***************
** Arguments **
***************
backbone: 
config_file: configs/trainers/CoOp/vit_b16_ep50_bs4.yaml
dataset_config_file: configs/datasets/sun397.yaml
eval_only: True
head: 
load_epoch: None
model_dir: 
no_train: False
opts: ['DATASET.SUBSAMPLE_CLASSES', 'new_ratio3']
output_dir: output/new_ratio3/ZeroshotCLIP/vit_b16_ep50_bs4/sun397/1
resume: 
root: /mnt/hdd/DATA
seed: 1
source_domains: None
target_domains: None
trainer: ZeroshotCLIP
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: SUN397
  NUM_LABELED: -1
  NUM_SHOTS: -1
  ROOT: /mnt/hdd/DATA
  SOURCE_DOMAINS: ()
  STL10_FOLD: -1
  SUBSAMPLE_CLASSES: new_ratio3
  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
  RRCROP_SCALE: (0.08, 1.0)
  SIZE: (224, 224)
  TRANSFORMS: ('random_resized_crop', 'random_flip', '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: 0.002
  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/new_ratio3/ZeroshotCLIP/vit_b16_ep50_bs4/sun397/1
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:
  CDAC:
    CLASS_LR_MULTI: 10
    P_THRESH: 0.95
    RAMPUP_COEF: 30
    RAMPUP_ITRS: 1000
    STRONG_TRANSFORMS: ()
    TOPK_MATCH: 5
  COCOOP:
    CTX_INIT: 
    N_CTX: 16
    PREC: fp16
  COOP:
    CLASS_TOKEN_POSITION: end
    CSC: False
    CTX_INIT: 
    N_CTX: 16
    PREC: fp16
  CROSSGRAD:
    ALPHA_D: 0.5
    ALPHA_F: 0.5
    EPS_D: 1.0
    EPS_F: 1.0
  DAEL:
    CONF_THRE: 0.95
    STRONG_TRANSFORMS: ()
    WEIGHT_U: 0.5
  DAELDG:
    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
  DOMAINMIX:
    ALPHA: 1.0
    BETA: 1.0
    TYPE: crossdomain
  ENTMIN:
    LMDA: 0.001
  FIXMATCH:
    CONF_THRE: 0.95
    STRONG_TRANSFORMS: ()
    WEIGHT_U: 1.0
  IVLP:
    CTX_INIT: a photo of a
    N_CTX_TEXT: 2
    N_CTX_VISION: 2
    PREC: fp16
    PROMPT_DEPTH_TEXT: 9
    PROMPT_DEPTH_VISION: 9
  M3SDA:
    LMDA: 0.5
    N_STEP_F: 4
  MAPLE:
    CTX_INIT: a photo of a
    N_CTX: 4
    PREC: fp16
    PROMPT_DEPTH: 9
  MCD:
    N_STEP_F: 4
  MEANTEACHER:
    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: ZeroshotCLIP
  ProDA:
    N_CTX: 16
    N_PROMPT: 32
    PREC: fp16
  SE:
    CONF_THRE: 0.95
    EMA_ALPHA: 0.999
    RAMPUP: 300
  VPT:
    CTX_INIT: a photo of a
    N_CTX_VISION: 2
    PREC: fp16
    PROMPT_DEPTH_VISION: 1
USE_CUDA: True
VERBOSE: True
VERSION: 1
Collecting env info ...
** System info **
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version: (Debian 12.2.0-14) 12.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.36

Python version: 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-6.5.13-3-pve-x86_64-with-glibc2.36
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A800 80GB PCIe
GPU 1: NVIDIA A800 80GB PCIe

Nvidia driver version: 525.147.05
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:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             64
On-line CPU(s) list:                18,20,22,23,25-27,29,31,32,34,37,46-49
Off-line CPU(s) list:               0-17,19,21,24,28,30,33,35,36,38-45,50-63
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          2
Stepping:                           6
CPU(s) scaling MHz:                 97%
CPU max MHz:                        3500.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5800.00
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          1.5 MiB (32 instances)
L1i cache:                          1 MiB (32 instances)
L2 cache:                           40 MiB (32 instances)
L3 cache:                           48 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-15,32-47
NUMA node1 CPU(s):                  16-31,48-63
Vulnerability Gather data sampling: Vulnerable: No microcode
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] flake8==3.7.9
[pip3] flake8==3.7.9
[pip3] numpy==1.26.4
[pip3] torch==2.2.1
[pip3] torchaudio==2.2.1
[pip3] torchvision==0.17.1
[pip3] triton==2.2.0
[conda] Could not collect
        Pillow (10.2.0)

Loading trainer: ZeroshotCLIP
Loading dataset: SUN397
Reading split from /mnt/hdd/DATA/sun397/split_zhou_SUN397.json
Loading preprocessed few-shot data from /mnt/hdd/DATA/sun397/split_fewshot/shot_-1_shuffled-seed_1.pkl
SUBSAMPLE NEW_RATIO3 CLASSES!
Building transform_train
+ random resized crop (size=(224, 224), scale=(0.08, 1.0))
+ random flip
+ 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    SUN397
# classes  199
# train_x  7,960
# val      1,990
# test     9,950
---------  ------
Loading CLIP (backbone: ViT-B/16)
Prompts: ['a photo of a server room.', 'a photo of a outdoor cabin.', 'a photo of a indoor seats theater.', 'a photo of a outdoor planetarium.', 'a photo of a lecture room.', 'a photo of a golf course.', 'a photo of a pavilion.', 'a photo of a patio.', 'a photo of a indoor greenhouse.', 'a photo of a lock chamber.', 'a photo of a residential neighborhood.', 'a photo of a bar.', 'a photo of a driveway.', 'a photo of a wild field.', 'a photo of a hayfield.', 'a photo of a platform train station.', 'a photo of a campsite.', 'a photo of a phone booth.', 'a photo of a indoor volleyball court.', 'a photo of a skatepark.', 'a photo of a living room.', 'a photo of a indoor ice skating rink.', 'a photo of a mansion.', 'a photo of a indoor cloister.', 'a photo of a baggage claim.', 'a photo of a sand desert.', 'a photo of a berth.', 'a photo of a indoor kennel.', 'a photo of a indoor escalator.', 'a photo of a vegetable garden.', 'a photo of a indoor badminton court.', 'a photo of a motel.', 'a photo of a indoor swimming pool.', 'a photo of a classroom.', 'a photo of a waiting room.', 'a photo of a outdoor synagogue.', 'a photo of a martial arts gym.', 'a photo of a pulpit.', 'a photo of a stable.', 'a photo of a sauna.', 'a photo of a supermarket.', 'a photo of a laundromat.', 'a photo of a interior elevator.', 'a photo of a indoor synagogue.', 'a photo of a banquet hall.', 'a photo of a outdoor labyrinth.', 'a photo of a elevator shaft.', 'a photo of a conference center.', 'a photo of a water moat.', 'a photo of a hotel room.', 'a photo of a outdoor lido deck.', 'a photo of a outdoor athletic field.', 'a photo of a assembly line.', 'a photo of a backseat car interior.', 'a photo of a arch.', 'a photo of a boathouse.', 'a photo of a bottle storage wine cellar.', 'a photo of a candy store.', 'a photo of a rock arch.', 'a photo of a thriftshop.', 'a photo of a nursery.', 'a photo of a outdoor power plant.', 'a photo of a village.', 'a photo of a excavation.', 'a photo of a outdoor general store.', 'a photo of a indoor jacuzzi.', 'a photo of a anechoic chamber.', 'a photo of a game room.', 'a photo of a discotheque.', 'a photo of a yard.', 'a photo of a ocean.', 'a photo of a dining car.', 'a photo of a parking lot.', 'a photo of a forest path.', 'a photo of a indoor booth.', 'a photo of a outdoor driving range.', 'a photo of a control room.', 'a photo of a gift shop.', 'a photo of a outdoor diner.', 'a photo of a promenade deck.', 'a photo of a cottage garden.', 'a photo of a bathroom.', 'a photo of a highway.', 'a photo of a indoor apse.', 'a photo of a baseball field.', 'a photo of a clothing store.', 'a photo of a tree farm.', 'a photo of a botanical garden.', 'a photo of a amusement arcade.', 'a photo of a kasbah.', 'a photo of a outdoor track.', 'a photo of a baseball stadium.', 'a photo of a boat deck.', 'a photo of a watering hole.', 'a photo of a physics laboratory.', 'a photo of a building facade.', 'a photo of a shop bakery.', 'a photo of a plaza.', 'a photo of a block waterfall.', 'a photo of a outdoor inn.', 'a photo of a fan waterfall.', 'a photo of a squash court.', 'a photo of a cliff.', 'a photo of a frontseat car interior.', 'a photo of a veranda.', 'a photo of a interior balcony.', 'a photo of a crosswalk.', 'a photo of a vegetation desert.', 'a photo of a castle.', 'a photo of a outdoor hangar.', 'a photo of a reception.', 'a photo of a urban canal.', 'a photo of a topiary garden.', 'a photo of a galley.', 'a photo of a butte.', 'a photo of a garbage dump.', 'a photo of a bayou.', 'a photo of a dock.', 'a photo of a bullring.', 'a photo of a outdoor basketball court.', 'a photo of a street.', 'a photo of a ski slope.', 'a photo of a natural canal.', 'a photo of a indoor shopping mall.', 'a photo of a beauty salon.', 'a photo of a broadleaf forest.', 'a photo of a islet.', 'a photo of a tree house.', 'a photo of a youth hostel.', 'a photo of a bridge.', 'a photo of a cafeteria.', 'a photo of a oast house.', 'a photo of a house.', 'a photo of a alley.', 'a photo of a courthouse.', 'a photo of a outdoor apartment building.', 'a photo of a batters box.', 'a photo of a barndoor.', 'a photo of a art school.', 'a photo of a barrel storage wine cellar.', 'a photo of a beach.', 'a photo of a outdoor chicken coop.', 'a photo of a chemistry lab.', 'a photo of a veterinarians office.', 'a photo of a tower.', 'a photo of a outdoor market.', 'a photo of a gas station.', 'a photo of a abbey.', 'a photo of a indoor gymnasium.', 'a photo of a shower.', 'a photo of a heliport.', 'a photo of a music store.', 'a photo of a outdoor kennel.', 'a photo of a slum.', 'a photo of a railroad track.', 'a photo of a home office.', 'a photo of a indoor movie theater.', 'a photo of a exterior balcony.', 'a photo of a landing deck.', 'a photo of a computer room.', 'a photo of a park.', 'a photo of a burial chamber.', 'a photo of a pagoda.', 'a photo of a ice floe.', 'a photo of a airport terminal.', 'a photo of a raceway.', 'a photo of a dorm room.', 'a photo of a south asia temple.', 'a photo of a harbor.', 'a photo of a train railway.', 'a photo of a palace.', 'a photo of a jail cell.', 'a photo of a outdoor tent.', 'a photo of a shoe shop.', 'a photo of a picnic area.', 'a photo of a coast.', 'a photo of a amusement park.', 'a photo of a courtroom.', 'a photo of a outdoor oil refinery.', 'a photo of a indoor cavern.', 'a photo of a cemetery.', 'a photo of a runway.', 'a photo of a rainforest.', 'a photo of a indoor florist shop.', 'a photo of a auditorium.', 'a photo of a outdoor monastery.', 'a photo of a valley.', 'a photo of a outdoor nuclear power plant.', 'a photo of a hot spring.', 'a photo of a skyscraper.', 'a photo of a indoor parking garage.', 'a photo of a catacomb.', 'a photo of a bookstore.', 'a photo of a orchard.', 'a photo of a ticket booth.', 'a photo of a indoor bazaar.', 'a photo of a outdoor hotel.', 'a photo of a playroom.', 'a photo of a hospital.']
Loading evaluator: Classification
Note that load_model() is skipped as no pretrained model is given (ignore this if it's done on purpose)
Evaluate on the *test* set
=> result
* total: 9,950
* correct: 7,062
* accuracy: 70.97%
* error: 29.03%
* macro_f1: 70.50%
