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@@ -225,3 +225,6 @@ base2new/train_base/lungcolon/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpe
 base2new/train_base/octmnist/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 filter=lfs diff=lfs merge=lfs -text
 base2new/train_base/octmnist/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 filter=lfs diff=lfs merge=lfs -text
 base2new/train_base/octmnist/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 filter=lfs diff=lfs merge=lfs -text
+base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed1/prompt_learner/model.pth.tar-50 filter=lfs diff=lfs merge=lfs -text
+base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed2/prompt_learner/model.pth.tar-50 filter=lfs diff=lfs merge=lfs -text
+base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50 filter=lfs diff=lfs merge=lfs -text

base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed1/log.txt

@@ -0,0 +1,733 @@
+***************
+** Arguments **
+***************
+backbone: 
+config_file: configs/trainers/BiomedCoOp_BiomedCLIP/base_to_novel/retina.yaml
+dataset_config_file: configs/datasets/retina.yaml
+eval_only: False
+head: 
+load_epoch: None
+model_dir: 
+no_train: False
+opts: ['DATASET.NUM_SHOTS', '16', 'DATASET.SUBSAMPLE_CLASSES', 'base']
+output_dir: output_reproduce/base2new/train_base/retina/shots_16/seed1
+resume: 
+root: DATA
+seed: 1
+source_domains: None
+target_domains: None
+trainer: BiomedCoOp_BiomedCLIP
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+  K_TRANSFORMS: 1
+  NUM_WORKERS: 8
+  RETURN_IMG0: False
+  TEST:
+    BATCH_SIZE: 100
+    SAMPLER: SequentialSampler
+  TRAIN_U:
+    BATCH_SIZE: 32
+    N_DOMAIN: 0
+    N_INS: 16
+    SAME_AS_X: True
+    SAMPLER: RandomSampler
+  TRAIN_X:
+    BATCH_SIZE: 4
+    N_DOMAIN: 0
+    N_INS: 16
+    SAMPLER: RandomSampler
+DATASET:
+  ALL_AS_UNLABELED: False
+  CIFAR_C_LEVEL: 1
+  CIFAR_C_TYPE: 
+  NAME: RETINA
+  NUM_LABELED: -1
+  NUM_SHOTS: 16
+  ROOT: DATA
+  SOURCE_DOMAINS: ()
+  STL10_FOLD: -1
+  SUBSAMPLE_CLASSES: base
+  TARGET_DOMAINS: ()
+  VAL_PERCENT: 0.1
+INPUT:
+  COLORJITTER_B: 0.4
+  COLORJITTER_C: 0.4
+  COLORJITTER_H: 0.1
+  COLORJITTER_S: 0.4
+  CROP_PADDING: 4
+  CUTOUT_LEN: 16
+  CUTOUT_N: 1
+  GB_K: 21
+  GB_P: 0.5
+  GN_MEAN: 0.0
+  GN_STD: 0.15
+  INTERPOLATION: bicubic
+  NO_TRANSFORM: False
+  PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+  PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+  RANDAUGMENT_M: 10
+  RANDAUGMENT_N: 2
+  RGS_P: 0.2
+  SIZE: (224, 224)
+  TRANSFORMS: ('random_resized_crop', 'normalize')
+MODEL:
+  BACKBONE:
+    NAME: ViT-B/16
+    PRETRAINED: True
+  HEAD:
+    ACTIVATION: relu
+    BN: True
+    DROPOUT: 0.0
+    HIDDEN_LAYERS: ()
+    NAME: 
+  INIT_WEIGHTS: 
+OPTIM:
+  ADAM_BETA1: 0.9
+  ADAM_BETA2: 0.999
+  BASE_LR_MULT: 0.1
+  GAMMA: 0.1
+  LR: 0.0025
+  LR_SCHEDULER: cosine
+  MAX_EPOCH: 50
+  MOMENTUM: 0.9
+  NAME: sgd
+  NEW_LAYERS: ()
+  RMSPROP_ALPHA: 0.99
+  SGD_DAMPNING: 0
+  SGD_NESTEROV: False
+  STAGED_LR: False
+  STEPSIZE: (-1,)
+  WARMUP_CONS_LR: 1e-05
+  WARMUP_EPOCH: 1
+  WARMUP_MIN_LR: 1e-05
+  WARMUP_RECOUNT: True
+  WARMUP_TYPE: constant
+  WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output_reproduce/base2new/train_base/retina/shots_16/seed1
+RESUME: 
+SEED: 1
+TEST:
+  COMPUTE_CMAT: False
+  EVALUATOR: Classification
+  FINAL_MODEL: last_step
+  NO_TEST: False
+  PER_CLASS_RESULT: False
+  SPLIT: test
+TRAIN:
+  CHECKPOINT_FREQ: 0
+  COUNT_ITER: train_x
+  PRINT_FREQ: 20
+TRAINER:
+  BIOMEDCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    KDSP_LAMBDA: 1.0
+    N_CTX: 4
+    N_PROMPTS: 50
+    PREC: fp32
+    SCCM_LAMBDA: 5.0
+    TAU: 2.0
+  CG:
+    ALPHA_D: 0.5
+    ALPHA_F: 0.5
+    EPS_D: 1.0
+    EPS_F: 1.0
+  COCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: 
+    N_CTX: 4
+    PREC: fp32
+  COOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: 
+    N_CTX: 4
+    PREC: fp32
+  DAEL:
+    CONF_THRE: 0.95
+    STRONG_TRANSFORMS: ()
+    WEIGHT_U: 0.5
+  DDAIG:
+    ALPHA: 0.5
+    CLAMP: False
+    CLAMP_MAX: 1.0
+    CLAMP_MIN: -1.0
+    G_ARCH: 
+    LMDA: 0.3
+    WARMUP: 0
+  ENTMIN:
+    LMDA: 0.001
+  FIXMATCH:
+    CONF_THRE: 0.95
+    STRONG_TRANSFORMS: ()
+    WEIGHT_U: 1.0
+  KGCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    N_CTX: 4
+    PREC: fp32
+    W: 1.0
+  M3SDA:
+    LMDA: 0.5
+    N_STEP_F: 4
+  MCD:
+    N_STEP_F: 4
+  MEANTEA:
+    EMA_ALPHA: 0.999
+    RAMPUP: 5
+    WEIGHT_U: 1.0
+  MIXMATCH:
+    MIXUP_BETA: 0.75
+    RAMPUP: 20000
+    TEMP: 2.0
+    WEIGHT_U: 100.0
+  MME:
+    LMDA: 0.1
+  NAME: BiomedCoOp_BiomedCLIP
+  PROGRAD:
+    ALPHA: 0.0
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    GM: False
+    LAMBDA: 1.0
+    NAME: 
+    N_CTX: 4
+    PREC: fp32
+    T: 1.0
+  SE:
+    CONF_THRE: 0.95
+    EMA_ALPHA: 0.999
+    RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.0.1+cu118
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 22.04.5 LTS (x86_64)
+GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
+Clang version: Could not collect
+CMake version: version 3.25.0
+Libc version: glibc-2.35
+
+Python version: 3.10.15 (main, Oct  3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.15.0-134-generic-x86_64-with-glibc2.35
+Is CUDA available: True
+CUDA runtime version: 12.8.61
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration: 
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 570.124.06
+cuDNN version: Could not collect
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture:                         x86_64
+CPU op-mode(s):                       32-bit, 64-bit
+Address sizes:                        43 bits physical, 48 bits virtual
+Byte Order:                           Little Endian
+CPU(s):                               128
+On-line CPU(s) list:                  0-127
+Vendor ID:                            AuthenticAMD
+Model name:                           AMD EPYC 7502 32-Core Processor
+CPU family:                           23
+Model:                                49
+Thread(s) per core:                   2
+Core(s) per socket:                   32
+Socket(s):                            2
+Stepping:                             0
+Frequency boost:                      enabled
+CPU max MHz:                          2500.0000
+CPU min MHz:                          1500.0000
+BogoMIPS:                             4999.71
+Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
+Virtualization:                       AMD-V
+L1d cache:                            2 MiB (64 instances)
+L1i cache:                            2 MiB (64 instances)
+L2 cache:                             32 MiB (64 instances)
+L3 cache:                             256 MiB (16 instances)
+NUMA node(s):                         2
+NUMA node0 CPU(s):                    0-31,64-95
+NUMA node1 CPU(s):                    32-63,96-127
+Vulnerability Gather data sampling:   Not affected
+Vulnerability Itlb multihit:          Not affected
+Vulnerability L1tf:                   Not affected
+Vulnerability Mds:                    Not affected
+Vulnerability Meltdown:               Not affected
+Vulnerability Mmio stale data:        Not affected
+Vulnerability Reg file data sampling: Not affected
+Vulnerability Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
+Vulnerability Spec rstack overflow:   Mitigation; safe RET
+Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
+Vulnerability Srbds:                  Not affected
+Vulnerability Tsx async abort:        Not affected
+
+Versions of relevant libraries:
+[pip3] numpy==1.26.3
+[pip3] torch==2.0.1+cu118
+[pip3] torchvision==0.15.2+cu118
+[conda] numpy                     1.26.3                   pypi_0    pypi
+[conda] torch                     2.0.1+cu118              pypi_0    pypi
+[conda] torchvision               0.15.2+cu118             pypi_0    pypi
+        Pillow (10.2.0)
+
+Loading trainer: BiomedCoOp_BiomedCLIP
+Loading dataset: RETINA
+Reading split from /data/t_kol/BiomedCoOp/DATA/RETINA/split_RETINA.json
+Loading preprocessed few-shot data from /data/t_kol/BiomedCoOp/DATA/RETINA/split_fewshot/shot_16-seed_1.pkl
+SUBSAMPLE BASE CLASSES!
+Building transform_train
++ random resized crop (size=(224, 224))
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+***** Dataset statistics *****
+  Dataset: RETINA
+  # classes: 2
+  # train_x: 32
+  # val: 8
+  # test: 633
+Loading BiomedCLIP (backbone: ViT-B/16)
+Building custom CLIP
+Initial text context: "a photo of a"
+Number of context words (tokens) for Language prompting: 4
+Turning off gradients in both the image and the text encoder
+Parameters to be updated: {'prompt_learner.ctx'}
+Parameters count: 1
+Loading evaluator: Classification
+Trainer built successfully.
+No checkpoint found, train from scratch
+Initializing summary writer for tensorboard with log_dir=output_reproduce/base2new/train_base/retina/shots_16/seed1/tensorboard
+epoch [1/50][1/8]	time 0.741 (0.741)	data 0.411 (0.411)	eta 0:04:55	loss 2.6931 (2.6931)	acc 0.0000 (0.0000)	lr 1.000000e-05
+epoch [1/50][2/8]	time 0.058 (0.399)	data 0.008 (0.210)	eta 0:02:38	loss 1.2585 (1.9758)	acc 25.0000 (12.5000)	lr 1.000000e-05
+epoch [1/50][3/8]	time 0.049 (0.283)	data 0.001 (0.140)	eta 0:01:52	loss 1.4346 (1.7954)	acc 75.0000 (33.3333)	lr 1.000000e-05
+epoch [1/50][4/8]	time 0.049 (0.224)	data 0.001 (0.105)	eta 0:01:28	loss 0.7699 (1.5390)	acc 75.0000 (43.7500)	lr 1.000000e-05
+epoch [1/50][5/8]	time 0.049 (0.189)	data 0.001 (0.084)	eta 0:01:14	loss 0.6434 (1.3599)	acc 50.0000 (45.0000)	lr 1.000000e-05
+epoch [1/50][6/8]	time 0.049 (0.166)	data 0.001 (0.071)	eta 0:01:05	loss 1.2018 (1.3336)	acc 50.0000 (45.8333)	lr 1.000000e-05
+epoch [1/50][7/8]	time 0.049 (0.149)	data 0.001 (0.061)	eta 0:00:58	loss 1.6145 (1.3737)	acc 50.0000 (46.4286)	lr 1.000000e-05
+epoch [1/50][8/8]	time 0.050 (0.137)	data 0.001 (0.053)	eta 0:00:53	loss 1.9011 (1.4396)	acc 25.0000 (43.7500)	lr 2.500000e-03
+epoch [2/50][1/8]	time 0.516 (0.516)	data 0.461 (0.461)	eta 0:03:21	loss 1.0545 (1.0545)	acc 75.0000 (75.0000)	lr 2.500000e-03
+epoch [2/50][2/8]	time 0.144 (0.330)	data 0.087 (0.274)	eta 0:02:08	loss 2.0463 (1.5504)	acc 75.0000 (75.0000)	lr 2.500000e-03
+epoch [2/50][3/8]	time 0.054 (0.238)	data 0.002 (0.183)	eta 0:01:32	loss 0.6278 (1.2429)	acc 75.0000 (75.0000)	lr 2.500000e-03
+epoch [2/50][4/8]	time 0.049 (0.191)	data 0.001 (0.138)	eta 0:01:13	loss 1.3656 (1.2736)	acc 75.0000 (75.0000)	lr 2.500000e-03
+epoch [2/50][5/8]	time 0.049 (0.162)	data 0.001 (0.110)	eta 0:01:02	loss 2.1961 (1.4581)	acc 75.0000 (75.0000)	lr 2.500000e-03
+epoch [2/50][6/8]	time 0.052 (0.144)	data 0.001 (0.092)	eta 0:00:55	loss 0.7012 (1.3319)	acc 50.0000 (70.8333)	lr 2.500000e-03
+epoch [2/50][7/8]	time 0.050 (0.130)	data 0.001 (0.079)	eta 0:00:50	loss 2.9500 (1.5631)	acc 25.0000 (64.2857)	lr 2.500000e-03
+epoch [2/50][8/8]	time 0.049 (0.120)	data 0.001 (0.069)	eta 0:00:46	loss 1.5855 (1.5659)	acc 25.0000 (59.3750)	lr 2.497533e-03
+epoch [3/50][1/8]	time 0.544 (0.544)	data 0.489 (0.489)	eta 0:03:28	loss 1.2156 (1.2156)	acc 75.0000 (75.0000)	lr 2.497533e-03
+epoch [3/50][2/8]	time 0.051 (0.297)	data 0.003 (0.246)	eta 0:01:53	loss 1.3087 (1.2622)	acc 75.0000 (75.0000)	lr 2.497533e-03
+epoch [3/50][3/8]	time 0.052 (0.216)	data 0.003 (0.165)	eta 0:01:22	loss 0.4833 (1.0025)	acc 75.0000 (75.0000)	lr 2.497533e-03
+epoch [3/50][4/8]	time 0.051 (0.174)	data 0.003 (0.124)	eta 0:01:06	loss 1.6289 (1.1591)	acc 50.0000 (68.7500)	lr 2.497533e-03
+epoch [3/50][5/8]	time 0.049 (0.149)	data 0.001 (0.100)	eta 0:00:56	loss 1.0188 (1.1311)	acc 50.0000 (65.0000)	lr 2.497533e-03
+epoch [3/50][6/8]	time 0.049 (0.133)	data 0.001 (0.083)	eta 0:00:50	loss 2.0422 (1.2829)	acc 0.0000 (54.1667)	lr 2.497533e-03
+epoch [3/50][7/8]	time 0.049 (0.121)	data 0.001 (0.072)	eta 0:00:45	loss 0.8786 (1.2252)	acc 75.0000 (57.1429)	lr 2.497533e-03
+epoch [3/50][8/8]	time 0.049 (0.112)	data 0.001 (0.063)	eta 0:00:41	loss 0.8791 (1.1819)	acc 75.0000 (59.3750)	lr 2.490143e-03
+epoch [4/50][1/8]	time 0.537 (0.537)	data 0.483 (0.483)	eta 0:03:21	loss 1.1123 (1.1123)	acc 50.0000 (50.0000)	lr 2.490143e-03
+epoch [4/50][2/8]	time 0.051 (0.294)	data 0.001 (0.242)	eta 0:01:49	loss 0.5127 (0.8125)	acc 75.0000 (62.5000)	lr 2.490143e-03
+epoch [4/50][3/8]	time 0.049 (0.212)	data 0.001 (0.162)	eta 0:01:19	loss 1.3723 (0.9991)	acc 25.0000 (50.0000)	lr 2.490143e-03
+epoch [4/50][4/8]	time 0.050 (0.172)	data 0.003 (0.122)	eta 0:01:03	loss 1.0062 (1.0009)	acc 25.0000 (43.7500)	lr 2.490143e-03
+epoch [4/50][5/8]	time 0.049 (0.147)	data 0.001 (0.098)	eta 0:00:54	loss 0.9806 (0.9968)	acc 75.0000 (50.0000)	lr 2.490143e-03
+epoch [4/50][6/8]	time 0.049 (0.131)	data 0.001 (0.082)	eta 0:00:48	loss 0.5734 (0.9262)	acc 75.0000 (54.1667)	lr 2.490143e-03
+epoch [4/50][7/8]	time 0.049 (0.119)	data 0.001 (0.070)	eta 0:00:43	loss 1.5205 (1.0111)	acc 50.0000 (53.5714)	lr 2.490143e-03
+epoch [4/50][8/8]	time 0.049 (0.110)	data 0.001 (0.061)	eta 0:00:40	loss 0.6203 (0.9623)	acc 75.0000 (56.2500)	lr 2.477859e-03
+epoch [5/50][1/8]	time 0.527 (0.527)	data 0.470 (0.470)	eta 0:03:13	loss 0.8679 (0.8679)	acc 50.0000 (50.0000)	lr 2.477859e-03
+epoch [5/50][2/8]	time 0.056 (0.291)	data 0.005 (0.238)	eta 0:01:46	loss 1.2213 (1.0446)	acc 75.0000 (62.5000)	lr 2.477859e-03
+epoch [5/50][3/8]	time 0.051 (0.211)	data 0.001 (0.159)	eta 0:01:17	loss 0.5916 (0.8936)	acc 75.0000 (66.6667)	lr 2.477859e-03
+epoch [5/50][4/8]	time 0.050 (0.171)	data 0.001 (0.119)	eta 0:01:02	loss 1.4360 (1.0292)	acc 75.0000 (68.7500)	lr 2.477859e-03
+epoch [5/50][5/8]	time 0.053 (0.147)	data 0.004 (0.096)	eta 0:00:53	loss 1.4105 (1.1055)	acc 50.0000 (65.0000)	lr 2.477859e-03
+epoch [5/50][6/8]	time 0.050 (0.131)	data 0.001 (0.080)	eta 0:00:47	loss 1.3438 (1.1452)	acc 25.0000 (58.3333)	lr 2.477859e-03
+epoch [5/50][7/8]	time 0.050 (0.119)	data 0.001 (0.069)	eta 0:00:43	loss 2.6720 (1.3633)	acc 0.0000 (50.0000)	lr 2.477859e-03
+epoch [5/50][8/8]	time 0.051 (0.111)	data 0.001 (0.061)	eta 0:00:39	loss 0.5211 (1.2580)	acc 75.0000 (53.1250)	lr 2.460729e-03
+epoch [6/50][1/8]	time 0.518 (0.518)	data 0.463 (0.463)	eta 0:03:05	loss 1.4891 (1.4891)	acc 50.0000 (50.0000)	lr 2.460729e-03
+epoch [6/50][2/8]	time 0.052 (0.285)	data 0.004 (0.233)	eta 0:01:42	loss 0.8768 (1.1829)	acc 50.0000 (50.0000)	lr 2.460729e-03
+epoch [6/50][3/8]	time 0.050 (0.207)	data 0.002 (0.156)	eta 0:01:13	loss 0.6757 (1.0139)	acc 75.0000 (58.3333)	lr 2.460729e-03
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+epoch [47/50][8/8]	time 0.055 (0.110)	data 0.001 (0.058)	eta 0:00:02	loss 0.7010 (0.7222)	acc 75.0000 (78.1250)	lr 3.927105e-05
+epoch [48/50][1/8]	time 0.553 (0.553)	data 0.498 (0.498)	eta 0:00:12	loss 0.8524 (0.8524)	acc 75.0000 (75.0000)	lr 3.927105e-05
+epoch [48/50][2/8]	time 0.049 (0.301)	data 0.001 (0.250)	eta 0:00:06	loss 0.9856 (0.9190)	acc 75.0000 (75.0000)	lr 3.927105e-05
+epoch [48/50][3/8]	time 0.049 (0.217)	data 0.001 (0.167)	eta 0:00:04	loss 0.4566 (0.7649)	acc 100.0000 (83.3333)	lr 3.927105e-05
+epoch [48/50][4/8]	time 0.049 (0.175)	data 0.001 (0.125)	eta 0:00:03	loss 0.8237 (0.7796)	acc 100.0000 (87.5000)	lr 3.927105e-05
+epoch [48/50][5/8]	time 0.053 (0.151)	data 0.004 (0.101)	eta 0:00:02	loss 0.4626 (0.7162)	acc 75.0000 (85.0000)	lr 3.927105e-05
+epoch [48/50][6/8]	time 0.049 (0.134)	data 0.001 (0.084)	eta 0:00:02	loss 0.9385 (0.7532)	acc 50.0000 (79.1667)	lr 3.927105e-05
+epoch [48/50][7/8]	time 0.051 (0.122)	data 0.002 (0.073)	eta 0:00:02	loss 0.7895 (0.7584)	acc 75.0000 (78.5714)	lr 3.927105e-05
+epoch [48/50][8/8]	time 0.049 (0.113)	data 0.001 (0.064)	eta 0:00:01	loss 1.4993 (0.8510)	acc 25.0000 (71.8750)	lr 2.214094e-05
+epoch [49/50][1/8]	time 0.676 (0.676)	data 0.623 (0.623)	eta 0:00:10	loss 0.5460 (0.5460)	acc 75.0000 (75.0000)	lr 2.214094e-05
+epoch [49/50][2/8]	time 0.049 (0.363)	data 0.001 (0.312)	eta 0:00:05	loss 0.7149 (0.6304)	acc 75.0000 (75.0000)	lr 2.214094e-05
+epoch [49/50][3/8]	time 0.048 (0.258)	data 0.001 (0.208)	eta 0:00:03	loss 0.7404 (0.6671)	acc 75.0000 (75.0000)	lr 2.214094e-05
+epoch [49/50][4/8]	time 0.051 (0.206)	data 0.001 (0.156)	eta 0:00:02	loss 0.9490 (0.7375)	acc 100.0000 (81.2500)	lr 2.214094e-05
+epoch [49/50][5/8]	time 0.048 (0.175)	data 0.001 (0.125)	eta 0:00:01	loss 0.8400 (0.7580)	acc 75.0000 (80.0000)	lr 2.214094e-05
+epoch [49/50][6/8]	time 0.049 (0.154)	data 0.001 (0.105)	eta 0:00:01	loss 0.5027 (0.7155)	acc 75.0000 (79.1667)	lr 2.214094e-05
+epoch [49/50][7/8]	time 0.048 (0.139)	data 0.001 (0.090)	eta 0:00:01	loss 0.4600 (0.6790)	acc 100.0000 (82.1429)	lr 2.214094e-05
+epoch [49/50][8/8]	time 0.049 (0.127)	data 0.001 (0.079)	eta 0:00:01	loss 0.6023 (0.6694)	acc 75.0000 (81.2500)	lr 9.856623e-06
+epoch [50/50][1/8]	time 0.506 (0.506)	data 0.452 (0.452)	eta 0:00:03	loss 1.2049 (1.2049)	acc 50.0000 (50.0000)	lr 9.856623e-06
+epoch [50/50][2/8]	time 0.054 (0.280)	data 0.004 (0.228)	eta 0:00:01	loss 0.6094 (0.9071)	acc 75.0000 (62.5000)	lr 9.856623e-06
+epoch [50/50][3/8]	time 0.052 (0.204)	data 0.002 (0.153)	eta 0:00:01	loss 0.7877 (0.8673)	acc 75.0000 (66.6667)	lr 9.856623e-06
+epoch [50/50][4/8]	time 0.054 (0.166)	data 0.004 (0.115)	eta 0:00:00	loss 0.7406 (0.8357)	acc 50.0000 (62.5000)	lr 9.856623e-06
+epoch [50/50][5/8]	time 0.050 (0.143)	data 0.001 (0.093)	eta 0:00:00	loss 1.4077 (0.9501)	acc 25.0000 (55.0000)	lr 9.856623e-06
+epoch [50/50][6/8]	time 0.050 (0.128)	data 0.001 (0.077)	eta 0:00:00	loss 0.5905 (0.8901)	acc 100.0000 (62.5000)	lr 9.856623e-06
+epoch [50/50][7/8]	time 0.050 (0.116)	data 0.001 (0.066)	eta 0:00:00	loss 0.3177 (0.8084)	acc 100.0000 (67.8571)	lr 9.856623e-06
+epoch [50/50][8/8]	time 0.050 (0.108)	data 0.001 (0.058)	eta 0:00:00	loss 0.8021 (0.8076)	acc 100.0000 (71.8750)	lr 2.466589e-06
+Checkpoint saved to "output_reproduce/base2new/train_base/retina/shots_16/seed1/prompt_learner/model.pth.tar-50"
+Finished training
+Do evaluation on test set
+=> result
+* total: 633
+* correct: 460
+* accuracy: 72.67%
+* error: 27.33%
+* macro_f1: 72.60%
+Elapsed: 0:00:58

base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed1/prompt_learner/checkpoint

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base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed1/tensorboard/events.out.tfevents.1742453511.impactlab-server.3826635.0

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base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed2/log.txt

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+***************
+** Arguments **
+***************
+backbone: 
+config_file: configs/trainers/BiomedCoOp_BiomedCLIP/base_to_novel/retina.yaml
+dataset_config_file: configs/datasets/retina.yaml
+eval_only: False
+head: 
+load_epoch: None
+model_dir: 
+no_train: False
+opts: ['DATASET.NUM_SHOTS', '16', 'DATASET.SUBSAMPLE_CLASSES', 'base']
+output_dir: output_reproduce/base2new/train_base/retina/shots_16/seed2
+resume: 
+root: DATA
+seed: 2
+source_domains: None
+target_domains: None
+trainer: BiomedCoOp_BiomedCLIP
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+  K_TRANSFORMS: 1
+  NUM_WORKERS: 8
+  RETURN_IMG0: False
+  TEST:
+    BATCH_SIZE: 100
+    SAMPLER: SequentialSampler
+  TRAIN_U:
+    BATCH_SIZE: 32
+    N_DOMAIN: 0
+    N_INS: 16
+    SAME_AS_X: True
+    SAMPLER: RandomSampler
+  TRAIN_X:
+    BATCH_SIZE: 4
+    N_DOMAIN: 0
+    N_INS: 16
+    SAMPLER: RandomSampler
+DATASET:
+  ALL_AS_UNLABELED: False
+  CIFAR_C_LEVEL: 1
+  CIFAR_C_TYPE: 
+  NAME: RETINA
+  NUM_LABELED: -1
+  NUM_SHOTS: 16
+  ROOT: DATA
+  SOURCE_DOMAINS: ()
+  STL10_FOLD: -1
+  SUBSAMPLE_CLASSES: base
+  TARGET_DOMAINS: ()
+  VAL_PERCENT: 0.1
+INPUT:
+  COLORJITTER_B: 0.4
+  COLORJITTER_C: 0.4
+  COLORJITTER_H: 0.1
+  COLORJITTER_S: 0.4
+  CROP_PADDING: 4
+  CUTOUT_LEN: 16
+  CUTOUT_N: 1
+  GB_K: 21
+  GB_P: 0.5
+  GN_MEAN: 0.0
+  GN_STD: 0.15
+  INTERPOLATION: bicubic
+  NO_TRANSFORM: False
+  PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+  PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+  RANDAUGMENT_M: 10
+  RANDAUGMENT_N: 2
+  RGS_P: 0.2
+  SIZE: (224, 224)
+  TRANSFORMS: ('random_resized_crop', 'normalize')
+MODEL:
+  BACKBONE:
+    NAME: ViT-B/16
+    PRETRAINED: True
+  HEAD:
+    ACTIVATION: relu
+    BN: True
+    DROPOUT: 0.0
+    HIDDEN_LAYERS: ()
+    NAME: 
+  INIT_WEIGHTS: 
+OPTIM:
+  ADAM_BETA1: 0.9
+  ADAM_BETA2: 0.999
+  BASE_LR_MULT: 0.1
+  GAMMA: 0.1
+  LR: 0.0025
+  LR_SCHEDULER: cosine
+  MAX_EPOCH: 50
+  MOMENTUM: 0.9
+  NAME: sgd
+  NEW_LAYERS: ()
+  RMSPROP_ALPHA: 0.99
+  SGD_DAMPNING: 0
+  SGD_NESTEROV: False
+  STAGED_LR: False
+  STEPSIZE: (-1,)
+  WARMUP_CONS_LR: 1e-05
+  WARMUP_EPOCH: 1
+  WARMUP_MIN_LR: 1e-05
+  WARMUP_RECOUNT: True
+  WARMUP_TYPE: constant
+  WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output_reproduce/base2new/train_base/retina/shots_16/seed2
+RESUME: 
+SEED: 2
+TEST:
+  COMPUTE_CMAT: False
+  EVALUATOR: Classification
+  FINAL_MODEL: last_step
+  NO_TEST: False
+  PER_CLASS_RESULT: False
+  SPLIT: test
+TRAIN:
+  CHECKPOINT_FREQ: 0
+  COUNT_ITER: train_x
+  PRINT_FREQ: 20
+TRAINER:
+  BIOMEDCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    KDSP_LAMBDA: 1.0
+    N_CTX: 4
+    N_PROMPTS: 50
+    PREC: fp32
+    SCCM_LAMBDA: 5.0
+    TAU: 2.0
+  CG:
+    ALPHA_D: 0.5
+    ALPHA_F: 0.5
+    EPS_D: 1.0
+    EPS_F: 1.0
+  COCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: 
+    N_CTX: 4
+    PREC: fp32
+  COOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: 
+    N_CTX: 4
+    PREC: fp32
+  DAEL:
+    CONF_THRE: 0.95
+    STRONG_TRANSFORMS: ()
+    WEIGHT_U: 0.5
+  DDAIG:
+    ALPHA: 0.5
+    CLAMP: False
+    CLAMP_MAX: 1.0
+    CLAMP_MIN: -1.0
+    G_ARCH: 
+    LMDA: 0.3
+    WARMUP: 0
+  ENTMIN:
+    LMDA: 0.001
+  FIXMATCH:
+    CONF_THRE: 0.95
+    STRONG_TRANSFORMS: ()
+    WEIGHT_U: 1.0
+  KGCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    N_CTX: 4
+    PREC: fp32
+    W: 1.0
+  M3SDA:
+    LMDA: 0.5
+    N_STEP_F: 4
+  MCD:
+    N_STEP_F: 4
+  MEANTEA:
+    EMA_ALPHA: 0.999
+    RAMPUP: 5
+    WEIGHT_U: 1.0
+  MIXMATCH:
+    MIXUP_BETA: 0.75
+    RAMPUP: 20000
+    TEMP: 2.0
+    WEIGHT_U: 100.0
+  MME:
+    LMDA: 0.1
+  NAME: BiomedCoOp_BiomedCLIP
+  PROGRAD:
+    ALPHA: 0.0
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    GM: False
+    LAMBDA: 1.0
+    NAME: 
+    N_CTX: 4
+    PREC: fp32
+    T: 1.0
+  SE:
+    CONF_THRE: 0.95
+    EMA_ALPHA: 0.999
+    RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.0.1+cu118
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 22.04.5 LTS (x86_64)
+GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
+Clang version: Could not collect
+CMake version: version 3.25.0
+Libc version: glibc-2.35
+
+Python version: 3.10.15 (main, Oct  3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.15.0-134-generic-x86_64-with-glibc2.35
+Is CUDA available: True
+CUDA runtime version: 12.8.61
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration: 
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 570.124.06
+cuDNN version: Could not collect
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture:                         x86_64
+CPU op-mode(s):                       32-bit, 64-bit
+Address sizes:                        43 bits physical, 48 bits virtual
+Byte Order:                           Little Endian
+CPU(s):                               128
+On-line CPU(s) list:                  0-127
+Vendor ID:                            AuthenticAMD
+Model name:                           AMD EPYC 7502 32-Core Processor
+CPU family:                           23
+Model:                                49
+Thread(s) per core:                   2
+Core(s) per socket:                   32
+Socket(s):                            2
+Stepping:                             0
+Frequency boost:                      enabled
+CPU max MHz:                          2500.0000
+CPU min MHz:                          1500.0000
+BogoMIPS:                             4999.71
+Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
+Virtualization:                       AMD-V
+L1d cache:                            2 MiB (64 instances)
+L1i cache:                            2 MiB (64 instances)
+L2 cache:                             32 MiB (64 instances)
+L3 cache:                             256 MiB (16 instances)
+NUMA node(s):                         2
+NUMA node0 CPU(s):                    0-31,64-95
+NUMA node1 CPU(s):                    32-63,96-127
+Vulnerability Gather data sampling:   Not affected
+Vulnerability Itlb multihit:          Not affected
+Vulnerability L1tf:                   Not affected
+Vulnerability Mds:                    Not affected
+Vulnerability Meltdown:               Not affected
+Vulnerability Mmio stale data:        Not affected
+Vulnerability Reg file data sampling: Not affected
+Vulnerability Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
+Vulnerability Spec rstack overflow:   Mitigation; safe RET
+Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
+Vulnerability Srbds:                  Not affected
+Vulnerability Tsx async abort:        Not affected
+
+Versions of relevant libraries:
+[pip3] numpy==1.26.3
+[pip3] torch==2.0.1+cu118
+[pip3] torchvision==0.15.2+cu118
+[conda] numpy                     1.26.3                   pypi_0    pypi
+[conda] torch                     2.0.1+cu118              pypi_0    pypi
+[conda] torchvision               0.15.2+cu118             pypi_0    pypi
+        Pillow (10.2.0)
+
+Loading trainer: BiomedCoOp_BiomedCLIP
+Loading dataset: RETINA
+Reading split from /data/t_kol/BiomedCoOp/DATA/RETINA/split_RETINA.json
+Loading preprocessed few-shot data from /data/t_kol/BiomedCoOp/DATA/RETINA/split_fewshot/shot_16-seed_2.pkl
+SUBSAMPLE BASE CLASSES!
+Building transform_train
++ random resized crop (size=(224, 224))
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+***** Dataset statistics *****
+  Dataset: RETINA
+  # classes: 2
+  # train_x: 32
+  # val: 8
+  # test: 633
+Loading BiomedCLIP (backbone: ViT-B/16)
+Building custom CLIP
+Initial text context: "a photo of a"
+Number of context words (tokens) for Language prompting: 4
+Turning off gradients in both the image and the text encoder
+Parameters to be updated: {'prompt_learner.ctx'}
+Parameters count: 1
+Loading evaluator: Classification
+Trainer built successfully.
+No checkpoint found, train from scratch
+Initializing summary writer for tensorboard with log_dir=output_reproduce/base2new/train_base/retina/shots_16/seed2/tensorboard
+epoch [1/50][1/8]	time 0.834 (0.834)	data 0.489 (0.489)	eta 0:05:32	loss 0.4281 (0.4281)	acc 75.0000 (75.0000)	lr 1.000000e-05
+epoch [1/50][2/8]	time 0.051 (0.443)	data 0.001 (0.245)	eta 0:02:56	loss 2.2672 (1.3476)	acc 50.0000 (62.5000)	lr 1.000000e-05
+epoch [1/50][3/8]	time 0.050 (0.312)	data 0.001 (0.164)	eta 0:02:03	loss 1.2523 (1.3159)	acc 75.0000 (66.6667)	lr 1.000000e-05
+epoch [1/50][4/8]	time 0.050 (0.246)	data 0.001 (0.123)	eta 0:01:37	loss 1.3550 (1.3257)	acc 50.0000 (62.5000)	lr 1.000000e-05
+epoch [1/50][5/8]	time 0.050 (0.207)	data 0.001 (0.099)	eta 0:01:21	loss 3.1148 (1.6835)	acc 0.0000 (50.0000)	lr 1.000000e-05
+epoch [1/50][6/8]	time 0.050 (0.181)	data 0.001 (0.082)	eta 0:01:11	loss 1.2138 (1.6052)	acc 50.0000 (50.0000)	lr 1.000000e-05
+epoch [1/50][7/8]	time 0.055 (0.163)	data 0.001 (0.071)	eta 0:01:03	loss 0.7380 (1.4813)	acc 100.0000 (57.1429)	lr 1.000000e-05
+epoch [1/50][8/8]	time 0.054 (0.149)	data 0.001 (0.062)	eta 0:00:58	loss 1.8307 (1.5250)	acc 50.0000 (56.2500)	lr 2.500000e-03
+epoch [2/50][1/8]	time 0.472 (0.472)	data 0.418 (0.418)	eta 0:03:04	loss 1.3739 (1.3739)	acc 50.0000 (50.0000)	lr 2.500000e-03
+epoch [2/50][2/8]	time 0.051 (0.261)	data 0.002 (0.210)	eta 0:01:41	loss 1.0699 (1.2219)	acc 50.0000 (50.0000)	lr 2.500000e-03
+epoch [2/50][3/8]	time 0.050 (0.191)	data 0.001 (0.140)	eta 0:01:14	loss 0.8153 (1.0864)	acc 75.0000 (58.3333)	lr 2.500000e-03
+epoch [2/50][4/8]	time 0.050 (0.156)	data 0.001 (0.106)	eta 0:01:00	loss 1.3224 (1.1454)	acc 50.0000 (56.2500)	lr 2.500000e-03
+epoch [2/50][5/8]	time 0.050 (0.135)	data 0.001 (0.085)	eta 0:00:52	loss 0.4795 (1.0122)	acc 75.0000 (60.0000)	lr 2.500000e-03
+epoch [2/50][6/8]	time 0.050 (0.120)	data 0.001 (0.071)	eta 0:00:46	loss 0.8950 (0.9927)	acc 75.0000 (62.5000)	lr 2.500000e-03
+epoch [2/50][7/8]	time 0.053 (0.111)	data 0.004 (0.061)	eta 0:00:42	loss 0.7824 (0.9626)	acc 75.0000 (64.2857)	lr 2.500000e-03
+epoch [2/50][8/8]	time 0.054 (0.104)	data 0.001 (0.054)	eta 0:00:39	loss 0.6731 (0.9264)	acc 75.0000 (65.6250)	lr 2.497533e-03
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+epoch [3/50][2/8]	time 0.050 (0.250)	data 0.001 (0.199)	eta 0:01:35	loss 1.5942 (1.4103)	acc 50.0000 (50.0000)	lr 2.497533e-03
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+epoch [44/50][5/8]	time 0.049 (0.137)	data 0.001 (0.086)	eta 0:00:06	loss 0.4498 (0.5861)	acc 100.0000 (85.0000)	lr 1.546166e-04
+epoch [44/50][6/8]	time 0.049 (0.122)	data 0.001 (0.072)	eta 0:00:06	loss 0.5495 (0.5800)	acc 100.0000 (87.5000)	lr 1.546166e-04
+epoch [44/50][7/8]	time 0.049 (0.112)	data 0.001 (0.062)	eta 0:00:05	loss 0.4169 (0.5567)	acc 100.0000 (89.2857)	lr 1.546166e-04
+epoch [44/50][8/8]	time 0.049 (0.104)	data 0.001 (0.054)	eta 0:00:04	loss 1.1675 (0.6331)	acc 50.0000 (84.3750)	lr 1.189662e-04
+epoch [45/50][1/8]	time 0.456 (0.456)	data 0.402 (0.402)	eta 0:00:21	loss 0.3518 (0.3518)	acc 100.0000 (100.0000)	lr 1.189662e-04
+epoch [45/50][2/8]	time 0.056 (0.256)	data 0.002 (0.202)	eta 0:00:11	loss 0.2078 (0.2798)	acc 100.0000 (100.0000)	lr 1.189662e-04
+epoch [45/50][3/8]	time 0.058 (0.190)	data 0.005 (0.136)	eta 0:00:08	loss 0.4465 (0.3354)	acc 100.0000 (100.0000)	lr 1.189662e-04
+epoch [45/50][4/8]	time 0.056 (0.156)	data 0.002 (0.103)	eta 0:00:06	loss 0.9498 (0.4890)	acc 50.0000 (87.5000)	lr 1.189662e-04
+epoch [45/50][5/8]	time 0.054 (0.136)	data 0.001 (0.082)	eta 0:00:05	loss 0.3735 (0.4659)	acc 100.0000 (90.0000)	lr 1.189662e-04
+epoch [45/50][6/8]	time 0.058 (0.123)	data 0.004 (0.069)	eta 0:00:05	loss 0.6596 (0.4982)	acc 75.0000 (87.5000)	lr 1.189662e-04
+epoch [45/50][7/8]	time 0.055 (0.113)	data 0.001 (0.060)	eta 0:00:04	loss 0.6993 (0.5269)	acc 75.0000 (85.7143)	lr 1.189662e-04
+epoch [45/50][8/8]	time 0.054 (0.106)	data 0.001 (0.052)	eta 0:00:04	loss 0.5338 (0.5278)	acc 75.0000 (84.3750)	lr 8.777939e-05
+epoch [46/50][1/8]	time 0.574 (0.574)	data 0.520 (0.520)	eta 0:00:22	loss 0.6053 (0.6053)	acc 75.0000 (75.0000)	lr 8.777939e-05
+epoch [46/50][2/8]	time 0.049 (0.311)	data 0.001 (0.261)	eta 0:00:11	loss 0.8872 (0.7462)	acc 75.0000 (75.0000)	lr 8.777939e-05
+epoch [46/50][3/8]	time 0.049 (0.224)	data 0.001 (0.174)	eta 0:00:08	loss 0.4800 (0.6575)	acc 75.0000 (75.0000)	lr 8.777939e-05
+epoch [46/50][4/8]	time 0.049 (0.180)	data 0.001 (0.131)	eta 0:00:06	loss 0.4479 (0.6051)	acc 75.0000 (75.0000)	lr 8.777939e-05
+epoch [46/50][5/8]	time 0.054 (0.155)	data 0.001 (0.105)	eta 0:00:05	loss 0.8426 (0.6526)	acc 100.0000 (80.0000)	lr 8.777939e-05
+epoch [46/50][6/8]	time 0.054 (0.138)	data 0.001 (0.088)	eta 0:00:04	loss 0.6981 (0.6602)	acc 75.0000 (79.1667)	lr 8.777939e-05
+epoch [46/50][7/8]	time 0.054 (0.126)	data 0.001 (0.075)	eta 0:00:04	loss 0.2910 (0.6074)	acc 100.0000 (82.1429)	lr 8.777939e-05
+epoch [46/50][8/8]	time 0.054 (0.117)	data 0.001 (0.066)	eta 0:00:03	loss 0.5383 (0.5988)	acc 75.0000 (81.2500)	lr 6.117935e-05
+epoch [47/50][1/8]	time 0.441 (0.441)	data 0.387 (0.387)	eta 0:00:13	loss 0.9108 (0.9108)	acc 75.0000 (75.0000)	lr 6.117935e-05
+epoch [47/50][2/8]	time 0.055 (0.248)	data 0.005 (0.196)	eta 0:00:07	loss 0.3510 (0.6309)	acc 100.0000 (87.5000)	lr 6.117935e-05
+epoch [47/50][3/8]	time 0.061 (0.185)	data 0.005 (0.133)	eta 0:00:05	loss 0.7324 (0.6647)	acc 100.0000 (91.6667)	lr 6.117935e-05
+epoch [47/50][4/8]	time 0.053 (0.152)	data 0.001 (0.100)	eta 0:00:04	loss 0.2975 (0.5729)	acc 100.0000 (93.7500)	lr 6.117935e-05
+epoch [47/50][5/8]	time 0.053 (0.133)	data 0.001 (0.080)	eta 0:00:03	loss 0.4768 (0.5537)	acc 75.0000 (90.0000)	lr 6.117935e-05
+epoch [47/50][6/8]	time 0.054 (0.119)	data 0.001 (0.067)	eta 0:00:03	loss 0.3674 (0.5226)	acc 100.0000 (91.6667)	lr 6.117935e-05
+epoch [47/50][7/8]	time 0.059 (0.111)	data 0.006 (0.058)	eta 0:00:02	loss 0.9016 (0.5768)	acc 75.0000 (89.2857)	lr 6.117935e-05
+epoch [47/50][8/8]	time 0.056 (0.104)	data 0.001 (0.051)	eta 0:00:02	loss 0.3570 (0.5493)	acc 100.0000 (90.6250)	lr 3.927105e-05
+epoch [48/50][1/8]	time 0.487 (0.487)	data 0.433 (0.433)	eta 0:00:11	loss 1.0506 (1.0506)	acc 25.0000 (25.0000)	lr 3.927105e-05
+epoch [48/50][2/8]	time 0.107 (0.297)	data 0.052 (0.242)	eta 0:00:06	loss 0.3359 (0.6933)	acc 100.0000 (62.5000)	lr 3.927105e-05
+epoch [48/50][3/8]	time 0.061 (0.218)	data 0.002 (0.162)	eta 0:00:04	loss 0.5501 (0.6455)	acc 50.0000 (58.3333)	lr 3.927105e-05
+epoch [48/50][4/8]	time 0.055 (0.177)	data 0.001 (0.122)	eta 0:00:03	loss 0.5201 (0.6142)	acc 100.0000 (68.7500)	lr 3.927105e-05
+epoch [48/50][5/8]	time 0.055 (0.153)	data 0.001 (0.098)	eta 0:00:02	loss 0.5343 (0.5982)	acc 75.0000 (70.0000)	lr 3.927105e-05
+epoch [48/50][6/8]	time 0.063 (0.138)	data 0.001 (0.082)	eta 0:00:02	loss 0.8022 (0.6322)	acc 75.0000 (70.8333)	lr 3.927105e-05
+epoch [48/50][7/8]	time 0.054 (0.126)	data 0.001 (0.070)	eta 0:00:02	loss 0.5335 (0.6181)	acc 100.0000 (75.0000)	lr 3.927105e-05
+epoch [48/50][8/8]	time 0.053 (0.117)	data 0.001 (0.061)	eta 0:00:01	loss 0.4901 (0.6021)	acc 100.0000 (78.1250)	lr 2.214094e-05
+epoch [49/50][1/8]	time 0.465 (0.465)	data 0.411 (0.411)	eta 0:00:06	loss 0.4649 (0.4649)	acc 100.0000 (100.0000)	lr 2.214094e-05
+epoch [49/50][2/8]	time 0.050 (0.258)	data 0.002 (0.207)	eta 0:00:03	loss 1.1464 (0.8056)	acc 75.0000 (87.5000)	lr 2.214094e-05
+epoch [49/50][3/8]	time 0.052 (0.189)	data 0.003 (0.139)	eta 0:00:02	loss 1.4243 (1.0119)	acc 50.0000 (75.0000)	lr 2.214094e-05
+epoch [49/50][4/8]	time 0.052 (0.155)	data 0.004 (0.105)	eta 0:00:01	loss 0.2932 (0.8322)	acc 100.0000 (81.2500)	lr 2.214094e-05
+epoch [49/50][5/8]	time 0.049 (0.134)	data 0.001 (0.084)	eta 0:00:01	loss 0.8771 (0.8412)	acc 50.0000 (75.0000)	lr 2.214094e-05
+epoch [49/50][6/8]	time 0.049 (0.119)	data 0.001 (0.070)	eta 0:00:01	loss 0.1597 (0.7276)	acc 100.0000 (79.1667)	lr 2.214094e-05
+epoch [49/50][7/8]	time 0.049 (0.109)	data 0.001 (0.061)	eta 0:00:00	loss 0.6331 (0.7141)	acc 75.0000 (78.5714)	lr 2.214094e-05
+epoch [49/50][8/8]	time 0.050 (0.102)	data 0.001 (0.053)	eta 0:00:00	loss 0.3306 (0.6662)	acc 75.0000 (78.1250)	lr 9.856623e-06
+epoch [50/50][1/8]	time 0.461 (0.461)	data 0.404 (0.404)	eta 0:00:03	loss 0.5674 (0.5674)	acc 100.0000 (100.0000)	lr 9.856623e-06
+epoch [50/50][2/8]	time 0.051 (0.256)	data 0.001 (0.202)	eta 0:00:01	loss 0.5151 (0.5412)	acc 75.0000 (87.5000)	lr 9.856623e-06
+epoch [50/50][3/8]	time 0.051 (0.188)	data 0.001 (0.135)	eta 0:00:00	loss 0.7511 (0.6112)	acc 75.0000 (83.3333)	lr 9.856623e-06
+epoch [50/50][4/8]	time 0.050 (0.153)	data 0.001 (0.102)	eta 0:00:00	loss 0.2966 (0.5326)	acc 100.0000 (87.5000)	lr 9.856623e-06
+epoch [50/50][5/8]	time 0.055 (0.134)	data 0.001 (0.082)	eta 0:00:00	loss 0.4091 (0.5079)	acc 75.0000 (85.0000)	lr 9.856623e-06
+epoch [50/50][6/8]	time 0.055 (0.121)	data 0.001 (0.068)	eta 0:00:00	loss 0.9554 (0.5824)	acc 50.0000 (79.1667)	lr 9.856623e-06
+epoch [50/50][7/8]	time 0.055 (0.111)	data 0.001 (0.059)	eta 0:00:00	loss 0.7335 (0.6040)	acc 75.0000 (78.5714)	lr 9.856623e-06
+epoch [50/50][8/8]	time 0.058 (0.105)	data 0.003 (0.052)	eta 0:00:00	loss 0.4917 (0.5900)	acc 75.0000 (78.1250)	lr 2.466589e-06
+Checkpoint saved to "output_reproduce/base2new/train_base/retina/shots_16/seed2/prompt_learner/model.pth.tar-50"
+Finished training
+Do evaluation on test set
+=> result
+* total: 633
+* correct: 425
+* accuracy: 67.14%
+* error: 32.86%
+* macro_f1: 66.06%
+Elapsed: 0:00:55

base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed2/prompt_learner/checkpoint

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base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed3/log.txt

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+***************
+** Arguments **
+***************
+backbone: 
+config_file: configs/trainers/BiomedCoOp_BiomedCLIP/base_to_novel/retina.yaml
+dataset_config_file: configs/datasets/retina.yaml
+eval_only: False
+head: 
+load_epoch: None
+model_dir: 
+no_train: False
+opts: ['DATASET.NUM_SHOTS', '16', 'DATASET.SUBSAMPLE_CLASSES', 'base']
+output_dir: output_reproduce/base2new/train_base/retina/shots_16/seed3
+resume: 
+root: DATA
+seed: 3
+source_domains: None
+target_domains: None
+trainer: BiomedCoOp_BiomedCLIP
+transforms: None
+************
+** Config **
+************
+DATALOADER:
+  K_TRANSFORMS: 1
+  NUM_WORKERS: 8
+  RETURN_IMG0: False
+  TEST:
+    BATCH_SIZE: 100
+    SAMPLER: SequentialSampler
+  TRAIN_U:
+    BATCH_SIZE: 32
+    N_DOMAIN: 0
+    N_INS: 16
+    SAME_AS_X: True
+    SAMPLER: RandomSampler
+  TRAIN_X:
+    BATCH_SIZE: 4
+    N_DOMAIN: 0
+    N_INS: 16
+    SAMPLER: RandomSampler
+DATASET:
+  ALL_AS_UNLABELED: False
+  CIFAR_C_LEVEL: 1
+  CIFAR_C_TYPE: 
+  NAME: RETINA
+  NUM_LABELED: -1
+  NUM_SHOTS: 16
+  ROOT: DATA
+  SOURCE_DOMAINS: ()
+  STL10_FOLD: -1
+  SUBSAMPLE_CLASSES: base
+  TARGET_DOMAINS: ()
+  VAL_PERCENT: 0.1
+INPUT:
+  COLORJITTER_B: 0.4
+  COLORJITTER_C: 0.4
+  COLORJITTER_H: 0.1
+  COLORJITTER_S: 0.4
+  CROP_PADDING: 4
+  CUTOUT_LEN: 16
+  CUTOUT_N: 1
+  GB_K: 21
+  GB_P: 0.5
+  GN_MEAN: 0.0
+  GN_STD: 0.15
+  INTERPOLATION: bicubic
+  NO_TRANSFORM: False
+  PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073]
+  PIXEL_STD: [0.26862954, 0.26130258, 0.27577711]
+  RANDAUGMENT_M: 10
+  RANDAUGMENT_N: 2
+  RGS_P: 0.2
+  SIZE: (224, 224)
+  TRANSFORMS: ('random_resized_crop', 'normalize')
+MODEL:
+  BACKBONE:
+    NAME: ViT-B/16
+    PRETRAINED: True
+  HEAD:
+    ACTIVATION: relu
+    BN: True
+    DROPOUT: 0.0
+    HIDDEN_LAYERS: ()
+    NAME: 
+  INIT_WEIGHTS: 
+OPTIM:
+  ADAM_BETA1: 0.9
+  ADAM_BETA2: 0.999
+  BASE_LR_MULT: 0.1
+  GAMMA: 0.1
+  LR: 0.0025
+  LR_SCHEDULER: cosine
+  MAX_EPOCH: 50
+  MOMENTUM: 0.9
+  NAME: sgd
+  NEW_LAYERS: ()
+  RMSPROP_ALPHA: 0.99
+  SGD_DAMPNING: 0
+  SGD_NESTEROV: False
+  STAGED_LR: False
+  STEPSIZE: (-1,)
+  WARMUP_CONS_LR: 1e-05
+  WARMUP_EPOCH: 1
+  WARMUP_MIN_LR: 1e-05
+  WARMUP_RECOUNT: True
+  WARMUP_TYPE: constant
+  WEIGHT_DECAY: 0.0005
+OUTPUT_DIR: output_reproduce/base2new/train_base/retina/shots_16/seed3
+RESUME: 
+SEED: 3
+TEST:
+  COMPUTE_CMAT: False
+  EVALUATOR: Classification
+  FINAL_MODEL: last_step
+  NO_TEST: False
+  PER_CLASS_RESULT: False
+  SPLIT: test
+TRAIN:
+  CHECKPOINT_FREQ: 0
+  COUNT_ITER: train_x
+  PRINT_FREQ: 20
+TRAINER:
+  BIOMEDCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    KDSP_LAMBDA: 1.0
+    N_CTX: 4
+    N_PROMPTS: 50
+    PREC: fp32
+    SCCM_LAMBDA: 5.0
+    TAU: 2.0
+  CG:
+    ALPHA_D: 0.5
+    ALPHA_F: 0.5
+    EPS_D: 1.0
+    EPS_F: 1.0
+  COCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: 
+    N_CTX: 4
+    PREC: fp32
+  COOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: 
+    N_CTX: 4
+    PREC: fp32
+  DAEL:
+    CONF_THRE: 0.95
+    STRONG_TRANSFORMS: ()
+    WEIGHT_U: 0.5
+  DDAIG:
+    ALPHA: 0.5
+    CLAMP: False
+    CLAMP_MAX: 1.0
+    CLAMP_MIN: -1.0
+    G_ARCH: 
+    LMDA: 0.3
+    WARMUP: 0
+  ENTMIN:
+    LMDA: 0.001
+  FIXMATCH:
+    CONF_THRE: 0.95
+    STRONG_TRANSFORMS: ()
+    WEIGHT_U: 1.0
+  KGCOOP:
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    N_CTX: 4
+    PREC: fp32
+    W: 1.0
+  M3SDA:
+    LMDA: 0.5
+    N_STEP_F: 4
+  MCD:
+    N_STEP_F: 4
+  MEANTEA:
+    EMA_ALPHA: 0.999
+    RAMPUP: 5
+    WEIGHT_U: 1.0
+  MIXMATCH:
+    MIXUP_BETA: 0.75
+    RAMPUP: 20000
+    TEMP: 2.0
+    WEIGHT_U: 100.0
+  MME:
+    LMDA: 0.1
+  NAME: BiomedCoOp_BiomedCLIP
+  PROGRAD:
+    ALPHA: 0.0
+    CLASS_TOKEN_POSITION: end
+    CSC: False
+    CTX_INIT: a photo of a
+    GM: False
+    LAMBDA: 1.0
+    NAME: 
+    N_CTX: 4
+    PREC: fp32
+    T: 1.0
+  SE:
+    CONF_THRE: 0.95
+    EMA_ALPHA: 0.999
+    RAMPUP: 300
+USE_CUDA: True
+VERBOSE: True
+VERSION: 1
+Collecting env info ...
+** System info **
+PyTorch version: 2.0.1+cu118
+Is debug build: False
+CUDA used to build PyTorch: 11.8
+ROCM used to build PyTorch: N/A
+
+OS: Ubuntu 22.04.5 LTS (x86_64)
+GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
+Clang version: Could not collect
+CMake version: version 3.25.0
+Libc version: glibc-2.35
+
+Python version: 3.10.15 (main, Oct  3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)
+Python platform: Linux-5.15.0-134-generic-x86_64-with-glibc2.35
+Is CUDA available: True
+CUDA runtime version: 12.8.61
+CUDA_MODULE_LOADING set to: LAZY
+GPU models and configuration: 
+GPU 0: NVIDIA A100-SXM4-40GB
+GPU 1: NVIDIA A100-SXM4-40GB
+GPU 2: NVIDIA A100-SXM4-40GB
+GPU 3: NVIDIA A100-SXM4-40GB
+
+Nvidia driver version: 570.124.06
+cuDNN version: Could not collect
+HIP runtime version: N/A
+MIOpen runtime version: N/A
+Is XNNPACK available: True
+
+CPU:
+Architecture:                         x86_64
+CPU op-mode(s):                       32-bit, 64-bit
+Address sizes:                        43 bits physical, 48 bits virtual
+Byte Order:                           Little Endian
+CPU(s):                               128
+On-line CPU(s) list:                  0-127
+Vendor ID:                            AuthenticAMD
+Model name:                           AMD EPYC 7502 32-Core Processor
+CPU family:                           23
+Model:                                49
+Thread(s) per core:                   2
+Core(s) per socket:                   32
+Socket(s):                            2
+Stepping:                             0
+Frequency boost:                      enabled
+CPU max MHz:                          2500.0000
+CPU min MHz:                          1500.0000
+BogoMIPS:                             4999.71
+Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
+Virtualization:                       AMD-V
+L1d cache:                            2 MiB (64 instances)
+L1i cache:                            2 MiB (64 instances)
+L2 cache:                             32 MiB (64 instances)
+L3 cache:                             256 MiB (16 instances)
+NUMA node(s):                         2
+NUMA node0 CPU(s):                    0-31,64-95
+NUMA node1 CPU(s):                    32-63,96-127
+Vulnerability Gather data sampling:   Not affected
+Vulnerability Itlb multihit:          Not affected
+Vulnerability L1tf:                   Not affected
+Vulnerability Mds:                    Not affected
+Vulnerability Meltdown:               Not affected
+Vulnerability Mmio stale data:        Not affected
+Vulnerability Reg file data sampling: Not affected
+Vulnerability Retbleed:               Mitigation; untrained return thunk; SMT enabled with STIBP protection
+Vulnerability Spec rstack overflow:   Mitigation; safe RET
+Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
+Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2:             Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
+Vulnerability Srbds:                  Not affected
+Vulnerability Tsx async abort:        Not affected
+
+Versions of relevant libraries:
+[pip3] numpy==1.26.3
+[pip3] torch==2.0.1+cu118
+[pip3] torchvision==0.15.2+cu118
+[conda] numpy                     1.26.3                   pypi_0    pypi
+[conda] torch                     2.0.1+cu118              pypi_0    pypi
+[conda] torchvision               0.15.2+cu118             pypi_0    pypi
+        Pillow (10.2.0)
+
+Loading trainer: BiomedCoOp_BiomedCLIP
+Loading dataset: RETINA
+Reading split from /data/t_kol/BiomedCoOp/DATA/RETINA/split_RETINA.json
+Loading preprocessed few-shot data from /data/t_kol/BiomedCoOp/DATA/RETINA/split_fewshot/shot_16-seed_3.pkl
+SUBSAMPLE BASE CLASSES!
+Building transform_train
++ random resized crop (size=(224, 224))
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+Building transform_test
++ resize the smaller edge to 224
++ 224x224 center crop
++ to torch tensor of range [0, 1]
++ normalization (mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711])
+***** Dataset statistics *****
+  Dataset: RETINA
+  # classes: 2
+  # train_x: 32
+  # val: 8
+  # test: 633
+Loading BiomedCLIP (backbone: ViT-B/16)
+Building custom CLIP
+Initial text context: "a photo of a"
+Number of context words (tokens) for Language prompting: 4
+Turning off gradients in both the image and the text encoder
+Parameters to be updated: {'prompt_learner.ctx'}
+Parameters count: 1
+Loading evaluator: Classification
+Trainer built successfully.
+No checkpoint found, train from scratch
+Initializing summary writer for tensorboard with log_dir=output_reproduce/base2new/train_base/retina/shots_16/seed3/tensorboard
+epoch [1/50][1/8]	time 0.701 (0.701)	data 0.375 (0.375)	eta 0:04:39	loss 2.7313 (2.7313)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][2/8]	time 0.060 (0.380)	data 0.010 (0.192)	eta 0:02:31	loss 2.7793 (2.7553)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][3/8]	time 0.050 (0.270)	data 0.001 (0.129)	eta 0:01:47	loss 0.8561 (2.1223)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][4/8]	time 0.050 (0.215)	data 0.001 (0.097)	eta 0:01:25	loss 3.0388 (2.3514)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][5/8]	time 0.054 (0.183)	data 0.001 (0.077)	eta 0:01:12	loss 3.3619 (2.5535)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][6/8]	time 0.058 (0.162)	data 0.004 (0.065)	eta 0:01:03	loss 4.6274 (2.8991)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][7/8]	time 0.054 (0.147)	data 0.001 (0.056)	eta 0:00:57	loss 1.7355 (2.7329)	acc 25.0000 (25.0000)	lr 1.000000e-05
+epoch [1/50][8/8]	time 0.054 (0.135)	data 0.001 (0.049)	eta 0:00:52	loss 1.5118 (2.5803)	acc 50.0000 (28.1250)	lr 2.500000e-03
+epoch [2/50][1/8]	time 0.500 (0.500)	data 0.446 (0.446)	eta 0:03:15	loss 1.1952 (1.1952)	acc 25.0000 (25.0000)	lr 2.500000e-03
+epoch [2/50][2/8]	time 0.049 (0.274)	data 0.001 (0.224)	eta 0:01:47	loss 1.7996 (1.4974)	acc 75.0000 (50.0000)	lr 2.500000e-03
+epoch [2/50][3/8]	time 0.049 (0.199)	data 0.001 (0.149)	eta 0:01:17	loss 2.0049 (1.6666)	acc 50.0000 (50.0000)	lr 2.500000e-03
+epoch [2/50][4/8]	time 0.049 (0.162)	data 0.001 (0.112)	eta 0:01:02	loss 1.4148 (1.6037)	acc 50.0000 (50.0000)	lr 2.500000e-03
+epoch [2/50][5/8]	time 0.049 (0.139)	data 0.001 (0.090)	eta 0:00:53	loss 1.4577 (1.5745)	acc 50.0000 (50.0000)	lr 2.500000e-03
+epoch [2/50][6/8]	time 0.054 (0.125)	data 0.001 (0.075)	eta 0:00:48	loss 1.4876 (1.5600)	acc 0.0000 (41.6667)	lr 2.500000e-03
+epoch [2/50][7/8]	time 0.054 (0.115)	data 0.001 (0.065)	eta 0:00:44	loss 1.6638 (1.5748)	acc 25.0000 (39.2857)	lr 2.500000e-03
+epoch [2/50][8/8]	time 0.056 (0.108)	data 0.003 (0.057)	eta 0:00:41	loss 1.5016 (1.5657)	acc 25.0000 (37.5000)	lr 2.497533e-03
+epoch [3/50][1/8]	time 0.488 (0.488)	data 0.432 (0.432)	eta 0:03:07	loss 2.2064 (2.2064)	acc 25.0000 (25.0000)	lr 2.497533e-03
+epoch [3/50][2/8]	time 0.054 (0.271)	data 0.005 (0.219)	eta 0:01:43	loss 6.7606 (4.4835)	acc 25.0000 (25.0000)	lr 2.497533e-03
+epoch [3/50][3/8]	time 0.053 (0.198)	data 0.004 (0.147)	eta 0:01:15	loss 1.4697 (3.4789)	acc 0.0000 (16.6667)	lr 2.497533e-03
+epoch [3/50][4/8]	time 0.053 (0.162)	data 0.004 (0.111)	eta 0:01:01	loss 1.1428 (2.8949)	acc 50.0000 (25.0000)	lr 2.497533e-03
+epoch [3/50][5/8]	time 0.050 (0.140)	data 0.001 (0.089)	eta 0:00:52	loss 1.8927 (2.6944)	acc 50.0000 (30.0000)	lr 2.497533e-03
+epoch [3/50][6/8]	time 0.050 (0.125)	data 0.001 (0.074)	eta 0:00:47	loss 1.1445 (2.4361)	acc 25.0000 (29.1667)	lr 2.497533e-03
+epoch [3/50][7/8]	time 0.050 (0.114)	data 0.001 (0.064)	eta 0:00:42	loss 0.8489 (2.2094)	acc 50.0000 (32.1429)	lr 2.497533e-03
+epoch [3/50][8/8]	time 0.050 (0.106)	data 0.001 (0.056)	eta 0:00:39	loss 1.0646 (2.0663)	acc 25.0000 (31.2500)	lr 2.490143e-03
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+epoch [4/50][2/8]	time 0.055 (0.287)	data 0.001 (0.229)	eta 0:01:47	loss 1.6110 (1.6172)	acc 0.0000 (0.0000)	lr 2.490143e-03
+epoch [4/50][3/8]	time 0.054 (0.209)	data 0.001 (0.153)	eta 0:01:18	loss 0.9765 (1.4036)	acc 75.0000 (25.0000)	lr 2.490143e-03
+epoch [4/50][4/8]	time 0.058 (0.171)	data 0.004 (0.116)	eta 0:01:03	loss 1.2489 (1.3649)	acc 25.0000 (25.0000)	lr 2.490143e-03
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+epoch [4/50][7/8]	time 0.054 (0.121)	data 0.001 (0.067)	eta 0:00:44	loss 0.8054 (1.2600)	acc 75.0000 (39.2857)	lr 2.490143e-03
+epoch [4/50][8/8]	time 0.054 (0.113)	data 0.001 (0.058)	eta 0:00:41	loss 0.5712 (1.1739)	acc 100.0000 (46.8750)	lr 2.477859e-03
+epoch [5/50][1/8]	time 0.509 (0.509)	data 0.446 (0.446)	eta 0:03:06	loss 1.0269 (1.0269)	acc 25.0000 (25.0000)	lr 2.477859e-03
+epoch [5/50][2/8]	time 0.049 (0.279)	data 0.001 (0.223)	eta 0:01:42	loss 0.9143 (0.9706)	acc 50.0000 (37.5000)	lr 2.477859e-03
+epoch [5/50][3/8]	time 0.052 (0.203)	data 0.001 (0.149)	eta 0:01:14	loss 1.2279 (1.0564)	acc 25.0000 (33.3333)	lr 2.477859e-03
+epoch [5/50][4/8]	time 0.051 (0.165)	data 0.001 (0.112)	eta 0:01:00	loss 0.8703 (1.0099)	acc 75.0000 (43.7500)	lr 2.477859e-03
+epoch [5/50][5/8]	time 0.050 (0.142)	data 0.001 (0.090)	eta 0:00:51	loss 1.5002 (1.1079)	acc 25.0000 (40.0000)	lr 2.477859e-03
+epoch [5/50][6/8]	time 0.050 (0.127)	data 0.001 (0.075)	eta 0:00:45	loss 0.9572 (1.0828)	acc 75.0000 (45.8333)	lr 2.477859e-03
+epoch [5/50][7/8]	time 0.050 (0.116)	data 0.001 (0.065)	eta 0:00:41	loss 1.1924 (1.0985)	acc 50.0000 (46.4286)	lr 2.477859e-03
+epoch [5/50][8/8]	time 0.050 (0.108)	data 0.001 (0.057)	eta 0:00:38	loss 1.0260 (1.0894)	acc 25.0000 (43.7500)	lr 2.460729e-03
+epoch [6/50][1/8]	time 0.486 (0.486)	data 0.423 (0.423)	eta 0:02:54	loss 0.5476 (0.5476)	acc 75.0000 (75.0000)	lr 2.460729e-03
+epoch [6/50][2/8]	time 0.052 (0.269)	data 0.004 (0.213)	eta 0:01:36	loss 2.4237 (1.4856)	acc 0.0000 (37.5000)	lr 2.460729e-03
+epoch [6/50][3/8]	time 0.054 (0.198)	data 0.001 (0.143)	eta 0:01:10	loss 0.7111 (1.2274)	acc 75.0000 (50.0000)	lr 2.460729e-03
+epoch [6/50][4/8]	time 0.055 (0.162)	data 0.001 (0.107)	eta 0:00:57	loss 1.2135 (1.2240)	acc 25.0000 (43.7500)	lr 2.460729e-03
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+epoch [6/50][8/8]	time 0.055 (0.109)	data 0.001 (0.055)	eta 0:00:38	loss 1.7933 (1.1319)	acc 50.0000 (46.8750)	lr 2.438821e-03
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+epoch [7/50][4/8]	time 0.050 (0.169)	data 0.001 (0.116)	eta 0:00:58	loss 0.9556 (1.1932)	acc 50.0000 (37.5000)	lr 2.438821e-03
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+epoch [7/50][7/8]	time 0.052 (0.118)	data 0.003 (0.067)	eta 0:00:40	loss 0.7831 (1.0276)	acc 50.0000 (39.2857)	lr 2.438821e-03
+epoch [7/50][8/8]	time 0.051 (0.110)	data 0.001 (0.059)	eta 0:00:37	loss 0.7143 (0.9884)	acc 50.0000 (40.6250)	lr 2.412221e-03
+epoch [8/50][1/8]	time 0.508 (0.508)	data 0.442 (0.442)	eta 0:02:54	loss 0.8433 (0.8433)	acc 100.0000 (100.0000)	lr 2.412221e-03
+epoch [8/50][2/8]	time 0.053 (0.281)	data 0.001 (0.222)	eta 0:01:36	loss 0.7297 (0.7865)	acc 75.0000 (87.5000)	lr 2.412221e-03
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+epoch [11/50][8/8]	time 0.055 (0.107)	data 0.001 (0.055)	eta 0:00:33	loss 0.6280 (0.9685)	acc 75.0000 (53.1250)	lr 2.261271e-03
+epoch [12/50][1/8]	time 0.498 (0.498)	data 0.431 (0.431)	eta 0:02:34	loss 0.8116 (0.8116)	acc 75.0000 (75.0000)	lr 2.261271e-03
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+epoch [12/50][8/8]	time 0.055 (0.111)	data 0.001 (0.055)	eta 0:00:33	loss 0.8535 (0.9410)	acc 75.0000 (46.8750)	lr 2.213142e-03
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+epoch [13/50][2/8]	time 0.055 (0.300)	data 0.001 (0.239)	eta 0:01:30	loss 1.0879 (1.0725)	acc 50.0000 (37.5000)	lr 2.213142e-03
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+epoch [13/50][4/8]	time 0.056 (0.177)	data 0.001 (0.120)	eta 0:00:53	loss 0.8871 (0.9348)	acc 25.0000 (43.7500)	lr 2.213142e-03
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+epoch [50/50][1/8]	time 0.492 (0.492)	data 0.439 (0.439)	eta 0:00:03	loss 0.6350 (0.6350)	acc 100.0000 (100.0000)	lr 9.856623e-06
+epoch [50/50][2/8]	time 0.049 (0.271)	data 0.001 (0.220)	eta 0:00:01	loss 0.7153 (0.6751)	acc 75.0000 (87.5000)	lr 9.856623e-06
+epoch [50/50][3/8]	time 0.054 (0.199)	data 0.001 (0.147)	eta 0:00:00	loss 0.6498 (0.6667)	acc 100.0000 (91.6667)	lr 9.856623e-06
+epoch [50/50][4/8]	time 0.061 (0.164)	data 0.007 (0.112)	eta 0:00:00	loss 0.3441 (0.5860)	acc 100.0000 (93.7500)	lr 9.856623e-06
+epoch [50/50][5/8]	time 0.055 (0.142)	data 0.001 (0.090)	eta 0:00:00	loss 0.8165 (0.6321)	acc 50.0000 (85.0000)	lr 9.856623e-06
+epoch [50/50][6/8]	time 0.055 (0.128)	data 0.001 (0.075)	eta 0:00:00	loss 0.9354 (0.6827)	acc 25.0000 (75.0000)	lr 9.856623e-06
+epoch [50/50][7/8]	time 0.055 (0.117)	data 0.001 (0.064)	eta 0:00:00	loss 0.8512 (0.7068)	acc 50.0000 (71.4286)	lr 9.856623e-06
+epoch [50/50][8/8]	time 0.055 (0.110)	data 0.001 (0.057)	eta 0:00:00	loss 0.9372 (0.7356)	acc 25.0000 (65.6250)	lr 2.466589e-06
+Checkpoint saved to "output_reproduce/base2new/train_base/retina/shots_16/seed3/prompt_learner/model.pth.tar-50"
+Finished training
+Do evaluation on test set
+=> result
+* total: 633
+* correct: 411
+* accuracy: 64.93%
+* error: 35.07%
+* macro_f1: 62.99%
+Elapsed: 0:00:57

base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed3/prompt_learner/checkpoint

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base2new/train_base/retina/shots_16/BiomedCoOp_BiomedCLIP/nctx4_cscFalse_ctpend/seed3/prompt_learner/model.pth.tar-50

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+size 1130209477

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