380|Test loss:21.28789 +2024-04-06 04:19:26,894 [INFO ] 47|bpp:0.01894|mask:0.00888|psnr:13.17232|msssim:0.39777|lpips:0.45881|dists:0.24288|Test loss:21.30505 +2024-04-06 05:13:14,246 [INFO ] 48|bpp:0.01892|mask:0.00888|psnr:13.15842|msssim:0.39939|lpips:0.45970|dists:0.24217|Test loss:21.28761 +2024-04-06 06:06:52,133 [INFO ] 49|bpp:0.01892|mask:0.00888|psnr:13.13974|msssim:0.40070|lpips:0.45912|dists:0.24283|Test loss:21.28643 +2024-04-06 07:00:34,570 [INFO ] 50|bpp:0.01891|mask:0.00888|psnr:13.15933|msssim:0.40199|lpips:0.45925|dists:0.24360|Test loss:21.26676 +2024-04-06 07:54:09,538 [INFO ] 51|bpp:0.01890|mask:0.00888|psnr:13.15301|msssim:0.40131|lpips:0.45862|dists:0.24211|Test loss:21.30303 +2024-04-06 08:47:47,550 [INFO ] 52|bpp:0.01889|mask:0.00888|psnr:13.18375|msssim:0.40323|lpips:0.45831|dists:0.24224|Test loss:21.27481 +2024-04-06 09:41:21,827 [INFO ] 53|bpp:0.01888|mask:0.00888|psnr:13.20155|msssim:0.40267|lpips:0.45854|dists:0.24149|Test loss:21.27408 +2024-04-06 10:35:01,946 [INFO ] 54|bpp:0.01888|mask:0.00888|psnr:13.15092|msssim:0.39922|lpips:0.45821|dists:0.24182|Test loss:21.26130 +2024-04-06 11:28:31,412 [INFO ] 55|bpp:0.01887|mask:0.00888|psnr:13.18632|msssim:0.40212|lpips:0.45831|dists:0.24298|Test loss:21.28109 +2024-04-06 12:22:10,321 [INFO ] 56|bpp:0.01886|mask:0.00888|psnr:13.15418|msssim:0.39572|lpips:0.45972|dists:0.24117|Test loss:21.27839 +2024-04-06 13:15:53,155 [INFO ] 57|bpp:0.01886|mask:0.00888|psnr:13.20347|msssim:0.39953|lpips:0.45948|dists:0.24265|Test loss:21.30376 +2024-04-06 14:09:35,642 [INFO ] 58|bpp:0.01885|mask:0.00888|psnr:13.18421|msssim:0.39955|lpips:0.46068|dists:0.24233|Test loss:21.27847 +2024-04-06 15:03:08,344 [INFO ] 59|bpp:0.01885|mask:0.00888|psnr:13.17552|msssim:0.39806|lpips:0.46013|dists:0.24226|Test loss:21.26041 +2024-04-06 15:56:45,344 [INFO ] 60|bpp:0.01884|mask:0.00888|psnr:13.11659|msssim:0.40079|lpips:0.45856|dists:0.24217|Test loss:21.29998 +2024-04-06 16:50:18,143 [INFO ] 61|bpp:0.01884|mask:0.00888|psnr:13.16615|msssim:0.40034|lpips:0.45914|dists:0.24267|Test loss:21.28585 +2024-04-06 17:43:49,593 [INFO ] 62|bpp:0.01883|mask:0.00888|psnr:13.19080|msssim:0.40082|lpips:0.45851|dists:0.24310|Test loss:21.27888 +2024-04-06 18:37:15,453 [INFO ] 63|bpp:0.01883|mask:0.00888|psnr:13.13621|msssim:0.40047|lpips:0.45837|dists:0.24225|Test loss:21.28103 +2024-04-06 19:30:53,548 [INFO ] 64|bpp:0.01883|mask:0.00888|psnr:13.26381|msssim:0.40252|lpips:0.45799|dists:0.24336|Test loss:21.28065 +2024-04-06 19:34:52,987 [INFO ] 65|bpp:0.01883|mask:0.00888|psnr:13.13076|msssim:0.39975|lpips:0.45852|dists:0.24184|Test loss:21.28312 +2024-04-06 20:28:29,657 [INFO ] 66|bpp:0.01882|mask:0.00888|psnr:13.20211|msssim:0.40009|lpips:0.45839|dists:0.24126|Test loss:21.29404 +2024-04-06 21:22:16,799 [INFO ] 67|bpp:0.01882|mask:0.00888|psnr:13.16327|msssim:0.40113|lpips:0.45909|dists:0.24202|Test loss:21.27699 +2024-04-06 22:16:06,216 [INFO ] 68|bpp:0.01882|mask:0.00888|psnr:13.18588|msssim:0.40282|lpips:0.45782|dists:0.24311|Test loss:21.27416 +2024-04-06 23:09:47,144 [INFO ] 69|bpp:0.01881|mask:0.00888|psnr:13.15015|msssim:0.39932|lpips:0.45851|dists:0.24151|Test loss:21.30159 +2024-04-07 00:03:21,497 [INFO ] 70|bpp:0.01881|mask:0.00888|psnr:13.10033|msssim:0.39983|lpips:0.45928|dists:0.24270|Test loss:21.29060 +2024-04-07 00:56:59,626 [INFO ] 71|bpp:0.01881|mask:0.00888|psnr:13.14018|msssim:0.40084|lpips:0.45975|dists:0.24190|Test loss:21.28356 +2024-04-07 01:50:33,358 [INFO ] 72|bpp:0.01881|mask:0.00888|psnr:13.12160|msssim:0.40126|lpips:0.45849|dists:0.24217|Test loss:21.26237 +2024-04-07 02:44:08,000 [INFO ] 73|bpp:0.01880|mask:0.00888|psnr:13.15619|msssim:0.39692|lpips:0.45923|dists:0.24271|Test loss:21.29595 +2024-04-07 03:37:39,585 [INFO ] 74|bpp:0.01880|mask:0.00888|psnr:13.13028|msssim:0.40184|lpips:0.45848|dists:0.24174|Test loss:21.29035 +2024-04-07 04:31:13,078 [INFO ] 75|bpp:0.01880|mask:0.00888|psnr:13.08685|msssim:0.39805|lpips:0.45909|dists:0.24092|Test loss:21.30299 +2024-04-07 05:24:57,582 [INFO ] 76|bpp:0.01880|mask:0.00888|psnr:13.19422|msssim:0.40299|lpips:0.45750|dists:0.24243|Test loss:21.27396 +2024-04-07 06:18:42,074 [INFO ] 77|bpp:0.01880|mask:0.00888|psnr:13.15509|msssim:0.39990|lpips:0.45978|dists:0.24168|Test loss:21.28555 +2024-04-07 07:12:20,064 [INFO ] 78|bpp:0.01879|mask:0.00888|psnr:13.15753|msssim:0.39970|lpips:0.46015|dists:0.24303|Test loss:21.28609 +2024-04-07 08:05:56,550 [INFO ] 79|bpp:0.01879|mask:0.00888|psnr:13.09374|msssim:0.39664|lpips:0.46007|dists:0.24141|Test loss:21.28756 +2024-04-07 08:59:37,982 [INFO ] 80|bpp:0.01879|mask:0.00888|psnr:13.10648|msssim:0.39951|lpips:0.45942|dists:0.24325|Test loss:21.27971 +2024-04-07 09:53:17,812 [INFO ] 81|bpp:0.01879|mask:0.00888|psnr:13.17104|msssim:0.39809|lpips:0.45951|dists:0.24069|Test loss:21.29235 +2024-04-07 10:46:55,982 [INFO ] 82|bpp:0.01879|mask:0.00888|psnr:13.07997|msssim:0.39833|lpips:0.45959|dists:0.24204|Test loss:21.28985 +2024-04-07 11:40:30,301 [INFO ] 83|bpp:0.01878|mask:0.00888|psnr:13.18234|msssim:0.39914|lpips:0.46006|dists:0.24284|Test loss:21.27786 +2024-04-07 12:33:58,249 [INFO ] 84|bpp:0.01878|mask:0.00888|psnr:13.10977|msssim:0.39820|lpips:0.46088|dists:0.24417|Test loss:21.31828 +2024-04-07 13:27:43,815 [INFO ] 85|bpp:0.01878|mask:0.00888|psnr:13.18486|msssim:0.39966|lpips:0.45862|dists:0.24222|Test loss:21.29355 +2024-04-07 14:21:25,501 [INFO ] 86|bpp:0.01878|mask:0.00888|psnr:13.11761|msssim:0.39849|lpips:0.45907|dists:0.24176|Test loss:21.32596 +2024-04-07 15:14:57,948 [INFO ] 87|bpp:0.01878|mask:0.00888|psnr:13.14036|msssim:0.39951|lpips:0.45901|dists:0.24303|Test loss:21.29274 +2024-04-07 16:08:26,816 [INFO ] 88|bpp:0.01878|mask:0.00888|psnr:13.10488|msssim:0.39725|lpips:0.46110|dists:0.24222|Test loss:21.27472 +2024-04-07 17:02:09,272 [INFO ] 89|bpp:0.01878|mask:0.00888|psnr:13.13665|msssim:0.40026|lpips:0.45941|dists:0.24232|Test loss:21.30573 +2024-04-07 17:55:41,944 [INFO ] 90|bpp:0.01877|mask:0.00888|psnr:13.18672|msssim:0.40023|lpips:0.46070|dists:0.24332|Test loss:21.32571 +2024-04-07 18:49:17,140 [INFO ] 91|bpp:0.01877|mask:0.00888|psnr:13.15499|msssim:0.39884|lpips:0.45965|dists:0.24332|Test loss:21.28087 +2024-04-07 19:42:54,397 [INFO ] 92|bpp:0.01877|mask:0.00888|psnr:13.19676|msssim:0.39717|lpips:0.46009|dists:0.24161|Test loss:21.27701 +2024-04-07 20:36:25,601 [INFO ] 93|bpp:0.01877|mask:0.00888|psnr:13.12814|msssim:0.40258|lpips:0.45921|dists:0.24219|Test loss:21.30234 +2024-04-07 21:30:14,036 [INFO ] 94|bpp:0.01877|mask:0.00888|psnr:13.14352|msssim:0.39705|lpips:0.46129|dists:0.24150|Test loss:21.30530 +2024-04-07 22:24:05,405 [INFO ] 95|bpp:0.01877|mask:0.00888|psnr:13.06017|msssim:0.39349|lpips:0.45980|dists:0.24162|Test loss:21.30218 +2024-04-07 23:17:48,959 [INFO ] 96|bpp:0.01877|mask:0.00888|psnr:13.11861|msssim:0.39969|lpips:0.45837|dists:0.24114|Test loss:21.27901 +2024-04-08 00:11:26,392 [INFO ] 97|bpp:0.01877|mask:0.00888|psnr:13.12178|msssim:0.39854|lpips:0.46021|dists:0.24196|Test loss:21.28097 +2024-04-08 00:15:34,226 [INFO ] 98|bpp:0.01877|mask:0.00888|psnr:13.17939|msssim:0.40138|lpips:0.45900|dists:0.24096|Test loss:21.29021 +2024-04-08 01:09:16,792 [INFO ] 99|bpp:0.01877|mask:0.00888|psnr:13.14410|msssim:0.39844|lpips:0.45837|dists:0.24220|Test loss:21.27590 +2024-04-08 02:02:52,660 [INFO ] 100|bpp:0.01877|mask:0.00888|psnr:13.11800|msssim:0.39906|lpips:0.46079|dists:0.24257|Test loss:21.29972 +2024-04-08 02:56:33,809 [INFO ] 101|bpp:0.01877|mask:0.00888|psnr:13.09951|msssim:0.39818|lpips:0.46034|dists:0.24260|Test loss:21.29719 +2024-04-08 03:50:09,424 [INFO ] 102|bpp:0.01877|mask:0.00888|psnr:13.09148|msssim:0.39664|lpips:0.46147|dists:0.24145|Test loss:21.27959 +2024-04-08 04:43:56,350 [INFO ] 103|bpp:0.01876|mask:0.00888|psnr:13.21087|msssim:0.40139|lpips:0.45962|dists:0.24261|Test loss:21.30489 +2024-04-08 05:37:37,081 [INFO ] 104|bpp:0.01876|mask:0.00888|psnr:13.11728|msssim:0.39728|lpips:0.45922|dists:0.24171|Test loss:21.28550 +2024-04-08 06:31:12,513 [INFO ] 105|bpp:0.01876|mask:0.00888|psnr:13.26630|msssim:0.40026|lpips:0.45839|dists:0.24208|Test loss:21.30695 +2024-04-08 07:25:02,277 [INFO ] 106|bpp:0.01876|mask:0.00888|psnr:13.19477|msssim:0.40017|lpips:0.45787|dists:0.24184|Test loss:21.29826 +2024-04-08 08:18:46,770 [INFO ] 107|bpp:0.01876|mask:0.00888|psnr:13.12502|msssim:0.39721|lpips:0.46076|dists:0.24250|Test loss:21.31212 +2024-04-08 09:12:12,793 [INFO ] 108|bpp:0.01876|mask:0.00888|psnr:13.15306|msssim:0.39866|lpips:0.45841|dists:0.24227|Test loss:21.27882 +2024-04-08 10:05:37,436 [INFO ] 109|bpp:0.01876|mask:0.00888|psnr:13.14274|msssim:0.39831|lpips:0.46015|dists:0.24187|Test loss:21.29269 +2024-04-08 10:58:48,255 [INFO ] 110|bpp:0.01876|mask:0.00888|psnr:13.06936|msssim:0.39817|lpips:0.45940|dists:0.24177|Test loss:21.27553 +2024-04-08 11:52:08,858 [INFO ] 111|bpp:0.01876|mask:0.00888|psnr:13.26804|msssim:0.40089|lpips:0.46035|dists:0.24314|Test loss:21.30732 +2024-04-08 12:45:29,692 [INFO ] 112|bpp:0.01876|mask:0.00888|psnr:13.10184|msssim:0.39918|lpips:0.45925|dists:0.24234|Test loss:21.28786 +2024-04-08 13:39:40,276 [INFO ] 113|bpp:0.01876|mask:0.00888|psnr:13.23099|msssim:0.39817|lpips:0.45826|dists:0.24208|Test loss:21.28473 +2024-04-08 14:32:44,746 [INFO ] 114|bpp:0.01876|mask:0.00888|psnr:13.13753|msssim:0.39948|lpips:0.45895|dists:0.24221|Test loss:21.28946 +2024-04-08 15:25:36,144 [INFO ] 115|bpp:0.01876|mask:0.00888|psnr:13.13848|msssim:0.39736|lpips:0.45826|dists:0.24160|Test loss:21.29336 +2024-04-08 16:18:23,555 [INFO ] 116|bpp:0.01876|mask:0.00888|psnr:13.13918|msssim:0.39702|lpips:0.45840|dists:0.24189|Test loss:21.28731 +2024-04-08 17:11:13,436 [INFO ] 117|bpp:0.01876|mask:0.00888|psnr:13.11501|msssim:0.39999|lpips:0.45810|dists:0.24232|Test loss:21.27105 +2024-04-08 18:04:06,768 [INFO ] 118|bpp:0.01875|mask:0.00888|psnr:13.20255|msssim:0.39889|lpips:0.45864|dists:0.24233|Test loss:21.29198 +2024-04-08 18:56:53,973 [INFO ] 119|bpp:0.01875|mask:0.00888|psnr:13.13821|msssim:0.39758|lpips:0.46021|dists:0.24270|Test loss:21.28407 +2024-04-08 19:49:51,865 [INFO ] 120|bpp:0.01876|mask:0.00888|psnr:13.15808|msssim:0.40112|lpips:0.45929|dists:0.24367|Test loss:21.27388 +2024-04-08 20:42:33,179 [INFO ] 121|bpp:0.01875|mask:0.00888|psnr:13.24289|msssim:0.40229|lpips:0.45788|dists:0.24267|Test loss:21.28680 +2024-04-08 21:35:27,077 [INFO ] 122|bpp:0.01876|mask:0.00888|psnr:13.16283|msssim:0.39867|lpips:0.45937|dists:0.24261|Test loss:21.27719 +2024-04-08 22:28:24,743 [INFO ] 123|bpp:0.01875|mask:0.00888|psnr:13.24087|msssim:0.40205|lpips:0.45881|dists:0.24227|Test loss:21.29647 +2024-04-08 23:21:16,449 [INFO ] 124|bpp:0.01875|mask:0.00888|psnr:13.16379|msssim:0.40038|lpips:0.45990|dists:0.24369|Test loss:21.33541 +2024-04-09 00:14:42,460 [INFO ] 125|bpp:0.01875|mask:0.00888|psnr:13.25599|msssim:0.40097|lpips:0.45870|dists:0.24218|Test loss:21.32172 +2024-04-09 01:07:24,766 [INFO ] 126|bpp:0.01875|mask:0.00888|psnr:13.17828|msssim:0.40155|lpips:0.45992|dists:0.24317|Test loss:21.31592 +2024-04-09 02:00:08,801 [INFO ] 127|bpp:0.01875|mask:0.00888|psnr:13.21902|msssim:0.40059|lpips:0.45914|dists:0.24168|Test loss:21.29630 +2024-04-09 02:52:48,278 [INFO ] 128|bpp:0.01875|mask:0.00888|psnr:13.23710|msssim:0.40199|lpips:0.45998|dists:0.24410|Test loss:21.31754 +2024-04-09 03:45:36,050 [INFO ] 129|bpp:0.01875|mask:0.00888|psnr:13.11098|msssim:0.39886|lpips:0.45885|dists:0.24248|Test loss:21.30143 +2024-04-09 04:38:11,855 [INFO ] 130|bpp:0.01875|mask:0.00888|psnr:13.15147|msssim:0.39957|lpips:0.45995|dists:0.24174|Test loss:21.28291 +2024-04-09 04:42:25,252 [INFO ] 131|bpp:0.01875|mask:0.00888|psnr:13.09123|msssim:0.39640|lpips:0.45994|dists:0.24242|Test loss:21.30394 +2024-04-09 05:36:58,110 [INFO ] 132|bpp:0.01875|mask:0.00888|psnr:13.10564|msssim:0.39795|lpips:0.46064|dists:0.24208|Test loss:21.31005 +2024-04-09 06:30:42,124 [INFO ] 133|bpp:0.01875|mask:0.00888|psnr:13.17493|msssim:0.40061|lpips:0.45805|dists:0.24231|Test loss:21.32013 +2024-04-09 07:23:51,320 [INFO ] 134|bpp:0.01875|mask:0.00888|psnr:13.20627|msssim:0.40202|lpips:0.45790|dists:0.24272|Test loss:21.27656 +2024-04-09 08:16:18,567 [INFO ] 135|bpp:0.01875|mask:0.00888|psnr:13.09275|msssim:0.39883|lpips:0.45940|dists:0.24203|Test loss:21.28377 +2024-04-09 09:08:26,530 [INFO ] 136|bpp:0.01875|mask:0.00888|psnr:13.16006|msssim:0.40012|lpips:0.45795|dists:0.24182|Test loss:21.27860 +2024-04-09 10:00:42,201 [INFO ] 137|bpp:0.01875|mask:0.00888|psnr:13.15080|msssim:0.40103|lpips:0.45842|dists:0.24056|Test loss:21.30526 +2024-04-09 10:52:45,034 [INFO ] 138|bpp:0.01875|mask:0.00888|psnr:13.11378|msssim:0.39720|lpips:0.45999|dists:0.24285|Test loss:21.29334 +2024-04-09 11:45:14,213 [INFO ] 139|bpp:0.01875|mask:0.00888|psnr:13.13135|msssim:0.39961|lpips:0.45970|dists:0.24280|Test loss:21.28495 +2024-04-09 12:41:18,240 [INFO ] 140|bpp:0.01875|mask:0.00888|psnr:13.20432|msssim:0.39960|lpips:0.45942|dists:0.24320|Test loss:21.28614 +2024-04-09 13:35:57,597 [INFO ] 141|bpp:0.01875|mask:0.00888|psnr:13.21269|msssim:0.39957|lpips:0.45982|dists:0.24293|Test loss:21.25552 +2024-04-09 14:29:18,842 [INFO ] 142|bpp:0.01875|mask:0.00888|psnr:13.17876|msssim:0.39893|lpips:0.45929|dists:0.24233|Test loss:21.27812 +2024-04-09 15:22:00,299 [INFO ] 143|bpp:0.01875|mask:0.00888|psnr:13.12684|msssim:0.39752|lpips:0.46154|dists:0.24323|Test loss:21.29375 +2024-04-09 16:14:22,225 [INFO ] 144|bpp:0.01875|mask:0.00888|psnr:13.14156|msssim:0.40313|lpips:0.45881|dists:0.24170|Test loss:21.26921 +2024-04-09 17:06:31,018 [INFO ] 145|bpp:0.01875|mask:0.00888|psnr:13.16159|msssim:0.39841|lpips:0.45947|dists:0.24182|Test loss:21.28990 +2024-04-09 17:58:55,864 [INFO ] 146|bpp:0.01875|mask:0.00888|psnr:13.17212|msssim:0.39959|lpips:0.45816|dists:0.24172|Test loss:21.29350 +2024-04-09 18:51:25,152 [INFO ] 147|bpp:0.01875|mask:0.00888|psnr:13.14617|msssim:0.39860|lpips:0.45997|dists:0.24274|Test loss:21.28954 +2024-04-09 19:43:52,789 [INFO ] 148|bpp:0.01875|mask:0.00888|psnr:13.15737|msssim:0.40086|lpips:0.45823|dists:0.24095|Test loss:21.29493 +2024-04-09 20:36:14,261 [INFO ] 149|bpp:0.01875|mask:0.00888|psnr:13.20810|msssim:0.39898|lpips:0.45875|dists:0.24274|Test loss:21.27807 +2024-04-09 21:28:40,986 [INFO ] 150|bpp:0.01875|mask:0.00888|psnr:13.13410|msssim:0.39909|lpips:0.45991|dists:0.24317|Test loss:21.30550 +2024-04-09 22:21:16,607 [INFO ] 151|bpp:0.01875|mask:0.00888|psnr:13.10259|msssim:0.39601|lpips:0.45975|dists:0.24113|Test loss:21.24937 +2024-04-09 23:13:36,098 [INFO ] 152|bpp:0.01875|mask:0.00888|psnr:13.18755|msssim:0.40077|lpips:0.45872|dists:0.24203|Test loss:21.27901 +2024-04-10 00:06:10,497 [INFO ] 153|bpp:0.01875|mask:0.00888|psnr:13.07590|msssim:0.39663|lpips:0.46009|dists:0.24311|Test loss:21.28411 +2024-04-10 00:59:16,202 [INFO ] 154|bpp:0.01875|mask:0.00888|psnr:13.20057|msssim:0.39944|lpips:0.45832|dists:0.24171|Test loss:21.28243 +2024-04-10 01:52:36,780 [INFO ] 155|bpp:0.01874|mask:0.00888|psnr:13.15526|msssim:0.39841|lpips:0.45863|dists:0.24194|Test loss:21.32053 +2024-04-10 02:45:43,233 [INFO ] 156|bpp:0.01875|mask:0.00888|psnr:13.14736|msssim:0.39925|lpips:0.45881|dists:0.24323|Test loss:21.31534 +2024-04-10 03:38:53,279 [INFO ] 157|bpp:0.01875|mask:0.00888|psnr:13.12221|msssim:0.39785|lpips:0.45998|dists:0.24348|Test loss:21.29007 +2024-04-10 04:31:50,894 [INFO ] 158|bpp:0.01875|mask:0.00888|psnr:13.16754|msssim:0.39913|lpips:0.45891|dists:0.24224|Test loss:21.28170 +2024-04-10 05:25:23,761 [INFO ] 159|bpp:0.01875|mask:0.00888|psnr:13.12215|msssim:0.39853|lpips:0.45929|dists:0.24165|Test loss:21.27042 +2024-04-10 06:18:40,470 [INFO ] 160|bpp:0.01875|mask:0.00888|psnr:13.16180|msssim:0.40104|lpips:0.45821|dists:0.24212|Test loss:21.28927 +2024-04-10 07:13:56,170 [INFO ] 161|bpp:0.01875|mask:0.00888|psnr:13.18172|msssim:0.40227|lpips:0.45785|dists:0.24253|Test loss:21.28629 +2024-04-10 08:08:42,016 [INFO ] 162|bpp:0.01875|mask:0.00888|psnr:13.23372|msssim:0.40003|lpips:0.45833|dists:0.24363|Test loss:21.29768 +2024-04-10 09:01:38,685 [INFO ] 163|bpp:0.01875|mask:0.00888|psnr:13.18086|msssim:0.39869|lpips:0.45988|dists:0.24313|Test loss:21.29734 +2024-04-10 09:05:36,721 [INFO ] 164|bpp:0.01875|mask:0.00888|psnr:13.20056|msssim:0.40166|lpips:0.45814|dists:0.24139|Test loss:21.29253 +2024-04-10 09:58:50,542 [INFO ] 165|bpp:0.01875|mask:0.00888|psnr:13.21326|msssim:0.39942|lpips:0.45956|dists:0.24205|Test loss:21.26868 +2024-04-10 10:52:14,650 [INFO ] 166|bpp:0.01875|mask:0.00888|psnr:13.18901|msssim:0.40232|lpips:0.45880|dists:0.24151|Test loss:21.28847 +2024-04-10 11:45:32,561 [INFO ] 167|bpp:0.01875|mask:0.00888|psnr:13.10461|msssim:0.39899|lpips:0.45891|dists:0.24205|Test loss:21.29590 +2024-04-10 12:38:21,301 [INFO ] 168|bpp:0.01874|mask:0.00888|psnr:13.14186|msssim:0.39919|lpips:0.45987|dists:0.24228|Test loss:21.29317 +2024-04-10 13:30:55,217 [INFO ] 169|bpp:0.01875|mask:0.00888|psnr:13.08116|msssim:0.39757|lpips:0.46033|dists:0.24233|Test loss:21.29024 +2024-04-10 14:23:38,843 [INFO ] 170|bpp:0.01875|mask:0.00888|psnr:13.15740|msssim:0.39724|lpips:0.45989|dists:0.24198|Test loss:21.27508 +2024-04-10 15:17:50,292 [INFO ] 171|bpp:0.01875|mask:0.00888|psnr:13.17207|msssim:0.40001|lpips:0.45793|dists:0.24249|Test loss:21.26488 +2024-04-10 16:10:24,656 [INFO ] 172|bpp:0.01875|mask:0.00888|psnr:13.11798|msssim:0.39845|lpips:0.45824|dists:0.24167|Test loss:21.31201 +2024-04-10 17:03:03,234 [INFO ] 173|bpp:0.01874|mask:0.00888|psnr:13.20667|msssim:0.40378|lpips:0.45846|dists:0.24155|Test loss:21.26096 +2024-04-10 17:56:42,762 [INFO ] 174|bpp:0.01875|mask:0.00888|psnr:13.19973|msssim:0.40048|lpips:0.45891|dists:0.24364|Test loss:21.28590 +2024-04-10 18:50:09,036 [INFO ] 175|bpp:0.01875|mask:0.00888|psnr:13.12961|msssim:0.40018|lpips:0.45959|dists:0.24142|Test loss:21.28239 +2024-04-10 19:43:04,714 [INFO ] 176|bpp:0.01875|mask:0.00888|psnr:13.13815|msssim:0.40162|lpips:0.45834|dists:0.24118|Test loss:21.29412 +2024-04-10 20:36:24,710 [INFO ] 177|bpp:0.01874|mask:0.00888|psnr:13.07813|msssim:0.39896|lpips:0.45908|dists:0.24120|Test loss:21.27041 +2024-04-10 21:29:20,996 [INFO ] 178|bpp:0.01874|mask:0.00888|psnr:13.12257|msssim:0.40213|lpips:0.45795|dists:0.24171|Test loss:21.28373 +2024-04-10 22:22:36,190 [INFO ] 179|bpp:0.01875|mask:0.00888|psnr:13.18746|msssim:0.39921|lpips:0.46022|dists:0.24245|Test loss:21.28558 +2024-04-10 23:15:48,184 [INFO ] 180|bpp:0.01875|mask:0.00888|psnr:13.16244|msssim:0.40015|lpips:0.45892|dists:0.24216|Test loss:21.28899 +2024-04-11 00:10:39,826 [INFO ] 181|bpp:0.01874|mask:0.00888|psnr:13.12945|msssim:0.39976|lpips:0.45877|dists:0.24072|Test loss:21.26417 +2024-04-11 01:03:45,895 [INFO ] 182|bpp:0.01875|mask:0.00888|psnr:13.10407|msssim:0.39961|lpips:0.45999|dists:0.24226|Test loss:21.28852 +2024-04-11 01:56:57,351 [INFO ] 183|bpp:0.01874|mask:0.00888|psnr:13.10647|msssim:0.39898|lpips:0.45848|dists:0.24025|Test loss:21.27169 +2024-04-11 02:50:10,273 [INFO ] 184|bpp:0.01874|mask:0.00888|psnr:13.16364|msssim:0.40170|lpips:0.45797|dists:0.24153|Test loss:21.26569 +2024-04-11 03:43:29,060 [INFO ] 185|bpp:0.01874|mask:0.00888|psnr:13.13477|msssim:0.40030|lpips:0.45906|dists:0.24200|Test loss:21.25362 +2024-04-11 04:36:56,345 [INFO ] 186|bpp:0.01874|mask:0.00888|psnr:13.17415|msssim:0.39882|lpips:0.45991|dists:0.24193|Test loss:21.29474 +2024-04-11 05:30:32,513 [INFO ] 187|bpp:0.01874|mask:0.00888|psnr:13.13313|msssim:0.39741|lpips:0.45878|dists:0.24064|Test loss:21.25000 +2024-04-11 06:24:34,261 [INFO ] 188|bpp:0.01874|mask:0.00888|psnr:13.19090|msssim:0.40097|lpips:0.45831|dists:0.24182|Test loss:21.27625 +2024-04-11 07:18:06,300 [INFO ] 189|bpp:0.01874|mask:0.00888|psnr:13.23289|msssim:0.39992|lpips:0.45882|dists:0.24302|Test loss:21.27963 +2024-04-11 08:11:33,478 [INFO ] 190|bpp:0.01875|mask:0.00888|psnr:13.15747|msssim:0.39966|lpips:0.45706|dists:0.24143|Test loss:21.26014 +2024-04-11 09:06:50,559 [INFO ] 191|bpp:0.01874|mask:0.00888|psnr:13.18624|msssim:0.40035|lpips:0.45932|dists:0.24235|Test loss:21.29921 +2024-04-11 10:00:50,986 [INFO ] 192|bpp:0.01874|mask:0.00888|psnr:13.14455|msssim:0.39701|lpips:0.45862|dists:0.24265|Test loss:21.26465 +2024-04-11 10:54:44,716 [INFO ] 193|bpp:0.01874|mask:0.00888|psnr:13.14073|msssim:0.39867|lpips:0.45792|dists:0.24107|Test loss:21.25963 +2024-04-11 11:48:18,924 [INFO ] 194|bpp:0.01874|mask:0.00888|psnr:13.11501|msssim:0.39874|lpips:0.45867|dists:0.24104|Test loss:21.27571 +2024-04-11 12:41:50,693 [INFO ] 195|bpp:0.01874|mask:0.00888|psnr:13.19901|msssim:0.40055|lpips:0.45669|dists:0.24048|Test loss:21.26514 +2024-04-11 13:36:33,943 [INFO ] 196|bpp:0.01874|mask:0.00888|psnr:13.15637|msssim:0.39909|lpips:0.45848|dists:0.24172|Test loss:21.27318 +2024-04-11 13:40:40,778 [INFO ] 197|bpp:0.01874|mask:0.00888|psnr:13.18917|msssim:0.40192|lpips:0.45828|dists:0.24054|Test loss:21.24191 +2024-04-11 14:35:18,952 [INFO ] 198|bpp:0.01874|mask:0.00888|psnr:13.17403|msssim:0.39968|lpips:0.45730|dists:0.24080|Test loss:21.26892 +2024-04-11 15:28:51,952 [INFO ] 199|bpp:0.01874|mask:0.00888|psnr:13.18546|msssim:0.39972|lpips:0.45769|dists:0.24070|Test loss:21.25120 +2024-04-11 16:22:48,955 [INFO ] 200|bpp:0.01874|mask:0.00888|psnr:13.18654|msssim:0.40035|lpips:0.45769|dists:0.24114|Test loss:21.28192 +2024-04-11 17:17:42,151 [INFO ] 201|bpp:0.01874|mask:0.00888|psnr:13.15212|msssim:0.39952|lpips:0.45796|dists:0.24034|Test loss:21.26880 +2024-04-11 18:11:38,165 [INFO ] 202|bpp:0.01874|mask:0.00888|psnr:13.19180|msssim:0.39978|lpips:0.45802|dists:0.24041|Test loss:21.24751 +2024-04-11 19:05:18,801 [INFO ] 203|bpp:0.01874|mask:0.00888|psnr:13.12211|msssim:0.39826|lpips:0.45958|dists:0.24199|Test loss:21.27601 +2024-04-11 19:59:13,696 [INFO ] 204|bpp:0.01874|mask:0.00888|psnr:13.15027|msssim:0.40008|lpips:0.45829|dists:0.24163|Test loss:21.27846 +2024-04-11 20:53:11,151 [INFO ] 205|bpp:0.01874|mask:0.00888|psnr:13.15788|msssim:0.40035|lpips:0.45882|dists:0.24190|Test loss:21.28137 +2024-04-11 21:47:14,491 [INFO ] 206|bpp:0.01874|mask:0.00888|psnr:13.15342|msssim:0.39839|lpips:0.45819|dists:0.24145|Test loss:21.27481 +2024-04-11 22:41:08,061 [INFO ] 207|bpp:0.01874|mask:0.00888|psnr:13.21074|msssim:0.40314|lpips:0.45719|dists:0.24202|Test loss:21.27130 +2024-04-11 23:34:54,712 [INFO ] 208|bpp:0.01874|mask:0.00888|psnr:13.19443|msssim:0.39929|lpips:0.45881|dists:0.24151|Test loss:21.25859 +2024-04-12 00:28:24,287 [INFO ] 209|bpp:0.01874|mask:0.00888|psnr:13.14517|msssim:0.39986|lpips:0.45752|dists:0.24017|Test loss:21.25162 +2024-04-12 01:22:06,068 [INFO ] 210|bpp:0.01874|mask:0.00888|psnr:13.11709|msssim:0.39900|lpips:0.45975|dists:0.24223|Test loss:21.24230 +2024-04-12 02:16:13,848 [INFO ] 211|bpp:0.01874|mask:0.00888|psnr:13.12799|msssim:0.39956|lpips:0.45940|dists:0.24210|Test loss:21.27789 +2024-04-12 03:09:56,089 [INFO ] 212|bpp:0.01874|mask:0.00888|psnr:13.24415|msssim:0.40205|lpips:0.45726|dists:0.24135|Test loss:21.26449 +2024-04-12 04:03:32,615 [INFO ] 213|bpp:0.01874|mask:0.00888|psnr:13.20839|msssim:0.40222|lpips:0.45821|dists:0.24254|Test loss:21.27056 +2024-04-12 04:57:31,110 [INFO ] 214|bpp:0.01874|mask:0.00888|psnr:13.14268|msssim:0.39945|lpips:0.45869|dists:0.24170|Test loss:21.26803 +2024-04-12 05:51:43,303 [INFO ] 215|bpp:0.01874|mask:0.00888|psnr:13.22896|msssim:0.40037|lpips:0.45741|dists:0.24157|Test loss:21.27512 +2024-04-12 06:45:41,695 [INFO ] 216|bpp:0.01874|mask:0.00888|psnr:13.08144|msssim:0.39900|lpips:0.45852|dists:0.24172|Test loss:21.25653 +2024-04-12 07:39:29,702 [INFO ] 217|bpp:0.01874|mask:0.00888|psnr:13.16652|msssim:0.40047|lpips:0.45791|dists:0.24152|Test loss:21.26339 +2024-04-12 08:33:39,403 [INFO ] 218|bpp:0.01874|mask:0.00888|psnr:13.17001|msssim:0.40174|lpips:0.45846|dists:0.24068|Test loss:21.28058 +2024-04-12 09:27:29,695 [INFO ] 219|bpp:0.01874|mask:0.00888|psnr:13.12841|msssim:0.40036|lpips:0.45889|dists:0.24117|Test loss:21.26918 +2024-04-12 10:21:23,286 [INFO ] 220|bpp:0.01874|mask:0.00888|psnr:13.20291|msssim:0.40021|lpips:0.45783|dists:0.24121|Test loss:21.25948 +2024-04-12 11:16:26,800 [INFO ] 221|bpp:0.01874|mask:0.00888|psnr:13.10802|msssim:0.39887|lpips:0.45869|dists:0.23948|Test loss:21.24364 +2024-04-12 12:10:30,085 [INFO ] 222|bpp:0.01874|mask:0.00888|psnr:13.19658|msssim:0.40342|lpips:0.45742|dists:0.24071|Test loss:21.25483 diff --git a/random_cls_ori_pos_embed.pth b/random_cls_ori_pos_embed.pth new file mode 100644 index 0000000000000000000000000000000000000000..bd7c344b57af4207948b89c88dddc5b75c910d14 --- /dev/null +++ b/random_cls_ori_pos_embed.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:da751689c99b91255ed9a9f0a01141dc1bf10b1d4b1bc6a8a2bec4807da5ba77 +size 1963532650 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..bd8fa7b4d8ed1e88733977cce9747b560de2a86d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,150 @@ +absl-py==2.1.0 +aiohttp==3.9.3 +aiosignal==1.3.1 +albumentations==0.4.3 +altair==5.3.0 +anykeystore==0.2 +apex==0.9.10.dev0 +async-timeout==4.0.3 +attrs==23.2.0 +blinker==1.7.0 +cachetools==5.3.3 +certifi==2024.2.2 +charset-normalizer==3.3.2 +click==8.1.7 +compressai==1.2.4 +contourpy==1.1.1 +cryptacular==1.6.2 +cycler==0.12.1 +datasets==2.18.0 +defusedxml==0.7.1 +dill==0.3.8 +DISTS-pytorch==0.1 +einops==0.3.0 +filelock==3.13.3 +fonttools==4.50.0 +frozenlist==1.4.1 +fsspec==2024.2.0 +future==1.0.0 +gitdb==4.0.11 +GitPython==3.1.43 +google-auth==2.29.0 +google-auth-oauthlib==1.0.0 +greenlet==3.0.3 +grpcio==1.62.1 +huggingface-hub==0.22.1 +hupper==1.12.1 +idna==3.6 +imageio==2.9.0 +imageio-ffmpeg==0.4.2 +imgaug==0.2.6 +importlib_metadata==7.1.0 +importlib_resources==6.4.0 +Jinja2==3.1.3 +joblib==1.3.2 +jsonschema==4.21.1 +jsonschema-specifications==2023.12.1 +kiwisolver==1.4.5 +lazy_loader==0.3 +lpips==0.1.4 +Markdown==3.6 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +matplotlib==3.7.5 +mdurl==0.1.2 +more-itertools==10.2.0 +multidict==6.0.5 +multiprocess==0.70.16 +networkx==3.1 +ninja==1.11.1.1 +numpy==1.24.4 +oauthlib==3.2.2 +omegaconf==2.0.0 +opencv-python==4.1.2.30 +opencv-python-headless==4.9.0.80 +packaging==23.2 +pandas==2.0.3 +PasteDeploy==3.1.0 +pbkdf2==1.3 +pillow==10.2.0 +pip==23.3.1 +pkgutil_resolve_name==1.3.10 +plaster==1.1.2 +plaster-pastedeploy==1.0.1 +protobuf==4.25.3 +psutil==5.9.8 +pudb==2019.2 +pyarrow==15.0.2 +pyarrow-hotfix==0.6 +pyasn1==0.6.0 +pyasn1_modules==0.4.0 +pydeck==0.8.1b0 +Pygments==2.17.2 +pyparsing==3.1.2 +pyramid==2.0.2 +pyramid-mailer==0.15.1 +python-dateutil==2.9.0.post0 +python3-openid==3.2.0 +pytorch-lightning==1.0.8 +pytorch-msssim==1.0.0 +pytz==2024.1 +PyWavelets==1.4.1 +PyYAML==6.0.1 +referencing==0.34.0 +regex==2023.12.25 +repoze.sendmail==4.4.1 +requests==2.31.0 +requests-oauthlib==2.0.0 +rich==13.7.1 +rpds-py==0.18.0 +rsa==4.9 +sacremoses==0.1.1 +safetensors==0.4.2 +scikit-image==0.20.0 +scikit-learn==1.3.2 +scipy==1.9.1 +setuptools==68.2.2 +six==1.16.0 +smmap==5.0.1 +SQLAlchemy==2.0.29 +streamlit==1.32.2 +tenacity==8.2.3 +tensorboard==2.14.0 +tensorboard-data-server==0.7.2 +test-tube==0.7.5 +threadpoolctl==3.4.0 +tifffile==2023.7.10 +timm==0.3.2 +tokenizers==0.10.3 +toml==0.10.2 +toolz==0.12.1 +torch==1.12.1+cu113 +torch-fidelity==0.3.0 +torch_geometric==2.5.2 +torchac==0.9.3 +torchaudio==0.12.1+cu113 +torchvision==0.13.1+cu113 +tornado==6.4 +tqdm==4.66.2 +transaction==4.0 +transformers==4.3.1 +translationstring==1.4 +typing_extensions==4.10.0 +tzdata==2024.1 +urllib3==2.2.1 +urwid==2.6.10 +velruse==1.1.1 +venusian==3.1.0 +watchdog==4.0.0 +wcwidth==0.2.13 +WebOb==1.8.7 +Werkzeug==3.0.2 +wheel==0.41.2 +WTForms==3.1.2 +wtforms-recaptcha==0.3.2 +xxhash==3.4.1 +yarl==1.9.4 +zipp==3.18.1 +zope.deprecation==5.0 +zope.interface==6.3 +zope.sqlalchemy==3.1 diff --git a/scripts/eval_only.sh b/scripts/eval_only.sh new file mode 100755 index 0000000000000000000000000000000000000000..90d48b6572da6baafb906e48652a7626dd3e9d64 --- /dev/null +++ b/scripts/eval_only.sh @@ -0,0 +1,11 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=3 + +torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12346 \ +eval_only.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval_high_res.yaml --world_size 1 + +# torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12345 \ +# test_inference.py \ +# -c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/train_det.yaml --world_size 1 \ No newline at end of file diff --git a/scripts/eval_sc.sh b/scripts/eval_sc.sh new file mode 100755 index 0000000000000000000000000000000000000000..e9198f8e69879292989a3e7582f71a5faf3f22b6 --- /dev/null +++ b/scripts/eval_sc.sh @@ -0,0 +1,7 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=1 + +torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12346 \ +eval.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval_config.yaml --world_size 1 \ No newline at end of file diff --git a/scripts/eval_vq.sh b/scripts/eval_vq.sh new file mode 100755 index 0000000000000000000000000000000000000000..96efe4354b1fec0bdd52836f342ccd543b7cf847 --- /dev/null +++ b/scripts/eval_vq.sh @@ -0,0 +1,6 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=0 + +python eval_vq.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml --world_size 1 \ No newline at end of file diff --git a/scripts/fine_tune_vq copy.sh b/scripts/fine_tune_vq copy.sh new file mode 100644 index 0000000000000000000000000000000000000000..e6d86ce06043520f3c0eb712fbe0c6b8ea110ade --- /dev/null +++ b/scripts/fine_tune_vq copy.sh @@ -0,0 +1,9 @@ +cd .. \ + +# export CUDA_VISIBLE_DEVICES=0 + +# python fine_tune_vq.py \ +# -c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml --world_size 1 + +python fine_tune_vq.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml \ No newline at end of file diff --git a/scripts/fine_tune_vq.sh b/scripts/fine_tune_vq.sh new file mode 100755 index 0000000000000000000000000000000000000000..e6d86ce06043520f3c0eb712fbe0c6b8ea110ade --- /dev/null +++ b/scripts/fine_tune_vq.sh @@ -0,0 +1,9 @@ +cd .. \ + +# export CUDA_VISIBLE_DEVICES=0 + +# python fine_tune_vq.py \ +# -c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml --world_size 1 + +python fine_tune_vq.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml \ No newline at end of file diff --git a/scripts/fine_tune_vq_w_mask.sh b/scripts/fine_tune_vq_w_mask.sh new file mode 100755 index 0000000000000000000000000000000000000000..20e2c5a6c042ee4709223200419204fbfe770f2c --- /dev/null +++ b/scripts/fine_tune_vq_w_mask.sh @@ -0,0 +1,10 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=0,1,2,3 + +torchrun --node_rank=0 --nproc_per_node=4 --nnodes=1 --master_addr="localhost" --master_port=12345 \ +fine_tune_vq_w_mask.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/fine_tune_vq_w_mask.yaml --world_size 4 + +# python fine_tune_vq_w_mask.py \ +# -c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/fine_tune_vq_w_mask.yaml \ No newline at end of file diff --git a/scripts/inference_fine_tune_vq.sh b/scripts/inference_fine_tune_vq.sh new file mode 100755 index 0000000000000000000000000000000000000000..e6d86ce06043520f3c0eb712fbe0c6b8ea110ade --- /dev/null +++ b/scripts/inference_fine_tune_vq.sh @@ -0,0 +1,9 @@ +cd .. \ + +# export CUDA_VISIBLE_DEVICES=0 + +# python fine_tune_vq.py \ +# -c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml --world_size 1 + +python fine_tune_vq.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/eval.yaml \ No newline at end of file diff --git a/scripts/pretrain_magevit.sh b/scripts/pretrain_magevit.sh new file mode 100644 index 0000000000000000000000000000000000000000..1b816f07a81d7a398f2e16adb3a3e687dbcf69fe --- /dev/null +++ b/scripts/pretrain_magevit.sh @@ -0,0 +1,15 @@ +cd .. \ + +python -m torch.distributed.launch --node_rank=0 --nproc_per_node=8 --nnodes=8 \ +--master_addr="${MASTER_SERVER_ADDRESS}" --master_port=12344 \ +main_pretrain.py \ +--batch_size 64 \ +--model mage_vit_base_patch16 \ +--mask_ratio_min 0.5 --mask_ratio_max 1.0 \ +--mask_ratio_mu 0.55 --mask_ratio_std 0.25 \ +--epochs 1600 \ +--warmup_epochs 40 \ +--blr 1.5e-4 --weight_decay 0.05 \ +--output_dir ${OUTPUT_DIR} \ +--data_path ${IMAGENET_DIR} \ +--dist_url tcp://${MASTER_SERVER_ADDRESS}:2214 \ No newline at end of file diff --git a/scripts/train_codec.sh b/scripts/train_codec.sh new file mode 100755 index 0000000000000000000000000000000000000000..0cfcfcc8a5afc0315cae4f47f8a7c911ac90df66 --- /dev/null +++ b/scripts/train_codec.sh @@ -0,0 +1,7 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=0 + +torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12345 \ +train_vbr_codec.py \ +-c /home/v-ruoyufeng/v-ruoyufeng/qyp/mage/config/train_mim_config.yaml --world_size 1 \ No newline at end of file diff --git a/scripts/train_codec_rope.sh b/scripts/train_codec_rope.sh new file mode 100755 index 0000000000000000000000000000000000000000..c70b6809474a272a0f8a0b1ebac2e614f8852799 --- /dev/null +++ b/scripts/train_codec_rope.sh @@ -0,0 +1,7 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=0,1,2,3 + +torchrun --node_rank=0 --nproc_per_node=4 --nnodes=1 --master_addr="localhost" --master_port=12345 \ +train_vbr_codec_rope.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/train_mim_config.yaml --world_size 4 \ No newline at end of file diff --git a/scripts/train_det.sh b/scripts/train_det.sh new file mode 100755 index 0000000000000000000000000000000000000000..0fbc13d6e5fd973b7e85f4093b7dac5dd9820e90 --- /dev/null +++ b/scripts/train_det.sh @@ -0,0 +1,11 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=3 + +torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12344 \ +train_on_coco.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/train_det.yaml --world_size 1 + +# torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12345 \ +# test_inference.py \ +# -c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/train_det.yaml --world_size 1 \ No newline at end of file diff --git a/scripts/train_im_cls.sh b/scripts/train_im_cls.sh new file mode 100755 index 0000000000000000000000000000000000000000..eb1088a6ef8515d700a33146f5194a3a0af294f2 --- /dev/null +++ b/scripts/train_im_cls.sh @@ -0,0 +1,7 @@ +cd .. \ + +export CUDA_VISIBLE_DEVICES=0 + +torchrun --node_rank=0 --nproc_per_node=1 --nnodes=1 --master_addr="localhost" --master_port=12345 \ +train_im_cls.py \ +-c /home/t2vg-a100-G4-10/project/qyp/mimc_rope/config/train_im_cls.yaml --world_size 1 \ No newline at end of file diff --git a/scripts/uncon_gen.sh b/scripts/uncon_gen.sh new file mode 100644 index 0000000000000000000000000000000000000000..c07e5f0b1622f52eaa64a10dfe16892ec6a81f85 --- /dev/null +++ b/scripts/uncon_gen.sh @@ -0,0 +1,6 @@ +cd .. \ + +python gen_img_uncond.py --temp 6.0 --num_iter 20 \ +--ckpt "./ckpt_pretrained/mage-vitb-1600.pth" --batch_size 16 --num_images 32 \ +--model mage_vit_base_patch16 --output_dir "./output/uncond_gen/mage-vitb/6.0" \ +--gpu_id 5 --cuda \ \ No newline at end of file diff --git a/taming/models/__pycache__/vqgan.cpython-38.pyc b/taming/models/__pycache__/vqgan.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7d571b0152f4c53e709b3a33d0040ecfa6706a4b Binary files /dev/null and b/taming/models/__pycache__/vqgan.cpython-38.pyc differ diff --git a/taming/models/vqgan.py b/taming/models/vqgan.py new file mode 100644 index 0000000000000000000000000000000000000000..026c9d480cbe82e3b703c70e57a2800390cb80cd --- /dev/null +++ b/taming/models/vqgan.py @@ -0,0 +1,142 @@ +import torch +import pytorch_lightning as pl + +from taming.modules.diffusionmodules.model import Encoder, Decoder, Decoder_w_Prompt +from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer + + +class VQModel(pl.LightningModule): + def __init__(self, + ddconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, sane_index_shape=sane_index_shape) + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.image_key = image_key + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu") + if "state_dict" in sd.keys(): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + print("Strict load") + self.load_state_dict(sd, strict=False) + # self.load_state_dict(sd, strict=True) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def decode(self, quant): + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input): + quant, diff, _ = self.encode(input) + dec = self.decode(quant) + return dec, diff + + +class VQModel_w_Prompt(pl.LightningModule): + def __init__(self, + ddconfig, + n_embed, + embed_dim, + ckpt_path=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + remap=None, + sane_index_shape=False, # tell vector quantizer to return indices as bhw + ): + super().__init__() + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder_w_Prompt(**ddconfig) + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, + remap=remap, sane_index_shape=sane_index_shape) + if ckpt_path is not None: + self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) + self.image_key = image_key + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + def remove_module_prefix(self, state_dict): + """ + 去除state_dict中键的'module.'前缀 + """ + new_state_dict = {} + for key in state_dict.keys(): + new_key = key.replace('module.vqgan.', '') if key.startswith('module.vqgan.') else key.replace('module.', '') + new_state_dict[new_key] = state_dict[key] + return new_state_dict + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu") + if "state_dict" in sd.keys(): + sd = sd["state_dict"] + keys = list(sd.keys()) + for k in keys: + for ik in ignore_keys: + if k.startswith(ik): + print("Deleting key {} from state_dict.".format(k)) + del sd[k] + # print("Strict load") + self.load_state_dict(sd, strict=False) + # sd = self.remove_module_prefix(sd) + # self.load_state_dict(sd, strict=True) + print(f"Restored from {path}") + + def encode(self, x): + h = self.encoder(x) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info + + def decode(self, quant): + dec = self.decoder(quant) + return dec + + def decode_code(self, code_b): + quant_b = self.quantize.embed_code(code_b) + dec = self.decode(quant_b) + return dec + + def forward(self, input): + quant, diff, _ = self.encode(input) + dec = self.decode(quant) + return dec, diff \ No newline at end of file diff --git a/taming/modules/diffusionmodules/__pycache__/model.cpython-38.pyc b/taming/modules/diffusionmodules/__pycache__/model.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f52b62504e44c13c59d3866a2bf6cc1b659f079d Binary files /dev/null and b/taming/modules/diffusionmodules/__pycache__/model.cpython-38.pyc differ diff --git a/taming/modules/diffusionmodules/model copy.py b/taming/modules/diffusionmodules/model copy.py new file mode 100644 index 0000000000000000000000000000000000000000..99ed04644884e0c6ace28e326e195742b41dfe6a --- /dev/null +++ b/taming/modules/diffusionmodules/model copy.py @@ -0,0 +1,608 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +import torch.nn.functional as F + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + else: + self.nin_shortcut = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=False) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(h) + else: + x = self.nin_shortcut(h) + + return x+h + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=False, in_channels, + resolution, z_channels, double_z=True, **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1, + bias=False) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + down = nn.Module() + down.block = block + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution) + + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, **ignorekwargs): + super().__init__() + self.ch = ch # 128 + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) # 4 + self.num_res_blocks = num_res_blocks # 2 + self.resolution = resolution # 256 + self.in_channels = in_channels # 3 + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) # 没有用 + block_in = ch*ch_mult[self.num_resolutions-1] # + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, # 0 + dropout=dropout) # 0.0 + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): # 4个 + block = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + # print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out) + block_in = block_out + up = nn.Module() + up.block = block + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) # Ture + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks): + h = self.up[i_level].block[i_block](h, temb) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + +def init_weights_zero(m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +def init_weights_kaiming(m): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +########################################################################## +##---------- Prompt Gen Module ----------------------- +class PromptGenBlock(nn.Module): + def __init__(self, prompt_dim=128, prompt_size=96, prompt_len=1): + super(PromptGenBlock,self).__init__() + self.prompt_param = nn.Parameter(torch.rand(1, prompt_len, prompt_dim, prompt_size, prompt_size)) # (1, 1, 128, 96, 96) + self.conv3x3 = nn.Conv2d(prompt_dim, prompt_dim, kernel_size=3, stride=1, padding=1, bias=False) + # self.conv3x3.apply(self.init_weights_zero) + + def init_weights_zero(self, m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + B, C, H, W = x.shape + prompt = self.prompt_param.unsqueeze(0).repeat(B,1,1,1,1,1).squeeze(1) + prompt = torch.sum(prompt, dim=1) # (B, prompt_dim, prompt_size, prompt_size) + prompt = F.interpolate(prompt, (H, W), mode="bilinear") # + prompt = self.conv3x3(prompt) # (B, prompt_dim, H, W) + return prompt + +class Attention(nn.Module): + def __init__(self, dim, num_heads, bias, prompt_dim=192): + super(Attention, self).__init__() + self.num_heads = num_heads + self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) + self.shared_mlp = nn.Sequential( + # nn.Linear(prompt_dim, dim*2, bias=False), + nn.Conv2d(prompt_dim, dim*2, kernel_size=1, bias=bias) + ) + self.qkv = nn.Conv2d(dim, dim*3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d(dim*3, dim*3, kernel_size=3, stride=1, padding=1, groups=dim*3, bias=bias) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + self.qkv.apply(init_weights_kaiming) + self.qkv_dwconv.apply(init_weights_kaiming) + + def forward(self, x, prompt): + b, c, h, w = x.shape + prompt = self.shared_mlp(prompt) + prompt = prompt.expand(b, -1, -1, -1) + gama, beta = prompt.chunk(2, dim=1) + x = x *( 1 + gama) + beta + qkv = self.qkv_dwconv(self.qkv(x)) + q,k,v = qkv.chunk(3, dim=1) + + q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads) + k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads) + v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + + out = (attn @ v) + + out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w) + + out = self.project_out(out) + return out + +class DepthwiseSeparableConv(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1): + super(DepthwiseSeparableConv, self).__init__() + + # Depthwise convolution + self.depthwise_conv = nn.Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=in_channels, + bias=False + ) + + # Pointwise convolution + self.pointwise_conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=False + ) + + def forward(self, x): + out = self.depthwise_conv(x) + out = self.pointwise_conv(out) + return out + +class SFT(nn.Module): + def __init__(self, x_dim, prompt_dim=192, ks=3, nhidden=128): + super(SFT, self).__init__() + pw = ks // 2 + self.mlp_shared = nn.Sequential( + nn.Conv2d(prompt_dim, nhidden, kernel_size=1), + nn.ReLU() + ) + self.mlp_gama = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw) + self.mlp_beta = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw) + + # self.mlp_shared.apply(init_weights_zero) # Initialize shared_mlp + # self.mlp_gama.apply(init_weights_zero) + # self.mlp_beta.apply(init_weights_zero) + + def forward(self, x, prompt): + actv = self.mlp_shared(prompt) + gama = self.mlp_gama(actv) + beta = self.mlp_beta(actv) + # print("gama_max=", gama.max()) + # print("beta_max=", beta.max()) + # print("gama_min=", gama.min()) + # print("beta_min=", beta.min()) + out = x * (1 + gama) + beta + return out + +def init_weights_gama(m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + center = m.kernel_size[0] // 2 + if m.groups == m.in_channels and m.in_channels == m.out_channels: # depthwise conv + nn.init.constant_(m.weight[:, :, center, center], 1) + else: # pointwise conv + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +class SFT_new(nn.Module): + def __init__(self, x_dim, prompt_dim=192, ks=3, nhidden=128): + super(SFT_new, self).__init__() + pw = ks // 2 + self.mlp_shared = nn.Sequential( + nn.Conv2d(prompt_dim, nhidden, kernel_size=1), + nn.ReLU() + ) + self.mlp_gama = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw) + self.mlp_beta = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw) + + # self.mlp_shared.apply(init_weights_zero) # Initialize shared_mlp + # self.mlp_gama.apply(init_weights_gama) + # self.mlp_beta.apply(init_weights_zero) + + def forward(self, x, prompt): + actv = self.mlp_shared(prompt) + gama = self.mlp_gama(actv) + beta = self.mlp_beta(actv) + out = x * gama + beta + return out + +class Decoder_w_Prompt(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, **ignorekwargs): + super().__init__() + self.ch = ch # 128 + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) # 4 + self.num_res_blocks = num_res_blocks # 2 + self.resolution = resolution # 256 + self.in_channels = in_channels # 3 + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) # 没有用 + block_in = ch*ch_mult[self.num_resolutions-1] # 128 * 4 + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, # 0 + dropout=dropout) # 0.0 + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): # 4,3,2,1,0 + block = nn.ModuleList() + block_out = ch*ch_mult[i_level] # ch*8 + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, # 512 + out_channels=block_out, # + temb_channels=self.temb_ch, + dropout=dropout)) + # print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out) + block_in = block_out + up = nn.Module() + up.block = block + if i_level != 0: + if i_level == 1: + up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=16) + up.prompt_attn = Attention(dim=128, num_heads=8, bias=True, prompt_dim=128) + # up.prompt_sft = SFT(x_dim=128, prompt_dim=128) + elif i_level == 2: + up.prompt = PromptGenBlock(prompt_dim=256, prompt_size=32) + # up.prompt_sft = SFT(x_dim=256, prompt_dim=256) + up.prompt_attn = Attention(dim=256, num_heads=8, bias=True, prompt_dim=256) + elif i_level == 3: + up.prompt = PromptGenBlock(prompt_dim=256, prompt_size=64) + # up.prompt_sft = SFT(x_dim=256, prompt_dim=256) + up.prompt_attn = Attention(dim=256, num_heads=8, bias=True, prompt_dim=256) + elif i_level == 4: + up.prompt = PromptGenBlock(prompt_dim=512, prompt_size=128) + # up.prompt_sft = SFT(x_dim=512, prompt_dim=512) + up.upsample = Upsample(block_in, resamp_with_conv) # Ture + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) # 256->512 + + # middle + h = self.mid.block_1(h, temb) # 512 + h = self.mid.block_2(h, temb) # 512 + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks): + h = self.up[i_level].block[i_block](h, temb) + if i_level != 0: + # prompt & attention + if i_level != 4: + prompt = self.up[i_level].prompt(h) + # h = self.up[i_level].prompt_sft(h, prompt) + h = self.up[i_level].prompt_attn(h, prompt) + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: # False + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h \ No newline at end of file diff --git a/taming/modules/diffusionmodules/model.py b/taming/modules/diffusionmodules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..c411891ba38f0edc483ed2f0055b8fcc417e969d --- /dev/null +++ b/taming/modules/diffusionmodules/model.py @@ -0,0 +1,573 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +import torch.nn.functional as F + + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + else: + self.nin_shortcut = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=False) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(h) + else: + x = self.nin_shortcut(h) + + return x+h + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=False, in_channels, + resolution, z_channels, double_z=True, **ignore_kwargs): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1, + bias=False) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + down = nn.Module() + down.block = block + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=1, + stride=1, + padding=0) + + + def forward(self, x): + #assert x.shape[2] == x.shape[3] == self.resolution, "{}, {}, {}".format(x.shape[2], x.shape[3], self.resolution) + + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, **ignorekwargs): + super().__init__() + self.ch = ch # 128 + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) # 4 + self.num_res_blocks = num_res_blocks # 2 + self.resolution = resolution # 256 + self.in_channels = in_channels # 3 + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) # 没有用 + block_in = ch*ch_mult[self.num_resolutions-1] # + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, # 0 + dropout=dropout) # 0.0 + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): # 4个 + block = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + # print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out) + block_in = block_out + up = nn.Module() + up.block = block + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) # Ture + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks): + h = self.up[i_level].block[i_block](h, temb) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + +def init_weights_zero(m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +def init_weights_kaiming(m): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +########################################################################## +##---------- Prompt Gen Module ----------------------- +class PromptGenBlock(nn.Module): + def __init__(self, prompt_dim=128, prompt_size=96, prompt_len=1): + super(PromptGenBlock,self).__init__() + self.prompt_param = nn.Parameter(torch.rand(1, prompt_len, prompt_dim, prompt_size, prompt_size)) # (1, 1, 128, 96, 96) + self.conv3x3 = nn.Conv2d(prompt_dim, prompt_dim, kernel_size=3, stride=1, padding=1, bias=False) + + def init_weights_zero(self, m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x): + B, C, H, W = x.shape + prompt = self.prompt_param.unsqueeze(0).repeat(B,1,1,1,1,1).squeeze(1) + prompt = torch.sum(prompt, dim=1) # (B, prompt_dim, prompt_size, prompt_size) + prompt = F.interpolate(prompt, (H, W), mode="bilinear") # + prompt = self.conv3x3(prompt) # (B, prompt_dim, H, W) + return prompt + +class Attention(nn.Module): + def __init__(self, dim, hidden_dim, bias, prompt_dim=192): + super(Attention, self).__init__() + self.shared_mlp = nn.Sequential( + nn.Conv2d(prompt_dim, hidden_dim, kernel_size=3, stride=1, padding=1, bias=bias) + ) + self.mlp_gama = nn.Conv2d(hidden_dim, dim, kernel_size=3, stride=1, padding=1, bias=bias) + self.mlp_beta = nn.Conv2d(hidden_dim, dim, kernel_size=3, stride=1, padding=1, bias=bias) + + self.mlp_gama.apply(init_weights_zero) + self.mlp_beta.apply(init_weights_zero) + + + def init_weights_zero(self, m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + + def forward(self, x, prompt): + b, c, h, w = x.shape + prompt = self.shared_mlp(prompt) + prompt = prompt.expand(b, -1, -1, -1) + # prompt = F.adaptive_avg_pool2d(prompt, x.size()[2:]) + gama = self.mlp_gama(prompt) + beta = self.mlp_beta(prompt) + # gama, beta = prompt.chunk(2, dim=1) + x = x *( 1 + gama) + beta + return x + +class DepthwiseSeparableConv(nn.Module): + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1): + super(DepthwiseSeparableConv, self).__init__() + + # Depthwise convolution + self.depthwise_conv = nn.Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=in_channels, + bias=False + ) + + # Pointwise convolution + self.pointwise_conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=False + ) + + def forward(self, x): + out = self.depthwise_conv(x) + out = self.pointwise_conv(out) + return out + +class SFT(nn.Module): + def __init__(self, x_dim, prompt_dim=192, ks=3, nhidden=128): + super(SFT, self).__init__() + pw = ks // 2 + self.mlp_shared = nn.Sequential( + nn.Conv2d(prompt_dim, nhidden, kernel_size=1), + nn.ReLU() + ) + self.mlp_gama = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw) + self.mlp_beta = DepthwiseSeparableConv(nhidden, x_dim, kernel_size=ks, padding=pw) + + # self.mlp_shared.apply(init_weights_zero) # Initialize shared_mlp + # self.mlp_gama.apply(init_weights_zero) + # self.mlp_beta.apply(init_weights_zero) + + def forward(self, x, prompt): + actv = self.mlp_shared(prompt) + gama = self.mlp_gama(actv) + beta = self.mlp_beta(actv) + # print("gama_max=", gama.max()) + # print("beta_max=", beta.max()) + # print("gama_min=", gama.min()) + # print("beta_min=", beta.min()) + out = x * (1 + gama) + beta + return out + +def init_weights_gama(m): + if isinstance(m, nn.Conv2d): + nn.init.constant_(m.weight, 0) + center = m.kernel_size[0] // 2 + if m.groups == m.in_channels and m.in_channels == m.out_channels: # depthwise conv + nn.init.constant_(m.weight[:, :, center, center], 1) + else: # pointwise conv + nn.init.constant_(m.weight, 1) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + +class Decoder_w_Prompt(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, **ignorekwargs): + super().__init__() + self.ch = ch # 128 + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) # 4 + self.num_res_blocks = num_res_blocks # 2 + self.resolution = resolution # 256 + self.in_channels = in_channels # 3 + self.give_pre_end = give_pre_end + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) # 没有用 + block_in = ch*ch_mult[self.num_resolutions-1] # 128 * 4 + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, # 0 + dropout=dropout) # 0.0 + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): # 4,3,2,1,0 + block = nn.ModuleList() + block_out = ch*ch_mult[i_level] # ch*8 + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, # 512 + out_channels=block_out, # + temb_channels=self.temb_ch, + dropout=dropout)) + # print("i_level=", i_level, "block_in=", block_in, "block_out=", block_out) + block_in = block_out + up = nn.Module() + up.block = block + if i_level != 0: + if i_level == 1: + up.prompt = PromptGenBlock(prompt_dim=64, prompt_size=16) + up.prompt_attn = Attention(dim=128, hidden_dim=64, bias=True, prompt_dim=64) + # up.prompt_sft = SFT(x_dim=128, prompt_dim=128) + elif i_level == 2: + up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=16) + # up.prompt_sft = SFT(x_dim=256, prompt_dim=256) + up.prompt_attn = Attention(dim=256, hidden_dim=128, bias=True, prompt_dim=128) + elif i_level == 3: + up.prompt = PromptGenBlock(prompt_dim=128, prompt_size=32) + # up.prompt_sft = SFT(x_dim=256, prompt_dim=256) + up.prompt_attn = Attention(dim=256, hidden_dim=128, bias=True, prompt_dim=128) + elif i_level == 4: + up.prompt = PromptGenBlock(prompt_dim=256, prompt_size=32) + # up.prompt_sft = SFT(x_dim=512, prompt_dim=512) + up.prompt_attn = Attention(dim=512, hidden_dim=192, bias=True, prompt_dim=256) + up.upsample = Upsample(block_in, resamp_with_conv) # Ture + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) # 256->512 + + # middle + h = self.mid.block_1(h, temb) # 512 + h = self.mid.block_2(h, temb) # 512 + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks): + h = self.up[i_level].block[i_block](h, temb) + if i_level != 0: + # prompt & attention + prompt = self.up[i_level].prompt(h) + # h = self.up[i_level].prompt_sft(h, prompt) + h = self.up[i_level].prompt_attn(h, prompt) + h = self.up[i_level].upsample(h) + # end + if self.give_pre_end: # False + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h \ No newline at end of file diff --git a/taming/modules/util.py b/taming/modules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee16385d8b1342a2d60a5f1aa5cadcfbe934bd8 --- /dev/null +++ b/taming/modules/util.py @@ -0,0 +1,130 @@ +import torch +import torch.nn as nn + + +def count_params(model): + total_params = sum(p.numel() for p in model.parameters()) + return total_params + + +class ActNorm(nn.Module): + def __init__(self, num_features, logdet=False, affine=True, + allow_reverse_init=False): + assert affine + super().__init__() + self.logdet = logdet + self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) + self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) + self.allow_reverse_init = allow_reverse_init + + self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) + + def initialize(self, input): + with torch.no_grad(): + flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) + mean = ( + flatten.mean(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + std = ( + flatten.std(1) + .unsqueeze(1) + .unsqueeze(2) + .unsqueeze(3) + .permute(1, 0, 2, 3) + ) + + self.loc.data.copy_(-mean) + self.scale.data.copy_(1 / (std + 1e-6)) + + def forward(self, input, reverse=False): + if reverse: + return self.reverse(input) + if len(input.shape) == 2: + input = input[:,:,None,None] + squeeze = True + else: + squeeze = False + + _, _, height, width = input.shape + + if self.training and self.initialized.item() == 0: + self.initialize(input) + self.initialized.fill_(1) + + h = self.scale * (input + self.loc) + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + + if self.logdet: + log_abs = torch.log(torch.abs(self.scale)) + logdet = height*width*torch.sum(log_abs) + logdet = logdet * torch.ones(input.shape[0]).to(input) + return h, logdet + + return h + + def reverse(self, output): + if self.training and self.initialized.item() == 0: + if not self.allow_reverse_init: + raise RuntimeError( + "Initializing ActNorm in reverse direction is " + "disabled by default. Use allow_reverse_init=True to enable." + ) + else: + self.initialize(output) + self.initialized.fill_(1) + + if len(output.shape) == 2: + output = output[:,:,None,None] + squeeze = True + else: + squeeze = False + + h = output / self.scale - self.loc + + if squeeze: + h = h.squeeze(-1).squeeze(-1) + return h + + +class AbstractEncoder(nn.Module): + def __init__(self): + super().__init__() + + def encode(self, *args, **kwargs): + raise NotImplementedError + + +class Labelator(AbstractEncoder): + """Net2Net Interface for Class-Conditional Model""" + def __init__(self, n_classes, quantize_interface=True): + super().__init__() + self.n_classes = n_classes + self.quantize_interface = quantize_interface + + def encode(self, c): + c = c[:,None] + if self.quantize_interface: + return c, None, [None, None, c.long()] + return c + + +class SOSProvider(AbstractEncoder): + # for unconditional training + def __init__(self, sos_token, quantize_interface=True): + super().__init__() + self.sos_token = sos_token + self.quantize_interface = quantize_interface + + def encode(self, x): + # get batch size from data and replicate sos_token + c = torch.ones(x.shape[0], 1)*self.sos_token + c = c.long().to(x.device) + if self.quantize_interface: + return c, None, [None, None, c] + return c diff --git a/taming/modules/vqvae/__pycache__/quantize.cpython-38.pyc b/taming/modules/vqvae/__pycache__/quantize.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a8d82f8a99e0a1f2eff351a5ec06187c1db8cd75 Binary files /dev/null and b/taming/modules/vqvae/__pycache__/quantize.cpython-38.pyc differ diff --git a/taming/modules/vqvae/quantize.py b/taming/modules/vqvae/quantize.py new file mode 100644 index 0000000000000000000000000000000000000000..d75544e41fa01bce49dd822b1037963d62f79b51 --- /dev/null +++ b/taming/modules/vqvae/quantize.py @@ -0,0 +1,445 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from torch import einsum +from einops import rearrange + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + # NOTE: this class contains a bug regarding beta; see VectorQuantizer2 for + # a fix and use legacy=False to apply that fix. VectorQuantizer2 can be + # used wherever VectorQuantizer has been used before and is additionally + # more efficient. + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.matmul(z_flattened, self.embedding.weight.t()) + + ## could possible replace this here + # #\start... + # find closest encodings + min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + #.........\end + + # with: + # .........\start + #min_encoding_indices = torch.argmin(d, dim=1) + #z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:,None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantize(nn.Module): + """ + credit to @karpathy: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py (thanks!) + Gumbel Softmax trick quantizer + Categorical Reparameterization with Gumbel-Softmax, Jang et al. 2016 + https://arxiv.org/abs/1611.01144 + """ + def __init__(self, num_hiddens, embedding_dim, n_embed, straight_through=True, + kl_weight=5e-4, temp_init=1.0, use_vqinterface=True, + remap=None, unknown_index="random"): + super().__init__() + + self.embedding_dim = embedding_dim + self.n_embed = n_embed + + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + + self.proj = nn.Conv2d(num_hiddens, n_embed, 1) + self.embed = nn.Embedding(n_embed, embedding_dim) + + self.use_vqinterface = use_vqinterface + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, return_logits=False): + # force hard = True when we are in eval mode, as we must quantize. actually, always true seems to work + hard = self.straight_through if self.training else True + temp = self.temperature if temp is None else temp + + logits = self.proj(z) + if self.remap is not None: + # continue only with used logits + full_zeros = torch.zeros_like(logits) + logits = logits[:,self.used,...] + + soft_one_hot = F.gumbel_softmax(logits, tau=temp, dim=1, hard=hard) + if self.remap is not None: + # go back to all entries but unused set to zero + full_zeros[:,self.used,...] = soft_one_hot + soft_one_hot = full_zeros + z_q = einsum('b n h w, n d -> b d h w', soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.n_embed + 1e-10), dim=1).mean() + + ind = soft_one_hot.argmax(dim=1) + if self.remap is not None: + ind = self.remap_to_used(ind) + if self.use_vqinterface: + if return_logits: + return z_q, diff, (None, None, ind), logits + return z_q, diff, (None, None, ind) + return z_q, diff, ind + + def get_codebook_entry(self, indices, shape): + b, h, w, c = shape + assert b*h*w == indices.shape[0] + indices = rearrange(indices, '(b h w) -> b h w', b=b, h=h, w=w) + if self.remap is not None: + indices = self.unmap_to_all(indices) + one_hot = F.one_hot(indices, num_classes=self.n_embed).permute(0, 3, 1, 2).float() + z_q = einsum('b n h w, n d -> b d h w', one_hot, self.embed.weight) + return z_q + + +class VectorQuantizer2(nn.Module): + """ + Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly + avoids costly matrix multiplications and allows for post-hoc remapping of indices. + """ + # NOTE: due to a bug the beta term was applied to the wrong term. for + # backwards compatibility we use the buggy version by default, but you can + # specify legacy=False to fix it. + def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random", + sane_index_shape=False, legacy=True): + super().__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + self.legacy = legacy + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_e} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_e + + self.sane_index_shape = sane_index_shape + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z, temp=None, rescale_logits=False, return_logits=False): + assert temp is None or temp==1.0, "Only for interface compatible with Gumbel" + assert rescale_logits==False, "Only for interface compatible with Gumbel" + assert return_logits==False, "Only for interface compatible with Gumbel" + # reshape z -> (batch, height, width, channel) and flatten + z = rearrange(z, 'b c h w -> b h w c').contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \ + torch.sum(self.embedding.weight**2, dim=1) - 2 * \ + torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n')) + + min_encoding_indices = torch.argmin(d, dim=1) + z_q = self.embedding(min_encoding_indices).view(z.shape) + perplexity = None + min_encodings = None + + # compute loss for embedding + if not self.legacy: + loss = self.beta * torch.mean((z_q.detach()-z)**2) + \ + torch.mean((z_q - z.detach()) ** 2) + else: + loss = torch.mean((z_q.detach()-z)**2) + self.beta * \ + torch.mean((z_q - z.detach()) ** 2) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous() + + if self.remap is not None: + min_encoding_indices = min_encoding_indices.reshape(z.shape[0],-1) # add batch axis + min_encoding_indices = self.remap_to_used(min_encoding_indices) + min_encoding_indices = min_encoding_indices.reshape(-1,1) # flatten + + if self.sane_index_shape: + min_encoding_indices = min_encoding_indices.reshape( + z_q.shape[0], z_q.shape[2], z_q.shape[3]) + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + if self.remap is not None: + indices = indices.reshape(shape[0],-1) # add batch axis + indices = self.unmap_to_all(indices) + indices = indices.reshape(-1) # flatten again + + # get quantized latent vectors + z_q = self.embedding(indices) + + if shape is not None: + z_q = z_q.view(shape) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + +class EmbeddingEMA(nn.Module): + def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5): + super().__init__() + self.decay = decay + self.eps = eps + weight = torch.randn(num_tokens, codebook_dim) + self.weight = nn.Parameter(weight, requires_grad = False) + self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad = False) + self.embed_avg = nn.Parameter(weight.clone(), requires_grad = False) + self.update = True + + def forward(self, embed_id): + return F.embedding(embed_id, self.weight) + + def cluster_size_ema_update(self, new_cluster_size): + self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) + + def embed_avg_ema_update(self, new_embed_avg): + self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay) + + def weight_update(self, num_tokens): + n = self.cluster_size.sum() + smoothed_cluster_size = ( + (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n + ) + #normalize embedding average with smoothed cluster size + embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) + self.weight.data.copy_(embed_normalized) + + +class EMAVectorQuantizer(nn.Module): + def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5, + remap=None, unknown_index="random"): + super().__init__() + self.codebook_dim = codebook_dim + self.num_tokens = num_tokens + self.beta = beta + self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps) + + self.remap = remap + if self.remap is not None: + self.register_buffer("used", torch.tensor(np.load(self.remap))) + self.re_embed = self.used.shape[0] + self.unknown_index = unknown_index # "random" or "extra" or integer + if self.unknown_index == "extra": + self.unknown_index = self.re_embed + self.re_embed = self.re_embed+1 + print(f"Remapping {self.n_embed} indices to {self.re_embed} indices. " + f"Using {self.unknown_index} for unknown indices.") + else: + self.re_embed = n_embed + + def remap_to_used(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + match = (inds[:,:,None]==used[None,None,...]).long() + new = match.argmax(-1) + unknown = match.sum(2)<1 + if self.unknown_index == "random": + new[unknown]=torch.randint(0,self.re_embed,size=new[unknown].shape).to(device=new.device) + else: + new[unknown] = self.unknown_index + return new.reshape(ishape) + + def unmap_to_all(self, inds): + ishape = inds.shape + assert len(ishape)>1 + inds = inds.reshape(ishape[0],-1) + used = self.used.to(inds) + if self.re_embed > self.used.shape[0]: # extra token + inds[inds>=self.used.shape[0]] = 0 # simply set to zero + back=torch.gather(used[None,:][inds.shape[0]*[0],:], 1, inds) + return back.reshape(ishape) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + #z, 'b c h w -> b h w c' + z = rearrange(z, 'b c h w -> b h w c') + z_flattened = z.reshape(-1, self.codebook_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \ + self.embedding.weight.pow(2).sum(dim=1) - 2 * \ + torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n' + + + encoding_indices = torch.argmin(d, dim=1) + + z_q = self.embedding(encoding_indices).view(z.shape) + encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype) + avg_probs = torch.mean(encodings, dim=0) + perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10))) + + if self.training and self.embedding.update: + #EMA cluster size + encodings_sum = encodings.sum(0) + self.embedding.cluster_size_ema_update(encodings_sum) + #EMA embedding average + embed_sum = encodings.transpose(0,1) @ z_flattened + self.embedding.embed_avg_ema_update(embed_sum) + #normalize embed_avg and update weight + self.embedding.weight_update(self.num_tokens) + + # compute loss for embedding + loss = self.beta * F.mse_loss(z_q.detach(), z) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # reshape back to match original input shape + #z_q, 'b h w c -> b c h w' + z_q = rearrange(z_q, 'b h w c -> b c h w') + return z_q, loss, (perplexity, encodings, encoding_indices) diff --git a/taming/util.py b/taming/util.py new file mode 100644 index 0000000000000000000000000000000000000000..06053e5defb87977f9ab07e69bf4da12201de9b7 --- /dev/null +++ b/taming/util.py @@ -0,0 +1,157 @@ +import os, hashlib +import requests +from tqdm import tqdm + +URL_MAP = { + "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1" +} + +CKPT_MAP = { + "vgg_lpips": "vgg.pth" +} + +MD5_MAP = { + "vgg_lpips": "d507d7349b931f0638a25a48a722f98a" +} + + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path + + +class KeyNotFoundError(Exception): + def __init__(self, cause, keys=None, visited=None): + self.cause = cause + self.keys = keys + self.visited = visited + messages = list() + if keys is not None: + messages.append("Key not found: {}".format(keys)) + if visited is not None: + messages.append("Visited: {}".format(visited)) + messages.append("Cause:\n{}".format(cause)) + message = "\n".join(messages) + super().__init__(message) + + +def retrieve( + list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False +): + """Given a nested list or dict return the desired value at key expanding + callable nodes if necessary and :attr:`expand` is ``True``. The expansion + is done in-place. + + Parameters + ---------- + list_or_dict : list or dict + Possibly nested list or dictionary. + key : str + key/to/value, path like string describing all keys necessary to + consider to get to the desired value. List indices can also be + passed here. + splitval : str + String that defines the delimiter between keys of the + different depth levels in `key`. + default : obj + Value returned if :attr:`key` is not found. + expand : bool + Whether to expand callable nodes on the path or not. + + Returns + ------- + The desired value or if :attr:`default` is not ``None`` and the + :attr:`key` is not found returns ``default``. + + Raises + ------ + Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is + ``None``. + """ + + keys = key.split(splitval) + + success = True + try: + visited = [] + parent = None + last_key = None + for key in keys: + if callable(list_or_dict): + if not expand: + raise KeyNotFoundError( + ValueError( + "Trying to get past callable node with expand=False." + ), + keys=keys, + visited=visited, + ) + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + + last_key = key + parent = list_or_dict + + try: + if isinstance(list_or_dict, dict): + list_or_dict = list_or_dict[key] + else: + list_or_dict = list_or_dict[int(key)] + except (KeyError, IndexError, ValueError) as e: + raise KeyNotFoundError(e, keys=keys, visited=visited) + + visited += [key] + # final expansion of retrieved value + if expand and callable(list_or_dict): + list_or_dict = list_or_dict() + parent[last_key] = list_or_dict + except KeyNotFoundError as e: + if default is None: + raise e + else: + list_or_dict = default + success = False + + if not pass_success: + return list_or_dict + else: + return list_or_dict, success + + +if __name__ == "__main__": + config = {"keya": "a", + "keyb": "b", + "keyc": + {"cc1": 1, + "cc2": 2, + } + } + from omegaconf import OmegaConf + config = OmegaConf.create(config) + print(config) + retrieve(config, "keya") + diff --git a/train_im_cls.py b/train_im_cls.py new file mode 100644 index 0000000000000000000000000000000000000000..6e19b011eae0aadd24840c9ba66d97d9faf63d2d --- /dev/null +++ b/train_im_cls.py @@ -0,0 +1,616 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec_cls +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import prepadding + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + bpp_mask = (bytes_length / num_pixels) * 8 + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, perceptual_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.perceptual_loss = perceptual_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return cls_loss, accu, perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def train_one_epoch(model, data_loader, metrics_criterion, cls_criterion, device, + optimizer, epoch, log_writer, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./train_im_cls/", stage) + os.makedirs(vis_path, exist_ok=True) + + tqdm_emu = tqdm.tqdm(enumerate(data_loader), total=len(data_loader), leave=False) + for data_iter_step, (samples, l) in tqdm_emu: + samples = samples.to(device, non_blocking=True) # samples = original image + l = l.to(device, non_blocking=True) + optimizer.zero_grad() + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + # rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + # _, _, perc_loss = cls_criterion(samples, rec, l) + loss = out_net['task_loss'] + + loss_value = loss.item() + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + loss.backward() + optimizer.step() + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 2000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + update_txt = ( + f'[{data_iter_step * len(samples)}/{len(data_loader.dataset)}]|' + f'Task loss: {loss.item():.4f}|' + f'Bpp: {out_criterion["bpp"].item():.4f}|' + f'Bpp mask: {out_criterion["bpp_mask"]:.4f}|' + f'mask rate: {out_net["mask_ratio"]:.4f}|' + f'lpips: {out_criterion["lpips"].item():.4f}|' + f'dists: {out_criterion["dists"].item():.4f}' + ) + tqdm_emu.set_postfix_str(update_txt, refresh=True) + +def inference(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, stage='val'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + percloss = AverageMeter() + accuracy = AverageMeter() + task_loss = AverageMeter() + cmap = plt.get_cmap('coolwarm') + + vis_path = os.path.join("./train_im_cls/", stage) + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + l = l.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + # rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + _, acc, perc = cls_criterion(d, rec, l) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + percloss.update(perc) + accuracy.update(acc) + task_loss.update(out_net['task_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 15 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + # 将其转换为numpy数组并移除批次和通道维度以进行可视化 + importance_map = out_net['im_score_vis'] + importance_scores_np = importance_map[0].squeeze(0).squeeze(0).cpu().numpy() + plt.figure(figsize=(6, 6)) + plt.imshow(importance_scores_np, cmap=cmap) + plt.colorbar() + plt.axis('off') # 不显示坐标轴 + plt.savefig(os.path.join(vis_path, f'feature_importance_batch_{epoch}_{i+1}.png'), bbox_inches='tight', pad_inches=0) # 保存图像 + plt.close() + model.train() + # 假设其它变量和环境已经正确设置 + # if torch.distributed.is_initialized(): + # rank = dist.get_rank() + # else: + # rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + # if rank == 0: + log_txt = f"{epoch}|loss:{task_loss.avg.item():.5f}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Acc:{accuracy.avg.item():.5f}" + logging.info(log_txt) + return percloss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + + if stage == 'test': + # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + d = prepadding(d) + + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], + out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + out_criterion = metrics_criterion(d, out_net, rec) + _, acc, _ = cls_criterion(d, rec, l) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + acc.update(acc) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + model.train() + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}" + logging.info(log_txt) + return accuracy.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def add_weight_decay(model, weight_decay=1e-5, skip_list=()): + decay, no_decay = [], [] + for name, param in model.named_parameters(): + if not param.requires_grad: + continue # 跳过冻结的参数 + if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list: + no_decay.append(param) + else: + decay.append(param) + return [ + {'params': no_decay, 'weight_decay': 0.0}, + {'params': decay, 'weight_decay': weight_decay}] + + +def configure_optimizers(net, args): + """Set optimizer for only the parameters for token_predictor""" + parameters = { + k + for k, p in net.named_parameters() + if "token_predictor" in k + } + params_dict = dict(net.named_parameters()) + optimizer = optim.Adam( + (params_dict[n] for n in sorted(parameters)), + lr=args.learning_rate, + ) + + return optimizer + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # # if args.distributed: + # if True: + # num_tasks = misc.get_world_size() + # global_rank = misc.get_rank() + # sampler_test = torch.utils.data.DistributedSampler( + # test_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + # ) + # sampler_val = torch.utils.data.DistributedSampler( + # val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + # ) + # else: + # sampler_train = torch.utils.data.RandomSampler(train_dataset) + + # if global_rank == 0 and args.log_dir is not None: + # os.makedirs(args.log_dir, exist_ok=True) + # log_writer = SummaryWriter(log_dir=args.log_dir) + # else: + # log_writer = None + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + + # val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + # num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + # test_dataloader = DataLoader(test_dataset, sampler=sampler_test, batch_size=1, + # num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + val_dataloader = DataLoader(val_dataset, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_cls.__dict__[args.model](mask_ratio_min=args.mask_ratio_min, + mask_ratio_max=args.mask_ratio_max, vqgan_ckpt_path=vqgan_ckpt_path) + # model.freeze_parameters() + + model.to(device) + model_without_ddp = model + + # 打印所有可学习的参数 + print("List of all learnable parameters:") + for name, param in model_without_ddp.named_parameters(): + if param.requires_grad: + print(f"{name}: {param.size()}") + # print("Model = %s" % str(model_without_ddp)) + # eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + # if args.lr is None: # only base_lr is specified + # args.lr = args.blr * eff_batch_size / 256 + # print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + # print("actual lr: %.2e" % args.lr) + + # print("accumulate grad iterations: %d" % args.accum_iter) + # print("effective batch size: %d" % eff_batch_size) + + # if args.distributed: + # model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + # model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + optimizer = configure_optimizers(model_without_ddp, args) + # param_groups = add_weight_decay(model_without_ddp, args.weight_decay) + # # 只传入 requires_grad=True 的参数给优化器 + # optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + # print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model_new(args=args, model_without_ddp=model_without_ddp, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + if args.TEST: # test only + # if True: + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.3]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.7 + # test_loss = inference(-1, val_dataloader, model, metrics_criterion, cls_criterion, device, val_mask_ratio, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + # sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + # data_loader_train = DataLoader( + # current_dataset, sampler=sampler_train, + # batch_size=args.batch_size, + # num_workers=args.num_workers, + # pin_memory=args.pin_mem, + # drop_last=True, + # ) + data_loader_train = DataLoader( + current_dataset, batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + # if args.distributed: + # data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, cls_criterion, device, + optimizer, epoch, log_writer=log_writer, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, cls_criterion, device, val_mask_ratio, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 5 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/train_im_det.py b/train_im_det.py new file mode 100644 index 0000000000000000000000000000000000000000..59fd701f7b7df9838805dce65187ae8d91b89447 --- /dev/null +++ b/train_im_det.py @@ -0,0 +1,653 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec_full +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import prepadding + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + bpp_mask = bytes_length / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) # 初始化对齐模块，用于图像大小调整。 + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) # imagenet mean + + def forward(self, output, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) # RGB to BGR, [0,1] to [0,255] + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(output["x_hat"], 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + d = prepadding(d) + + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W'], num_iter=12) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + transform_test_pos = transforms.Compose( # crop to 256x256 + [transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + test_dataset_pos = torchvision.datasets.ImageFolder(os.path.join('/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets', "COCO"), transform=transform_test_pos) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset_pos, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset_pos, sampler=sampler_test, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_high_resolu.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + # model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + # vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + # if args.TEST: # test only + if True: + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.3]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.75 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + data_loader_train = DataLoader( + current_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/train_im_seg.py b/train_im_seg.py new file mode 100644 index 0000000000000000000000000000000000000000..59fd701f7b7df9838805dce65187ae8d91b89447 --- /dev/null +++ b/train_im_seg.py @@ -0,0 +1,653 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec_full +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import prepadding + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['mask_len'] + bytes_length = len(bs_mask_token) + bpp_mask = bytes_length / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +class TaskLoss(nn.Module): + def __init__(self, cfg, device) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.task_net = build_resnet_fpn_backbone(cfg, ShapeSpec(channels=3)) + checkpoint = OrderedDict() + with open(cfg.MODEL.WEIGHTS, 'rb') as f: + FPN_ckpt = pickle.load(f) + for k, v in FPN_ckpt['model'].items(): + if 'backbone' in k: + checkpoint['.'.join(k.split('.')[1:])] = torch.from_numpy(v) + self.task_net.load_state_dict(checkpoint, strict=True) + self.task_net = self.task_net.to(device) + for k, p in self.task_net.named_parameters(): + p.requires_grad = False + self.task_net.eval() + self.align = Alignment(divisor=32).to(device) # 初始化对齐模块，用于图像大小调整。 + self.pixel_mean = torch.Tensor([103.530, 116.280, 123.675]).view(-1, 1, 1).to(device) # imagenet mean + + def forward(self, output, d, train_mode=False): + with torch.no_grad(): + ## Ground truth for perceptual loss + d = d.flip(1).mul(255) # RGB to BGR, [0,1] to [0,255] + d = d - self.pixel_mean + if not train_mode: + d = self.align.align(d) + gt_out = self.task_net(d) + + x_hat = torch.clamp(output["x_hat"], 0, 1) + x_hat = x_hat.flip(1).mul(255) + x_hat = x_hat - self.pixel_mean + if not train_mode: + x_hat = self.align.align(x_hat) + task_net_out = self.task_net(x_hat) + + distortion_p2 = nn.MSELoss(reduction='none')(gt_out["p2"], task_net_out["p2"]) + distortion_p3 = nn.MSELoss(reduction='none')(gt_out["p3"], task_net_out["p3"]) + distortion_p4 = nn.MSELoss(reduction='none')(gt_out["p4"], task_net_out["p4"]) + distortion_p5 = nn.MSELoss(reduction='none')(gt_out["p5"], task_net_out["p5"]) + distortion_p6 = nn.MSELoss(reduction='none')(gt_out["p6"], task_net_out["p6"]) + + return 0.2*(distortion_p2.mean()+distortion_p3.mean()+distortion_p4.mean()+distortion_p5.mean()+distortion_p6.mean()) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + d = prepadding(d) + + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W'], num_iter=12) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + transform_test_pos = transforms.Compose( # crop to 256x256 + [transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + test_dataset_pos = torchvision.datasets.ImageFolder(os.path.join('/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets', "COCO"), transform=transform_test_pos) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset_pos, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset_pos, sampler=sampler_test, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_high_resolu.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + # model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + # vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + # if args.TEST: # test only + if True: + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.3]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.75 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + data_loader_train = DataLoader( + current_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/train_on_coco.py b/train_on_coco.py new file mode 100644 index 0000000000000000000000000000000000000000..b59680fc4a0f7924637a6a6ea75017fbf3abe3a7 --- /dev/null +++ b/train_on_coco.py @@ -0,0 +1,536 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import models_mage_codec_high_resolu +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import MSCOCO, Kodak, prepadding, crop_to_original_shape + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + # bpp = sum( + # (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)) + # for likelihoods in out_net["likelihoods"].values() + # ) + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + +def save_patches(blocks, save_path='/home/t2vg-a100-G4-10/project/qyp/patches'): + """ Save each patch as an image file. """ + os.makedirs(save_path, exist_ok=True) + for i, block in enumerate(blocks): + # Convert the tensor to an image format + block_image = block.permute(1, 2, 0).cpu().numpy().squeeze() + block_image = (block_image * 255).astype(np.uint8) + + # Create an image and save + img = Image.fromarray(block_image) + img.save(f"{save_path}/patch_{i}.png") +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_test_new/", stage) + os.makedirs(vis_path, exist_ok=True) + + # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) + for data_iter_step, samples in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + samples, h_ori, w_ori = prepadding(samples) + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W']) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + samples = samples[:, :, :h_ori, :w_ori] + rec = rec[:, :, :h_ori, :w_ori] + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr_kodak/", stage) + vis_path = os.path.join(vis_path, str(manual_mask_ratio)) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + tqdm_meter = tqdm.tqdm(enumerate(test_loader), leave=False, total=len(test_loader)) + for i, d in tqdm_meter: + d = d.to(device) + d, h_ori, w_ori = prepadding(d, factor=256) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + # rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape']) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['ori_shape'], out_net['patch_sizes'], out_net['num_blocks_h'], out_net['num_blocks_w']) + rec = rec.to(device) + + d = crop_to_original_shape(d, h_ori, w_ori) + rec = crop_to_original_shape(rec, h_ori, w_ori) + # rec = rec[:, :, :hx, :wx] + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + with torch.no_grad(): + # real_fake_images = torch.cat((d, rec), dim=0) + # vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{i}.jpg")) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg")) + # print('new_mask_vis:', out_net['new_mask_vis'].shape) + # save_patches(out_net['new_mask_vis']) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_det = transforms.Compose([ + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_val = transforms.Compose([ + # transforms.Resize(512), + # transforms.CenterCrop(512), + transforms.ToTensor() + ]) + + + if args.dataset=='coco': + train_dataset = MSCOCO(args.dataset_path + "/train2017/", + transform_det, + "/home/t2vg-a100-G4-10/project/qyp/mimc_rope/util/img_list.txt") + val_dataset = Kodak(args.kodak_path, transform_val) + # val_dataset = MSCOCO(args.kodak_path, transform_val) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_high_resolu.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + # model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + # vqgan_ckpt_path=vqgan_ckpt_path) + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + # cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + # if args.TEST: # test only + # best_loss = float("inf") + # tqrange = tqdm.trange(last_epoch, args.epochs) + # # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + # for manual_mask_ratio in [0.3]: + # loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + # return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + for val_mask_ratio in [0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05, 0.01]: + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + sampler_train = torch.utils.data.DistributedSampler(train_dataset, shuffle=True) + data_loader_train = DataLoader( + train_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/train_vbr_codec.py b/train_vbr_codec.py new file mode 100644 index 0000000000000000000000000000000000000000..bd815b5371650fbc4a051a7a98a1cd7334e9d4fa --- /dev/null +++ b/train_vbr_codec.py @@ -0,0 +1,665 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + # bpp = sum( + # (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)) + # for likelihoods in out_net["likelihoods"].values() + # ) + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1,1,3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + x = F.pad(x, (0, dw, 0, dh)) + return x, h, w + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + _, accu, _ = cls_criterion(d, rec, l) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + accuracy.update(accu) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + action='store_true', + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset, sampler=sampler_test, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/v-ruoyufeng/v-ruoyufeng/qyp/mage/ckpt_pretrained/models--Qiyp--mage/snapshots/b0692a453d4725bd80c37c2362549a46b4ff5c33/vqgan_jax_strongaug.ckpt' + model = models_mage_codec.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + if args.TEST: # test only + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.1, 0.2, 0.3, 0.4, 0.5]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.75 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + data_loader_train = DataLoader( + current_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/train_vbr_codec_high_resolu.py b/train_vbr_codec_high_resolu.py new file mode 100644 index 0000000000000000000000000000000000000000..c4952e2c64157a23d68cfd6be6e00e715fe72d49 --- /dev/null +++ b/train_vbr_codec_high_resolu.py @@ -0,0 +1,662 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec_full +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist +from util.dataloader import prepadding + + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + # bpp = sum( + # (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)) + # for likelihoods in out_net["likelihoods"].values() + # ) + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1,1,3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + x = F.pad(x, (0, dw, 0, dh)) + return x, h, w + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + d = prepadding(d) + + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W'], num_iter=12) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + transform_test_pos = transforms.Compose( # crop to 256x256 + [transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + test_dataset_pos = torchvision.datasets.ImageFolder(os.path.join('/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets', "COCO"), transform=transform_test_pos) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset_pos, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset_pos, sampler=sampler_test, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/t2vg-a100-G4-10/project/qyp/mage/vqgan_jax_strongaug.ckpt' + model = models_mage_codec_full.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + # model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + # vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + # if args.TEST: # test only + if True: + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.3]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.75 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + data_loader_train = DataLoader( + current_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/train_vbr_codec_random_classification.py b/train_vbr_codec_random_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..3ced009664ef86b960e8647c85c0001addeebaa6 --- /dev/null +++ b/train_vbr_codec_random_classification.py @@ -0,0 +1,677 @@ +import tqdm +import argparse +import math +# import torchac +import sys +import os +import time +import logging +from datetime import datetime + +import torch +import torch.nn as nn +import torch.optim as optim + +import torchvision +from torch.utils.data import DataLoader +from torchvision import transforms +from torchvision.models import resnet50 + +import yaml +from pytorch_msssim import ms_ssim +from DISTS_pytorch import DISTS +from util.lpips import LPIPS +from torch.nn import functional as F +from torchvision import utils as vutils +import matplotlib.pyplot as plt +import numpy as np +import glob + +import util.misc as misc +import util.lr_sched as lr_sched +from torch.utils.tensorboard import SummaryWriter +import models_mage_codec +import mage.models_mage_codec_rope as models_mage_codec_rope +import timm.optim.optim_factory as optim_factory +from util.misc import NativeScalerWithGradNormCount as NativeScaler +import json +import PIL.Image as Image +import torch.backends.cudnn as cudnn +from pathlib import Path +import random +import torch.distributed as dist + +class CalMetrics(nn.Module): + """Calculate BPP, PSNR, MS-SSIM, LPIPS and DISTS for the reconstructed image.""" + + def __init__(self): + super().__init__() + self.mse = nn.MSELoss() + + def bpp_loss(self, ori, out_net): + b, _, h, w = ori.shape + num_pixels = b * h * w + # bpp = sum( + # (torch.log(likelihoods).sum() / (-math.log(2) * num_pixels)) + # for likelihoods in out_net["likelihoods"].values() + # ) + bpp = torch.log(out_net["likelihoods"]).sum() / (-math.log(2) * num_pixels) + bs_mask_token = out_net['bs_mask_token'] + bytes_length = len(bs_mask_token) + # 因为每个字节包含8位，所以总位数是字节数 * 8 + total_bits = bytes_length * 8 + # 计算每像素的位数（bpp） + bpp_mask = total_bits / num_pixels + return bpp, bpp_mask + + def psnr(self, rec, ori): + mse = torch.mean((rec - ori) ** 2) + if(mse == 0): + return 100 + max_pixel = 1. + psnr = 10 * torch.log10(max_pixel / mse) + return torch.mean(psnr) + + def lpips(self, rec, ori): + lpips_func = LPIPS().eval().to(device=rec.device) + lipis_value = lpips_func(rec, ori) + return lipis_value.mean() + + def dists(self, rec, ori): + D = DISTS().cuda() + dists_value = D(rec, ori) + return dists_value.mean() + + def cal_total_loss(self, lpips, bpp, out_net): + # task_loss = out_net['task_loss'] + 0.1 * lpips + task_loss = out_net['task_loss'] + total_loss = bpp + out_net['lambda'] * task_loss + return total_loss + + def forward(self, ori, out_net, rec=None): + out = {} + out["bpp"], out["bpp_mask"] = self.bpp_loss(ori, out_net) + out["bpp_loss"] = out["bpp"] + out["bpp_mask"] + # out["total_loss"] = self.cal_total_loss(out["bpp_loss"], out_net) + if rec is not None: + out["psnr"] = self.psnr(torch.clamp(rec, 0, 1), ori) + out["msssim"] = ms_ssim(torch.clamp(rec, 0, 1), ori, data_range=1, size_average=True) + out["lpips"] = self.lpips(torch.clamp(rec, 0, 1), ori) + out["dists"] = self.dists(torch.clamp(rec, 0, 1), ori) + out["total_loss"] = self.cal_total_loss(out["lpips"], out["bpp_loss"], out_net) + return out + + +class FeatureHook(): + def __init__(self, module): + module.register_forward_hook(self.attach) + + def attach(self, model, input, output): + self.feature = output + + +class Clsloss(nn.Module): + def __init__(self, device, cls_loss=True) -> None: + super().__init__() + self.ce = nn.CrossEntropyLoss() + self.classifier = resnet50(True) + self.classifier.requires_grad_(False) + self.hooks = [FeatureHook(i) for i in [ # for calculating perceptual loss + self.classifier.layer1, + self.classifier.layer2, + self.classifier.layer3, + self.classifier.layer4, + ]] + self.classifier = self.classifier.to(device) + for k, p in self.classifier.named_parameters(): + p.requires_grad = False + self.classifier.eval() + self.cls_loss = cls_loss + self.transform = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) + + def accuracy(output, target, topk=(1,)): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def forward(self, d, rec, y_true): + # output = codec(d), d = original image, y_true = label + x_hat = torch.clamp(rec,0,1) + pred = self.classifier(self.transform(x_hat)) # transform is normalization + cls_loss = self.ce(pred, y_true) + accu = sum(torch.argmax(pred,-1)==y_true)/pred.shape[0] + if self.perceptual_loss: + pred_feat = [i.feature.clone() for i in self.hooks] + _ = self.classifier(self.transform(d)) + ori_feat = [i.feature.clone() for i in self.hooks] + perc_loss = torch.stack([nn.functional.mse_loss(p,o, reduction='none').mean((1,2,3)) for p,o in zip(pred_feat, ori_feat)]) + perc_loss = perc_loss.mean() + return perc_loss + + return cls_loss, accu, None + + +class AverageMeter: + """Compute running average.""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + +class CustomDataParallel(nn.DataParallel): + """Custom DataParallel to access the module methods.""" + + def __getattr__(self, key): + try: + return super().__getattr__(key) + except AttributeError: + return getattr(self.module, key) + + +def init(args): + base_dir = f'{args.root}/{args.exp_name}/' + os.makedirs(base_dir, exist_ok=True) + return base_dir + +def setup_logger(log_dir): + log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s") + root_logger = logging.getLogger() + root_logger.setLevel(logging.INFO) + + log_file_handler = logging.FileHandler(log_dir, encoding='utf-8') + log_file_handler.setFormatter(log_formatter) + root_logger.addHandler(log_file_handler) + + log_stream_handler = logging.StreamHandler(sys.stdout) + log_stream_handler.setFormatter(log_formatter) + root_logger.addHandler(log_stream_handler) + + logging.info('Logging file is %s' % log_dir) + + +def load_img(p, padding=True, factor=64): + x = Image.open(p) + x = torch.from_numpy(np.asarray(x)) + if len(x.shape) == 2: + x = x.unsqueeze(-1).repeat(1,1,3) # h,w -> h,w,3 + x = x.permute(2, 0, 1).unsqueeze(0).float().div(255) + h, w = x.shape[2:4] + + if padding: + dh = factor * math.ceil(h / factor) - h + dw = factor * math.ceil(w / factor) - w + x = F.pad(x, (0, dw, 0, dh)) + return x, h, w + +def save_img(img: torch.Tensor, vis_path, input_p, mask=False): + img = img.clone().detach() + img = img.to(torch.device('cpu')) + if os.path.isdir(vis_path) is not True: + os.makedirs(vis_path) + end = '/' + if mask: + img_name = vis_path + 'mask_' + str(input_p[input_p.rfind(end):]) + else: + img_name = vis_path + str(input_p[input_p.rfind(end):]) + vutils.save_image(img, os.path.join(vis_path, img_name), nrow=8) + +def train_one_epoch(model, data_loader, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer, args, val_dataloader=None, stage='train'): + ## ======================= set configs ======================= ## + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 20 + accum_iter = args.accum_iter + optimizer.zero_grad() + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + + # tqdm_emu = tqdm.tqdm(enumerate(data_loader_train), total=len(data_loader_train), leave=False) + for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + samples = samples.to(device, non_blocking=True) # samples = original image + + # we use a per iteration (instead of per epoch) lr scheduler + if data_iter_step % accum_iter == 0: + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + with torch.cuda.amp.autocast(): + out_net = model(samples, is_training=True, manual_mask_rate=None) + + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + # rec = model.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + rec = rec.to(device) + out_criterion = metrics_criterion(samples, out_net, rec) + loss_value = out_criterion['total_loss'].item() + + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + out_criterion['total_loss'] /= accum_iter + loss_scaler(out_criterion['total_loss'], optimizer, clip_grad=args.grad_clip, parameters=model.parameters(), + update_grad=(data_iter_step + 1) % accum_iter == 0) + if (data_iter_step + 1) % accum_iter == 0: + optimizer.zero_grad() + + torch.cuda.synchronize() + + metric_logger.update(loss=loss_value) + + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + metric_logger.update(bpp=out_criterion['bpp_loss']) + metric_logger.update(bpp_mask=out_criterion['bpp_mask']) + metric_logger.update(task_loss=out_net['task_loss'].item()) # task_loss未更新，均值更新了 + metric_logger.update(lmbda=out_net['lambda']) + metric_logger.update(mask_ratio=out_net['mask_ratio']) # mask_ratio未更新，均值更新了 + metric_logger.update(lpips=out_criterion['lpips'].item()) # lpips未更新，均值更新了 + metric_logger.update(dists=out_criterion['dists'].item()) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + if log_writer is not None and (data_iter_step + 1) % accum_iter == 0: + """ We use epoch_1000x as the x-axis in tensorboard. + This calibrates different curves when batch size changes. + """ + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + + ## ======================= update progress bar & visualization ======================= ## + if data_iter_step % 1000 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((samples, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{data_iter_step}.jpg"), nrow=8) + # vutils.save_image(samples, os.path.join(vis_path, f"{epoch}_{data_iter_step}_ori.jpg"), nrow=6) + # vutils.save_image(rec, os.path.join(vis_path, f"{epoch}_{data_iter_step}_rec.jpg"), nrow=6) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{data_iter_step}_mask.jpg"), nrow=8) + + # if (data_iter_step % 10000 == 0) and (data_iter_step != 0): + # test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, 0.75, args, 'val') + # gather the stats from all processes + metric_logger.synchronize_between_processes() + print("Averaged stats:", metric_logger) + return {k: meter.global_avg for k, meter in metric_logger.meters.items()} + +def inference(epoch, test_loader, model, metrics_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + test_loss = AverageMeter() + + vis_path = os.path.join("./MIM_vbr/", stage) + os.makedirs(vis_path, exist_ok=True) + if stage == 'test': + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices']) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + out_criterion = metrics_criterion(d, out_net, rec) + + bpp_loss.update(out_criterion["bpp_loss"]) + bpp_mask.update(out_criterion["bpp_mask"]) + psnr.update(out_criterion['psnr']) + msssim.update(out_criterion['msssim']) + lpips.update(out_criterion['lpips']) + dists.update(out_criterion['dists']) + test_loss.update(out_criterion['total_loss']) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'val': + if i % 5 == 0: + with torch.no_grad(): + real_fake_images = torch.cat((d, rec), dim=0) + vutils.save_image(real_fake_images, os.path.join(vis_path, f"{epoch}_{i}.jpg"), nrow=8) + vutils.save_image(out_net['mask_vis'], os.path.join(vis_path, f"{epoch}_{i}_mask.jpg"), nrow=8) + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg"), nrow=8) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def inference_with_acc(epoch, test_loader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, stage='test'): + model.eval() + bpp_loss = AverageMeter() + bpp_mask = AverageMeter() + psnr = AverageMeter() + msssim = AverageMeter() + lpips = AverageMeter() + dists = AverageMeter() + accuracy = AverageMeter() + test_loss = AverageMeter() + + if stage == 'test': + # test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/rec_fid", manual_mask_ratio) + test_vis_path = os.path.join("/home/v-ruoyufeng/v-ruoyufeng/qyp/test_pos", str(manual_mask_ratio)) + os.makedirs(test_vis_path, exist_ok=True) + + with torch.no_grad(): + # tqdm_meter = tqdm.tqdm(eval_path, leave=False) + tqdm_meter = tqdm.tqdm(enumerate(test_loader),leave=False, total=len(test_loader)) + for i, (d, l) in tqdm_meter: + d = d.to(device) + # l = l.to(device) + # for input_p in eval_path: + # x, hx, wx = load_img(input_p, padding=True, factor=64) + # x = x.to(device) + # _, _, hx, wx = d.shape + out_net = model(d, is_training=False, manual_mask_rate=manual_mask_ratio) + rec = model.module.gen_img(out_net['logits'], out_net['token_all_mask'], out_net['token_indices'], out_net['z_H'], out_net['z_W'], num_iter=12) + + # x = x[:, :, :hx, :wx] + # rec = rec[:, :, :hx, :wx] + rec = rec.to(device) + # out_criterion = metrics_criterion(d, out_net, rec) + # _, accu, _ = cls_criterion(d, rec, l) + + # bpp_loss.update(out_criterion["bpp_loss"]) + # bpp_mask.update(out_criterion["bpp_mask"]) + # psnr.update(out_criterion['psnr']) + # msssim.update(out_criterion['msssim']) + # lpips.update(out_criterion['lpips']) + # dists.update(out_criterion['dists']) + # test_loss.update(out_criterion['total_loss']) + # accuracy.update(accu) + + ## ======================= update progress bar & visualization ======================= ## + if stage == 'test': + with torch.no_grad(): + vutils.save_image(rec, os.path.join(test_vis_path, f"{i}.jpg")) + # txt = f"Rec Loss:{test_loss.avg:.4f}|Bpp:{bpp_loss.avg:.4f}|lpips:{lpips.avg:.4f}|msssim:{msssim.avg:.4f}|dists:{dists.avg:.4f}|psnr:{psnr.avg:.4f}\n" + # tqdm_meter.set_postfix_str(txt) + + model.train() + + # 假设其它变量和环境已经正确设置 + if torch.distributed.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 # 假设未使用DDP，则默认为单进程模式，rank为0 + + if rank == 0: + log_txt = f"{epoch}|bpp:{bpp_loss.avg.item():.5f}|mask:{bpp_mask.avg:.5f}|mask_ratio:{manual_mask_ratio}|psnr:{psnr.avg.item():.5f}|msssim:{msssim.avg.item():.5f}|lpips:{lpips.avg.item():.5f}|dists:{dists.avg.item():.5f}|accu:{accuracy.avg:.5f}|Test loss:{test_loss.avg.item():.5f}" + logging.info(log_txt) + return test_loss.avg + +def save_checkpoint(state, is_best, base_dir, filename="checkpoint.pth.tar"): + torch.save(state, base_dir+filename) + if is_best: + torch.save(state, base_dir+"checkpoint_best.pth.tar") + # shutil.copyfile(base_dir+filename, base_dir+"checkpoint_best_loss.pth.tar") + +def parse_args(argv): + parser = argparse.ArgumentParser(description="Example training script.") + parser.add_argument( + "-c", + "--config", + default="config/vpt_default.yaml", + help="Path to config file", + ) + parser.add_argument( + '--name', + default=datetime.now().strftime('%Y-%m-%d_%H_%M_%S'), + type=str, + help='Result dir name', + ) + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='learning rate (absolute lr)') + given_configs, remaining = parser.parse_known_args(argv) + # distributed training parameters + parser.add_argument('--world_size', default=1, type=int, + help='number of distributed processes') + parser.add_argument('--local-rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='url used to set up distributed training') + with open(given_configs.config) as file: + yaml_data= yaml.safe_load(file) + parser.set_defaults(**yaml_data) + + parser.add_argument( + "-T", + "--TEST", + # action='store_true', + default=False, + help='Testing' + ) + args = parser.parse_args(remaining) + return args + +def load_eval_ps(eval_path): + eval_ps = sorted(glob.glob(os.path.join(eval_path, '*.png'))) + return eval_ps + +def main(argv): + args = parse_args(argv) + base_dir = init(args) # create the base dir for saving the results + + if args.output_dir: + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + args.log_dir = args.output_dir + + misc.init_distributed_mode(args) + + print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__)))) + print("{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + # fix the seed for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + + cudnn.benchmark = True + + setup_logger(base_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log') + msg = f'======================= {args.name} =======================' + logging.info(msg) + for k in args.__dict__: + logging.info(k + ':' + str(args.__dict__[k])) + logging.info('=' * len(msg)) + + ## ======================= prepare dataset ======================= ## + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(args.input_size, scale=(0.8, 1.0)), + transforms.RandomHorizontalFlip(), + transforms.ToTensor()]) + transform_test = transforms.Compose( # crop to 256x256 + [transforms.Resize(256), transforms.CenterCrop(256), transforms.ToTensor()] + ) + transform_test_pos = transforms.Compose( # crop to 256x256 + [transforms.ToTensor()] + ) + + if args.dataset=='imagenet': + train_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "train"), transform=transform_train) + test_dataset = torchvision.datasets.ImageFolder(os.path.join(args.dataset_path, "val"), transform=transform_test) + test_dataset_pos = torchvision.datasets.ImageFolder(os.path.join('/home/v-ruoyufeng/v-ruoyufeng/qyp/datasets', "COCO"), transform=transform_test_pos) + val_dataset, _ = torch.utils.data.random_split(test_dataset, [2000, 48000]) + small_train_datasets = torch.utils.data.random_split(train_dataset, [40000]*32+[1167]) + eval_path = sorted(glob.glob(os.path.join(args.eval_path, '*.png'))) + + device = "cuda" if args.cuda and torch.cuda.is_available() else "cpu" + + # if args.distributed: + if True: + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + sampler_test = torch.utils.data.DistributedSampler( + test_dataset_pos, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + sampler_val = torch.utils.data.DistributedSampler( + val_dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True + ) + else: + sampler_train = torch.utils.data.RandomSampler(train_dataset) + + if global_rank == 0 and args.log_dir is not None: + os.makedirs(args.log_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.log_dir) + else: + log_writer = None + + val_dataloader = DataLoader(val_dataset, sampler=sampler_val, batch_size=args.test_batch_size, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem, drop_last=True) + test_dataloader = DataLoader(test_dataset_pos, sampler=sampler_test, batch_size=1, + num_workers=args.num_workers, shuffle=False, pin_memory=args.pin_mem,) + + ## ======================= prepare model ======================= ## + vqgan_ckpt_path = '/home/v-ruoyufeng/v-ruoyufeng/qyp/mage_copy/ckpt_pretrained/models--Qiyp--mage/snapshots/b0692a453d4725bd80c37c2362549a46b4ff5c33/vqgan_jax_strongaug.ckpt' + # model = models_mage_codec.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + # mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + # vqgan_ckpt_path=vqgan_ckpt_path) + model = models_mage_codec_rope.__dict__[args.model](mask_ratio_mu=args.mask_ratio_mu, mask_ratio_std=args.mask_ratio_std, + mask_ratio_min=args.mask_ratio_min, mask_ratio_max=args.mask_ratio_max, + vqgan_ckpt_path=vqgan_ckpt_path) + + model.to(device) + model_without_ddp = model + print("Model = %s" % str(model_without_ddp)) + eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size() + if args.lr is None: # only base_lr is specified + args.lr = args.blr * eff_batch_size / 256 + print("base lr: %.2e" % (args.lr * 256 / eff_batch_size)) + print("actual lr: %.2e" % args.lr) + + print("accumulate grad iterations: %d" % args.accum_iter) + print("effective batch size: %d" % eff_batch_size) + + if args.distributed: + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) + model_without_ddp = model.module + + # following timm: set wd as 0 for bias and norm layers + param_groups = optim_factory.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + loss_scaler = NativeScaler() + + # resume from a checkpoint + misc.load_model(args=args, model_without_ddp=model_without_ddp, + optimizer=optimizer, loss_scaler=loss_scaler, strict=False) + + metrics_criterion = CalMetrics() + cls_criterion = Clsloss(device, True) + ## ======================= Start Training ======================= ## + last_epoch = args.start_epoch + + # if args.TEST: # test only + if True: + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + # loss = test_epoch(-1, test_dataloader, net, rate_criterion, args.VPT_lmbda, args, 'test') + for manual_mask_ratio in [0.3]: + loss = inference_with_acc(-1, test_dataloader, model, metrics_criterion, cls_criterion, device, manual_mask_ratio, args, 'test') + return + + ## ======================= pre validation ======================= ## + print("############## pre validation ##############") + best_loss = float("inf") + tqrange = tqdm.trange(last_epoch, args.epochs) + val_mask_ratio = 0.75 + test_loss = inference(-1, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + ## ======================= start training ======================= ## + print(f"############## Start training for {args.epochs} epochs ##############") + start_time = time.time() + for epoch in tqrange: + current_dataset = small_train_datasets[epoch % len(small_train_datasets)] + sampler_train = torch.utils.data.DistributedSampler(current_dataset, shuffle=True) + data_loader_train = DataLoader( + current_dataset, sampler=sampler_train, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + drop_last=True, + ) + if args.distributed: + data_loader_train.sampler.set_epoch(epoch) + train_stats = train_one_epoch(model, data_loader_train, metrics_criterion, device, + optimizer, epoch, loss_scaler, log_writer=log_writer, args=args, val_dataloader=val_dataloader, stage='train') + + test_loss = inference(epoch, val_dataloader, model, metrics_criterion, device, val_mask_ratio, args, 'val') + + is_best = test_loss < best_loss + best_loss = min(test_loss, best_loss) + + if args.output_dir and (epoch % 10 == 0 or epoch + 1 == args.epochs): + misc.save_model( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch) + if is_best: + misc.save_model_last( + args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + loss_scaler=loss_scaler, epoch=epoch, is_best=is_best) + # misc.save_model_last( + # args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, + # loss_scaler=loss_scaler, epoch=epoch) + log_stats = {**{f'train_{k}': v for k, v in train_stats.items()}, + 'epoch': epoch,} + if args.output_dir and misc.is_main_process(): + if log_writer is not None: + log_writer.flush() + with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f: + f.write(json.dumps(log_stats) + "\n") + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time {}'.format(total_time_str)) + + + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/util/RoPE.py b/util/RoPE.py new file mode 100644 index 0000000000000000000000000000000000000000..1db88f7302688460fa1e5274350ddcf5557efae6 --- /dev/null +++ b/util/RoPE.py @@ -0,0 +1,116 @@ +import torch + + +class LlamaRotaryEmbedding(torch.nn.Module): + def __init__(self, dim, max_position_embeddings, base=10000, device=None): + super().__init__() + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) #persistent=False将不会作为state_dict + + # Build here to make `torch.jit.trace` work. + self._set_cos_sin_cache( + seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() + ) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + def forward(self, x, seq_len=None): + # x: [bs, num_attention_heads, seq_len, head_size] + #超过预设的max_position_embeddings则重新计算更大的Rope缓存，否则直接在缓存上切片 + if seq_len > self.max_seq_len_cached: + self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) + + return ( + self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), + ) + + +class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def __init__(self, dim, max_position_embeddings, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + t = t / self.scaling_factor #线性内插相当于将位置序号等比例缩小 + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding): + """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def __init__(self, dim, max_position_embeddings, base=10000, device=None, scaling_factor=1.0): + self.scaling_factor = scaling_factor + super().__init__(dim, max_position_embeddings, base, device) + + def _set_cos_sin_cache(self, seq_len, device, dtype): + self.max_seq_len_cached = seq_len + + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) #NTK扩展方式直接对base进行缩放 + inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) + + freqs = torch.einsum("i,j->ij", t, self.inv_freq) + + #此处处理逻辑与原始的ROPE有差异，原始逻辑如下 + #emb = torch.cat((freqs, freqs), dim=-1) + #emb[...,0::2]=freqs + #emb[...,1::2]=freqs + + + # Different from paper, but it uses a different permutation in order to obtain the same calculation + emb = torch.cat((freqs, freqs), dim=-1) + self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) + self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + + #此处逻辑与原始的ROPE有所差异，原始逻辑如下 + #x1 = x[..., 0::2] + #x2 = x[..., 1::2] + #res = torch.cat((x1, x2), dim=-1) + #res[...,0::2]=-x2 + #res[...,1::2]=x1 + #return res + + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. + cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] + sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] + cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed \ No newline at end of file diff --git a/util/__pycache__/RoPE.cpython-38.pyc b/util/__pycache__/RoPE.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1b6f22264f9836d6dda3f8425a5f198a2961401c Binary files /dev/null and b/util/__pycache__/RoPE.cpython-38.pyc differ diff --git a/util/__pycache__/alignment.cpython-38.pyc b/util/__pycache__/alignment.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..06ce525e72fad5c2bdf4f94cba0f04719dac2298 Binary files /dev/null and b/util/__pycache__/alignment.cpython-38.pyc differ diff --git a/util/__pycache__/dataloader.cpython-38.pyc b/util/__pycache__/dataloader.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..35531edc2133b6784bb5dd190fc75ad8181353e4 Binary files /dev/null and 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ceil + + +def cat_k(input): + """concat second dimesion to batch""" + return input.flatten(0, 1) + + +def split_k(input, size: int, dim: int = 0): + """reshape input to original batch size""" + if dim < 0: + dim = input.dim() + dim + split_size = list(input.size()) + split_size[dim] = size + split_size.insert(dim+1, -1) + return input.view(split_size) + + +class Alignment(torch.nn.Module): + """Image Alignment for model downsample requirement""" + # 调整图像大小以适应特定的下采样要求，确保输入图像的尺寸是指定除数的倍数。 + def __init__(self, divisor=64., mode='pad', padding_mode='replicate'): + super().__init__() + self.divisor = float(divisor) + self.mode = mode + self.padding_mode = padding_mode + self._tmp_shape = None + + def extra_repr(self): + s = 'divisor={divisor}, mode={mode}' + if self.mode == 'pad': + s += ', padding_mode={padding_mode}' + return s.format(**self.__dict__) + + @staticmethod + def _resize(input, size): # _resize方法使用双线性插值调整图像大小 + return F.interpolate(input, size, mode='bilinear', align_corners=False) + + def _align(self, input): # _align方法将图像尺寸调整为指定除数的倍数，可以通过填充或缩放实现 + H, W = input.size()[-2:] + H_ = int(ceil(H / self.divisor) * self.divisor) + W_ = int(ceil(W / self.divisor) * self.divisor) + pad_H, pad_W = H_-H, W_-W + if pad_H == pad_W == 0: + self._tmp_shape = None + return input + + self._tmp_shape = input.size() + if self.mode == 'pad': + return F.pad(input, (0, pad_W, 0, pad_H), mode=self.padding_mode) + elif self.mode == 'resize': + return self._resize(input, size=(H_, W_)) + + def _resume(self, input, shape=None): # 将图像恢复到调整前的原始形状 + if shape is not None: + self._tmp_shape = shape + if self._tmp_shape is None: + return input + + if self.mode == 'pad': + output = input[..., :self._tmp_shape[-2], :self._tmp_shape[-1]] + elif self.mode == 'resize': + output = self._resize(input, size=self._tmp_shape[-2:]) + + return output + + def align(self, input): + """align""" + if input.dim() == 4: + return self._align(input) + elif input.dim() == 5: + return split_k(self._align(cat_k(input)), input.size(0)) + + def resume(self, input, shape=None): + """resume""" + if input.dim() == 4: + return self._resume(input, shape) + elif input.dim() == 5: + return split_k(self._resume(cat_k(input), shape), input.size(0)) + + def forward(self, func, *args, **kwargs): + pass \ No newline at end of file diff --git a/util/crop.py b/util/crop.py new file mode 100644 index 0000000000000000000000000000000000000000..22658c9a6895946696bdfc572788db4d457473ea --- /dev/null +++ b/util/crop.py @@ -0,0 +1,36 @@ +import math + +import torch + +from torchvision import transforms +from torchvision.transforms import functional as F + + +class RandomResizedCrop(transforms.RandomResizedCrop): + """ + RandomResizedCrop for matching TF/TPU implementation: no for-loop is used. + This may lead to results different with torchvision's version. + Following BYOL's TF code: + https://github.com/deepmind/deepmind-research/blob/master/byol/utils/dataset.py#L206 + """ + @staticmethod + def get_params(img, scale, ratio): + width, height = F._get_image_size(img) + area = height * width + + target_area = area * torch.empty(1).uniform_(scale[0], scale[1]).item() + log_ratio = torch.log(torch.tensor(ratio)) + aspect_ratio = torch.exp( + torch.empty(1).uniform_(log_ratio[0], log_ratio[1]) + ).item() + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + w = min(w, width) + h = min(h, height) + + i = torch.randint(0, height - h + 1, size=(1,)).item() + j = torch.randint(0, width - w + 1, size=(1,)).item() + + return i, j, h, w \ No newline at end of file diff --git a/util/dataloader.py b/util/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6f410bd14ba629fa55900e9b2ab3b94b8627db95 --- /dev/null +++ b/util/dataloader.py @@ -0,0 +1,127 @@ +from glob import glob + +from torch.utils.data import Dataset +from PIL import Image +import math +import torch.nn.functional as F +import os + +def prepadding(latent, factor=64): + h, w = latent.size(2), latent.size(3) + target_h = ((h - 1) // factor + 1) * factor + target_w = ((w - 1) // factor + 1) * factor + pad_h = (target_h - h) // 2 + pad_w = (target_w - w) // 2 + # 额外处理奇数padding的情况 + pad_h_extra = (target_h - h) % 2 + pad_w_extra = (target_w - w) % 2 + padded_latent = F.pad(latent, (pad_w, pad_w + pad_w_extra, pad_h, pad_h + pad_h_extra), mode='constant', value=0) # 指定左、右、上、下的填充宽度 + # print("After padding: ", padded_latent.shape) + return padded_latent, h, w + +def crop_to_original_shape(blocks, ori_h, ori_w): + _, _, padded_height, padded_width = blocks.shape + start_h = (padded_height - ori_h) // 2 + end_h = start_h + ori_h + start_w = (padded_width - ori_w) // 2 + end_w = start_w + ori_w + cropped_blocks = blocks[:, :, start_h:end_h, start_w:end_w] + # print("After cropping to original shape: ", cropped_blocks.shape) + return cropped_blocks + +class MSCOCO(Dataset): + def __init__(self, root, transform, img_list=None): + assert root[-1] == '/', "root to COCO dataset should end with \'/\', not {}.".format( + root) + + if img_list: + self.image_paths = [] + with open(img_list, 'r') as r: + lines = r.read().splitlines() + for line in lines: + self.image_paths.append(root + line) + else: + self.image_paths = sorted(glob(root + "*.jpg")) + self.transform = transform + + def __getitem__(self, index): + """ + Args: + index (int): Index + Returns: + object: image. + """ + img_path = self.image_paths[index] + + img = Image.open(img_path).convert('RGB') + + if self.transform is not None: + img = self.transform(img) + + return img + + def __len__(self): + return len(self.image_paths) + +class MSCOCO_inference(Dataset): + def __init__(self, root, transform, img_list=None): + assert root[-1] == '/', "root to COCO dataset should end with \'/\', not {}.".format( + root) + + if img_list: + self.image_paths = [] + with open(img_list, 'r') as r: + lines = r.read().splitlines() + for line in lines: + self.image_paths.append(root + line) + else: + self.image_paths = sorted(glob(root + "*.jpg")) + self.transform = transform + + def __getitem__(self, index): + """ + Args: + index (int): Index + Returns: + object: (image, filename). + """ + img_path = self.image_paths[index] + img = Image.open(img_path).convert('RGB') + + if self.transform is not None: + img = self.transform(img) + # print("img path=", img_path) + filename = os.path.basename(img_path) # 确保返回文件名字符串 + return img, filename + + def __len__(self): + return len(self.image_paths) + + +class Kodak(Dataset): + def __init__(self, root, transform): + + assert root[-1] == '/', "root to Kodak dataset should end with \'/\', not {}.".format( + root) + + self.image_paths = sorted(glob(root + "*.png")) + self.transform = transform + + def __getitem__(self, index): + """ + Args: + index (int): Index + Returns: + object: image. + """ + img_path = self.image_paths[index] + + img = Image.open(img_path).convert('RGB') + + if self.transform is not None: + img = self.transform(img) + + return img + + def __len__(self): + return len(self.image_paths) \ No newline at end of file diff --git a/util/datasets.py b/util/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..9cc50c4afb9b1ffff87b12b3d06f54a492de0dbe --- /dev/null +++ b/util/datasets.py @@ -0,0 +1,55 @@ +import os +import PIL + +from torchvision import datasets, transforms + +from timm.data import create_transform +from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD + + +def build_dataset(is_train, args): + transform = build_transform(is_train, args) + + root = os.path.join(args.data_path, 'train' if is_train else 'val') + dataset = datasets.ImageFolder(root, transform=transform) + + print(dataset) + + return dataset + + +def build_transform(is_train, args): + mean = IMAGENET_DEFAULT_MEAN + std = IMAGENET_DEFAULT_STD + # mean = (0, 0, 0) + # std = (1, 1, 1) + # train transform + if is_train: + # this should always dispatch to transforms_imagenet_train + transform = create_transform( + scale=(0.2, 1.0), + input_size=args.input_size, + is_training=True, + color_jitter=args.color_jitter, + auto_augment=args.aa, + interpolation=args.interpolation, + re_prob=args.reprob, + re_mode=args.remode, + re_count=args.recount, + mean=mean, + std=std, + ) + return transform + + # eval transform + t = [] + size = 292 + t.append( + transforms.Resize(size, interpolation=PIL.Image.BILINEAR if args.interpolation == 'bilinear' else + PIL.Image.BICUBIC), # to maintain same ratio w.r.t. 224 images + ) + t.append(transforms.CenterCrop(args.input_size)) + + t.append(transforms.ToTensor()) + t.append(transforms.Normalize(mean, std)) + return transforms.Compose(t) diff --git a/util/img_list.txt b/util/img_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..d7af7b27e6c241b92b7ffda8b38412c9c254b245 --- /dev/null +++ b/util/img_list.txt @@ -0,0 +1,117465 @@ +000000479474.jpg 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+000000269171.jpg +000000547417.jpg +000000207093.jpg +000000367732.jpg +000000277440.jpg +000000156098.jpg +000000427832.jpg +000000219329.jpg +000000536167.jpg +000000527866.jpg +000000255124.jpg +000000204147.jpg +000000428718.jpg +000000080801.jpg +000000041397.jpg +000000279385.jpg +000000058253.jpg +000000356953.jpg +000000455543.jpg +000000100744.jpg +000000258679.jpg +000000512023.jpg +000000144182.jpg +000000189278.jpg +000000117768.jpg +000000323970.jpg +000000179914.jpg +000000574155.jpg +000000143010.jpg +000000095948.jpg +000000437118.jpg +000000024107.jpg +000000044520.jpg +000000011625.jpg +000000133908.jpg +000000495171.jpg +000000282766.jpg +000000062552.jpg +000000283286.jpg diff --git a/util/lars.py b/util/lars.py new file mode 100644 index 0000000000000000000000000000000000000000..30bcb5baf406b678a96baf07aeb9dfc83497b91d --- /dev/null +++ b/util/lars.py @@ -0,0 +1,37 @@ +import torch + + +class LARS(torch.optim.Optimizer): + """ + LARS optimizer, no rate scaling or weight decay for parameters <= 1D. + """ + def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_coefficient=0.001): + defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, trust_coefficient=trust_coefficient) + super().__init__(params, defaults) + + @torch.no_grad() + def step(self): + for g in self.param_groups: + for p in g['params']: + dp = p.grad + + if dp is None: + continue + + if p.ndim > 1: # if not normalization gamma/beta or bias + dp = dp.add(p, alpha=g['weight_decay']) + param_norm = torch.norm(p) + update_norm = torch.norm(dp) + one = torch.ones_like(param_norm) + q = torch.where(param_norm > 0., + torch.where(update_norm > 0, + (g['trust_coefficient'] * param_norm / update_norm), one), + one) + dp = dp.mul(q) + + param_state = self.state[p] + if 'mu' not in param_state: + param_state['mu'] = torch.zeros_like(p) + mu = param_state['mu'] + mu.mul_(g['momentum']).add_(dp) + p.add_(mu, alpha=-g['lr']) \ No newline at end of file diff --git a/util/lpips.py b/util/lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..a0eca3f0671b09c6bae7f4cae6fddc2d3c1bb552 --- /dev/null +++ b/util/lpips.py @@ -0,0 +1,170 @@ +import os, hashlib +import torch +import torch.nn as nn +from torchvision import models +from collections import namedtuple +import requests +from tqdm import tqdm + + +URL_MAP = { + "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1" +} + +CKPT_MAP = { + "vgg_lpips": "vgg.pth" +} + +MD5_MAP = { + "vgg_lpips": "d507d7349b931f0638a25a48a722f98a" +} + +def download(url, local_path, chunk_size=1024): + os.makedirs(os.path.split(local_path)[0], exist_ok=True) + with requests.get(url, stream=True) as r: + total_size = int(r.headers.get("content-length", 0)) + with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: + with open(local_path, "wb") as f: + for data in r.iter_content(chunk_size=chunk_size): + if data: + f.write(data) + pbar.update(chunk_size) + +def md5_hash(path): + with open(path, "rb") as f: + content = f.read() + return hashlib.md5(content).hexdigest() + +def get_ckpt_path(name, root, check=False): + assert name in URL_MAP + path = os.path.join(root, CKPT_MAP[name]) + if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): + # print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) + download(URL_MAP[name], path) + md5 = md5_hash(path) + assert md5 == MD5_MAP[name], md5 + return path + + +class LPIPS(nn.Module): + # Learned perceptual metric + def __init__(self, use_dropout=True): + super().__init__() + self.scaling_layer = ScalingLayer() + self.chns = [64, 128, 256, 512, 512] # vg16 features + self.net = vgg16(pretrained=True, requires_grad=False) + self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout) + self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout) + self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout) + self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout) + self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout) + self.load_from_pretrained() + for param in self.parameters(): + param.requires_grad = False + + def load_from_pretrained(self, name="vgg_lpips"): + ckpt = get_ckpt_path(name, "ckpt_pretrained") + self.load_state_dict(torch.load(ckpt, map_location=torch.device("cuda")), strict=False) + # print("loaded pretrained LPIPS loss from {}".format(ckpt)) + + @classmethod + def from_pretrained(cls, name="vgg_lpips"): + if name != "vgg_lpips": + raise NotImplementedError + model = cls() + ckpt = get_ckpt_path(name) + model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False) + return model + + def forward(self, input, target): + in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target)) + outs0, outs1 = self.net(in0_input), self.net(in1_input) + feats0, feats1, diffs = {}, {}, {} + lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4] + for kk in range(len(self.chns)): + feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk]) + diffs[kk] = (feats0[kk] - feats1[kk]) ** 2 + + res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))] + val = res[0] + for l in range(1, len(self.chns)): + val += res[l] + return val + + +class ScalingLayer(nn.Module): + def __init__(self): + super(ScalingLayer, self).__init__() + self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) + self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None]) + + def forward(self, inp): + return (inp - self.shift) / self.scale + + +class NetLinLayer(nn.Module): + """ A single linear layer which does a 1x1 conv """ + def __init__(self, chn_in, chn_out=1, use_dropout=False): + super(NetLinLayer, self).__init__() + layers = [nn.Dropout(), ] if (use_dropout) else [] + layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ] + self.model = nn.Sequential(*layers) + + +class vgg16(torch.nn.Module): + def __init__(self, requires_grad=False, pretrained=True): + super(vgg16, self).__init__() + vgg_pretrained_features = models.vgg16(pretrained=pretrained).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + self.N_slices = 5 + for x in range(4): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(4, 9): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(9, 16): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(16, 23): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(23, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h = self.slice1(X) + h_relu1_2 = h + h = self.slice2(h) + h_relu2_2 = h + h = self.slice3(h) + h_relu3_3 = h + h = self.slice4(h) + h_relu4_3 = h + h = self.slice5(h) + h_relu5_3 = h + vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) + out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) + return out + + +def normalize_tensor(x, eps=1e-10): + """ + Normalize images by their length to make them unit vector? + :param x: batch of images + :return: normalized batch of images + """ + norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True)) + return x / (norm_factor + eps) + + +def spatial_average(x, keepdim=True): + """ + imgs have: batch_size x channels x width x height --> average over width and height channel + :param x: batch of images + :return: averaged images along width and height + """ + return x.mean([2, 3], keepdim=keepdim) \ No newline at end of file diff --git a/util/lr_decay.py b/util/lr_decay.py new file mode 100644 index 0000000000000000000000000000000000000000..5fbe25c54fd829f22613bf5c861b80d43b216024 --- /dev/null +++ b/util/lr_decay.py @@ -0,0 +1,64 @@ +import json + + +def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75): + """ + Parameter groups for layer-wise lr decay + Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58 + """ + param_group_names = {} + param_groups = {} + + num_layers = len(model.blocks) + 1 + + layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1)) + + for n, p in model.named_parameters(): + if not p.requires_grad: + continue + + # no decay: all 1D parameters and model specific ones + if p.ndim == 1 or n in no_weight_decay_list: + g_decay = "no_decay" + this_decay = 0. + else: + g_decay = "decay" + this_decay = weight_decay + + layer_id = get_layer_id_for_vit(n, num_layers) + group_name = "layer_%d_%s" % (layer_id, g_decay) + + if group_name not in param_group_names: + this_scale = layer_scales[layer_id] + + param_group_names[group_name] = { + "lr_scale": this_scale, + "weight_decay": this_decay, + "params": [], + } + param_groups[group_name] = { + "lr_scale": this_scale, + "weight_decay": this_decay, + "params": [], + } + + param_group_names[group_name]["params"].append(n) + param_groups[group_name]["params"].append(p) + + # print("parameter groups: \n%s" % json.dumps(param_group_names, indent=2)) + return list(param_groups.values()) + + +def get_layer_id_for_vit(name, num_layers): + """ + Assign a parameter with its layer id + Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33 + """ + if name in ['cls_token', 'pos_embed']: + return 0 + elif name.startswith('patch_embed') or name.startswith('token_emb'): + return 0 + elif name.startswith('blocks'): + return int(name.split('.')[1]) + 1 + else: + return num_layers \ No newline at end of file diff --git a/util/lr_sched.py b/util/lr_sched.py new file mode 100644 index 0000000000000000000000000000000000000000..e4ab743ddc905c86889cd0fa686ba2205ebe354f --- /dev/null +++ b/util/lr_sched.py @@ -0,0 +1,15 @@ +import math + +def adjust_learning_rate(optimizer, epoch, args): + """Decay the learning rate with half-cycle cosine after warmup""" + if epoch < args.warmup_epochs: + lr = args.lr * epoch / args.warmup_epochs + else: + lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \ + (1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs))) + for param_group in optimizer.param_groups: + if "lr_scale" in param_group: + param_group["lr"] = lr * param_group["lr_scale"] + else: + param_group["lr"] = lr + return lr diff --git a/util/misc.py b/util/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..5709cc7d9acb2ed3d6452f511d36d95599c80cca --- /dev/null +++ b/util/misc.py @@ -0,0 +1,367 @@ +import builtins +import datetime +import os +import time +from collections import defaultdict, deque +from pathlib import Path + +import torch +import torch.distributed as dist + +inf = float('inf') + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +class MetricLogger(object): # training过程中记录各种指标 + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if v is None: + continue + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.4f}') + data_time = SmoothedValue(fmt='{avg:.4f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + log_msg = [ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ] + if torch.cuda.is_available(): + log_msg.append('max mem: {memory:.0f}') + log_msg = self.delimiter.join(log_msg) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.4f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + builtin_print = builtins.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + force = force or (get_world_size() > 8) + if is_master or force: + now = datetime.datetime.now().time() + builtin_print('[{}] '.format(now), end='') # print with time stamp + builtin_print(*args, **kwargs) + + builtins.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if args.dist_on_itp: + args.rank = int(os.environ['OMPI_COMM_WORLD_RANK']) # 当前进程的全局排名 + args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE']) # 运行的总进程数 + args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK']) # 当前进程在其所在节点上的局部排名，通常用于分配GPU + args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT']) + os.environ['LOCAL_RANK'] = str(args.gpu) + os.environ['RANK'] = str(args.rank) + os.environ['WORLD_SIZE'] = str(args.world_size) + # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"] + elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + else: + print('Not using distributed mode') + setup_for_distributed(is_master=True) # hack + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = 'nccl' + print('| distributed init (rank {}): {}, gpu {}'.format( + args.rank, args.dist_url, args.gpu), flush=True) + torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +class NativeScalerWithGradNormCount: # 用于混合精度训练 + state_dict_key = "amp_scaler" + + def __init__(self): + self._scaler = torch.cuda.amp.GradScaler() + + def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True): + self._scaler.scale(loss).backward(create_graph=create_graph) + if update_grad: + if clip_grad is not None: + assert parameters is not None + self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place + norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad) + else: + self._scaler.unscale_(optimizer) + norm = get_grad_norm_(parameters) + self._scaler.step(optimizer) + self._scaler.update() + else: + norm = None + return norm + + def state_dict(self): + return self._scaler.state_dict() + + def load_state_dict(self, state_dict): + self._scaler.load_state_dict(state_dict) + + +def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor: + if isinstance(parameters, torch.Tensor): + parameters = [parameters] + parameters = [p for p in parameters if p.grad is not None] + norm_type = float(norm_type) + if len(parameters) == 0: + return torch.tensor(0.) + device = parameters[0].grad.device + if norm_type == inf: + total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters) + else: + total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type) + return total_norm + + +def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler): + output_dir = Path(args.output_dir) + epoch_name = str(epoch) + if loss_scaler is not None: + checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)] + for checkpoint_path in checkpoint_paths: + to_save = { + 'model': model_without_ddp.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + 'scaler': loss_scaler.state_dict(), + 'args': args, + } + + save_on_master(to_save, checkpoint_path) + else: + client_state = {'epoch': epoch} + model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state) + + +def save_model_last(args, epoch, model, model_without_ddp, optimizer, loss_scaler, is_best): + output_dir = Path(args.output_dir) + epoch_name = 'last' + if loss_scaler is not None: + checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)] + for checkpoint_path in checkpoint_paths: + to_save = { + 'model': model_without_ddp.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + 'scaler': loss_scaler.state_dict(), + 'args': args, + } + + save_on_master(to_save, checkpoint_path) + + if is_best: + best_ckpt_path = output_dir / (f'checkpoint-{epoch_name}best.pth') + save_on_master(to_save, best_ckpt_path) + else: + client_state = {'epoch': epoch} + model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state) + + if is_best: + best_tag = "checkpoint-%sbest" % epoch_name + model.save_checkpoint(save_dir=args.output_dir, tag=best_tag, client_state=client_state) + + +def load_model(args, model_without_ddp, optimizer, loss_scaler, strict=True): + if args.resume: + if args.resume.startswith('https'): + checkpoint = torch.hub.load_state_dict_from_url( + args.resume, map_location='cpu', check_hash=True) + else: + checkpoint = torch.load(args.resume, map_location='cpu') + model_without_ddp.load_state_dict(checkpoint['model'], strict=strict) + print("Resume checkpoint %s" % args.resume) + # if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval): + # optimizer.load_state_dict(checkpoint['optimizer']) + # args.start_epoch = checkpoint['epoch'] + 1 + # if 'scaler' in checkpoint: + # loss_scaler.load_state_dict(checkpoint['scaler']) + # print("With optim & sched!") + +def load_model_new(args, model_without_ddp, strict=True): + if args.resume: + if args.resume.startswith('https'): + checkpoint = torch.hub.load_state_dict_from_url( + args.resume, map_location='cpu', check_hash=True) + else: + checkpoint = torch.load(args.resume, map_location='cpu') + model_without_ddp.load_state_dict(checkpoint['model'], strict=strict) + print("Resume checkpoint %s" % args.resume) + +def all_reduce_mean(x): + world_size = get_world_size() + if world_size > 1: + x_reduce = torch.tensor(x).cuda() + dist.all_reduce(x_reduce) + x_reduce /= world_size + return x_reduce.item() + else: + return x \ No newline at end of file diff --git a/util/pos_embed.py b/util/pos_embed.py new file mode 100644 index 0000000000000000000000000000000000000000..37ca475a756aed6d0b21a79d6c2d55a181b43127 --- /dev/null +++ b/util/pos_embed.py @@ -0,0 +1,82 @@ +import numpy as np + +import torch + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token: + pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=float) + omega /= embed_dim / 2. + omega = 1. / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + + +# -------------------------------------------------------- +# Interpolate position embeddings for high-resolution +# References: +# DeiT: https://github.com/facebookresearch/deit +# -------------------------------------------------------- +def interpolate_pos_embed(model, checkpoint_model): + if 'pos_embed' in checkpoint_model: + pos_embed_checkpoint = checkpoint_model['pos_embed'] + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = model.patch_embed.num_patches + num_extra_tokens = model.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + # class_token and dist_token are kept unchanged + if orig_size != new_size: + print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + checkpoint_model['pos_embed'] = new_pos_embed diff --git a/util/predictor.py b/util/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..94d8bc4d2609997f4fb51779ecc680a810534b69 --- /dev/null +++ b/util/predictor.py @@ -0,0 +1,232 @@ +import torch + +from detectron2.data import MetadataCatalog + +from torch import nn +import detectron2.data.transforms as T +from detectron2.config import configurable +from detectron2.modeling import build_model +from detectron2.checkpoint import DetectionCheckpointer +from detectron2.modeling.backbone.backbone import Backbone + +from detectron2.structures import ImageList +from detectron2.modeling import (build_backbone, META_ARCH_REGISTRY, + build_proposal_generator, build_roi_heads, + detector_postprocess) +from typing import Optional, Tuple + +@META_ARCH_REGISTRY.register() +class GeneralizedRCNN_with_Rate(nn.Module): + """ + Generalized R-CNN. Any models that contains the following three components: + 1. Per-image feature extraction (aka backbone) + 2. Region proposal generation + 3. Per-region feature extraction and prediction + """ + + @configurable + def __init__( + self, + *, + backbone: Backbone, + proposal_generator: nn.Module, + roi_heads: nn.Module, + pixel_mean: Tuple[float], + pixel_std: Tuple[float], + input_format: Optional[str] = None, + vis_period: int = 0, + ): + """ + NOTE: this interface is experimental. + + Args: + backbone: a backbone module, must follow detectron2's backbone interface + proposal_generator: a module that generates proposals using backbone features + roi_heads: a ROI head that performs per-region computation + pixel_mean, pixel_std: list or tuple with #channels element, + representing the per-channel mean and std to be used to normalize + the input image + input_format: describe the meaning of channels of input. Needed by visualization + vis_period: the period to run visualization. Set to 0 to disable. + """ + super().__init__() + self.backbone = backbone + self.proposal_generator = proposal_generator + self.roi_heads = roi_heads + + self.input_format = input_format + self.vis_period = vis_period + if vis_period > 0: + assert input_format is not None, "input_format is required for visualization!" + + self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1)) + self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1)) + assert ( + self.pixel_mean.shape == self.pixel_std.shape + ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!" + + @classmethod + def from_config(cls, cfg): + backbone = build_backbone(cfg) + return { + "backbone": backbone, + "proposal_generator": build_proposal_generator(cfg, backbone.output_shape()), + "roi_heads": build_roi_heads(cfg, backbone.output_shape()), + "input_format": cfg.INPUT.FORMAT, + "vis_period": cfg.VIS_PERIOD, + "pixel_mean": cfg.MODEL.PIXEL_MEAN, + "pixel_std": cfg.MODEL.PIXEL_STD, + } + + @property + def device(self): + return self.pixel_mean.device + + def forward(self, batched_inputs, trand_y_tilde): + """ + Args: + batched_inputs: a list, batched outputs of :class:`DatasetMapper` . + Each item in the list contains the inputs for one image. + For now, each item in the list is a dict that contains: + + * image: Tensor, image in (C, H, W) format. + * instances (optional): groundtruth :class:`Instances` + * proposals (optional): :class:`Instances`, precomputed proposals. + + Other information that's included in the original dicts, such as: + + * "height", "width" (int): the output resolution of the model, used in inference. + See :meth:`postprocess` for details. + + Returns: + list[dict]: + Each dict is the output for one input image. + The dict contains one key "instances" whose value is a :class:`Instances`. + The :class:`Instances` object has the following keys: + "pred_boxes", "pred_classes", "scores", "pred_masks", "pred_keypoints" + """ + if not self.training: + return self.inference(batched_inputs, trand_y_tilde=trand_y_tilde) + + images = self.preprocess_image(batched_inputs) + if "instances" in batched_inputs[0]: + gt_instances = [x["instances"].to(self.device) for x in batched_inputs] + else: + gt_instances = None + + features, distortion, rate = self.backbone(images.tensor) + + if self.proposal_generator: + proposals, proposal_losses = self.proposal_generator(images, features, gt_instances) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + proposal_losses = {} + + _, detector_losses = self.roi_heads(images, features, proposals, gt_instances) + if self.vis_period > 0: + storage = get_event_storage() + if storage.iter % self.vis_period == 0: + self.visualize_training(batched_inputs, proposals) + + losses = {} + losses.update(detector_losses) + losses.update(proposal_losses) + return losses, distortion, rate + + def inference(self, batched_inputs, detected_instances=None, do_postprocess=True, trand_y_tilde=None): + """ + Run inference on the given inputs. + + Args: + batched_inputs (list[dict]): same as in :meth:`forward` + detected_instances (None or list[Instances]): if not None, it + contains an `Instances` object per image. The `Instances` + object contains "pred_boxes" and "pred_classes" which are + known boxes in the image. + The inference will then skip the detection of bounding boxes, + and only predict other per-ROI outputs. + do_postprocess (bool): whether to apply post-processing on the outputs. + + Returns: + same as in :meth:`forward`. + """ + assert not self.training + + images = self.preprocess_image(batched_inputs) + features = self.backbone(trand_y_tilde) + + if detected_instances is None: + if self.proposal_generator: + proposals, _ = self.proposal_generator(images, features, None) + else: + assert "proposals" in batched_inputs[0] + proposals = [x["proposals"].to(self.device) for x in batched_inputs] + + results, _ = self.roi_heads(images, features, proposals, None) + else: + detected_instances = [x.to(self.device) for x in detected_instances] + results = self.roi_heads.forward_with_given_boxes(features, detected_instances) + + if do_postprocess: + return self._postprocess(results, batched_inputs, images.image_sizes) + else: + return results + + def preprocess_image(self, batched_inputs): + """ + Normalize, pad and batch the input images. + """ + images = [x["image"].to(self.device) for x in batched_inputs] + images = [(x - self.pixel_mean) / self.pixel_std for x in images] + images = ImageList.from_tensors(images, self.backbone.size_divisibility) + return images + + @staticmethod + def _postprocess(instances, batched_inputs, image_sizes): + """ + Rescale the output instances to the target size. + """ + # note: private function; subject to changes + processed_results = [] + for results_per_image, input_per_image, image_size in zip( + instances, batched_inputs, image_sizes + ): + height = input_per_image.get("height", image_size[0]) + width = input_per_image.get("width", image_size[1]) + r = detector_postprocess(results_per_image, height, width) + processed_results.append({"instances": r}) + return processed_results + + +class ModPredictor: + def __init__(self, cfg): + self.cfg = cfg.clone() # cfg can be modified by model + self.model = build_model(self.cfg) + self.model.eval() + if len(cfg.DATASETS.TEST): + self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0]) + + checkpointer = DetectionCheckpointer(self.model) + checkpointer.load(cfg.MODEL.WEIGHTS) + + self.aug = T.ResizeShortestEdge( + [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST + ) + + self.input_format = cfg.INPUT.FORMAT + assert self.input_format in ["RGB", "BGR"], self.input_format + + def __call__(self, original_image, trand_y_tilde): + with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258 + # Apply pre-processing to image. + if self.input_format == "RGB": + # whether the model expects BGR inputs or RGB + original_image = original_image[:, :, ::-1] + height, width = original_image.shape[:2] + image = self.aug.get_transform(original_image).apply_image(original_image) + image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1)) + + inputs = {"image": image[0], "height": height, "width": width} + predictions = self.model([inputs], trand_y_tilde)[0] + return predictions diff --git a/util/rle.py b/util/rle.py new file mode 100644 index 0000000000000000000000000000000000000000..ae62df704a86675fdc33899c26a8eb9fd28017d1 --- /dev/null +++ b/util/rle.py @@ -0,0 +1,105 @@ +import torch +import numpy as np + +def mask_to_rle(mask): + """ + Convert a binary mask to RLE. + + Args: + mask (torch.Tensor): 1D tensor of binary mask (0s and 1s). + + Returns: + list: RLE as a list of start and length pairs. + """ + pixels = mask.detach().cpu().numpy().flatten() + rle = [] + last_val = pixels[0] + start = 0 + length = 1 + + for idx in range(1, len(pixels)): + pixel = pixels[idx] + if pixel == last_val: + length += 1 + else: + if last_val == 1: + rle.extend([start, length]) + start = idx + length = 1 + last_val = pixel + + # Handle the last run + if last_val == 1: + rle.extend([start, length]) + + return rle + +def rle_encode(tensor): + """ + Encode a batch of binary masks using RLE. + + Args: + tensor (torch.Tensor): 2D tensor of shape [b, n]. + + Returns: + list, list: RLE encoded masks and their lengths. + """ + rle_batches = [] + lengths = [] + for batch_item in tensor: + rle_code = mask_to_rle(batch_item) + rle_batches.append(rle_code) + lengths.append(len(rle_code)) + return rle_batches, lengths + +def rle_to_mask(rle, shape): + """ + Convert RLE back to binary mask. + + Args: + rle (list): RLE as a list of start and length pairs. + shape (tuple): Shape of the original mask (height, width). + + Returns: + numpy.ndarray: Reconstructed binary mask. + """ + mask = np.zeros(shape[0] * shape[1], dtype=np.uint8) + + for i in range(0, len(rle), 2): + start = rle[i] + length = rle[i + 1] + mask[start:start + length] = 1 + + return mask.reshape(shape) + +def rle_decode(encoded_batches, shape): + """ + Decode a batch of RLE encoded masks. + + Args: + encoded_batches (list): List of RLE encoded masks. + lengths (list): List of lengths of the RLE encoded masks. + shape (tuple): Shape of the original mask (b, n). + + Returns: + torch.Tensor: Decoded binary masks. + """ + decoded_batches = [] + for encoded in encoded_batches: + decoded = rle_to_mask(encoded, (1, shape[1])) # 恢复每个mask的形状 + decoded_batches.append(decoded) + return torch.tensor(decoded_batches).squeeze(1) + + +if __name__ == "__main__": + tensor = torch.tensor([[0, 0, 1, 1, 0, 0, 1, 0, 1], + [1, 1, 0, 0, 0, 0, 1, 0, 0], + [0, 0, 1, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 1, 0]]).float() + encoded, lengths = rle_encode(tensor) + print("Encoded RLE:", encoded) + print("Lengths:", lengths) + + shape = tensor.shape # shape = (b, n) + decoded = rle_decode(encoded, shape) + print("Decoded Tensors:\n", decoded.float()) \ No newline at end of file diff --git a/util/utils.py b/util/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9e498d7457d75ac1150bfbd0b17e925f4520931b --- /dev/null +++ b/util/utils.py @@ -0,0 +1,150 @@ +import torch +import numpy as np +from PIL import Image +import torch.nn.functional as F + +def pad_to_multiple_of_16(latent, pad_value, patch_size=16): + h, w = latent.size(2), latent.size(3) + target_h = ((h - 1) // patch_size + 1) * patch_size + target_w = ((w - 1) // patch_size + 1) * patch_size + pad_h = (target_h - h) // 2 + pad_w = (target_w - w) // 2 + # 额外处理奇数padding的情况 + pad_h_extra = (target_h - h) % 2 + pad_w_extra = (target_w - w) % 2 + padded_latent = F.pad(latent, (pad_w, pad_w + pad_w_extra, pad_h, pad_h + pad_h_extra), mode='constant', value=pad_value) + # print("After padding: ", padded_latent.shape) + return padded_latent + +def split_into_blocks(latent, patch_size=16): + b, c, h, w = latent.size() + blocks = latent.view(b, c, h // patch_size, patch_size, w // patch_size, patch_size) + blocks = blocks.permute(0, 2, 4, 1, 3, 5).contiguous().view(-1, c, patch_size, patch_size) + # print("After splitting into blocks: ", blocks.shape) + return blocks + +def merge_blocks(blocks, original_shape, patch_size=16): + b, c, h, w = original_shape + num_blocks_per_row = w // patch_size + num_blocks_per_col = h // patch_size + + # 恢复到原始形状的顺序 + blocks = blocks.view(b, num_blocks_per_col, num_blocks_per_row, c, patch_size, patch_size) + blocks = blocks.permute(0, 3, 1, 4, 2, 5).contiguous() + blocks = blocks.view(b, c, h, w) + # print("After merging blocks: ", blocks.shape) + return blocks + +def crop_to_original_shape(blocks, original_shape): + _, _, padded_height, padded_width = blocks.shape + original_height, original_width = original_shape[2], original_shape[3] + start_h = (padded_height - original_height) // 2 + end_h = start_h + original_height + start_w = (padded_width - original_width) // 2 + end_w = start_w + original_width + cropped_blocks = blocks[:, :, start_h:end_h, start_w:end_w] + # print("After cropping to original shape: ", cropped_blocks.shape) + return cropped_blocks + +def adaptively_split_and_pad(image_tensor, pad_value, target_patch_size=16): + """ + return: + patches_tensor: (N * num_blocks_h * num_blocks_w, c, target_patch_size, target_patch_size) patched tensors after spilt + patch_sizes: a list, ori size of each blocks + num_blocks_h, num_blocks_w + """ + c, h, w = image_tensor.size(1), image_tensor.size(2), image_tensor.size(3) + # 计算每个方向上的块数量 + num_blocks_h = h // target_patch_size if h % target_patch_size == 0 else h // target_patch_size + 1 + num_blocks_w = w // target_patch_size if w % target_patch_size == 0 else w // target_patch_size + 1 + + # 确定每个块的尺寸 + block_h = h // num_blocks_h + block_w = w // num_blocks_w + patches = [] + patch_sizes = [] + + for i in range(num_blocks_h): + for j in range(num_blocks_w): + # 计算每个块的起始和结束索引 + start_h = i * block_h + start_w = j * block_w + end_h = start_h + block_h if i < num_blocks_h - 1 else h + end_w = start_w + block_w if j < num_blocks_w - 1 else w + # 切割块 + patch = image_tensor[:, :, start_h:end_h, start_w:end_w] + # 打印每个block在padding前的分辨率 + # print(f"Block {i*num_blocks_w + j} size before padding: {end_h - start_h}x{end_w - start_w}") + + # 计算每个块的padding需求 + pad_top = (target_patch_size - (end_h - start_h)) // 2 + pad_bottom = target_patch_size - (end_h - start_h) - pad_top + pad_left = (target_patch_size - (end_w - start_w)) // 2 + pad_right = target_patch_size - (end_w - start_w) - pad_left + + # 应用padding + patch_padded = F.pad(patch, (pad_left, pad_right, pad_top, pad_bottom), mode='constant', value=pad_value) + + patches.append(patch_padded) + patch_sizes.append((end_h - start_h, end_w - start_w)) + # 将所有patch合并成一个tensor + patches_tensor = torch.cat(patches, dim=0) + return patches_tensor, patch_sizes, num_blocks_h, num_blocks_w + + +def crop_and_reconstruct(patches, patch_sizes, num_blocks_h, num_blocks_w, target_patch_size=16): + """ + inverse operation of adaptively_split_and_pad + """ + index = 0 + reconstructed_rows = [] + + for i in range(num_blocks_h): + row_patches = [] + for j in range(num_blocks_w): + patch = patches[index] + patch_height, patch_width = patch_sizes[index] + + valid_h_start = (target_patch_size - patch_height) // 2 + valid_w_start = (target_patch_size - patch_width) // 2 + valid_h_end = valid_h_start + patch_height + valid_w_end = valid_w_start + patch_width + + cropped_patch = patch[:, valid_h_start:valid_h_end, valid_w_start:valid_w_end] + row_patches.append(cropped_patch) + index += 1 + row_tensor = torch.cat(row_patches, dim=2) + reconstructed_rows.append(row_tensor) + + reconstructed_image = torch.cat(reconstructed_rows, dim=1) + return reconstructed_image + +def save_image(tensor, file_path): + # 将张量转换为PIL图像并保存 + image = tensor.to('cpu').clone().detach() + image = image.squeeze(0) + image = torch.clamp(image, 0, 1) + image = Image.fromarray((image.permute(1, 2, 0).numpy() * 255).astype(np.uint8)) + image.save(file_path) + print(f"Image saved to {file_path}") + + +if __name__ == "__main__": + # 假设有一个随机初始化的图像张量 + N, C, H, W = 1, 3, 36, 33 # 非标准尺寸，测试目的 + image_tensor = torch.rand(N, C, H, W) + + # 使用adaptively_split_and_pad函数 + target_patch_size = 16 + pad_value = 0 # 通常用于图像是黑色填充 + patches_tensor, patch_sizes, num_blocks_h, num_blocks_w = adaptively_split_and_pad(image_tensor, pad_value, target_patch_size) + + # 可视化每个block的crop结果 + for i, patch in enumerate(patches_tensor): + save_image(patch, f"patch_{i}.png") + + # 使用crop_and_reconstruct函数 + reconstructed_image = crop_and_reconstruct(patches_tensor, patch_sizes, num_blocks_h, num_blocks_w, target_patch_size) + + # 保存和显示重建的图像 + save_image(reconstructed_image, "reconstructed_image.png") \ No newline at end of file