From 5478ed6e62328dda467d945925ed72904de8c41f Mon Sep 17 00:00:00 2001 From: John-Ge <1017457635@qq.com> Date: Tue, 31 Jan 2023 12:57:27 +0000 Subject: [PATCH] add --- output/eval.txt | 21 + .../1.0_0.5_1.0_t0/seed_2/eval.txt | 281 -- .../1.0_0.5_1.0_t0/seed_2/train.txt | 2474 ----------------- 3 files changed, 21 insertions(+), 2755 deletions(-) create mode 100644 output/eval.txt delete mode 100644 output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/eval.txt delete mode 100644 output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/train.txt diff --git a/output/eval.txt b/output/eval.txt new file mode 100644 index 0000000..4569930 --- /dev/null +++ b/output/eval.txt @@ -0,0 +1,21 @@ +Do evaluation on test set +=> result +* total: 55,388 +* correct: 47,096 +* accuracy: 85.03% +* error: 14.97% +* macro_f1: 85.34% +=> per-class result +* class: 0 (aeroplane) total: 3,646 correct: 3,583 acc: 98.27% +* class: 1 (bicycle) total: 3,475 correct: 2,943 acc: 84.69% +* class: 2 (bus) total: 4,690 correct: 4,258 acc: 90.79% +* class: 3 (car) total: 10,401 correct: 7,853 acc: 75.50% +* class: 4 (horse) total: 4,691 correct: 4,568 acc: 97.38% +* class: 5 (knife) total: 2,075 correct: 1,897 acc: 91.42% +* class: 6 (motorcycle) total: 5,796 correct: 5,517 acc: 95.19% +* class: 7 (person) total: 4,000 correct: 3,101 acc: 77.53% +* class: 8 (plant) total: 4,549 correct: 3,935 acc: 86.50% +* class: 9 (skateboard) total: 2,281 correct: 2,028 acc: 88.91% +* class: 10 (train) total: 4,236 correct: 3,935 acc: 92.89% +* class: 11 (truck) total: 5,548 correct: 3,478 acc: 62.69% +* average: 86.81% \ No newline at end of file diff --git a/output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/eval.txt b/output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/eval.txt deleted file mode 100644 index 31a26c6..0000000 --- a/output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/eval.txt +++ /dev/null @@ -1,281 +0,0 @@ -*************** -** Arguments ** -*************** -backbone: -config_file: configs/trainers/DAPL/ep25-32-v1.yaml -dataset_config_file: configs/datasets/visda17.yaml -eval_only: True -head: -load_epoch: None -model_dir: output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2 -no_train: False -opts: [] -output_dir: output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2 -resume: -root: /home/data -seed: 2 -source_domains: None -target_domains: None -trainer: DAPL -transforms: None -************ -** Config ** -************ -DATALOADER: - K_TRANSFORMS: 1 - NUM_WORKERS: 4 - RETURN_IMG0: False - TEST: - BATCH_SIZE: 128 - SAMPLER: SequentialSampler - TRAIN_U: - BATCH_SIZE: 32 - N_DOMAIN: 0 - N_INS: 16 - SAME_AS_X: True - SAMPLER: RandomSampler - TRAIN_X: - BATCH_SIZE: 32 - N_DOMAIN: 0 - N_INS: 16 - SAMPLER: RandomSampler -DATASET: - ALL_AS_UNLABELED: False - CIFAR_C_LEVEL: 1 - CIFAR_C_TYPE: - NAME: VisDA17 - NUM_LABELED: -1 - NUM_SHOTS: -1 - ROOT: /home/data - SOURCE_DOMAINS: ('synthetic',) - STL10_FOLD: -1 - TARGET_DOMAINS: ('real',) - 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', 'random_flip', 'normalize') -MODEL: - BACKBONE: - NAME: RN101 - PATH: ./assets - 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: 0.003 - LR_SCHEDULER: cosine - MAX_EPOCH: 25 - 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: linear - WEIGHT_DECAY: 0.0005 -OUTPUT_DIR: output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2 -RESUME: -SEED: 2 -TEST: - COMPUTE_CMAT: False - EVALUATOR: Classification - FINAL_MODEL: last_step - NO_TEST: False - PER_CLASS_RESULT: True - SPLIT: test -TRAIN: - CHECKPOINT_FREQ: 0 - COUNT_ITER: train_x - PRINT_FREQ: 100 -TRAINER: - CG: - 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 - DAPL: - CSC: True - N_CTX: 16 - N_DMX: 16 - PREC: amp - T: 1.0 - TAU: 0.5 - U: 1.0 - 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 - 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: DAPL - 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: 1.13.1+cu116 -Is debug build: False -CUDA used to build PyTorch: 11.6 -ROCM used to build PyTorch: N/A - -OS: Ubuntu 18.04.5 LTS (x86_64) -GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 -Clang version: Could not collect -CMake version: Could not collect -Libc version: glibc-2.27 - -Python version: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0] (64-bit runtime) -Python platform: Linux-4.15.0-194-generic-x86_64-with-glibc2.10 -Is CUDA available: True -CUDA runtime version: 11.1.74 -CUDA_MODULE_LOADING set to: LAZY -GPU models and configuration: -GPU 0: NVIDIA GeForce RTX 3090 -GPU 1: NVIDIA GeForce RTX 3090 -GPU 2: NVIDIA GeForce RTX 3090 -GPU 3: NVIDIA GeForce RTX 3090 -GPU 4: NVIDIA GeForce RTX 3090 -GPU 5: NVIDIA GeForce RTX 3090 -GPU 6: NVIDIA GeForce RTX 3090 -GPU 7: NVIDIA GeForce RTX 3090 - -Nvidia driver version: 510.47.03 -cuDNN version: Probably one of the following: -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn.so.8 -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 -/usr/local/cuda-11.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 -HIP runtime version: N/A -MIOpen runtime version: N/A -Is XNNPACK available: True - -Versions of relevant libraries: -[pip3] numpy==1.24.1 -[pip3] torch==1.13.1+cu116 -[pip3] torchaudio==0.13.1+cu116 -[pip3] torchvision==0.14.1+cu116 -[conda] numpy 1.24.1 pypi_0 pypi -[conda] torch 1.13.1+cu116 pypi_0 pypi -[conda] torchaudio 0.13.1+cu116 pypi_0 pypi -[conda] torchvision 0.14.1+cu116 pypi_0 pypi - Pillow (9.4.0) - -Loading trainer: DAPL -Loading dataset: VisDA17 -Building transform_train -+ random resized crop (size=(224, 224)) -+ 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 statistics ***** - Dataset: VisDA17 - Source domains: ('synthetic',) - Target domains: ('real',) - # classes: 12 - # train_x: 152,397 - # train_u: 55,388 - # test: 55,388 -Loading CLIP (backbone: RN101) -Building custom CLIP -Initializing class-specific contexts -ctx vectors size: -Initial context: "X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X" -Number of context words (tokens): 16 -Number of domain context words (tokens): 16 -Turning off gradients in both the image and the text encoder -Loading evaluator: Classification -Loading weights to prompt_learner from "output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2/prompt_learner/model-best.pth.tar" (epoch = 2) -Do evaluation on test set -=> result -* total: 55,388 -* correct: 47,096 -* accuracy: 85.03% -* error: 14.97% -* macro_f1: 85.34% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,583 acc: 98.27% -* class: 1 (bicycle) total: 3,475 correct: 2,943 acc: 84.69% -* class: 2 (bus) total: 4,690 correct: 4,258 acc: 90.79% -* class: 3 (car) total: 10,401 correct: 7,853 acc: 75.50% -* class: 4 (horse) total: 4,691 correct: 4,568 acc: 97.38% -* class: 5 (knife) total: 2,075 correct: 1,897 acc: 91.42% -* class: 6 (motorcycle) total: 5,796 correct: 5,517 acc: 95.19% -* class: 7 (person) total: 4,000 correct: 3,101 acc: 77.53% -* class: 8 (plant) total: 4,549 correct: 3,935 acc: 86.50% -* class: 9 (skateboard) total: 2,281 correct: 2,028 acc: 88.91% -* class: 10 (train) total: 4,236 correct: 3,935 acc: 92.89% -* class: 11 (truck) total: 5,548 correct: 3,478 acc: 62.69% -* average: 86.81% diff --git a/output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/train.txt b/output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/train.txt deleted file mode 100644 index b8ee442..0000000 --- a/output/visda17/DAPL/ep25-32-csc/1.0_0.5_1.0_t0/seed_2/train.txt +++ /dev/null @@ -1,2474 +0,0 @@ -*************** -** Arguments ** -*************** -backbone: -config_file: configs/trainers/DAPL/ep25-32-v1.yaml -dataset_config_file: configs/datasets/visda17.yaml -eval_only: False -head: -load_epoch: None -model_dir: -no_train: False -opts: ['TRAINER.DAPL.T', '1.0', 'TRAINER.DAPL.TAU', '0.6', 'TRAINER.DAPL.U', '1.0'] -output_dir: output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2 -resume: -root: /home/data -seed: 2 -source_domains: None -target_domains: None -trainer: DAPL -transforms: None -************ -** Config ** -************ -DATALOADER: - K_TRANSFORMS: 1 - NUM_WORKERS: 4 - RETURN_IMG0: False - TEST: - BATCH_SIZE: 128 - SAMPLER: SequentialSampler - TRAIN_U: - BATCH_SIZE: 32 - N_DOMAIN: 0 - N_INS: 16 - SAME_AS_X: True - SAMPLER: RandomSampler - TRAIN_X: - BATCH_SIZE: 32 - N_DOMAIN: 0 - N_INS: 16 - SAMPLER: RandomSampler -DATASET: - ALL_AS_UNLABELED: False - CIFAR_C_LEVEL: 1 - CIFAR_C_TYPE: - NAME: VisDA17 - NUM_LABELED: -1 - NUM_SHOTS: -1 - ROOT: /home/data - SOURCE_DOMAINS: ('synthetic',) - STL10_FOLD: -1 - TARGET_DOMAINS: ('real',) - 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', 'random_flip', 'normalize') -MODEL: - BACKBONE: - NAME: RN101 - PATH: ./assets - 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: 0.003 - LR_SCHEDULER: cosine - MAX_EPOCH: 25 - 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: linear - WEIGHT_DECAY: 0.0005 -OUTPUT_DIR: output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2 -RESUME: -SEED: 2 -TEST: - COMPUTE_CMAT: False - EVALUATOR: Classification - FINAL_MODEL: last_step - NO_TEST: False - PER_CLASS_RESULT: True - SPLIT: test -TRAIN: - CHECKPOINT_FREQ: 0 - COUNT_ITER: train_x - PRINT_FREQ: 100 -TRAINER: - CG: - 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 - DAPL: - CSC: True - N_CTX: 16 - N_DMX: 16 - PREC: amp - T: 1.0 - TAU: 0.6 - U: 1.0 - 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 - 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: DAPL - 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: 1.13.1+cu116 -Is debug build: False -CUDA used to build PyTorch: 11.6 -ROCM used to build PyTorch: N/A - -OS: Ubuntu 18.04.5 LTS (x86_64) -GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 -Clang version: Could not collect -CMake version: Could not collect -Libc version: glibc-2.27 - -Python version: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0] (64-bit runtime) -Python platform: Linux-4.15.0-194-generic-x86_64-with-glibc2.10 -Is CUDA available: True -CUDA runtime version: 10.2.89 -CUDA_MODULE_LOADING set to: LAZY -GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2080 Ti -Nvidia driver version: 510.47.03 -cuDNN version: Could not collect -HIP runtime version: N/A -MIOpen runtime version: N/A -Is XNNPACK available: True - -Versions of relevant libraries: -[pip3] numpy==1.24.1 -[pip3] torch==1.13.1+cu116 -[pip3] torchaudio==0.13.1+cu116 -[pip3] torchvision==0.14.1+cu116 -[conda] numpy 1.24.1 pypi_0 pypi -[conda] torch 1.13.1+cu116 pypi_0 pypi -[conda] torchaudio 0.13.1+cu116 pypi_0 pypi -[conda] torchvision 0.14.1+cu116 pypi_0 pypi - Pillow (9.4.0) - -Loading trainer: DAPL -Loading dataset: VisDA17 -Building transform_train -+ random resized crop (size=(224, 224)) -+ 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 statistics ***** - Dataset: VisDA17 - Source domains: ('synthetic',) - Target domains: ('real',) - # classes: 12 - # train_x: 152,397 - # train_u: 55,388 - # test: 55,388 -Loading CLIP (backbone: RN101) -Building custom CLIP -Initializing class-specific contexts -ctx vectors size: -Initial context: "X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X" -Number of context words (tokens): 16 -Number of domain context words (tokens): 16 -Turning off gradients in both the image and the text encoder -Loading evaluator: Classification -No checkpoint found, train from scratch -Initializing summary writer for tensorboard with log_dir=output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2/tensorboard -epoch [1/25][100/4762] time 0.134 (1.031) data 0.001 (0.007) eta 1 day, 10:04:39 loss 0.6585 (1.0379) loss_x 0.6223 (0.8392) loss_u 0.0361 (0.1987) acc_x 84.3750 (72.3750) lr 2.988172e-03 -epoch [1/25][200/4762] time 0.173 (0.589) data 0.001 (0.004) eta 19:27:18 loss 0.5128 (0.8366) loss_x 0.4728 (0.7117) loss_u 0.0399 (0.1249) acc_x 84.3750 (76.0469) lr 2.988172e-03 -epoch [1/25][300/4762] time 0.142 (0.442) data 0.001 (0.003) eta 14:35:17 loss 0.4899 (0.7608) loss_x 0.4680 (0.6634) loss_u 0.0219 (0.0975) acc_x 81.2500 (77.4792) lr 2.988172e-03 -epoch [1/25][400/4762] time 0.186 (0.368) data 0.001 (0.002) eta 12:08:18 loss 0.3339 (0.7141) loss_x 0.2720 (0.6305) loss_u 0.0619 (0.0836) acc_x 90.6250 (78.3906) lr 2.988172e-03 -epoch [1/25][500/4762] time 0.158 (0.325) data 0.001 (0.002) eta 10:42:51 loss 0.7445 (0.6885) loss_x 0.6690 (0.6136) loss_u 0.0755 (0.0750) acc_x 71.8750 (78.8812) lr 2.988172e-03 -epoch [1/25][600/4762] time 0.142 (0.296) data 0.002 (0.002) eta 9:44:45 loss 0.5613 (0.6600) loss_x 0.5362 (0.5917) loss_u 0.0251 (0.0682) acc_x 87.5000 (79.5990) lr 2.988172e-03 -epoch [1/25][700/4762] time 0.142 (0.275) data 0.001 (0.002) eta 9:01:44 loss 0.5968 (0.6400) loss_x 0.5921 (0.5774) loss_u 0.0048 (0.0626) acc_x 81.2500 (80.0357) lr 2.988172e-03 -epoch [1/25][800/4762] time 0.140 (0.259) data 0.001 (0.002) eta 8:30:21 loss 0.8172 (0.6245) loss_x 0.7709 (0.5656) loss_u 0.0463 (0.0588) acc_x 78.1250 (80.3789) lr 2.988172e-03 -epoch [1/25][900/4762] time 0.148 (0.247) data 0.001 (0.001) eta 8:05:28 loss 0.4158 (0.6109) loss_x 0.3673 (0.5553) loss_u 0.0485 (0.0556) acc_x 87.5000 (80.7153) lr 2.988172e-03 -epoch [1/25][1000/4762] time 0.153 (0.237) data 0.001 (0.001) eta 7:46:15 loss 0.5199 (0.6016) loss_x 0.4869 (0.5485) loss_u 0.0331 (0.0531) acc_x 81.2500 (80.9000) lr 2.988172e-03 -epoch [1/25][1100/4762] time 0.145 (0.229) data 0.001 (0.001) eta 7:30:08 loss 0.5803 (0.5933) loss_x 0.5592 (0.5420) loss_u 0.0211 (0.0512) acc_x 78.1250 (81.0170) lr 2.988172e-03 -epoch [1/25][1200/4762] time 0.138 (0.222) data 0.001 (0.001) eta 7:16:54 loss 0.6341 (0.5848) loss_x 0.6193 (0.5356) loss_u 0.0148 (0.0491) acc_x 75.0000 (81.2422) lr 2.988172e-03 -epoch [1/25][1300/4762] time 0.154 (0.217) data 0.001 (0.001) eta 7:05:16 loss 0.1869 (0.5770) loss_x 0.1229 (0.5290) loss_u 0.0640 (0.0479) acc_x 96.8750 (81.4760) lr 2.988172e-03 -epoch [1/25][1400/4762] time 0.144 (0.212) data 0.001 (0.001) eta 6:55:12 loss 0.7570 (0.5697) loss_x 0.7150 (0.5235) loss_u 0.0420 (0.0462) acc_x 71.8750 (81.6272) lr 2.988172e-03 -epoch [1/25][1500/4762] time 0.157 (0.208) data 0.001 (0.001) eta 6:46:36 loss 0.3898 (0.5631) loss_x 0.3793 (0.5183) loss_u 0.0105 (0.0448) acc_x 87.5000 (81.7833) lr 2.988172e-03 -epoch [1/25][1600/4762] time 0.138 (0.204) data 0.001 (0.001) eta 6:39:11 loss 0.3253 (0.5579) loss_x 0.3137 (0.5142) loss_u 0.0116 (0.0437) acc_x 87.5000 (81.9355) lr 2.988172e-03 -epoch [1/25][1700/4762] time 0.139 (0.201) data 0.001 (0.001) eta 6:32:36 loss 0.5881 (0.5533) loss_x 0.5487 (0.5108) loss_u 0.0395 (0.0425) acc_x 78.1250 (82.0515) lr 2.988172e-03 -epoch [1/25][1800/4762] time 0.146 (0.198) data 0.001 (0.001) eta 6:27:18 loss 0.8350 (0.5497) loss_x 0.8279 (0.5081) loss_u 0.0071 (0.0415) acc_x 78.1250 (82.1510) lr 2.988172e-03 -epoch [1/25][1900/4762] time 0.140 (0.196) data 0.001 (0.001) eta 6:22:04 loss 0.5239 (0.5460) loss_x 0.4896 (0.5056) loss_u 0.0344 (0.0404) acc_x 81.2500 (82.2171) lr 2.988172e-03 -epoch [1/25][2000/4762] time 0.137 (0.193) data 0.001 (0.001) eta 6:17:14 loss 0.3003 (0.5425) loss_x 0.2926 (0.5030) loss_u 0.0076 (0.0395) acc_x 87.5000 (82.3516) lr 2.988172e-03 -epoch [1/25][2100/4762] time 0.171 (0.191) data 0.001 (0.001) eta 6:12:50 loss 0.6710 (0.5381) loss_x 0.5994 (0.4993) loss_u 0.0716 (0.0388) acc_x 84.3750 (82.5208) lr 2.988172e-03 -epoch [1/25][2200/4762] time 0.161 (0.189) data 0.001 (0.001) eta 6:08:56 loss 0.5864 (0.5355) loss_x 0.5308 (0.4972) loss_u 0.0557 (0.0383) acc_x 81.2500 (82.6151) lr 2.988172e-03 -epoch [1/25][2300/4762] time 0.139 (0.188) data 0.001 (0.001) eta 6:05:27 loss 0.3637 (0.5333) loss_x 0.3313 (0.4957) loss_u 0.0324 (0.0376) acc_x 90.6250 (82.6685) lr 2.988172e-03 -epoch [1/25][2400/4762] time 0.141 (0.186) data 0.001 (0.001) eta 6:01:54 loss 0.7609 (0.5309) loss_x 0.7547 (0.4938) loss_u 0.0061 (0.0371) acc_x 68.7500 (82.7396) lr 2.988172e-03 -epoch [1/25][2500/4762] time 0.147 (0.185) data 0.001 (0.001) eta 5:58:40 loss 0.2973 (0.5272) loss_x 0.2914 (0.4906) loss_u 0.0059 (0.0366) acc_x 93.7500 (82.8650) lr 2.988172e-03 -epoch [1/25][2600/4762] time 0.141 (0.183) data 0.001 (0.001) eta 5:55:45 loss 0.3171 (0.5237) loss_x 0.2943 (0.4876) loss_u 0.0229 (0.0361) acc_x 93.7500 (82.9279) lr 2.988172e-03 -epoch [1/25][2700/4762] time 0.159 (0.182) data 0.001 (0.001) eta 5:52:59 loss 0.4126 (0.5202) loss_x 0.4099 (0.4847) loss_u 0.0027 (0.0355) acc_x 78.1250 (83.0324) lr 2.988172e-03 -epoch [1/25][2800/4762] time 0.154 (0.181) data 0.001 (0.001) eta 5:50:24 loss 0.5728 (0.5173) loss_x 0.5243 (0.4822) loss_u 0.0484 (0.0351) acc_x 84.3750 (83.1016) lr 2.988172e-03 -epoch [1/25][2900/4762] time 0.141 (0.180) data 0.001 (0.001) eta 5:48:04 loss 0.3855 (0.5156) loss_x 0.3403 (0.4809) loss_u 0.0452 (0.0346) acc_x 90.6250 (83.1444) lr 2.988172e-03 -epoch [1/25][3000/4762] time 0.139 (0.179) data 0.001 (0.001) eta 5:45:50 loss 0.3192 (0.5136) loss_x 0.3155 (0.4795) loss_u 0.0037 (0.0341) acc_x 87.5000 (83.1865) lr 2.988172e-03 -epoch [1/25][3100/4762] time 0.143 (0.178) data 0.001 (0.001) eta 5:43:34 loss 0.5259 (0.5117) loss_x 0.5124 (0.4781) loss_u 0.0135 (0.0336) acc_x 78.1250 (83.2349) lr 2.988172e-03 -epoch [1/25][3200/4762] time 0.156 (0.177) data 0.001 (0.001) eta 5:41:31 loss 0.3962 (0.5096) loss_x 0.3774 (0.4763) loss_u 0.0189 (0.0333) acc_x 87.5000 (83.2900) lr 2.988172e-03 -epoch [1/25][3300/4762] time 0.153 (0.176) data 0.001 (0.001) eta 5:39:42 loss 0.3410 (0.5084) loss_x 0.3297 (0.4755) loss_u 0.0114 (0.0328) acc_x 90.6250 (83.2964) lr 2.988172e-03 -epoch [1/25][3400/4762] time 0.138 (0.175) data 0.001 (0.001) eta 5:37:52 loss 0.5321 (0.5064) loss_x 0.5274 (0.4739) loss_u 0.0047 (0.0325) acc_x 75.0000 (83.3502) lr 2.988172e-03 -epoch [1/25][3500/4762] time 0.147 (0.175) data 0.001 (0.001) eta 5:36:20 loss 0.4327 (0.5044) loss_x 0.4235 (0.4722) loss_u 0.0091 (0.0322) acc_x 84.3750 (83.4223) lr 2.988172e-03 -epoch [1/25][3600/4762] time 0.166 (0.174) data 0.001 (0.001) eta 5:34:53 loss 0.6917 (0.5026) loss_x 0.6828 (0.4706) loss_u 0.0089 (0.0320) acc_x 78.1250 (83.4722) lr 2.988172e-03 -epoch [1/25][3700/4762] time 0.140 (0.173) data 0.001 (0.001) eta 5:33:14 loss 0.2427 (0.5006) loss_x 0.2264 (0.4689) loss_u 0.0164 (0.0317) acc_x 90.6250 (83.5312) lr 2.988172e-03 -epoch [1/25][3800/4762] time 0.156 (0.173) data 0.001 (0.001) eta 5:31:40 loss 0.3139 (0.4987) loss_x 0.2937 (0.4672) loss_u 0.0202 (0.0314) acc_x 90.6250 (83.5987) lr 2.988172e-03 -epoch [1/25][3900/4762] time 0.148 (0.172) data 0.001 (0.001) eta 5:30:09 loss 0.2815 (0.4972) loss_x 0.2755 (0.4658) loss_u 0.0060 (0.0314) acc_x 90.6250 (83.6546) lr 2.988172e-03 -epoch [1/25][4000/4762] time 0.143 (0.171) data 0.001 (0.001) eta 5:28:45 loss 0.5124 (0.4958) loss_x 0.4888 (0.4648) loss_u 0.0236 (0.0310) acc_x 84.3750 (83.6922) lr 2.988172e-03 -epoch [1/25][4100/4762] time 0.143 (0.171) data 0.001 (0.001) eta 5:27:29 loss 0.5051 (0.4941) loss_x 0.4614 (0.4634) loss_u 0.0437 (0.0307) acc_x 78.1250 (83.7279) lr 2.988172e-03 -epoch [1/25][4200/4762] time 0.143 (0.170) data 0.001 (0.001) eta 5:26:12 loss 0.3838 (0.4931) loss_x 0.3732 (0.4626) loss_u 0.0106 (0.0305) acc_x 87.5000 (83.7604) lr 2.988172e-03 -epoch [1/25][4300/4762] time 0.161 (0.170) data 0.001 (0.001) eta 5:24:58 loss 0.2307 (0.4914) loss_x 0.2261 (0.4612) loss_u 0.0047 (0.0303) acc_x 87.5000 (83.8190) lr 2.988172e-03 -epoch [1/25][4400/4762] time 0.143 (0.169) data 0.001 (0.001) eta 5:23:27 loss 0.4639 (0.4900) loss_x 0.4517 (0.4599) loss_u 0.0122 (0.0301) acc_x 81.2500 (83.8501) lr 2.988172e-03 -epoch [1/25][4500/4762] time 0.146 (0.169) data 0.001 (0.001) eta 5:22:28 loss 0.3632 (0.4887) loss_x 0.3562 (0.4588) loss_u 0.0070 (0.0299) acc_x 87.5000 (83.8944) lr 2.988172e-03 -epoch [1/25][4600/4762] time 0.145 (0.168) data 0.001 (0.001) eta 5:21:22 loss 0.4493 (0.4877) loss_x 0.4446 (0.4581) loss_u 0.0047 (0.0296) acc_x 78.1250 (83.9287) lr 2.988172e-03 -epoch [1/25][4700/4762] time 0.141 (0.168) data 0.001 (0.001) eta 5:20:13 loss 0.3544 (0.4862) loss_x 0.3524 (0.4568) loss_u 0.0020 (0.0294) acc_x 81.2500 (83.9608) lr 2.988172e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 47,096 -* accuracy: 85.03% -* error: 14.97% -* macro_f1: 85.38% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,585 acc: 98.33% -* class: 1 (bicycle) total: 3,475 correct: 2,938 acc: 84.55% -* class: 2 (bus) total: 4,690 correct: 4,255 acc: 90.72% -* class: 3 (car) total: 10,401 correct: 7,849 acc: 75.46% -* class: 4 (horse) total: 4,691 correct: 4,572 acc: 97.46% -* class: 5 (knife) total: 2,075 correct: 1,891 acc: 91.13% -* class: 6 (motorcycle) total: 5,796 correct: 5,513 acc: 95.12% -* class: 7 (person) total: 4,000 correct: 3,118 acc: 77.95% -* class: 8 (plant) total: 4,549 correct: 3,933 acc: 86.46% -* class: 9 (skateboard) total: 2,281 correct: 2,023 acc: 88.69% -* class: 10 (train) total: 4,236 correct: 3,926 acc: 92.68% -* class: 11 (truck) total: 5,548 correct: 3,493 acc: 62.96% -* average: 86.79% -Checkpoint saved to "output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2/prompt_learner/model-best.pth.tar" -epoch [2/25][100/4762] time 0.139 (0.153) data 0.001 (0.003) eta 4:51:37 loss 0.2271 (0.4132) loss_x 0.2255 (0.3932) loss_u 0.0016 (0.0199) acc_x 90.6250 (86.2500) lr 1.781072e-03 -epoch [2/25][200/4762] time 0.137 (0.156) data 0.001 (0.002) eta 4:55:48 loss 0.5199 (0.4259) loss_x 0.5159 (0.4075) loss_u 0.0041 (0.0185) acc_x 78.1250 (85.7656) lr 1.781072e-03 -epoch [2/25][300/4762] time 0.139 (0.154) data 0.001 (0.002) eta 4:52:16 loss 0.3605 (0.4284) loss_x 0.3516 (0.4097) loss_u 0.0089 (0.0187) acc_x 84.3750 (85.5729) lr 1.781072e-03 -epoch [2/25][400/4762] time 0.144 (0.153) data 0.001 (0.001) eta 4:49:50 loss 0.6421 (0.4254) loss_x 0.6340 (0.4069) loss_u 0.0081 (0.0185) acc_x 81.2500 (85.7656) lr 1.781072e-03 -epoch [2/25][500/4762] time 0.144 (0.152) data 0.001 (0.001) eta 4:48:17 loss 0.5567 (0.4256) loss_x 0.5195 (0.4068) loss_u 0.0373 (0.0188) acc_x 84.3750 (85.7687) lr 1.781072e-03 -epoch [2/25][600/4762] time 0.149 (0.152) data 0.001 (0.001) eta 4:47:34 loss 0.7983 (0.4271) loss_x 0.7871 (0.4085) loss_u 0.0111 (0.0185) acc_x 75.0000 (85.7031) lr 1.781072e-03 -epoch [2/25][700/4762] time 0.185 (0.151) data 0.000 (0.001) eta 4:46:41 loss 0.1380 (0.4309) loss_x 0.1308 (0.4126) loss_u 0.0072 (0.0184) acc_x 93.7500 (85.5402) lr 1.781072e-03 -epoch [2/25][800/4762] time 0.137 (0.151) data 0.001 (0.001) eta 4:45:41 loss 0.4070 (0.4319) loss_x 0.3876 (0.4138) loss_u 0.0194 (0.0181) acc_x 81.2500 (85.5039) lr 1.781072e-03 -epoch [2/25][900/4762] time 0.141 (0.150) data 0.001 (0.001) eta 4:44:02 loss 0.3967 (0.4315) loss_x 0.3888 (0.4131) loss_u 0.0079 (0.0184) acc_x 87.5000 (85.4896) lr 1.781072e-03 -epoch [2/25][1000/4762] time 0.170 (0.150) data 0.001 (0.001) eta 4:43:33 loss 0.3016 (0.4308) loss_x 0.2788 (0.4123) loss_u 0.0228 (0.0185) acc_x 90.6250 (85.4688) lr 1.781072e-03 -epoch [2/25][1100/4762] time 0.140 (0.150) data 0.001 (0.001) eta 4:42:32 loss 0.8198 (0.4308) loss_x 0.8047 (0.4125) loss_u 0.0151 (0.0182) acc_x 71.8750 (85.4631) lr 1.781072e-03 -epoch [2/25][1200/4762] time 0.148 (0.150) data 0.001 (0.001) eta 4:42:31 loss 0.3901 (0.4297) loss_x 0.3784 (0.4115) loss_u 0.0117 (0.0181) acc_x 84.3750 (85.4896) lr 1.781072e-03 -epoch [2/25][1300/4762] time 0.147 (0.150) data 0.001 (0.001) eta 4:42:06 loss 0.3606 (0.4288) loss_x 0.3519 (0.4105) loss_u 0.0087 (0.0182) acc_x 87.5000 (85.5144) lr 1.781072e-03 -epoch [2/25][1400/4762] time 0.178 (0.150) data 0.001 (0.001) eta 4:42:02 loss 0.4103 (0.4281) loss_x 0.4086 (0.4098) loss_u 0.0017 (0.0182) acc_x 87.5000 (85.5804) lr 1.781072e-03 -epoch [2/25][1500/4762] time 0.145 (0.150) data 0.001 (0.001) eta 4:41:40 loss 0.2030 (0.4272) loss_x 0.1825 (0.4090) loss_u 0.0205 (0.0183) acc_x 93.7500 (85.6146) lr 1.781072e-03 -epoch [2/25][1600/4762] time 0.203 (0.150) data 0.001 (0.001) eta 4:41:46 loss 0.6413 (0.4258) loss_x 0.6306 (0.4077) loss_u 0.0107 (0.0181) acc_x 75.0000 (85.6289) lr 1.781072e-03 -epoch [2/25][1700/4762] time 0.156 (0.150) data 0.001 (0.001) eta 4:41:12 loss 0.3572 (0.4257) loss_x 0.3475 (0.4077) loss_u 0.0097 (0.0180) acc_x 84.3750 (85.6544) lr 1.781072e-03 -epoch [2/25][1800/4762] time 0.143 (0.150) data 0.001 (0.001) eta 4:41:08 loss 0.4326 (0.4254) loss_x 0.4174 (0.4076) loss_u 0.0152 (0.0178) acc_x 87.5000 (85.6997) lr 1.781072e-03 -epoch [2/25][1900/4762] time 0.161 (0.150) data 0.001 (0.001) eta 4:40:54 loss 0.4231 (0.4248) loss_x 0.3971 (0.4071) loss_u 0.0260 (0.0177) acc_x 87.5000 (85.7516) lr 1.781072e-03 -epoch [2/25][2000/4762] time 0.168 (0.150) data 0.001 (0.001) eta 4:40:40 loss 0.2124 (0.4238) loss_x 0.1988 (0.4062) loss_u 0.0136 (0.0177) acc_x 96.8750 (85.7719) lr 1.781072e-03 -epoch [2/25][2100/4762] time 0.163 (0.150) data 0.001 (0.001) eta 4:40:52 loss 0.5660 (0.4238) loss_x 0.5571 (0.4063) loss_u 0.0088 (0.0176) acc_x 84.3750 (85.7693) lr 1.781072e-03 -epoch [2/25][2200/4762] time 0.139 (0.150) data 0.004 (0.001) eta 4:40:51 loss 0.5204 (0.4233) loss_x 0.5076 (0.4057) loss_u 0.0127 (0.0176) acc_x 84.3750 (85.7784) lr 1.781072e-03 -epoch [2/25][2300/4762] time 0.146 (0.150) data 0.001 (0.001) eta 4:40:18 loss 0.3418 (0.4244) loss_x 0.3299 (0.4066) loss_u 0.0118 (0.0178) acc_x 87.5000 (85.7364) lr 1.781072e-03 -epoch [2/25][2400/4762] time 0.142 (0.150) data 0.001 (0.001) eta 4:40:12 loss 0.6266 (0.4250) loss_x 0.6047 (0.4071) loss_u 0.0218 (0.0179) acc_x 78.1250 (85.6992) lr 1.781072e-03 -epoch [2/25][2500/4762] time 0.137 (0.150) data 0.001 (0.001) eta 4:39:52 loss 0.6323 (0.4237) loss_x 0.6165 (0.4059) loss_u 0.0158 (0.0178) acc_x 78.1250 (85.7362) lr 1.781072e-03 -epoch [2/25][2600/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:39:33 loss 0.2573 (0.4227) loss_x 0.2536 (0.4050) loss_u 0.0037 (0.0177) acc_x 90.6250 (85.7764) lr 1.781072e-03 -epoch [2/25][2700/4762] time 0.136 (0.150) data 0.001 (0.001) eta 4:39:00 loss 0.3939 (0.4220) loss_x 0.3835 (0.4043) loss_u 0.0104 (0.0177) acc_x 87.5000 (85.7998) lr 1.781072e-03 -epoch [2/25][2800/4762] time 0.140 (0.150) data 0.001 (0.001) eta 4:38:40 loss 0.3759 (0.4214) loss_x 0.3705 (0.4037) loss_u 0.0054 (0.0177) acc_x 84.3750 (85.8259) lr 1.781072e-03 -epoch [2/25][2900/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:38:25 loss 0.3443 (0.4207) loss_x 0.3307 (0.4030) loss_u 0.0136 (0.0177) acc_x 84.3750 (85.8341) lr 1.781072e-03 -epoch [2/25][3000/4762] time 0.143 (0.150) data 0.001 (0.001) eta 4:38:03 loss 0.4107 (0.4202) loss_x 0.4043 (0.4024) loss_u 0.0064 (0.0177) acc_x 87.5000 (85.8729) lr 1.781072e-03 -epoch [2/25][3100/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:37:50 loss 0.6647 (0.4208) loss_x 0.6623 (0.4032) loss_u 0.0025 (0.0176) acc_x 78.1250 (85.8377) lr 1.781072e-03 -epoch [2/25][3200/4762] time 0.144 (0.150) data 0.001 (0.001) eta 4:37:26 loss 0.3344 (0.4208) loss_x 0.3246 (0.4032) loss_u 0.0098 (0.0176) acc_x 84.3750 (85.8262) lr 1.781072e-03 -epoch [2/25][3300/4762] time 0.143 (0.150) data 0.001 (0.001) eta 4:37:16 loss 0.5272 (0.4212) loss_x 0.5198 (0.4036) loss_u 0.0074 (0.0176) acc_x 78.1250 (85.8163) lr 1.781072e-03 -epoch [2/25][3400/4762] time 0.147 (0.150) data 0.001 (0.001) eta 4:36:57 loss 0.5194 (0.4214) loss_x 0.4303 (0.4039) loss_u 0.0891 (0.0175) acc_x 81.2500 (85.8006) lr 1.781072e-03 -epoch [2/25][3500/4762] time 0.141 (0.150) data 0.001 (0.001) eta 4:36:49 loss 0.3493 (0.4216) loss_x 0.3097 (0.4042) loss_u 0.0396 (0.0174) acc_x 93.7500 (85.7884) lr 1.781072e-03 -epoch [2/25][3600/4762] time 0.149 (0.150) data 0.001 (0.001) eta 4:36:40 loss 0.3418 (0.4219) loss_x 0.3367 (0.4046) loss_u 0.0051 (0.0174) acc_x 84.3750 (85.7674) lr 1.781072e-03 -epoch [2/25][3700/4762] time 0.137 (0.150) data 0.001 (0.001) eta 4:36:20 loss 0.3596 (0.4214) loss_x 0.3531 (0.4041) loss_u 0.0065 (0.0174) acc_x 84.3750 (85.7644) lr 1.781072e-03 -epoch [2/25][3800/4762] time 0.138 (0.150) data 0.001 (0.001) eta 4:36:05 loss 0.3050 (0.4213) loss_x 0.2131 (0.4039) loss_u 0.0919 (0.0174) acc_x 93.7500 (85.7804) lr 1.781072e-03 -epoch [2/25][3900/4762] time 0.194 (0.150) data 0.001 (0.001) eta 4:35:48 loss 0.4961 (0.4206) loss_x 0.4929 (0.4032) loss_u 0.0032 (0.0173) acc_x 81.2500 (85.8085) lr 1.781072e-03 -epoch [2/25][4000/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:35:30 loss 0.2330 (0.4206) loss_x 0.2281 (0.4033) loss_u 0.0049 (0.0173) acc_x 90.6250 (85.8086) lr 1.781072e-03 -epoch [2/25][4100/4762] time 0.148 (0.150) data 0.001 (0.001) eta 4:35:20 loss 0.6046 (0.4208) loss_x 0.5896 (0.4035) loss_u 0.0150 (0.0172) acc_x 81.2500 (85.8056) lr 1.781072e-03 -epoch [2/25][4200/4762] time 0.138 (0.150) data 0.001 (0.001) eta 4:35:06 loss 0.6031 (0.4206) loss_x 0.5814 (0.4034) loss_u 0.0217 (0.0173) acc_x 75.0000 (85.8006) lr 1.781072e-03 -epoch [2/25][4300/4762] time 0.137 (0.150) data 0.001 (0.001) eta 4:34:43 loss 0.5167 (0.4205) loss_x 0.4829 (0.4033) loss_u 0.0338 (0.0172) acc_x 81.2500 (85.8009) lr 1.781072e-03 -epoch [2/25][4400/4762] time 0.152 (0.150) data 0.001 (0.001) eta 4:34:31 loss 0.3201 (0.4203) loss_x 0.2866 (0.4031) loss_u 0.0335 (0.0171) acc_x 90.6250 (85.8011) lr 1.781072e-03 -epoch [2/25][4500/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:34:07 loss 0.2576 (0.4201) loss_x 0.2521 (0.4031) loss_u 0.0055 (0.0171) acc_x 87.5000 (85.7931) lr 1.781072e-03 -epoch [2/25][4600/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:33:45 loss 0.4657 (0.4201) loss_x 0.4641 (0.4030) loss_u 0.0016 (0.0171) acc_x 78.1250 (85.7928) lr 1.781072e-03 -epoch [2/25][4700/4762] time 0.137 (0.150) data 0.001 (0.001) eta 4:33:32 loss 0.2334 (0.4200) loss_x 0.2203 (0.4029) loss_u 0.0131 (0.0171) acc_x 93.7500 (85.8059) lr 1.781072e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,935 -* accuracy: 84.74% -* error: 15.26% -* macro_f1: 85.04% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,585 acc: 98.33% -* class: 1 (bicycle) total: 3,475 correct: 2,950 acc: 84.89% -* class: 2 (bus) total: 4,690 correct: 4,279 acc: 91.24% -* class: 3 (car) total: 10,401 correct: 7,613 acc: 73.19% -* class: 4 (horse) total: 4,691 correct: 4,579 acc: 97.61% -* class: 5 (knife) total: 2,075 correct: 1,885 acc: 90.84% -* class: 6 (motorcycle) total: 5,796 correct: 5,517 acc: 95.19% -* class: 7 (person) total: 4,000 correct: 3,092 acc: 77.30% -* class: 8 (plant) total: 4,549 correct: 3,984 acc: 87.58% -* class: 9 (skateboard) total: 2,281 correct: 2,066 acc: 90.57% -* class: 10 (train) total: 4,236 correct: 3,921 acc: 92.56% -* class: 11 (truck) total: 5,548 correct: 3,464 acc: 62.44% -* average: 86.81% -epoch [3/25][100/4762] time 0.161 (0.152) data 0.001 (0.004) eta 4:36:52 loss 0.1658 (0.4088) loss_x 0.1623 (0.3901) loss_u 0.0036 (0.0186) acc_x 93.7500 (86.6250) lr 4.712526e-05 -epoch [3/25][200/4762] time 0.143 (0.152) data 0.001 (0.002) eta 4:36:08 loss 0.2701 (0.4098) loss_x 0.2636 (0.3935) loss_u 0.0065 (0.0163) acc_x 90.6250 (86.4375) lr 4.712526e-05 -epoch [3/25][300/4762] time 0.164 (0.152) data 0.001 (0.002) eta 4:36:56 loss 0.5506 (0.4149) loss_x 0.5357 (0.3988) loss_u 0.0149 (0.0161) acc_x 84.3750 (86.1146) lr 4.712526e-05 -epoch [3/25][400/4762] time 0.144 (0.152) data 0.001 (0.002) eta 4:36:28 loss 0.3046 (0.4121) loss_x 0.2902 (0.3963) loss_u 0.0144 (0.0158) acc_x 93.7500 (86.0156) lr 4.712526e-05 -epoch [3/25][500/4762] time 0.139 (0.152) data 0.001 (0.001) eta 4:36:09 loss 0.6418 (0.4144) loss_x 0.6333 (0.3986) loss_u 0.0085 (0.0158) acc_x 75.0000 (86.0563) lr 4.712526e-05 -epoch [3/25][600/4762] time 0.144 (0.152) data 0.001 (0.001) eta 4:35:10 loss 0.2365 (0.4088) loss_x 0.2236 (0.3931) loss_u 0.0129 (0.0156) acc_x 90.6250 (86.3229) lr 4.712526e-05 -epoch [3/25][700/4762] time 0.150 (0.151) data 0.001 (0.001) eta 4:34:09 loss 0.2715 (0.4102) loss_x 0.2562 (0.3951) loss_u 0.0153 (0.0150) acc_x 90.6250 (86.2321) lr 4.712526e-05 -epoch [3/25][800/4762] time 0.139 (0.151) data 0.003 (0.001) eta 4:33:27 loss 0.2145 (0.4077) loss_x 0.2072 (0.3929) loss_u 0.0073 (0.0148) acc_x 93.7500 (86.3281) lr 4.712526e-05 -epoch [3/25][900/4762] time 0.145 (0.151) data 0.001 (0.001) eta 4:33:10 loss 0.3023 (0.4076) loss_x 0.2934 (0.3922) loss_u 0.0089 (0.0153) acc_x 87.5000 (86.2674) lr 4.712526e-05 -epoch [3/25][1000/4762] time 0.140 (0.151) data 0.001 (0.001) eta 4:32:59 loss 0.4890 (0.4083) loss_x 0.4857 (0.3929) loss_u 0.0033 (0.0154) acc_x 84.3750 (86.2438) lr 4.712526e-05 -epoch [3/25][1100/4762] time 0.140 (0.151) data 0.001 (0.001) eta 4:32:13 loss 0.4479 (0.4090) loss_x 0.4214 (0.3935) loss_u 0.0265 (0.0156) acc_x 84.3750 (86.1790) lr 4.712526e-05 -epoch [3/25][1200/4762] time 0.149 (0.150) data 0.001 (0.001) eta 4:31:32 loss 0.5784 (0.4086) loss_x 0.5596 (0.3927) loss_u 0.0189 (0.0159) acc_x 78.1250 (86.1562) lr 4.712526e-05 -epoch [3/25][1300/4762] time 0.177 (0.150) data 0.001 (0.001) eta 4:31:07 loss 0.3913 (0.4085) loss_x 0.3890 (0.3927) loss_u 0.0023 (0.0158) acc_x 90.6250 (86.1154) lr 4.712526e-05 -epoch [3/25][1400/4762] time 0.142 (0.150) data 0.000 (0.001) eta 4:30:40 loss 0.6119 (0.4077) loss_x 0.6005 (0.3919) loss_u 0.0114 (0.0158) acc_x 84.3750 (86.1473) lr 4.712526e-05 -epoch [3/25][1500/4762] time 0.153 (0.150) data 0.001 (0.001) eta 4:29:59 loss 0.4729 (0.4086) loss_x 0.4536 (0.3928) loss_u 0.0193 (0.0157) acc_x 87.5000 (86.1500) lr 4.712526e-05 -epoch [3/25][1600/4762] time 0.149 (0.150) data 0.001 (0.001) eta 4:29:14 loss 0.1773 (0.4086) loss_x 0.1647 (0.3930) loss_u 0.0127 (0.0155) acc_x 93.7500 (86.1582) lr 4.712526e-05 -epoch [3/25][1700/4762] time 0.163 (0.149) data 0.001 (0.001) eta 4:28:13 loss 0.2766 (0.4089) loss_x 0.2736 (0.3933) loss_u 0.0029 (0.0156) acc_x 87.5000 (86.1324) lr 4.712526e-05 -epoch [3/25][1800/4762] time 0.152 (0.150) data 0.001 (0.001) eta 4:28:28 loss 0.5267 (0.4076) loss_x 0.5137 (0.3919) loss_u 0.0131 (0.0156) acc_x 84.3750 (86.1719) lr 4.712526e-05 -epoch [3/25][1900/4762] time 0.139 (0.150) data 0.001 (0.001) eta 4:28:24 loss 0.3037 (0.4075) loss_x 0.3021 (0.3920) loss_u 0.0016 (0.0155) acc_x 93.7500 (86.1612) lr 4.712526e-05 -epoch [3/25][2000/4762] time 0.138 (0.150) data 0.001 (0.001) eta 4:28:02 loss 0.5398 (0.4073) loss_x 0.5257 (0.3919) loss_u 0.0140 (0.0154) acc_x 90.6250 (86.1719) lr 4.712526e-05 -epoch [3/25][2100/4762] time 0.151 (0.150) data 0.001 (0.001) eta 4:27:43 loss 0.2971 (0.4089) loss_x 0.2927 (0.3934) loss_u 0.0045 (0.0154) acc_x 90.6250 (86.1265) lr 4.712526e-05 -epoch [3/25][2200/4762] time 0.141 (0.149) data 0.001 (0.001) eta 4:27:19 loss 0.7813 (0.4102) loss_x 0.7794 (0.3948) loss_u 0.0019 (0.0154) acc_x 75.0000 (86.0582) lr 4.712526e-05 -epoch [3/25][2300/4762] time 0.146 (0.149) data 0.001 (0.001) eta 4:27:00 loss 0.5303 (0.4105) loss_x 0.5256 (0.3951) loss_u 0.0047 (0.0154) acc_x 81.2500 (86.0435) lr 4.712526e-05 -epoch [3/25][2400/4762] time 0.147 (0.149) data 0.001 (0.001) eta 4:26:48 loss 0.4165 (0.4095) loss_x 0.4000 (0.3942) loss_u 0.0165 (0.0153) acc_x 87.5000 (86.0651) lr 4.712526e-05 -epoch [3/25][2500/4762] time 0.136 (0.149) data 0.001 (0.001) eta 4:26:27 loss 0.2568 (0.4099) loss_x 0.2501 (0.3947) loss_u 0.0067 (0.0152) acc_x 93.7500 (86.0550) lr 4.712526e-05 -epoch [3/25][2600/4762] time 0.165 (0.149) data 0.001 (0.001) eta 4:26:25 loss 0.6299 (0.4096) loss_x 0.6278 (0.3944) loss_u 0.0020 (0.0152) acc_x 75.0000 (86.0541) lr 4.712526e-05 -epoch [3/25][2700/4762] time 0.207 (0.149) data 0.001 (0.001) eta 4:26:08 loss 0.2346 (0.4088) loss_x 0.2283 (0.3936) loss_u 0.0062 (0.0152) acc_x 87.5000 (86.0891) lr 4.712526e-05 -epoch [3/25][2800/4762] time 0.143 (0.149) data 0.001 (0.001) eta 4:25:48 loss 0.4946 (0.4091) loss_x 0.4489 (0.3941) loss_u 0.0457 (0.0151) acc_x 78.1250 (86.0781) lr 4.712526e-05 -epoch [3/25][2900/4762] time 0.155 (0.149) data 0.001 (0.001) eta 4:25:39 loss 0.2459 (0.4079) loss_x 0.2443 (0.3928) loss_u 0.0016 (0.0151) acc_x 90.6250 (86.1293) lr 4.712526e-05 -epoch [3/25][3000/4762] time 0.143 (0.149) data 0.001 (0.001) eta 4:25:19 loss 0.3561 (0.4078) loss_x 0.3464 (0.3927) loss_u 0.0097 (0.0151) acc_x 81.2500 (86.1490) lr 4.712526e-05 -epoch [3/25][3100/4762] time 0.139 (0.149) data 0.001 (0.001) eta 4:25:03 loss 0.3743 (0.4076) loss_x 0.3687 (0.3926) loss_u 0.0056 (0.0150) acc_x 87.5000 (86.1764) lr 4.712526e-05 -epoch [3/25][3200/4762] time 0.141 (0.149) data 0.001 (0.001) eta 4:24:46 loss 0.2108 (0.4069) loss_x 0.2057 (0.3919) loss_u 0.0050 (0.0150) acc_x 90.6250 (86.2051) lr 4.712526e-05 -epoch [3/25][3300/4762] time 0.183 (0.149) data 0.001 (0.001) eta 4:24:38 loss 0.2624 (0.4069) loss_x 0.2374 (0.3920) loss_u 0.0250 (0.0149) acc_x 93.7500 (86.2150) lr 4.712526e-05 -epoch [3/25][3400/4762] time 0.143 (0.150) data 0.001 (0.001) eta 4:24:26 loss 0.2742 (0.4063) loss_x 0.2654 (0.3914) loss_u 0.0088 (0.0149) acc_x 87.5000 (86.2399) lr 4.712526e-05 -epoch [3/25][3500/4762] time 0.169 (0.150) data 0.001 (0.001) eta 4:24:32 loss 0.2492 (0.4059) loss_x 0.2397 (0.3910) loss_u 0.0095 (0.0149) acc_x 90.6250 (86.2482) lr 4.712526e-05 -epoch [3/25][3600/4762] time 0.159 (0.150) data 0.001 (0.001) eta 4:24:10 loss 0.4120 (0.4063) loss_x 0.4101 (0.3915) loss_u 0.0019 (0.0148) acc_x 90.6250 (86.2214) lr 4.712526e-05 -epoch [3/25][3700/4762] time 0.142 (0.150) data 0.001 (0.001) eta 4:24:10 loss 0.4295 (0.4067) loss_x 0.4267 (0.3918) loss_u 0.0028 (0.0149) acc_x 90.6250 (86.2331) lr 4.712526e-05 -epoch [3/25][3800/4762] time 0.146 (0.150) data 0.001 (0.001) eta 4:23:53 loss 0.3134 (0.4062) loss_x 0.2845 (0.3914) loss_u 0.0289 (0.0148) acc_x 90.6250 (86.2549) lr 4.712526e-05 -epoch [3/25][3900/4762] time 0.190 (0.150) data 0.001 (0.001) eta 4:23:46 loss 0.3672 (0.4059) loss_x 0.3393 (0.3910) loss_u 0.0279 (0.0149) acc_x 90.6250 (86.2492) lr 4.712526e-05 -epoch [3/25][4000/4762] time 0.147 (0.150) data 0.001 (0.001) eta 4:23:28 loss 0.2185 (0.4057) loss_x 0.2084 (0.3908) loss_u 0.0101 (0.0149) acc_x 93.7500 (86.2461) lr 4.712526e-05 -epoch [3/25][4100/4762] time 0.154 (0.150) data 0.001 (0.001) eta 4:23:08 loss 0.2306 (0.4057) loss_x 0.1866 (0.3908) loss_u 0.0440 (0.0149) acc_x 93.7500 (86.2416) lr 4.712526e-05 -epoch [3/25][4200/4762] time 0.140 (0.150) data 0.001 (0.001) eta 4:22:51 loss 0.4700 (0.4052) loss_x 0.4017 (0.3904) loss_u 0.0683 (0.0149) acc_x 84.3750 (86.2634) lr 4.712526e-05 -epoch [3/25][4300/4762] time 0.137 (0.150) data 0.001 (0.001) eta 4:22:22 loss 0.3561 (0.4053) loss_x 0.3499 (0.3904) loss_u 0.0062 (0.0148) acc_x 90.6250 (86.2580) lr 4.712526e-05 -epoch [3/25][4400/4762] time 0.154 (0.150) data 0.001 (0.001) eta 4:22:07 loss 0.2047 (0.4048) loss_x 0.1932 (0.3900) loss_u 0.0115 (0.0148) acc_x 93.7500 (86.2607) lr 4.712526e-05 -epoch [3/25][4500/4762] time 0.183 (0.150) data 0.001 (0.001) eta 4:21:49 loss 0.4130 (0.4043) loss_x 0.3521 (0.3895) loss_u 0.0609 (0.0148) acc_x 93.7500 (86.2764) lr 4.712526e-05 -epoch [3/25][4600/4762] time 0.178 (0.150) data 0.001 (0.001) eta 4:21:33 loss 0.3988 (0.4040) loss_x 0.3776 (0.3892) loss_u 0.0212 (0.0148) acc_x 84.3750 (86.2880) lr 4.712526e-05 -epoch [3/25][4700/4762] time 0.151 (0.150) data 0.001 (0.001) eta 4:21:13 loss 0.2546 (0.4033) loss_x 0.2405 (0.3885) loss_u 0.0141 (0.0148) acc_x 87.5000 (86.3132) lr 4.712526e-05 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,862 -* accuracy: 84.61% -* error: 15.39% -* macro_f1: 84.94% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,587 acc: 98.38% -* class: 1 (bicycle) total: 3,475 correct: 2,889 acc: 83.14% -* class: 2 (bus) total: 4,690 correct: 4,254 acc: 90.70% -* class: 3 (car) total: 10,401 correct: 7,654 acc: 73.59% -* class: 4 (horse) total: 4,691 correct: 4,574 acc: 97.51% -* class: 5 (knife) total: 2,075 correct: 1,899 acc: 91.52% -* class: 6 (motorcycle) total: 5,796 correct: 5,513 acc: 95.12% -* class: 7 (person) total: 4,000 correct: 3,095 acc: 77.38% -* class: 8 (plant) total: 4,549 correct: 3,949 acc: 86.81% -* class: 9 (skateboard) total: 2,281 correct: 2,059 acc: 90.27% -* class: 10 (train) total: 4,236 correct: 3,923 acc: 92.61% -* class: 11 (truck) total: 5,548 correct: 3,466 acc: 62.47% -* average: 86.62% -epoch [4/25][100/4762] time 0.139 (0.156) data 0.001 (0.003) eta 4:32:43 loss 0.4255 (0.4075) loss_x 0.4111 (0.3940) loss_u 0.0144 (0.0136) acc_x 87.5000 (85.6250) lr 1.036475e-03 -epoch [4/25][200/4762] time 0.169 (0.155) data 0.001 (0.002) eta 4:30:55 loss 0.3651 (0.4224) loss_x 0.3617 (0.4067) loss_u 0.0035 (0.0157) acc_x 87.5000 (85.3906) lr 1.036475e-03 -epoch [4/25][300/4762] time 0.145 (0.154) data 0.001 (0.002) eta 4:28:47 loss 0.3026 (0.4192) loss_x 0.2934 (0.4035) loss_u 0.0092 (0.0157) acc_x 90.6250 (85.8021) lr 1.036475e-03 -epoch [4/25][400/4762] time 0.146 (0.153) data 0.001 (0.001) eta 4:26:00 loss 0.5834 (0.4142) loss_x 0.5540 (0.3992) loss_u 0.0295 (0.0150) acc_x 75.0000 (85.9766) lr 1.036475e-03 -epoch [4/25][500/4762] time 0.138 (0.152) data 0.001 (0.001) eta 4:24:39 loss 0.3038 (0.4137) loss_x 0.2957 (0.3991) loss_u 0.0081 (0.0145) acc_x 96.8750 (85.9562) lr 1.036475e-03 -epoch [4/25][600/4762] time 0.153 (0.152) data 0.001 (0.001) eta 4:24:40 loss 0.6490 (0.4114) loss_x 0.6347 (0.3966) loss_u 0.0143 (0.0149) acc_x 75.0000 (86.0312) lr 1.036475e-03 -epoch [4/25][700/4762] time 0.182 (0.152) data 0.001 (0.001) eta 4:24:28 loss 0.4589 (0.4104) loss_x 0.4583 (0.3957) loss_u 0.0006 (0.0148) acc_x 81.2500 (86.0536) lr 1.036475e-03 -epoch [4/25][800/4762] time 0.157 (0.153) data 0.001 (0.001) eta 4:24:52 loss 0.1869 (0.4076) loss_x 0.1800 (0.3927) loss_u 0.0069 (0.0149) acc_x 96.8750 (86.1328) lr 1.036475e-03 -epoch [4/25][900/4762] time 0.139 (0.153) data 0.001 (0.001) eta 4:24:14 loss 0.4899 (0.4088) loss_x 0.4537 (0.3939) loss_u 0.0362 (0.0149) acc_x 84.3750 (86.0938) lr 1.036475e-03 -epoch [4/25][1000/4762] time 0.140 (0.152) data 0.001 (0.001) eta 4:23:22 loss 0.3814 (0.4077) loss_x 0.3744 (0.3930) loss_u 0.0070 (0.0147) acc_x 90.6250 (86.1125) lr 1.036475e-03 -epoch [4/25][1100/4762] time 0.144 (0.152) data 0.001 (0.001) eta 4:22:16 loss 0.3487 (0.4062) loss_x 0.3344 (0.3917) loss_u 0.0143 (0.0144) acc_x 90.6250 (86.1818) lr 1.036475e-03 -epoch [4/25][1200/4762] time 0.137 (0.152) data 0.001 (0.001) eta 4:21:34 loss 0.2337 (0.4044) loss_x 0.2324 (0.3900) loss_u 0.0013 (0.0144) acc_x 90.6250 (86.2526) lr 1.036475e-03 -epoch [4/25][1300/4762] time 0.143 (0.151) data 0.001 (0.001) eta 4:21:07 loss 0.3472 (0.4053) loss_x 0.3464 (0.3906) loss_u 0.0008 (0.0147) acc_x 87.5000 (86.2260) lr 1.036475e-03 -epoch [4/25][1400/4762] time 0.137 (0.151) data 0.001 (0.001) eta 4:20:39 loss 0.3338 (0.4044) loss_x 0.3208 (0.3897) loss_u 0.0130 (0.0148) acc_x 90.6250 (86.2121) lr 1.036475e-03 -epoch [4/25][1500/4762] time 0.138 (0.151) data 0.001 (0.001) eta 4:20:22 loss 0.3863 (0.4036) loss_x 0.3817 (0.3890) loss_u 0.0046 (0.0145) acc_x 78.1250 (86.2313) lr 1.036475e-03 -epoch [4/25][1600/4762] time 0.139 (0.151) data 0.001 (0.001) eta 4:19:31 loss 0.3448 (0.4021) loss_x 0.3348 (0.3876) loss_u 0.0100 (0.0145) acc_x 90.6250 (86.2734) lr 1.036475e-03 -epoch [4/25][1700/4762] time 0.162 (0.151) data 0.001 (0.001) eta 4:19:08 loss 0.4707 (0.4014) loss_x 0.4527 (0.3869) loss_u 0.0179 (0.0145) acc_x 81.2500 (86.3033) lr 1.036475e-03 -epoch [4/25][1800/4762] time 0.149 (0.151) data 0.001 (0.001) eta 4:19:19 loss 0.2797 (0.4020) loss_x 0.2691 (0.3876) loss_u 0.0106 (0.0145) acc_x 90.6250 (86.2882) lr 1.036475e-03 -epoch [4/25][1900/4762] time 0.165 (0.151) data 0.001 (0.001) eta 4:19:13 loss 0.4538 (0.4009) loss_x 0.4219 (0.3864) loss_u 0.0319 (0.0145) acc_x 84.3750 (86.3569) lr 1.036475e-03 -epoch [4/25][2000/4762] time 0.141 (0.151) data 0.001 (0.001) eta 4:18:27 loss 0.3386 (0.3998) loss_x 0.3345 (0.3853) loss_u 0.0041 (0.0145) acc_x 93.7500 (86.3891) lr 1.036475e-03 -epoch [4/25][2100/4762] time 0.140 (0.151) data 0.001 (0.001) eta 4:18:02 loss 0.3171 (0.3994) loss_x 0.2908 (0.3850) loss_u 0.0264 (0.0145) acc_x 90.6250 (86.3914) lr 1.036475e-03 -epoch [4/25][2200/4762] time 0.165 (0.151) data 0.001 (0.001) eta 4:17:46 loss 0.2493 (0.3994) loss_x 0.2367 (0.3849) loss_u 0.0126 (0.0145) acc_x 90.6250 (86.3878) lr 1.036475e-03 -epoch [4/25][2300/4762] time 0.137 (0.151) data 0.001 (0.001) eta 4:17:27 loss 0.6040 (0.3995) loss_x 0.5893 (0.3852) loss_u 0.0147 (0.0144) acc_x 84.3750 (86.3913) lr 1.036475e-03 -epoch [4/25][2400/4762] time 0.137 (0.151) data 0.001 (0.001) eta 4:17:14 loss 0.4351 (0.3997) loss_x 0.4329 (0.3854) loss_u 0.0022 (0.0143) acc_x 87.5000 (86.3854) lr 1.036475e-03 -epoch [4/25][2500/4762] time 0.143 (0.151) data 0.001 (0.001) eta 4:16:49 loss 0.3521 (0.3987) loss_x 0.3483 (0.3845) loss_u 0.0037 (0.0142) acc_x 87.5000 (86.4213) lr 1.036475e-03 -epoch [4/25][2600/4762] time 0.138 (0.151) data 0.001 (0.001) eta 4:16:33 loss 0.3296 (0.3979) loss_x 0.3228 (0.3837) loss_u 0.0067 (0.0142) acc_x 87.5000 (86.4639) lr 1.036475e-03 -epoch [4/25][2700/4762] time 0.169 (0.151) data 0.001 (0.001) eta 4:16:29 loss 0.6523 (0.3974) loss_x 0.6314 (0.3831) loss_u 0.0208 (0.0142) acc_x 75.0000 (86.4803) lr 1.036475e-03 -epoch [4/25][2800/4762] time 0.166 (0.151) data 0.001 (0.001) eta 4:16:22 loss 0.3224 (0.3973) loss_x 0.2724 (0.3831) loss_u 0.0500 (0.0142) acc_x 93.7500 (86.4699) lr 1.036475e-03 -epoch [4/25][2900/4762] time 0.148 (0.151) data 0.001 (0.001) eta 4:16:02 loss 0.1836 (0.3974) loss_x 0.1835 (0.3831) loss_u 0.0001 (0.0142) acc_x 90.6250 (86.4580) lr 1.036475e-03 -epoch [4/25][3000/4762] time 0.163 (0.151) data 0.001 (0.001) eta 4:15:42 loss 0.3282 (0.3977) loss_x 0.3198 (0.3834) loss_u 0.0084 (0.0142) acc_x 90.6250 (86.4365) lr 1.036475e-03 -epoch [4/25][3100/4762] time 0.150 (0.151) data 0.001 (0.001) eta 4:15:06 loss 0.4308 (0.3975) loss_x 0.4052 (0.3832) loss_u 0.0255 (0.0143) acc_x 87.5000 (86.4446) lr 1.036475e-03 -epoch [4/25][3200/4762] time 0.146 (0.150) data 0.001 (0.001) eta 4:14:39 loss 0.3496 (0.3977) loss_x 0.3466 (0.3834) loss_u 0.0031 (0.0143) acc_x 87.5000 (86.4336) lr 1.036475e-03 -epoch [4/25][3300/4762] time 0.181 (0.150) data 0.001 (0.001) eta 4:14:18 loss 0.3939 (0.3981) loss_x 0.3793 (0.3837) loss_u 0.0146 (0.0144) acc_x 87.5000 (86.4441) lr 1.036475e-03 -epoch [4/25][3400/4762] time 0.145 (0.150) data 0.001 (0.001) eta 4:14:03 loss 0.2750 (0.3980) loss_x 0.2445 (0.3836) loss_u 0.0306 (0.0144) acc_x 93.7500 (86.4577) lr 1.036475e-03 -epoch [4/25][3500/4762] time 0.150 (0.150) data 0.001 (0.001) eta 4:14:00 loss 0.2877 (0.3981) loss_x 0.2779 (0.3837) loss_u 0.0098 (0.0144) acc_x 87.5000 (86.4589) lr 1.036475e-03 -epoch [4/25][3600/4762] time 0.152 (0.151) data 0.001 (0.001) eta 4:13:46 loss 0.3855 (0.3976) loss_x 0.3777 (0.3832) loss_u 0.0078 (0.0143) acc_x 87.5000 (86.4774) lr 1.036475e-03 -epoch [4/25][3700/4762] time 0.150 (0.151) data 0.001 (0.001) eta 4:13:31 loss 0.4350 (0.3974) loss_x 0.4243 (0.3831) loss_u 0.0107 (0.0143) acc_x 81.2500 (86.4924) lr 1.036475e-03 -epoch [4/25][3800/4762] time 0.136 (0.150) data 0.001 (0.001) eta 4:13:14 loss 0.5051 (0.3970) loss_x 0.4945 (0.3827) loss_u 0.0105 (0.0143) acc_x 81.2500 (86.5099) lr 1.036475e-03 -epoch [4/25][3900/4762] time 0.156 (0.151) data 0.001 (0.001) eta 4:13:03 loss 0.4176 (0.3972) loss_x 0.3919 (0.3829) loss_u 0.0257 (0.0143) acc_x 84.3750 (86.4968) lr 1.036475e-03 -epoch [4/25][4000/4762] time 0.136 (0.150) data 0.001 (0.001) eta 4:12:34 loss 0.2104 (0.3968) loss_x 0.1954 (0.3826) loss_u 0.0149 (0.0143) acc_x 90.6250 (86.5234) lr 1.036475e-03 -epoch [4/25][4100/4762] time 0.148 (0.150) data 0.001 (0.001) eta 4:12:21 loss 0.1803 (0.3964) loss_x 0.1645 (0.3822) loss_u 0.0158 (0.0142) acc_x 96.8750 (86.5373) lr 1.036475e-03 -epoch [4/25][4200/4762] time 0.162 (0.150) data 0.001 (0.001) eta 4:12:08 loss 0.2871 (0.3962) loss_x 0.2860 (0.3819) loss_u 0.0011 (0.0143) acc_x 87.5000 (86.5424) lr 1.036475e-03 -epoch [4/25][4300/4762] time 0.161 (0.150) data 0.001 (0.001) eta 4:11:46 loss 0.7086 (0.3951) loss_x 0.6927 (0.3809) loss_u 0.0160 (0.0142) acc_x 75.0000 (86.5712) lr 1.036475e-03 -epoch [4/25][4400/4762] time 0.151 (0.150) data 0.001 (0.001) eta 4:11:29 loss 0.5003 (0.3952) loss_x 0.4868 (0.3811) loss_u 0.0135 (0.0142) acc_x 81.2500 (86.5597) lr 1.036475e-03 -epoch [4/25][4500/4762] time 0.137 (0.150) data 0.001 (0.001) eta 4:11:12 loss 0.3977 (0.3954) loss_x 0.3426 (0.3812) loss_u 0.0551 (0.0142) acc_x 90.6250 (86.5569) lr 1.036475e-03 -epoch [4/25][4600/4762] time 0.168 (0.150) data 0.001 (0.001) eta 4:10:56 loss 0.3147 (0.3951) loss_x 0.3031 (0.3810) loss_u 0.0116 (0.0141) acc_x 90.6250 (86.5571) lr 1.036475e-03 -epoch [4/25][4700/4762] time 0.141 (0.150) data 0.001 (0.001) eta 4:10:43 loss 0.2830 (0.3944) loss_x 0.2743 (0.3804) loss_u 0.0086 (0.0141) acc_x 84.3750 (86.5831) lr 1.036475e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,907 -* accuracy: 84.69% -* error: 15.31% -* macro_f1: 84.85% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,595 acc: 98.60% -* class: 1 (bicycle) total: 3,475 correct: 2,955 acc: 85.04% -* class: 2 (bus) total: 4,690 correct: 4,249 acc: 90.60% -* class: 3 (car) total: 10,401 correct: 7,756 acc: 74.57% -* class: 4 (horse) total: 4,691 correct: 4,581 acc: 97.66% -* class: 5 (knife) total: 2,075 correct: 1,891 acc: 91.13% -* class: 6 (motorcycle) total: 5,796 correct: 5,514 acc: 95.13% -* class: 7 (person) total: 4,000 correct: 2,931 acc: 73.28% -* class: 8 (plant) total: 4,549 correct: 3,961 acc: 87.07% -* class: 9 (skateboard) total: 2,281 correct: 2,044 acc: 89.61% -* class: 10 (train) total: 4,236 correct: 3,930 acc: 92.78% -* class: 11 (truck) total: 5,548 correct: 3,500 acc: 63.09% -* average: 86.55% -epoch [5/25][100/4762] time 0.175 (0.156) data 0.001 (0.003) eta 4:19:59 loss 0.1869 (0.3699) loss_x 0.1754 (0.3569) loss_u 0.0115 (0.0130) acc_x 93.7500 (86.9062) lr 2.894665e-03 -epoch [5/25][200/4762] time 0.152 (0.155) data 0.001 (0.002) eta 4:18:21 loss 0.3189 (0.3855) loss_x 0.2928 (0.3728) loss_u 0.0260 (0.0127) acc_x 87.5000 (86.4688) lr 2.894665e-03 -epoch [5/25][300/4762] time 0.138 (0.153) data 0.001 (0.001) eta 4:15:02 loss 0.3665 (0.3882) loss_x 0.3587 (0.3740) loss_u 0.0078 (0.0142) acc_x 90.6250 (86.4479) lr 2.894665e-03 -epoch [5/25][400/4762] time 0.136 (0.154) data 0.001 (0.001) eta 4:14:51 loss 0.4246 (0.3866) loss_x 0.4163 (0.3729) loss_u 0.0083 (0.0137) acc_x 87.5000 (86.5859) lr 2.894665e-03 -epoch [5/25][500/4762] time 0.167 (0.154) data 0.001 (0.001) eta 4:14:53 loss 0.3705 (0.3877) loss_x 0.3671 (0.3747) loss_u 0.0034 (0.0130) acc_x 90.6250 (86.7000) lr 2.894665e-03 -epoch [5/25][600/4762] time 0.148 (0.154) data 0.001 (0.001) eta 4:14:27 loss 0.3326 (0.3912) loss_x 0.3248 (0.3783) loss_u 0.0079 (0.0128) acc_x 90.6250 (86.5833) lr 2.894665e-03 -epoch [5/25][700/4762] time 0.153 (0.154) data 0.001 (0.001) eta 4:14:07 loss 0.3316 (0.3906) loss_x 0.3248 (0.3776) loss_u 0.0068 (0.0130) acc_x 90.6250 (86.6875) lr 2.894665e-03 -epoch [5/25][800/4762] time 0.143 (0.154) data 0.001 (0.001) eta 4:14:23 loss 0.2428 (0.3885) loss_x 0.2225 (0.3758) loss_u 0.0204 (0.0126) acc_x 90.6250 (86.7305) lr 2.894665e-03 -epoch [5/25][900/4762] time 0.141 (0.154) data 0.001 (0.001) eta 4:14:06 loss 0.3337 (0.3887) loss_x 0.3254 (0.3759) loss_u 0.0083 (0.0128) acc_x 87.5000 (86.7708) lr 2.894665e-03 -epoch [5/25][1000/4762] time 0.160 (0.154) data 0.001 (0.001) eta 4:14:00 loss 0.4584 (0.3896) loss_x 0.4471 (0.3766) loss_u 0.0113 (0.0129) acc_x 81.2500 (86.7344) lr 2.894665e-03 -epoch [5/25][1100/4762] time 0.147 (0.154) data 0.001 (0.001) eta 4:13:53 loss 0.3198 (0.3889) loss_x 0.2972 (0.3759) loss_u 0.0227 (0.0130) acc_x 90.6250 (86.7812) lr 2.894665e-03 -epoch [5/25][1200/4762] time 0.168 (0.154) data 0.001 (0.001) eta 4:13:44 loss 0.6577 (0.3903) loss_x 0.6519 (0.3771) loss_u 0.0058 (0.0132) acc_x 81.2500 (86.7083) lr 2.894665e-03 -epoch [5/25][1300/4762] time 0.140 (0.154) data 0.001 (0.001) eta 4:13:14 loss 0.5842 (0.3896) loss_x 0.5638 (0.3763) loss_u 0.0204 (0.0132) acc_x 78.1250 (86.7861) lr 2.894665e-03 -epoch [5/25][1400/4762] time 0.144 (0.154) data 0.001 (0.001) eta 4:13:26 loss 0.2147 (0.3908) loss_x 0.2073 (0.3776) loss_u 0.0074 (0.0132) acc_x 96.8750 (86.7835) lr 2.894665e-03 -epoch [5/25][1500/4762] time 0.143 (0.154) data 0.001 (0.001) eta 4:12:34 loss 0.5653 (0.3926) loss_x 0.5428 (0.3795) loss_u 0.0225 (0.0131) acc_x 84.3750 (86.6958) lr 2.894665e-03 -epoch [5/25][1600/4762] time 0.149 (0.154) data 0.001 (0.001) eta 4:12:12 loss 0.3448 (0.3921) loss_x 0.2789 (0.3790) loss_u 0.0659 (0.0131) acc_x 87.5000 (86.6797) lr 2.894665e-03 -epoch [5/25][1700/4762] time 0.148 (0.154) data 0.001 (0.001) eta 4:11:40 loss 0.4003 (0.3938) loss_x 0.3786 (0.3808) loss_u 0.0218 (0.0130) acc_x 84.3750 (86.6250) lr 2.894665e-03 -epoch [5/25][1800/4762] time 0.139 (0.154) data 0.001 (0.001) eta 4:11:58 loss 0.1810 (0.3938) loss_x 0.1782 (0.3808) loss_u 0.0029 (0.0129) acc_x 90.6250 (86.6024) lr 2.894665e-03 -epoch [5/25][1900/4762] time 0.176 (0.154) data 0.001 (0.001) eta 4:11:30 loss 0.4923 (0.3934) loss_x 0.4784 (0.3804) loss_u 0.0139 (0.0130) acc_x 78.1250 (86.6036) lr 2.894665e-03 -epoch [5/25][2000/4762] time 0.144 (0.154) data 0.001 (0.001) eta 4:11:00 loss 0.3694 (0.3936) loss_x 0.3512 (0.3806) loss_u 0.0182 (0.0130) acc_x 87.5000 (86.5953) lr 2.894665e-03 -epoch [5/25][2100/4762] time 0.136 (0.154) data 0.001 (0.001) eta 4:10:35 loss 0.4880 (0.3937) loss_x 0.4588 (0.3807) loss_u 0.0292 (0.0130) acc_x 84.3750 (86.5670) lr 2.894665e-03 -epoch [5/25][2200/4762] time 0.138 (0.153) data 0.001 (0.001) eta 4:10:00 loss 0.7425 (0.3942) loss_x 0.7272 (0.3812) loss_u 0.0153 (0.0130) acc_x 75.0000 (86.5511) lr 2.894665e-03 -epoch [5/25][2300/4762] time 0.168 (0.153) data 0.001 (0.001) eta 4:09:26 loss 0.2956 (0.3941) loss_x 0.2922 (0.3811) loss_u 0.0034 (0.0130) acc_x 96.8750 (86.5720) lr 2.894665e-03 -epoch [5/25][2400/4762] time 0.140 (0.153) data 0.001 (0.001) eta 4:09:08 loss 0.3882 (0.3947) loss_x 0.3835 (0.3815) loss_u 0.0048 (0.0132) acc_x 87.5000 (86.5794) lr 2.894665e-03 -epoch [5/25][2500/4762] time 0.140 (0.153) data 0.001 (0.001) eta 4:08:41 loss 0.3922 (0.3944) loss_x 0.3885 (0.3812) loss_u 0.0037 (0.0133) acc_x 84.3750 (86.5775) lr 2.894665e-03 -epoch [5/25][2600/4762] time 0.165 (0.153) data 0.000 (0.001) eta 4:08:32 loss 0.4946 (0.3942) loss_x 0.4869 (0.3810) loss_u 0.0076 (0.0132) acc_x 84.3750 (86.5841) lr 2.894665e-03 -epoch [5/25][2700/4762] time 0.167 (0.153) data 0.001 (0.001) eta 4:08:08 loss 0.5814 (0.3933) loss_x 0.5776 (0.3801) loss_u 0.0038 (0.0131) acc_x 81.2500 (86.6111) lr 2.894665e-03 -epoch [5/25][2800/4762] time 0.149 (0.153) data 0.001 (0.001) eta 4:07:59 loss 0.3370 (0.3925) loss_x 0.3273 (0.3794) loss_u 0.0097 (0.0131) acc_x 90.6250 (86.6373) lr 2.894665e-03 -epoch [5/25][2900/4762] time 0.162 (0.153) data 0.001 (0.001) eta 4:07:36 loss 0.2185 (0.3920) loss_x 0.2124 (0.3789) loss_u 0.0061 (0.0132) acc_x 90.6250 (86.6530) lr 2.894665e-03 -epoch [5/25][3000/4762] time 0.154 (0.153) data 0.001 (0.001) eta 4:07:13 loss 0.3585 (0.3915) loss_x 0.3504 (0.3783) loss_u 0.0081 (0.0133) acc_x 84.3750 (86.6500) lr 2.894665e-03 -epoch [5/25][3100/4762] time 0.153 (0.153) data 0.001 (0.001) eta 4:06:47 loss 0.2878 (0.3916) loss_x 0.2863 (0.3783) loss_u 0.0016 (0.0133) acc_x 87.5000 (86.6522) lr 2.894665e-03 -epoch [5/25][3200/4762] time 0.140 (0.153) data 0.001 (0.001) eta 4:06:28 loss 0.5606 (0.3906) loss_x 0.5386 (0.3773) loss_u 0.0221 (0.0133) acc_x 84.3750 (86.6875) lr 2.894665e-03 -epoch [5/25][3300/4762] time 0.151 (0.153) data 0.001 (0.001) eta 4:06:13 loss 0.5958 (0.3902) loss_x 0.5652 (0.3768) loss_u 0.0306 (0.0133) acc_x 78.1250 (86.7045) lr 2.894665e-03 -epoch [5/25][3400/4762] time 0.158 (0.153) data 0.001 (0.001) eta 4:05:53 loss 0.4531 (0.3902) loss_x 0.4400 (0.3768) loss_u 0.0131 (0.0134) acc_x 84.3750 (86.6958) lr 2.894665e-03 -epoch [5/25][3500/4762] time 0.154 (0.153) data 0.001 (0.001) eta 4:05:54 loss 0.2422 (0.3903) loss_x 0.2289 (0.3769) loss_u 0.0133 (0.0134) acc_x 93.7500 (86.6902) lr 2.894665e-03 -epoch [5/25][3600/4762] time 0.161 (0.153) data 0.001 (0.001) eta 4:05:43 loss 0.2577 (0.3904) loss_x 0.2256 (0.3770) loss_u 0.0321 (0.0134) acc_x 93.7500 (86.6753) lr 2.894665e-03 -epoch [5/25][3700/4762] time 0.161 (0.153) data 0.001 (0.001) eta 4:05:34 loss 0.6599 (0.3904) loss_x 0.6344 (0.3770) loss_u 0.0255 (0.0134) acc_x 78.1250 (86.6588) lr 2.894665e-03 -epoch [5/25][3800/4762] time 0.149 (0.153) data 0.001 (0.001) eta 4:05:16 loss 0.5907 (0.3903) loss_x 0.5832 (0.3769) loss_u 0.0075 (0.0134) acc_x 81.2500 (86.6579) lr 2.894665e-03 -epoch [5/25][3900/4762] time 0.142 (0.153) data 0.003 (0.001) eta 4:05:03 loss 0.4301 (0.3905) loss_x 0.4290 (0.3772) loss_u 0.0011 (0.0133) acc_x 84.3750 (86.6514) lr 2.894665e-03 -epoch [5/25][4000/4762] time 0.155 (0.153) data 0.001 (0.001) eta 4:04:46 loss 0.4853 (0.3904) loss_x 0.4652 (0.3771) loss_u 0.0200 (0.0133) acc_x 84.3750 (86.6602) lr 2.894665e-03 -epoch [5/25][4100/4762] time 0.172 (0.153) data 0.001 (0.001) eta 4:04:26 loss 0.3693 (0.3903) loss_x 0.3543 (0.3770) loss_u 0.0151 (0.0133) acc_x 84.3750 (86.6502) lr 2.894665e-03 -epoch [5/25][4200/4762] time 0.151 (0.153) data 0.001 (0.001) eta 4:04:03 loss 0.2840 (0.3902) loss_x 0.2825 (0.3769) loss_u 0.0015 (0.0132) acc_x 93.7500 (86.6555) lr 2.894665e-03 -epoch [5/25][4300/4762] time 0.156 (0.153) data 0.001 (0.001) eta 4:03:50 loss 0.2367 (0.3904) loss_x 0.2307 (0.3772) loss_u 0.0059 (0.0132) acc_x 87.5000 (86.6533) lr 2.894665e-03 -epoch [5/25][4400/4762] time 0.137 (0.153) data 0.001 (0.001) eta 4:03:29 loss 0.5597 (0.3906) loss_x 0.5583 (0.3774) loss_u 0.0014 (0.0132) acc_x 81.2500 (86.6513) lr 2.894665e-03 -epoch [5/25][4500/4762] time 0.177 (0.153) data 0.001 (0.001) eta 4:03:05 loss 0.3093 (0.3908) loss_x 0.2851 (0.3776) loss_u 0.0242 (0.0132) acc_x 93.7500 (86.6569) lr 2.894665e-03 -epoch [5/25][4600/4762] time 0.139 (0.153) data 0.001 (0.001) eta 4:02:53 loss 0.2583 (0.3912) loss_x 0.2548 (0.3780) loss_u 0.0034 (0.0132) acc_x 90.6250 (86.6406) lr 2.894665e-03 -epoch [5/25][4700/4762] time 0.145 (0.153) data 0.001 (0.001) eta 4:02:40 loss 0.5104 (0.3915) loss_x 0.4974 (0.3782) loss_u 0.0130 (0.0132) acc_x 75.0000 (86.6243) lr 2.894665e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,619 -* accuracy: 84.17% -* error: 15.83% -* macro_f1: 84.26% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,578 acc: 98.13% -* class: 1 (bicycle) total: 3,475 correct: 2,935 acc: 84.46% -* class: 2 (bus) total: 4,690 correct: 4,278 acc: 91.22% -* class: 3 (car) total: 10,401 correct: 7,832 acc: 75.30% -* class: 4 (horse) total: 4,691 correct: 4,590 acc: 97.85% -* class: 5 (knife) total: 2,075 correct: 1,858 acc: 89.54% -* class: 6 (motorcycle) total: 5,796 correct: 5,493 acc: 94.77% -* class: 7 (person) total: 4,000 correct: 2,789 acc: 69.72% -* class: 8 (plant) total: 4,549 correct: 3,865 acc: 84.96% -* class: 9 (skateboard) total: 2,281 correct: 2,075 acc: 90.97% -* class: 10 (train) total: 4,236 correct: 3,915 acc: 92.42% -* class: 11 (truck) total: 5,548 correct: 3,411 acc: 61.48% -* average: 85.90% -epoch [6/25][100/4762] time 0.147 (0.155) data 0.001 (0.003) eta 4:05:26 loss 0.6436 (0.3920) loss_x 0.6407 (0.3788) loss_u 0.0030 (0.0132) acc_x 78.1250 (86.3125) lr 2.138669e-03 -epoch [6/25][200/4762] time 0.163 (0.153) data 0.001 (0.002) eta 4:02:01 loss 0.1849 (0.3860) loss_x 0.1767 (0.3728) loss_u 0.0083 (0.0131) acc_x 93.7500 (86.5625) lr 2.138669e-03 -epoch [6/25][300/4762] time 0.142 (0.153) data 0.001 (0.001) eta 4:02:04 loss 0.4493 (0.3928) loss_x 0.4356 (0.3798) loss_u 0.0137 (0.0129) acc_x 78.1250 (86.3646) lr 2.138669e-03 -epoch [6/25][400/4762] time 0.182 (0.154) data 0.001 (0.001) eta 4:02:38 loss 0.4433 (0.3889) loss_x 0.4413 (0.3758) loss_u 0.0020 (0.0131) acc_x 81.2500 (86.5391) lr 2.138669e-03 -epoch [6/25][500/4762] time 0.149 (0.153) data 0.000 (0.001) eta 4:01:56 loss 0.2680 (0.3857) loss_x 0.2658 (0.3728) loss_u 0.0022 (0.0129) acc_x 90.6250 (86.7500) lr 2.138669e-03 -epoch [6/25][600/4762] time 0.142 (0.154) data 0.001 (0.001) eta 4:02:43 loss 0.4165 (0.3866) loss_x 0.4062 (0.3736) loss_u 0.0103 (0.0130) acc_x 84.3750 (86.8177) lr 2.138669e-03 -epoch [6/25][700/4762] time 0.181 (0.154) data 0.001 (0.001) eta 4:02:44 loss 0.4358 (0.3843) loss_x 0.3772 (0.3711) loss_u 0.0586 (0.0133) acc_x 84.3750 (86.9241) lr 2.138669e-03 -epoch [6/25][800/4762] time 0.144 (0.154) data 0.001 (0.001) eta 4:02:19 loss 0.5306 (0.3811) loss_x 0.5199 (0.3680) loss_u 0.0107 (0.0131) acc_x 78.1250 (87.0117) lr 2.138669e-03 -epoch [6/25][900/4762] time 0.194 (0.154) data 0.001 (0.001) eta 4:01:53 loss 0.5874 (0.3823) loss_x 0.5759 (0.3693) loss_u 0.0115 (0.0130) acc_x 81.2500 (86.9514) lr 2.138669e-03 -epoch [6/25][1000/4762] time 0.182 (0.154) data 0.005 (0.001) eta 4:01:30 loss 0.2889 (0.3830) loss_x 0.2856 (0.3702) loss_u 0.0033 (0.0128) acc_x 87.5000 (86.9719) lr 2.138669e-03 -epoch [6/25][1100/4762] time 0.147 (0.154) data 0.001 (0.001) eta 4:01:09 loss 0.3795 (0.3829) loss_x 0.3678 (0.3701) loss_u 0.0117 (0.0128) acc_x 84.3750 (86.9858) lr 2.138669e-03 -epoch [6/25][1200/4762] time 0.182 (0.154) data 0.001 (0.001) eta 4:01:03 loss 0.4231 (0.3829) loss_x 0.4218 (0.3700) loss_u 0.0013 (0.0130) acc_x 81.2500 (86.9635) lr 2.138669e-03 -epoch [6/25][1300/4762] time 0.150 (0.154) data 0.001 (0.001) eta 4:01:08 loss 0.2691 (0.3839) loss_x 0.2664 (0.3707) loss_u 0.0027 (0.0132) acc_x 87.5000 (86.9255) lr 2.138669e-03 -epoch [6/25][1400/4762] time 0.152 (0.154) data 0.001 (0.001) eta 4:00:42 loss 0.2486 (0.3841) loss_x 0.2461 (0.3710) loss_u 0.0026 (0.0131) acc_x 90.6250 (86.9018) lr 2.138669e-03 -epoch [6/25][1500/4762] time 0.146 (0.154) data 0.001 (0.001) eta 4:00:07 loss 0.4530 (0.3840) loss_x 0.4483 (0.3709) loss_u 0.0047 (0.0130) acc_x 87.5000 (86.9250) lr 2.138669e-03 -epoch [6/25][1600/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:59:20 loss 0.5226 (0.3839) loss_x 0.5135 (0.3709) loss_u 0.0091 (0.0130) acc_x 78.1250 (86.9004) lr 2.138669e-03 -epoch [6/25][1700/4762] time 0.160 (0.153) data 0.001 (0.001) eta 3:58:38 loss 0.3511 (0.3844) loss_x 0.3461 (0.3715) loss_u 0.0050 (0.0129) acc_x 81.2500 (86.8842) lr 2.138669e-03 -epoch [6/25][1800/4762] time 0.161 (0.153) data 0.001 (0.001) eta 3:58:36 loss 0.2162 (0.3861) loss_x 0.1936 (0.3733) loss_u 0.0226 (0.0128) acc_x 93.7500 (86.8351) lr 2.138669e-03 -epoch [6/25][1900/4762] time 0.136 (0.153) data 0.001 (0.001) eta 3:58:16 loss 0.4565 (0.3863) loss_x 0.4459 (0.3736) loss_u 0.0106 (0.0128) acc_x 78.1250 (86.8174) lr 2.138669e-03 -epoch [6/25][2000/4762] time 0.145 (0.153) data 0.001 (0.001) eta 3:57:49 loss 0.3042 (0.3848) loss_x 0.3027 (0.3721) loss_u 0.0015 (0.0128) acc_x 90.6250 (86.8688) lr 2.138669e-03 -epoch [6/25][2100/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:57:21 loss 0.3875 (0.3839) loss_x 0.3627 (0.3712) loss_u 0.0248 (0.0127) acc_x 87.5000 (86.8958) lr 2.138669e-03 -epoch [6/25][2200/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:56:54 loss 0.2628 (0.3840) loss_x 0.2512 (0.3713) loss_u 0.0116 (0.0128) acc_x 90.6250 (86.9261) lr 2.138669e-03 -epoch [6/25][2300/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:56:34 loss 0.3099 (0.3844) loss_x 0.3006 (0.3714) loss_u 0.0093 (0.0130) acc_x 93.7500 (86.9185) lr 2.138669e-03 -epoch [6/25][2400/4762] time 0.149 (0.153) data 0.001 (0.001) eta 3:56:07 loss 0.2261 (0.3857) loss_x 0.2127 (0.3727) loss_u 0.0134 (0.0130) acc_x 96.8750 (86.8581) lr 2.138669e-03 -epoch [6/25][2500/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:55:55 loss 0.4290 (0.3862) loss_x 0.4199 (0.3732) loss_u 0.0092 (0.0130) acc_x 81.2500 (86.7963) lr 2.138669e-03 -epoch [6/25][2600/4762] time 0.139 (0.153) data 0.003 (0.001) eta 3:55:38 loss 0.5384 (0.3868) loss_x 0.5287 (0.3738) loss_u 0.0097 (0.0129) acc_x 84.3750 (86.7728) lr 2.138669e-03 -epoch [6/25][2700/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:55:22 loss 0.4892 (0.3871) loss_x 0.4841 (0.3742) loss_u 0.0051 (0.0129) acc_x 84.3750 (86.7627) lr 2.138669e-03 -epoch [6/25][2800/4762] time 0.145 (0.153) data 0.001 (0.001) eta 3:55:04 loss 0.4131 (0.3868) loss_x 0.4040 (0.3739) loss_u 0.0091 (0.0129) acc_x 87.5000 (86.7746) lr 2.138669e-03 -epoch [6/25][2900/4762] time 0.138 (0.153) data 0.001 (0.001) eta 3:54:46 loss 0.5375 (0.3863) loss_x 0.5365 (0.3733) loss_u 0.0011 (0.0130) acc_x 78.1250 (86.7996) lr 2.138669e-03 -epoch [6/25][3000/4762] time 0.139 (0.152) data 0.001 (0.001) eta 3:54:20 loss 0.3357 (0.3856) loss_x 0.3332 (0.3727) loss_u 0.0025 (0.0130) acc_x 84.3750 (86.8240) lr 2.138669e-03 -epoch [6/25][3100/4762] time 0.138 (0.152) data 0.001 (0.001) eta 3:54:01 loss 0.3060 (0.3852) loss_x 0.3032 (0.3723) loss_u 0.0028 (0.0129) acc_x 90.6250 (86.8337) lr 2.138669e-03 -epoch [6/25][3200/4762] time 0.155 (0.152) data 0.001 (0.001) eta 3:53:39 loss 0.4397 (0.3854) loss_x 0.4206 (0.3724) loss_u 0.0191 (0.0130) acc_x 87.5000 (86.8379) lr 2.138669e-03 -epoch [6/25][3300/4762] time 0.141 (0.152) data 0.001 (0.001) eta 3:53:18 loss 0.2733 (0.3856) loss_x 0.2659 (0.3726) loss_u 0.0074 (0.0130) acc_x 87.5000 (86.8381) lr 2.138669e-03 -epoch [6/25][3400/4762] time 0.151 (0.152) data 0.001 (0.001) eta 3:52:56 loss 0.4943 (0.3864) loss_x 0.4795 (0.3735) loss_u 0.0148 (0.0129) acc_x 84.3750 (86.8079) lr 2.138669e-03 -epoch [6/25][3500/4762] time 0.145 (0.152) data 0.001 (0.001) eta 3:52:50 loss 0.3726 (0.3866) loss_x 0.3718 (0.3737) loss_u 0.0008 (0.0129) acc_x 84.3750 (86.8054) lr 2.138669e-03 -epoch [6/25][3600/4762] time 0.138 (0.152) data 0.001 (0.001) eta 3:52:39 loss 0.2379 (0.3866) loss_x 0.2150 (0.3737) loss_u 0.0229 (0.0129) acc_x 93.7500 (86.8142) lr 2.138669e-03 -epoch [6/25][3700/4762] time 0.161 (0.152) data 0.001 (0.001) eta 3:52:27 loss 0.1861 (0.3870) loss_x 0.1718 (0.3741) loss_u 0.0142 (0.0129) acc_x 93.7500 (86.8066) lr 2.138669e-03 -epoch [6/25][3800/4762] time 0.139 (0.152) data 0.001 (0.001) eta 3:52:07 loss 0.3028 (0.3867) loss_x 0.2927 (0.3739) loss_u 0.0101 (0.0129) acc_x 93.7500 (86.8306) lr 2.138669e-03 -epoch [6/25][3900/4762] time 0.158 (0.152) data 0.001 (0.001) eta 3:51:43 loss 0.3983 (0.3864) loss_x 0.3878 (0.3736) loss_u 0.0105 (0.0129) acc_x 84.3750 (86.8221) lr 2.138669e-03 -epoch [6/25][4000/4762] time 0.136 (0.152) data 0.001 (0.001) eta 3:51:24 loss 0.5625 (0.3865) loss_x 0.5481 (0.3736) loss_u 0.0145 (0.0129) acc_x 84.3750 (86.8172) lr 2.138669e-03 -epoch [6/25][4100/4762] time 0.141 (0.152) data 0.001 (0.001) eta 3:51:02 loss 0.3066 (0.3863) loss_x 0.3039 (0.3734) loss_u 0.0027 (0.0128) acc_x 87.5000 (86.8232) lr 2.138669e-03 -epoch [6/25][4200/4762] time 0.143 (0.152) data 0.001 (0.001) eta 3:50:46 loss 0.2854 (0.3866) loss_x 0.2731 (0.3737) loss_u 0.0123 (0.0128) acc_x 90.6250 (86.8162) lr 2.138669e-03 -epoch [6/25][4300/4762] time 0.158 (0.152) data 0.001 (0.001) eta 3:50:30 loss 0.4136 (0.3863) loss_x 0.4092 (0.3735) loss_u 0.0044 (0.0128) acc_x 87.5000 (86.8103) lr 2.138669e-03 -epoch [6/25][4400/4762] time 0.164 (0.152) data 0.001 (0.001) eta 3:50:14 loss 0.4662 (0.3869) loss_x 0.4636 (0.3741) loss_u 0.0026 (0.0128) acc_x 78.1250 (86.7862) lr 2.138669e-03 -epoch [6/25][4500/4762] time 0.166 (0.152) data 0.001 (0.001) eta 3:49:56 loss 0.4565 (0.3868) loss_x 0.4536 (0.3739) loss_u 0.0029 (0.0128) acc_x 84.3750 (86.7972) lr 2.138669e-03 -epoch [6/25][4600/4762] time 0.155 (0.152) data 0.001 (0.001) eta 3:49:38 loss 0.3518 (0.3869) loss_x 0.3495 (0.3741) loss_u 0.0023 (0.0128) acc_x 87.5000 (86.7894) lr 2.138669e-03 -epoch [6/25][4700/4762] time 0.152 (0.152) data 0.001 (0.001) eta 3:49:15 loss 0.4270 (0.3871) loss_x 0.4196 (0.3743) loss_u 0.0074 (0.0128) acc_x 84.3750 (86.7799) lr 2.138669e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,919 -* accuracy: 84.71% -* error: 15.29% -* macro_f1: 85.01% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,574 acc: 98.03% -* class: 1 (bicycle) total: 3,475 correct: 2,944 acc: 84.72% -* class: 2 (bus) total: 4,690 correct: 4,257 acc: 90.77% -* class: 3 (car) total: 10,401 correct: 7,807 acc: 75.06% -* class: 4 (horse) total: 4,691 correct: 4,575 acc: 97.53% -* class: 5 (knife) total: 2,075 correct: 1,916 acc: 92.34% -* class: 6 (motorcycle) total: 5,796 correct: 5,509 acc: 95.05% -* class: 7 (person) total: 4,000 correct: 3,093 acc: 77.33% -* class: 8 (plant) total: 4,549 correct: 3,819 acc: 83.95% -* class: 9 (skateboard) total: 2,281 correct: 2,035 acc: 89.22% -* class: 10 (train) total: 4,236 correct: 3,933 acc: 92.85% -* class: 11 (truck) total: 5,548 correct: 3,457 acc: 62.31% -* average: 86.59% -epoch [7/25][100/4762] time 0.154 (0.156) data 0.001 (0.003) eta 3:54:28 loss 0.3011 (0.3947) loss_x 0.2983 (0.3832) loss_u 0.0029 (0.0115) acc_x 87.5000 (85.8750) lr 1.855400e-04 -epoch [7/25][200/4762] time 0.160 (0.155) data 0.001 (0.002) eta 3:52:33 loss 0.3732 (0.3846) loss_x 0.3668 (0.3730) loss_u 0.0063 (0.0116) acc_x 87.5000 (86.2500) lr 1.855400e-04 -epoch [7/25][300/4762] time 0.167 (0.154) data 0.001 (0.002) eta 3:51:44 loss 0.3313 (0.3833) loss_x 0.3280 (0.3717) loss_u 0.0034 (0.0116) acc_x 87.5000 (86.4479) lr 1.855400e-04 -epoch [7/25][400/4762] time 0.170 (0.154) data 0.001 (0.001) eta 3:51:01 loss 0.2383 (0.3856) loss_x 0.2352 (0.3735) loss_u 0.0031 (0.0121) acc_x 93.7500 (86.4297) lr 1.855400e-04 -epoch [7/25][500/4762] time 0.139 (0.153) data 0.001 (0.001) eta 3:49:59 loss 0.2570 (0.3842) loss_x 0.2550 (0.3725) loss_u 0.0020 (0.0117) acc_x 90.6250 (86.6250) lr 1.855400e-04 -epoch [7/25][600/4762] time 0.141 (0.153) data 0.001 (0.001) eta 3:49:23 loss 0.3335 (0.3797) loss_x 0.3327 (0.3682) loss_u 0.0008 (0.0115) acc_x 87.5000 (86.8333) lr 1.855400e-04 -epoch [7/25][700/4762] time 0.137 (0.153) data 0.001 (0.001) eta 3:48:52 loss 0.3466 (0.3796) loss_x 0.3371 (0.3680) loss_u 0.0095 (0.0116) acc_x 87.5000 (86.8929) lr 1.855400e-04 -epoch [7/25][800/4762] time 0.151 (0.153) data 0.001 (0.001) eta 3:48:46 loss 0.3760 (0.3803) loss_x 0.3583 (0.3683) loss_u 0.0177 (0.0119) acc_x 84.3750 (86.9180) lr 1.855400e-04 -epoch [7/25][900/4762] time 0.168 (0.153) data 0.001 (0.001) eta 3:48:34 loss 0.4272 (0.3847) loss_x 0.3608 (0.3724) loss_u 0.0664 (0.0122) acc_x 84.3750 (86.8194) lr 1.855400e-04 -epoch [7/25][1000/4762] time 0.154 (0.153) data 0.001 (0.001) eta 3:48:17 loss 0.2600 (0.3863) loss_x 0.2566 (0.3741) loss_u 0.0034 (0.0123) acc_x 90.6250 (86.7687) lr 1.855400e-04 -epoch [7/25][1100/4762] time 0.151 (0.153) data 0.001 (0.001) eta 3:48:06 loss 0.5132 (0.3854) loss_x 0.4850 (0.3730) loss_u 0.0283 (0.0124) acc_x 81.2500 (86.8523) lr 1.855400e-04 -epoch [7/25][1200/4762] time 0.143 (0.153) data 0.001 (0.001) eta 3:47:53 loss 0.4311 (0.3845) loss_x 0.4217 (0.3722) loss_u 0.0094 (0.0123) acc_x 81.2500 (86.8568) lr 1.855400e-04 -epoch [7/25][1300/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:47:31 loss 0.5988 (0.3848) loss_x 0.5694 (0.3726) loss_u 0.0295 (0.0122) acc_x 78.1250 (86.8534) lr 1.855400e-04 -epoch [7/25][1400/4762] time 0.154 (0.153) data 0.001 (0.001) eta 3:47:39 loss 0.7177 (0.3855) loss_x 0.7021 (0.3731) loss_u 0.0156 (0.0123) acc_x 68.7500 (86.7991) lr 1.855400e-04 -epoch [7/25][1500/4762] time 0.142 (0.154) data 0.001 (0.001) eta 3:47:47 loss 0.2455 (0.3857) loss_x 0.2357 (0.3733) loss_u 0.0098 (0.0124) acc_x 93.7500 (86.8208) lr 1.855400e-04 -epoch [7/25][1600/4762] time 0.153 (0.154) data 0.001 (0.001) eta 3:47:26 loss 0.4727 (0.3861) loss_x 0.4690 (0.3737) loss_u 0.0037 (0.0124) acc_x 81.2500 (86.8477) lr 1.855400e-04 -epoch [7/25][1700/4762] time 0.136 (0.153) data 0.001 (0.001) eta 3:46:58 loss 0.4191 (0.3851) loss_x 0.4165 (0.3727) loss_u 0.0025 (0.0124) acc_x 84.3750 (86.8640) lr 1.855400e-04 -epoch [7/25][1800/4762] time 0.137 (0.154) data 0.001 (0.001) eta 3:47:02 loss 0.2900 (0.3848) loss_x 0.2810 (0.3726) loss_u 0.0090 (0.0123) acc_x 96.8750 (86.8542) lr 1.855400e-04 -epoch [7/25][1900/4762] time 0.151 (0.153) data 0.001 (0.001) eta 3:46:28 loss 0.4972 (0.3858) loss_x 0.4926 (0.3736) loss_u 0.0046 (0.0122) acc_x 81.2500 (86.8405) lr 1.855400e-04 -epoch [7/25][2000/4762] time 0.157 (0.153) data 0.001 (0.001) eta 3:46:09 loss 0.1498 (0.3857) loss_x 0.1444 (0.3735) loss_u 0.0053 (0.0122) acc_x 100.0000 (86.8531) lr 1.855400e-04 -epoch [7/25][2100/4762] time 0.143 (0.153) data 0.001 (0.001) eta 3:45:54 loss 0.3306 (0.3857) loss_x 0.3145 (0.3735) loss_u 0.0161 (0.0122) acc_x 87.5000 (86.8438) lr 1.855400e-04 -epoch [7/25][2200/4762] time 0.137 (0.153) data 0.001 (0.001) eta 3:45:35 loss 0.2581 (0.3855) loss_x 0.2482 (0.3734) loss_u 0.0099 (0.0122) acc_x 90.6250 (86.8580) lr 1.855400e-04 -epoch [7/25][2300/4762] time 0.138 (0.153) data 0.001 (0.001) eta 3:45:12 loss 0.4316 (0.3851) loss_x 0.4117 (0.3730) loss_u 0.0200 (0.0121) acc_x 87.5000 (86.8804) lr 1.855400e-04 -epoch [7/25][2400/4762] time 0.179 (0.153) data 0.001 (0.001) eta 3:44:54 loss 0.2796 (0.3851) loss_x 0.2652 (0.3731) loss_u 0.0144 (0.0121) acc_x 90.6250 (86.8971) lr 1.855400e-04 -epoch [7/25][2500/4762] time 0.147 (0.153) data 0.001 (0.001) eta 3:44:32 loss 0.3825 (0.3856) loss_x 0.3669 (0.3735) loss_u 0.0155 (0.0121) acc_x 87.5000 (86.8538) lr 1.855400e-04 -epoch [7/25][2600/4762] time 0.154 (0.153) data 0.001 (0.001) eta 3:44:16 loss 0.5065 (0.3853) loss_x 0.4251 (0.3733) loss_u 0.0814 (0.0120) acc_x 87.5000 (86.8245) lr 1.855400e-04 -epoch [7/25][2700/4762] time 0.163 (0.153) data 0.001 (0.001) eta 3:44:04 loss 0.4033 (0.3853) loss_x 0.3920 (0.3732) loss_u 0.0113 (0.0121) acc_x 87.5000 (86.8322) lr 1.855400e-04 -epoch [7/25][2800/4762] time 0.163 (0.153) data 0.001 (0.001) eta 3:43:49 loss 0.4703 (0.3852) loss_x 0.4610 (0.3731) loss_u 0.0094 (0.0120) acc_x 78.1250 (86.8493) lr 1.855400e-04 -epoch [7/25][2900/4762] time 0.146 (0.153) data 0.001 (0.001) eta 3:43:28 loss 0.3375 (0.3854) loss_x 0.3356 (0.3735) loss_u 0.0020 (0.0120) acc_x 87.5000 (86.8103) lr 1.855400e-04 -epoch [7/25][3000/4762] time 0.149 (0.153) data 0.001 (0.001) eta 3:43:12 loss 0.1449 (0.3850) loss_x 0.1414 (0.3730) loss_u 0.0036 (0.0120) acc_x 96.8750 (86.8281) lr 1.855400e-04 -epoch [7/25][3100/4762] time 0.138 (0.153) data 0.001 (0.001) eta 3:42:59 loss 0.5701 (0.3840) loss_x 0.5675 (0.3719) loss_u 0.0025 (0.0120) acc_x 78.1250 (86.8478) lr 1.855400e-04 -epoch [7/25][3200/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:42:34 loss 0.5508 (0.3841) loss_x 0.5478 (0.3722) loss_u 0.0030 (0.0119) acc_x 84.3750 (86.8281) lr 1.855400e-04 -epoch [7/25][3300/4762] time 0.136 (0.153) data 0.001 (0.001) eta 3:42:05 loss 0.3941 (0.3840) loss_x 0.3927 (0.3721) loss_u 0.0015 (0.0119) acc_x 84.3750 (86.8125) lr 1.855400e-04 -epoch [7/25][3400/4762] time 0.175 (0.153) data 0.001 (0.001) eta 3:41:52 loss 0.5527 (0.3838) loss_x 0.5495 (0.3719) loss_u 0.0032 (0.0118) acc_x 81.2500 (86.8235) lr 1.855400e-04 -epoch [7/25][3500/4762] time 0.162 (0.153) data 0.001 (0.001) eta 3:41:49 loss 0.2992 (0.3833) loss_x 0.2780 (0.3714) loss_u 0.0212 (0.0119) acc_x 90.6250 (86.8268) lr 1.855400e-04 -epoch [7/25][3600/4762] time 0.139 (0.153) data 0.001 (0.001) eta 3:41:33 loss 0.2881 (0.3838) loss_x 0.2782 (0.3719) loss_u 0.0099 (0.0119) acc_x 93.7500 (86.8047) lr 1.855400e-04 -epoch [7/25][3700/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:41:11 loss 0.2099 (0.3838) loss_x 0.1949 (0.3719) loss_u 0.0150 (0.0119) acc_x 90.6250 (86.8167) lr 1.855400e-04 -epoch [7/25][3800/4762] time 0.139 (0.153) data 0.001 (0.001) eta 3:40:50 loss 0.4479 (0.3839) loss_x 0.3711 (0.3720) loss_u 0.0768 (0.0119) acc_x 84.3750 (86.8117) lr 1.855400e-04 -epoch [7/25][3900/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:40:21 loss 0.2229 (0.3842) loss_x 0.2199 (0.3722) loss_u 0.0030 (0.0119) acc_x 93.7500 (86.7901) lr 1.855400e-04 -epoch [7/25][4000/4762] time 0.146 (0.153) data 0.001 (0.001) eta 3:40:09 loss 0.3665 (0.3846) loss_x 0.3517 (0.3727) loss_u 0.0148 (0.0119) acc_x 84.3750 (86.7695) lr 1.855400e-04 -epoch [7/25][4100/4762] time 0.136 (0.153) data 0.001 (0.001) eta 3:39:47 loss 0.5542 (0.3852) loss_x 0.5524 (0.3734) loss_u 0.0018 (0.0119) acc_x 71.8750 (86.7401) lr 1.855400e-04 -epoch [7/25][4200/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:39:23 loss 0.2258 (0.3850) loss_x 0.2029 (0.3731) loss_u 0.0229 (0.0119) acc_x 90.6250 (86.7515) lr 1.855400e-04 -epoch [7/25][4300/4762] time 0.179 (0.153) data 0.001 (0.001) eta 3:39:06 loss 0.2234 (0.3849) loss_x 0.1829 (0.3730) loss_u 0.0404 (0.0119) acc_x 90.6250 (86.7624) lr 1.855400e-04 -epoch [7/25][4400/4762] time 0.136 (0.152) data 0.000 (0.001) eta 3:38:40 loss 0.3609 (0.3846) loss_x 0.3602 (0.3727) loss_u 0.0007 (0.0118) acc_x 84.3750 (86.7649) lr 1.855400e-04 -epoch [7/25][4500/4762] time 0.145 (0.152) data 0.001 (0.001) eta 3:38:20 loss 0.5518 (0.3841) loss_x 0.5443 (0.3723) loss_u 0.0075 (0.0118) acc_x 81.2500 (86.7972) lr 1.855400e-04 -epoch [7/25][4600/4762] time 0.144 (0.152) data 0.001 (0.001) eta 3:38:00 loss 0.3120 (0.3843) loss_x 0.3054 (0.3723) loss_u 0.0066 (0.0119) acc_x 90.6250 (86.7976) lr 1.855400e-04 -epoch [7/25][4700/4762] time 0.140 (0.152) data 0.001 (0.001) eta 3:37:38 loss 0.4648 (0.3844) loss_x 0.4485 (0.3725) loss_u 0.0163 (0.0119) acc_x 81.2500 (86.7939) lr 1.855400e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,836 -* accuracy: 84.56% -* error: 15.44% -* macro_f1: 84.90% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,574 acc: 98.03% -* class: 1 (bicycle) total: 3,475 correct: 2,974 acc: 85.58% -* class: 2 (bus) total: 4,690 correct: 4,236 acc: 90.32% -* class: 3 (car) total: 10,401 correct: 7,597 acc: 73.04% -* class: 4 (horse) total: 4,691 correct: 4,591 acc: 97.87% -* class: 5 (knife) total: 2,075 correct: 1,858 acc: 89.54% -* class: 6 (motorcycle) total: 5,796 correct: 5,502 acc: 94.93% -* class: 7 (person) total: 4,000 correct: 3,156 acc: 78.90% -* class: 8 (plant) total: 4,549 correct: 3,854 acc: 84.72% -* class: 9 (skateboard) total: 2,281 correct: 2,047 acc: 89.74% -* class: 10 (train) total: 4,236 correct: 3,900 acc: 92.07% -* class: 11 (truck) total: 5,548 correct: 3,547 acc: 63.93% -* average: 86.56% -epoch [8/25][100/4762] time 0.156 (0.155) data 0.001 (0.003) eta 3:40:40 loss 0.5320 (0.3980) loss_x 0.5232 (0.3849) loss_u 0.0088 (0.0131) acc_x 90.6250 (86.0312) lr 6.962598e-04 -epoch [8/25][200/4762] time 0.154 (0.154) data 0.001 (0.002) eta 3:40:07 loss 0.4346 (0.3804) loss_x 0.4288 (0.3689) loss_u 0.0058 (0.0115) acc_x 81.2500 (86.9531) lr 6.962598e-04 -epoch [8/25][300/4762] time 0.147 (0.154) data 0.001 (0.002) eta 3:39:23 loss 0.6886 (0.3765) loss_x 0.6844 (0.3655) loss_u 0.0042 (0.0110) acc_x 75.0000 (87.0000) lr 6.962598e-04 -epoch [8/25][400/4762] time 0.171 (0.155) data 0.001 (0.001) eta 3:39:56 loss 0.2988 (0.3738) loss_x 0.2948 (0.3630) loss_u 0.0040 (0.0108) acc_x 90.6250 (87.1094) lr 6.962598e-04 -epoch [8/25][500/4762] time 0.140 (0.154) data 0.001 (0.001) eta 3:38:48 loss 0.5920 (0.3741) loss_x 0.5893 (0.3633) loss_u 0.0027 (0.0108) acc_x 81.2500 (87.0812) lr 6.962598e-04 -epoch [8/25][600/4762] time 0.152 (0.154) data 0.001 (0.001) eta 3:37:53 loss 0.1483 (0.3757) loss_x 0.1123 (0.3649) loss_u 0.0360 (0.0108) acc_x 100.0000 (87.0156) lr 6.962598e-04 -epoch [8/25][700/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:37:12 loss 0.3555 (0.3763) loss_x 0.3474 (0.3654) loss_u 0.0081 (0.0109) acc_x 90.6250 (87.0223) lr 6.962598e-04 -epoch [8/25][800/4762] time 0.141 (0.154) data 0.001 (0.001) eta 3:37:46 loss 0.3906 (0.3778) loss_x 0.3722 (0.3666) loss_u 0.0184 (0.0112) acc_x 84.3750 (87.0117) lr 6.962598e-04 -epoch [8/25][900/4762] time 0.147 (0.154) data 0.001 (0.001) eta 3:37:26 loss 0.4379 (0.3784) loss_x 0.4334 (0.3672) loss_u 0.0045 (0.0112) acc_x 84.3750 (87.1319) lr 6.962598e-04 -epoch [8/25][1000/4762] time 0.151 (0.154) data 0.001 (0.001) eta 3:37:22 loss 0.2812 (0.3781) loss_x 0.2552 (0.3667) loss_u 0.0260 (0.0114) acc_x 90.6250 (87.1094) lr 6.962598e-04 -epoch [8/25][1100/4762] time 0.144 (0.154) data 0.001 (0.001) eta 3:36:50 loss 0.4403 (0.3798) loss_x 0.4189 (0.3685) loss_u 0.0213 (0.0113) acc_x 81.2500 (87.0085) lr 6.962598e-04 -epoch [8/25][1200/4762] time 0.143 (0.153) data 0.001 (0.001) eta 3:35:57 loss 0.3895 (0.3797) loss_x 0.3848 (0.3684) loss_u 0.0047 (0.0114) acc_x 81.2500 (86.9818) lr 6.962598e-04 -epoch [8/25][1300/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:35:33 loss 0.2424 (0.3810) loss_x 0.2401 (0.3695) loss_u 0.0023 (0.0115) acc_x 87.5000 (86.9591) lr 6.962598e-04 -epoch [8/25][1400/4762] time 0.167 (0.153) data 0.001 (0.001) eta 3:35:31 loss 0.3447 (0.3806) loss_x 0.3229 (0.3692) loss_u 0.0218 (0.0114) acc_x 90.6250 (86.9598) lr 6.962598e-04 -epoch [8/25][1500/4762] time 0.172 (0.154) data 0.001 (0.001) eta 3:35:50 loss 0.2914 (0.3805) loss_x 0.2474 (0.3691) loss_u 0.0439 (0.0114) acc_x 93.7500 (86.9813) lr 6.962598e-04 -epoch [8/25][1600/4762] time 0.157 (0.154) data 0.001 (0.001) eta 3:35:54 loss 0.2856 (0.3805) loss_x 0.2652 (0.3692) loss_u 0.0204 (0.0114) acc_x 96.8750 (86.9785) lr 6.962598e-04 -epoch [8/25][1700/4762] time 0.138 (0.154) data 0.001 (0.001) eta 3:35:16 loss 0.4461 (0.3805) loss_x 0.4304 (0.3691) loss_u 0.0158 (0.0114) acc_x 90.6250 (87.0000) lr 6.962598e-04 -epoch [8/25][1800/4762] time 0.188 (0.154) data 0.001 (0.001) eta 3:35:17 loss 0.4590 (0.3810) loss_x 0.4521 (0.3695) loss_u 0.0070 (0.0115) acc_x 90.6250 (86.9844) lr 6.962598e-04 -epoch [8/25][1900/4762] time 0.159 (0.154) data 0.001 (0.001) eta 3:34:57 loss 0.4040 (0.3817) loss_x 0.4027 (0.3702) loss_u 0.0013 (0.0116) acc_x 87.5000 (86.9424) lr 6.962598e-04 -epoch [8/25][2000/4762] time 0.157 (0.154) data 0.001 (0.001) eta 3:34:30 loss 0.5494 (0.3809) loss_x 0.5393 (0.3693) loss_u 0.0101 (0.0115) acc_x 78.1250 (86.9969) lr 6.962598e-04 -epoch [8/25][2100/4762] time 0.142 (0.154) data 0.001 (0.001) eta 3:33:55 loss 0.2604 (0.3810) loss_x 0.2553 (0.3695) loss_u 0.0051 (0.0115) acc_x 90.6250 (87.0179) lr 6.962598e-04 -epoch [8/25][2200/4762] time 0.139 (0.153) data 0.001 (0.001) eta 3:33:35 loss 0.4628 (0.3817) loss_x 0.4622 (0.3702) loss_u 0.0005 (0.0115) acc_x 81.2500 (87.0071) lr 6.962598e-04 -epoch [8/25][2300/4762] time 0.161 (0.154) data 0.001 (0.001) eta 3:33:25 loss 0.4078 (0.3823) loss_x 0.4049 (0.3708) loss_u 0.0030 (0.0115) acc_x 87.5000 (86.9606) lr 6.962598e-04 -epoch [8/25][2400/4762] time 0.203 (0.154) data 0.001 (0.001) eta 3:33:22 loss 0.5792 (0.3820) loss_x 0.5281 (0.3705) loss_u 0.0511 (0.0115) acc_x 71.8750 (86.9740) lr 6.962598e-04 -epoch [8/25][2500/4762] time 0.168 (0.154) data 0.001 (0.001) eta 3:33:10 loss 0.2660 (0.3815) loss_x 0.2445 (0.3699) loss_u 0.0215 (0.0116) acc_x 93.7500 (87.0000) lr 6.962598e-04 -epoch [8/25][2600/4762] time 0.158 (0.154) data 0.001 (0.001) eta 3:32:53 loss 0.2974 (0.3816) loss_x 0.2927 (0.3700) loss_u 0.0047 (0.0115) acc_x 90.6250 (86.9784) lr 6.962598e-04 -epoch [8/25][2700/4762] time 0.142 (0.153) data 0.001 (0.001) eta 3:32:22 loss 0.5293 (0.3818) loss_x 0.5236 (0.3703) loss_u 0.0056 (0.0115) acc_x 81.2500 (86.9734) lr 6.962598e-04 -epoch [8/25][2800/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:32:07 loss 0.1985 (0.3822) loss_x 0.1793 (0.3708) loss_u 0.0192 (0.0115) acc_x 90.6250 (86.9621) lr 6.962598e-04 -epoch [8/25][2900/4762] time 0.149 (0.153) data 0.001 (0.001) eta 3:31:50 loss 0.1960 (0.3829) loss_x 0.1920 (0.3714) loss_u 0.0040 (0.0115) acc_x 93.7500 (86.9386) lr 6.962598e-04 -epoch [8/25][3000/4762] time 0.153 (0.153) data 0.001 (0.001) eta 3:31:24 loss 0.3805 (0.3823) loss_x 0.3771 (0.3709) loss_u 0.0035 (0.0114) acc_x 84.3750 (86.9542) lr 6.962598e-04 -epoch [8/25][3100/4762] time 0.170 (0.153) data 0.001 (0.001) eta 3:31:07 loss 0.5749 (0.3819) loss_x 0.5635 (0.3705) loss_u 0.0113 (0.0114) acc_x 78.1250 (86.9677) lr 6.962598e-04 -epoch [8/25][3200/4762] time 0.137 (0.153) data 0.001 (0.001) eta 3:30:44 loss 0.5704 (0.3828) loss_x 0.5691 (0.3714) loss_u 0.0013 (0.0114) acc_x 84.3750 (86.9443) lr 6.962598e-04 -epoch [8/25][3300/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:30:28 loss 0.1590 (0.3822) loss_x 0.1554 (0.3708) loss_u 0.0035 (0.0114) acc_x 93.7500 (86.9640) lr 6.962598e-04 -epoch [8/25][3400/4762] time 0.149 (0.153) data 0.001 (0.001) eta 3:30:12 loss 0.4117 (0.3822) loss_x 0.4096 (0.3709) loss_u 0.0021 (0.0113) acc_x 84.3750 (86.9550) lr 6.962598e-04 -epoch [8/25][3500/4762] time 0.138 (0.153) data 0.001 (0.001) eta 3:30:09 loss 0.2433 (0.3825) loss_x 0.2360 (0.3712) loss_u 0.0074 (0.0113) acc_x 90.6250 (86.9357) lr 6.962598e-04 -epoch [8/25][3600/4762] time 0.184 (0.153) data 0.001 (0.001) eta 3:29:46 loss 0.3136 (0.3818) loss_x 0.3099 (0.3706) loss_u 0.0038 (0.0113) acc_x 96.8750 (86.9696) lr 6.962598e-04 -epoch [8/25][3700/4762] time 0.137 (0.153) data 0.001 (0.001) eta 3:29:23 loss 0.4843 (0.3815) loss_x 0.4737 (0.3703) loss_u 0.0106 (0.0113) acc_x 81.2500 (86.9916) lr 6.962598e-04 -epoch [8/25][3800/4762] time 0.157 (0.153) data 0.001 (0.001) eta 3:29:09 loss 0.2629 (0.3815) loss_x 0.2512 (0.3702) loss_u 0.0117 (0.0113) acc_x 87.5000 (86.9868) lr 6.962598e-04 -epoch [8/25][3900/4762] time 0.150 (0.153) data 0.001 (0.001) eta 3:28:52 loss 0.4443 (0.3817) loss_x 0.4434 (0.3705) loss_u 0.0008 (0.0112) acc_x 84.3750 (86.9808) lr 6.962598e-04 -epoch [8/25][4000/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:28:33 loss 0.5338 (0.3822) loss_x 0.5306 (0.3710) loss_u 0.0032 (0.0112) acc_x 84.3750 (86.9719) lr 6.962598e-04 -epoch [8/25][4100/4762] time 0.143 (0.153) data 0.001 (0.001) eta 3:28:15 loss 0.5745 (0.3820) loss_x 0.5655 (0.3708) loss_u 0.0090 (0.0112) acc_x 78.1250 (86.9733) lr 6.962598e-04 -epoch [8/25][4200/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:27:59 loss 0.2753 (0.3822) loss_x 0.2593 (0.3709) loss_u 0.0160 (0.0112) acc_x 90.6250 (86.9643) lr 6.962598e-04 -epoch [8/25][4300/4762] time 0.139 (0.153) data 0.001 (0.001) eta 3:27:42 loss 0.1997 (0.3814) loss_x 0.1947 (0.3702) loss_u 0.0050 (0.0112) acc_x 93.7500 (86.9818) lr 6.962598e-04 -epoch [8/25][4400/4762] time 0.160 (0.153) data 0.001 (0.001) eta 3:27:26 loss 0.2589 (0.3815) loss_x 0.2318 (0.3704) loss_u 0.0271 (0.0112) acc_x 93.7500 (86.9830) lr 6.962598e-04 -epoch [8/25][4500/4762] time 0.137 (0.153) data 0.001 (0.001) eta 3:27:03 loss 0.2235 (0.3816) loss_x 0.2057 (0.3704) loss_u 0.0178 (0.0112) acc_x 90.6250 (86.9875) lr 6.962598e-04 -epoch [8/25][4600/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:26:44 loss 0.2681 (0.3817) loss_x 0.2621 (0.3704) loss_u 0.0060 (0.0112) acc_x 93.7500 (86.9803) lr 6.962598e-04 -epoch [8/25][4700/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:26:23 loss 0.6027 (0.3815) loss_x 0.5941 (0.3703) loss_u 0.0086 (0.0112) acc_x 87.5000 (86.9860) lr 6.962598e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,880 -* accuracy: 84.64% -* error: 15.36% -* macro_f1: 84.84% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,589 acc: 98.44% -* class: 1 (bicycle) total: 3,475 correct: 2,924 acc: 84.14% -* class: 2 (bus) total: 4,690 correct: 4,260 acc: 90.83% -* class: 3 (car) total: 10,401 correct: 7,859 acc: 75.56% -* class: 4 (horse) total: 4,691 correct: 4,579 acc: 97.61% -* class: 5 (knife) total: 2,075 correct: 1,849 acc: 89.11% -* class: 6 (motorcycle) total: 5,796 correct: 5,507 acc: 95.01% -* class: 7 (person) total: 4,000 correct: 3,133 acc: 78.33% -* class: 8 (plant) total: 4,549 correct: 3,790 acc: 83.32% -* class: 9 (skateboard) total: 2,281 correct: 2,036 acc: 89.26% -* class: 10 (train) total: 4,236 correct: 3,935 acc: 92.89% -* class: 11 (truck) total: 5,548 correct: 3,419 acc: 61.63% -* average: 86.34% -epoch [9/25][100/4762] time 0.141 (0.155) data 0.001 (0.004) eta 3:28:48 loss 0.2533 (0.3845) loss_x 0.2422 (0.3712) loss_u 0.0111 (0.0134) acc_x 93.7500 (86.7188) lr 2.713525e-03 -epoch [9/25][200/4762] time 0.157 (0.154) data 0.001 (0.002) eta 3:26:42 loss 0.3632 (0.3760) loss_x 0.3432 (0.3638) loss_u 0.0200 (0.0122) acc_x 90.6250 (87.2188) lr 2.713525e-03 -epoch [9/25][300/4762] time 0.157 (0.153) data 0.001 (0.002) eta 3:25:43 loss 0.3165 (0.3756) loss_x 0.3014 (0.3642) loss_u 0.0151 (0.0113) acc_x 84.3750 (87.2188) lr 2.713525e-03 -epoch [9/25][400/4762] time 0.168 (0.153) data 0.001 (0.002) eta 3:25:20 loss 0.2205 (0.3758) loss_x 0.2089 (0.3647) loss_u 0.0116 (0.0111) acc_x 93.7500 (87.3438) lr 2.713525e-03 -epoch [9/25][500/4762] time 0.147 (0.153) data 0.001 (0.001) eta 3:25:08 loss 0.4555 (0.3758) loss_x 0.4514 (0.3650) loss_u 0.0042 (0.0108) acc_x 81.2500 (87.2500) lr 2.713525e-03 -epoch [9/25][600/4762] time 0.143 (0.153) data 0.001 (0.001) eta 3:24:36 loss 0.3992 (0.3758) loss_x 0.3955 (0.3652) loss_u 0.0037 (0.0106) acc_x 81.2500 (87.1979) lr 2.713525e-03 -epoch [9/25][700/4762] time 0.147 (0.153) data 0.001 (0.001) eta 3:24:23 loss 0.2700 (0.3750) loss_x 0.2599 (0.3644) loss_u 0.0102 (0.0105) acc_x 96.8750 (87.2723) lr 2.713525e-03 -epoch [9/25][800/4762] time 0.150 (0.153) data 0.001 (0.001) eta 3:24:20 loss 0.5612 (0.3761) loss_x 0.5605 (0.3653) loss_u 0.0007 (0.0108) acc_x 78.1250 (87.1797) lr 2.713525e-03 -epoch [9/25][900/4762] time 0.193 (0.153) data 0.001 (0.001) eta 3:24:32 loss 0.3823 (0.3746) loss_x 0.3297 (0.3638) loss_u 0.0526 (0.0108) acc_x 87.5000 (87.2083) lr 2.713525e-03 -epoch [9/25][1000/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:24:22 loss 0.5412 (0.3753) loss_x 0.5355 (0.3645) loss_u 0.0056 (0.0108) acc_x 84.3750 (87.2313) lr 2.713525e-03 -epoch [9/25][1100/4762] time 0.162 (0.153) data 0.001 (0.001) eta 3:24:07 loss 0.3914 (0.3760) loss_x 0.3770 (0.3653) loss_u 0.0144 (0.0107) acc_x 87.5000 (87.2188) lr 2.713525e-03 -epoch [9/25][1200/4762] time 0.156 (0.153) data 0.001 (0.001) eta 3:23:57 loss 0.1785 (0.3765) loss_x 0.1760 (0.3658) loss_u 0.0025 (0.0107) acc_x 93.7500 (87.2083) lr 2.713525e-03 -epoch [9/25][1300/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:23:27 loss 0.3328 (0.3747) loss_x 0.3160 (0.3638) loss_u 0.0167 (0.0108) acc_x 84.3750 (87.2548) lr 2.713525e-03 -epoch [9/25][1400/4762] time 0.164 (0.153) data 0.001 (0.001) eta 3:23:14 loss 0.5484 (0.3732) loss_x 0.5465 (0.3625) loss_u 0.0019 (0.0107) acc_x 84.3750 (87.2969) lr 2.713525e-03 -epoch [9/25][1500/4762] time 0.174 (0.153) data 0.001 (0.001) eta 3:23:14 loss 0.1981 (0.3738) loss_x 0.1884 (0.3631) loss_u 0.0096 (0.0107) acc_x 93.7500 (87.2938) lr 2.713525e-03 -epoch [9/25][1600/4762] time 0.152 (0.154) data 0.001 (0.001) eta 3:23:18 loss 0.4244 (0.3753) loss_x 0.4111 (0.3645) loss_u 0.0133 (0.0107) acc_x 84.3750 (87.2227) lr 2.713525e-03 -epoch [9/25][1700/4762] time 0.149 (0.154) data 0.001 (0.001) eta 3:23:21 loss 0.4804 (0.3744) loss_x 0.4799 (0.3636) loss_u 0.0005 (0.0108) acc_x 87.5000 (87.2794) lr 2.713525e-03 -epoch [9/25][1800/4762] time 0.156 (0.154) data 0.001 (0.001) eta 3:23:42 loss 0.2999 (0.3736) loss_x 0.2979 (0.3629) loss_u 0.0020 (0.0107) acc_x 90.6250 (87.3003) lr 2.713525e-03 -epoch [9/25][1900/4762] time 0.140 (0.155) data 0.001 (0.001) eta 3:23:34 loss 0.5080 (0.3738) loss_x 0.4930 (0.3631) loss_u 0.0149 (0.0107) acc_x 75.0000 (87.2845) lr 2.713525e-03 -epoch [9/25][2000/4762] time 0.151 (0.155) data 0.001 (0.001) eta 3:23:21 loss 0.3838 (0.3753) loss_x 0.3820 (0.3645) loss_u 0.0018 (0.0108) acc_x 84.3750 (87.2500) lr 2.713525e-03 -epoch [9/25][2100/4762] time 0.147 (0.154) data 0.001 (0.001) eta 3:22:51 loss 0.3457 (0.3746) loss_x 0.3442 (0.3639) loss_u 0.0016 (0.0107) acc_x 81.2500 (87.2872) lr 2.713525e-03 -epoch [9/25][2200/4762] time 0.152 (0.154) data 0.001 (0.001) eta 3:22:16 loss 0.3549 (0.3751) loss_x 0.3490 (0.3643) loss_u 0.0059 (0.0107) acc_x 93.7500 (87.2599) lr 2.713525e-03 -epoch [9/25][2300/4762] time 0.148 (0.154) data 0.001 (0.001) eta 3:21:48 loss 0.1642 (0.3759) loss_x 0.1395 (0.3652) loss_u 0.0247 (0.0107) acc_x 96.8750 (87.2269) lr 2.713525e-03 -epoch [9/25][2400/4762] time 0.159 (0.154) data 0.001 (0.001) eta 3:21:24 loss 0.4284 (0.3755) loss_x 0.4209 (0.3648) loss_u 0.0076 (0.0107) acc_x 87.5000 (87.2695) lr 2.713525e-03 -epoch [9/25][2500/4762] time 0.145 (0.154) data 0.001 (0.001) eta 3:20:59 loss 0.4722 (0.3770) loss_x 0.4644 (0.3662) loss_u 0.0079 (0.0108) acc_x 87.5000 (87.1937) lr 2.713525e-03 -epoch [9/25][2600/4762] time 0.160 (0.154) data 0.001 (0.001) eta 3:20:40 loss 0.2611 (0.3766) loss_x 0.2442 (0.3658) loss_u 0.0168 (0.0108) acc_x 90.6250 (87.2284) lr 2.713525e-03 -epoch [9/25][2700/4762] time 0.152 (0.154) data 0.001 (0.001) eta 3:20:17 loss 0.3688 (0.3776) loss_x 0.3389 (0.3667) loss_u 0.0299 (0.0109) acc_x 87.5000 (87.1921) lr 2.713525e-03 -epoch [9/25][2800/4762] time 0.139 (0.153) data 0.001 (0.001) eta 3:19:48 loss 0.3405 (0.3772) loss_x 0.3339 (0.3664) loss_u 0.0067 (0.0109) acc_x 84.3750 (87.1998) lr 2.713525e-03 -epoch [9/25][2900/4762] time 0.154 (0.153) data 0.001 (0.001) eta 3:19:29 loss 0.3224 (0.3769) loss_x 0.3094 (0.3661) loss_u 0.0129 (0.0109) acc_x 87.5000 (87.2101) lr 2.713525e-03 -epoch [9/25][3000/4762] time 0.189 (0.153) data 0.001 (0.001) eta 3:19:10 loss 0.2793 (0.3769) loss_x 0.2742 (0.3660) loss_u 0.0052 (0.0109) acc_x 87.5000 (87.2021) lr 2.713525e-03 -epoch [9/25][3100/4762] time 0.157 (0.153) data 0.001 (0.001) eta 3:18:50 loss 0.3946 (0.3771) loss_x 0.3898 (0.3662) loss_u 0.0049 (0.0109) acc_x 87.5000 (87.1855) lr 2.713525e-03 -epoch [9/25][3200/4762] time 0.146 (0.153) data 0.001 (0.001) eta 3:18:26 loss 0.6825 (0.3775) loss_x 0.6611 (0.3666) loss_u 0.0213 (0.0109) acc_x 84.3750 (87.1729) lr 2.713525e-03 -epoch [9/25][3300/4762] time 0.153 (0.153) data 0.001 (0.001) eta 3:18:09 loss 0.3131 (0.3779) loss_x 0.3108 (0.3670) loss_u 0.0023 (0.0110) acc_x 93.7500 (87.1686) lr 2.713525e-03 -epoch [9/25][3400/4762] time 0.157 (0.153) data 0.001 (0.001) eta 3:17:59 loss 0.2922 (0.3777) loss_x 0.2894 (0.3668) loss_u 0.0028 (0.0109) acc_x 84.3750 (87.1562) lr 2.713525e-03 -epoch [9/25][3500/4762] time 0.157 (0.153) data 0.001 (0.001) eta 3:17:58 loss 0.4489 (0.3783) loss_x 0.4404 (0.3674) loss_u 0.0085 (0.0109) acc_x 81.2500 (87.1321) lr 2.713525e-03 -epoch [9/25][3600/4762] time 0.149 (0.153) data 0.001 (0.001) eta 3:17:44 loss 0.3249 (0.3789) loss_x 0.3214 (0.3681) loss_u 0.0036 (0.0108) acc_x 90.6250 (87.1172) lr 2.713525e-03 -epoch [9/25][3700/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:17:28 loss 0.5411 (0.3791) loss_x 0.5388 (0.3683) loss_u 0.0023 (0.0108) acc_x 84.3750 (87.0861) lr 2.713525e-03 -epoch [9/25][3800/4762] time 0.146 (0.153) data 0.001 (0.001) eta 3:17:13 loss 0.2175 (0.3795) loss_x 0.2003 (0.3688) loss_u 0.0172 (0.0107) acc_x 96.8750 (87.0551) lr 2.713525e-03 -epoch [9/25][3900/4762] time 0.153 (0.153) data 0.001 (0.001) eta 3:16:54 loss 0.5562 (0.3801) loss_x 0.5483 (0.3693) loss_u 0.0079 (0.0107) acc_x 87.5000 (87.0264) lr 2.713525e-03 -epoch [9/25][4000/4762] time 0.145 (0.153) data 0.001 (0.001) eta 3:16:36 loss 0.3774 (0.3801) loss_x 0.3703 (0.3694) loss_u 0.0071 (0.0107) acc_x 87.5000 (87.0133) lr 2.713525e-03 -epoch [9/25][4100/4762] time 0.146 (0.153) data 0.001 (0.001) eta 3:16:21 loss 0.3622 (0.3801) loss_x 0.3517 (0.3694) loss_u 0.0106 (0.0107) acc_x 84.3750 (87.0091) lr 2.713525e-03 -epoch [9/25][4200/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:16:07 loss 0.3569 (0.3805) loss_x 0.3301 (0.3698) loss_u 0.0268 (0.0107) acc_x 90.6250 (86.9903) lr 2.713525e-03 -epoch [9/25][4300/4762] time 0.141 (0.153) data 0.001 (0.001) eta 3:15:42 loss 0.3721 (0.3799) loss_x 0.3696 (0.3693) loss_u 0.0025 (0.0106) acc_x 84.3750 (87.0196) lr 2.713525e-03 -epoch [9/25][4400/4762] time 0.157 (0.153) data 0.001 (0.001) eta 3:15:21 loss 0.3998 (0.3797) loss_x 0.3886 (0.3690) loss_u 0.0112 (0.0107) acc_x 84.3750 (87.0284) lr 2.713525e-03 -epoch [9/25][4500/4762] time 0.153 (0.153) data 0.001 (0.001) eta 3:15:03 loss 0.3868 (0.3803) loss_x 0.3846 (0.3696) loss_u 0.0022 (0.0107) acc_x 84.3750 (87.0090) lr 2.713525e-03 -epoch [9/25][4600/4762] time 0.150 (0.153) data 0.001 (0.001) eta 3:14:45 loss 0.4442 (0.3803) loss_x 0.4339 (0.3696) loss_u 0.0103 (0.0107) acc_x 84.3750 (87.0095) lr 2.713525e-03 -epoch [9/25][4700/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:14:27 loss 0.2324 (0.3797) loss_x 0.2083 (0.3690) loss_u 0.0240 (0.0107) acc_x 90.6250 (87.0339) lr 2.713525e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,750 -* accuracy: 84.40% -* error: 15.60% -* macro_f1: 84.67% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,575 acc: 98.05% -* class: 1 (bicycle) total: 3,475 correct: 2,907 acc: 83.65% -* class: 2 (bus) total: 4,690 correct: 4,271 acc: 91.07% -* class: 3 (car) total: 10,401 correct: 7,681 acc: 73.85% -* class: 4 (horse) total: 4,691 correct: 4,586 acc: 97.76% -* class: 5 (knife) total: 2,075 correct: 1,901 acc: 91.61% -* class: 6 (motorcycle) total: 5,796 correct: 5,529 acc: 95.39% -* class: 7 (person) total: 4,000 correct: 3,092 acc: 77.30% -* class: 8 (plant) total: 4,549 correct: 3,883 acc: 85.36% -* class: 9 (skateboard) total: 2,281 correct: 2,031 acc: 89.04% -* class: 10 (train) total: 4,236 correct: 3,922 acc: 92.59% -* class: 11 (truck) total: 5,548 correct: 3,372 acc: 60.78% -* average: 86.37% -epoch [10/25][100/4762] time 0.138 (0.156) data 0.001 (0.004) eta 3:17:44 loss 0.6911 (0.3956) loss_x 0.6866 (0.3845) loss_u 0.0045 (0.0111) acc_x 71.8750 (85.9375) lr 2.456136e-03 -epoch [10/25][200/4762] time 0.144 (0.154) data 0.001 (0.002) eta 3:14:34 loss 0.4096 (0.3940) loss_x 0.4075 (0.3825) loss_u 0.0022 (0.0116) acc_x 84.3750 (86.2969) lr 2.456136e-03 -epoch [10/25][300/4762] time 0.178 (0.153) data 0.001 (0.002) eta 3:13:24 loss 0.3456 (0.3828) loss_x 0.3375 (0.3711) loss_u 0.0081 (0.0117) acc_x 90.6250 (86.8229) lr 2.456136e-03 -epoch [10/25][400/4762] time 0.148 (0.153) data 0.001 (0.002) eta 3:12:53 loss 0.3492 (0.3824) loss_x 0.3251 (0.3713) loss_u 0.0240 (0.0111) acc_x 90.6250 (86.9922) lr 2.456136e-03 -epoch [10/25][500/4762] time 0.165 (0.153) data 0.001 (0.001) eta 3:12:25 loss 0.3312 (0.3812) loss_x 0.3167 (0.3699) loss_u 0.0146 (0.0113) acc_x 90.6250 (87.1000) lr 2.456136e-03 -epoch [10/25][600/4762] time 0.139 (0.152) data 0.001 (0.001) eta 3:11:57 loss 0.4937 (0.3803) loss_x 0.4806 (0.3685) loss_u 0.0131 (0.0117) acc_x 81.2500 (87.0573) lr 2.456136e-03 -epoch [10/25][700/4762] time 0.164 (0.153) data 0.001 (0.001) eta 3:11:52 loss 0.3850 (0.3802) loss_x 0.3770 (0.3687) loss_u 0.0080 (0.0115) acc_x 90.6250 (87.1518) lr 2.456136e-03 -epoch [10/25][800/4762] time 0.138 (0.152) data 0.001 (0.001) eta 3:11:18 loss 0.2863 (0.3808) loss_x 0.2838 (0.3692) loss_u 0.0025 (0.0116) acc_x 93.7500 (87.1133) lr 2.456136e-03 -epoch [10/25][900/4762] time 0.140 (0.152) data 0.001 (0.001) eta 3:10:57 loss 0.5540 (0.3806) loss_x 0.5478 (0.3693) loss_u 0.0062 (0.0114) acc_x 78.1250 (87.0347) lr 2.456136e-03 -epoch [10/25][1000/4762] time 0.159 (0.152) data 0.001 (0.001) eta 3:10:58 loss 0.3803 (0.3789) loss_x 0.3786 (0.3676) loss_u 0.0018 (0.0112) acc_x 84.3750 (87.0687) lr 2.456136e-03 -epoch [10/25][1100/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:10:54 loss 0.2665 (0.3784) loss_x 0.2577 (0.3674) loss_u 0.0088 (0.0110) acc_x 90.6250 (87.0341) lr 2.456136e-03 -epoch [10/25][1200/4762] time 0.173 (0.153) data 0.002 (0.001) eta 3:10:49 loss 0.3196 (0.3788) loss_x 0.3180 (0.3680) loss_u 0.0016 (0.0108) acc_x 87.5000 (86.9766) lr 2.456136e-03 -epoch [10/25][1300/4762] time 0.177 (0.153) data 0.001 (0.001) eta 3:10:30 loss 0.4469 (0.3806) loss_x 0.4409 (0.3695) loss_u 0.0060 (0.0111) acc_x 84.3750 (86.9111) lr 2.456136e-03 -epoch [10/25][1400/4762] time 0.156 (0.153) data 0.001 (0.001) eta 3:10:08 loss 0.2107 (0.3793) loss_x 0.2065 (0.3680) loss_u 0.0042 (0.0113) acc_x 96.8750 (86.9955) lr 2.456136e-03 -epoch [10/25][1500/4762] time 0.169 (0.153) data 0.016 (0.001) eta 3:09:53 loss 0.4380 (0.3796) loss_x 0.4291 (0.3683) loss_u 0.0089 (0.0114) acc_x 84.3750 (86.9792) lr 2.456136e-03 -epoch [10/25][1600/4762] time 0.155 (0.153) data 0.001 (0.001) eta 3:09:48 loss 0.3304 (0.3793) loss_x 0.3091 (0.3679) loss_u 0.0213 (0.0114) acc_x 90.6250 (86.9766) lr 2.456136e-03 -epoch [10/25][1700/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:09:48 loss 0.3104 (0.3805) loss_x 0.3050 (0.3692) loss_u 0.0054 (0.0113) acc_x 87.5000 (86.9375) lr 2.456136e-03 -epoch [10/25][1800/4762] time 0.167 (0.153) data 0.001 (0.001) eta 3:10:07 loss 0.2766 (0.3805) loss_x 0.2743 (0.3692) loss_u 0.0022 (0.0113) acc_x 81.2500 (86.9531) lr 2.456136e-03 -epoch [10/25][1900/4762] time 0.138 (0.153) data 0.001 (0.001) eta 3:09:54 loss 0.4524 (0.3809) loss_x 0.4462 (0.3697) loss_u 0.0063 (0.0112) acc_x 84.3750 (86.9260) lr 2.456136e-03 -epoch [10/25][2000/4762] time 0.151 (0.153) data 0.001 (0.001) eta 3:09:38 loss 0.4203 (0.3807) loss_x 0.3431 (0.3695) loss_u 0.0772 (0.0112) acc_x 90.6250 (86.9266) lr 2.456136e-03 -epoch [10/25][2100/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:09:30 loss 0.4143 (0.3800) loss_x 0.4043 (0.3688) loss_u 0.0100 (0.0112) acc_x 78.1250 (86.9464) lr 2.456136e-03 -epoch [10/25][2200/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:09:15 loss 0.3725 (0.3803) loss_x 0.3682 (0.3691) loss_u 0.0043 (0.0112) acc_x 93.7500 (86.9545) lr 2.456136e-03 -epoch [10/25][2300/4762] time 0.153 (0.153) data 0.001 (0.001) eta 3:08:57 loss 0.4156 (0.3806) loss_x 0.3908 (0.3694) loss_u 0.0248 (0.0113) acc_x 87.5000 (86.9606) lr 2.456136e-03 -epoch [10/25][2400/4762] time 0.194 (0.153) data 0.001 (0.001) eta 3:08:25 loss 0.3384 (0.3806) loss_x 0.3241 (0.3694) loss_u 0.0143 (0.0113) acc_x 84.3750 (86.9753) lr 2.456136e-03 -epoch [10/25][2500/4762] time 0.152 (0.153) data 0.001 (0.001) eta 3:08:09 loss 0.3230 (0.3805) loss_x 0.3192 (0.3692) loss_u 0.0038 (0.0113) acc_x 87.5000 (86.9613) lr 2.456136e-03 -epoch [10/25][2600/4762] time 0.153 (0.153) data 0.001 (0.001) eta 3:07:45 loss 0.2547 (0.3803) loss_x 0.2433 (0.3690) loss_u 0.0114 (0.0112) acc_x 93.7500 (86.9832) lr 2.456136e-03 -epoch [10/25][2700/4762] time 0.160 (0.153) data 0.001 (0.001) eta 3:07:20 loss 0.4457 (0.3814) loss_x 0.4355 (0.3700) loss_u 0.0102 (0.0114) acc_x 87.5000 (86.9352) lr 2.456136e-03 -epoch [10/25][2800/4762] time 0.140 (0.153) data 0.001 (0.001) eta 3:07:04 loss 0.3325 (0.3805) loss_x 0.3255 (0.3692) loss_u 0.0070 (0.0113) acc_x 84.3750 (86.9531) lr 2.456136e-03 -epoch [10/25][2900/4762] time 0.148 (0.153) data 0.003 (0.001) eta 3:06:47 loss 0.1978 (0.3802) loss_x 0.1889 (0.3689) loss_u 0.0089 (0.0113) acc_x 93.7500 (86.9547) lr 2.456136e-03 -epoch [10/25][3000/4762] time 0.145 (0.153) data 0.001 (0.001) eta 3:06:31 loss 0.3056 (0.3801) loss_x 0.2928 (0.3689) loss_u 0.0128 (0.0112) acc_x 90.6250 (86.9583) lr 2.456136e-03 -epoch [10/25][3100/4762] time 0.158 (0.153) data 0.001 (0.001) eta 3:06:11 loss 0.2444 (0.3799) loss_x 0.2280 (0.3687) loss_u 0.0164 (0.0112) acc_x 93.7500 (86.9819) lr 2.456136e-03 -epoch [10/25][3200/4762] time 0.166 (0.153) data 0.001 (0.001) eta 3:05:50 loss 0.1615 (0.3806) loss_x 0.1599 (0.3693) loss_u 0.0016 (0.0112) acc_x 96.8750 (86.9541) lr 2.456136e-03 -epoch [10/25][3300/4762] time 0.150 (0.153) data 0.001 (0.001) eta 3:05:34 loss 0.2173 (0.3806) loss_x 0.2054 (0.3694) loss_u 0.0120 (0.0112) acc_x 96.8750 (86.9536) lr 2.456136e-03 -epoch [10/25][3400/4762] time 0.166 (0.153) data 0.001 (0.001) eta 3:05:18 loss 0.3543 (0.3809) loss_x 0.3492 (0.3697) loss_u 0.0051 (0.0112) acc_x 90.6250 (86.9476) lr 2.456136e-03 -epoch [10/25][3500/4762] time 0.186 (0.153) data 0.001 (0.001) eta 3:05:12 loss 0.3644 (0.3811) loss_x 0.3567 (0.3698) loss_u 0.0078 (0.0112) acc_x 78.1250 (86.9366) lr 2.456136e-03 -epoch [10/25][3600/4762] time 0.167 (0.153) data 0.001 (0.001) eta 3:04:51 loss 0.4784 (0.3814) loss_x 0.4708 (0.3702) loss_u 0.0076 (0.0112) acc_x 84.3750 (86.9141) lr 2.456136e-03 -epoch [10/25][3700/4762] time 0.138 (0.153) data 0.001 (0.001) eta 3:04:42 loss 0.4040 (0.3808) loss_x 0.3920 (0.3696) loss_u 0.0120 (0.0112) acc_x 84.3750 (86.9400) lr 2.456136e-03 -epoch [10/25][3800/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:04:22 loss 0.4520 (0.3807) loss_x 0.4447 (0.3696) loss_u 0.0073 (0.0112) acc_x 81.2500 (86.9276) lr 2.456136e-03 -epoch [10/25][3900/4762] time 0.149 (0.153) data 0.001 (0.001) eta 3:04:04 loss 0.4280 (0.3806) loss_x 0.4073 (0.3695) loss_u 0.0207 (0.0112) acc_x 87.5000 (86.9311) lr 2.456136e-03 -epoch [10/25][4000/4762] time 0.167 (0.153) data 0.001 (0.001) eta 3:03:43 loss 0.5558 (0.3806) loss_x 0.5546 (0.3695) loss_u 0.0012 (0.0111) acc_x 87.5000 (86.9352) lr 2.456136e-03 -epoch [10/25][4100/4762] time 0.148 (0.153) data 0.001 (0.001) eta 3:03:26 loss 0.2541 (0.3805) loss_x 0.2450 (0.3694) loss_u 0.0091 (0.0111) acc_x 93.7500 (86.9413) lr 2.456136e-03 -epoch [10/25][4200/4762] time 0.171 (0.153) data 0.001 (0.001) eta 3:03:12 loss 0.4578 (0.3802) loss_x 0.4560 (0.3691) loss_u 0.0018 (0.0111) acc_x 87.5000 (86.9539) lr 2.456136e-03 -epoch [10/25][4300/4762] time 0.164 (0.153) data 0.001 (0.001) eta 3:02:57 loss 0.5703 (0.3802) loss_x 0.5683 (0.3690) loss_u 0.0020 (0.0111) acc_x 84.3750 (86.9440) lr 2.456136e-03 -epoch [10/25][4400/4762] time 0.179 (0.153) data 0.001 (0.001) eta 3:02:44 loss 0.3383 (0.3802) loss_x 0.3296 (0.3690) loss_u 0.0087 (0.0112) acc_x 87.5000 (86.9467) lr 2.456136e-03 -epoch [10/25][4500/4762] time 0.144 (0.153) data 0.001 (0.001) eta 3:02:31 loss 0.2963 (0.3801) loss_x 0.2871 (0.3689) loss_u 0.0093 (0.0112) acc_x 90.6250 (86.9597) lr 2.456136e-03 -epoch [10/25][4600/4762] time 0.141 (0.153) data 0.001 (0.001) eta 3:02:14 loss 0.4709 (0.3791) loss_x 0.4589 (0.3679) loss_u 0.0120 (0.0112) acc_x 81.2500 (86.9891) lr 2.456136e-03 -epoch [10/25][4700/4762] time 0.166 (0.153) data 0.001 (0.001) eta 3:02:01 loss 0.4247 (0.3793) loss_x 0.3924 (0.3681) loss_u 0.0323 (0.0112) acc_x 87.5000 (86.9767) lr 2.456136e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,944 -* accuracy: 84.75% -* error: 15.25% -* macro_f1: 84.91% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,573 acc: 98.00% -* class: 1 (bicycle) total: 3,475 correct: 2,890 acc: 83.17% -* class: 2 (bus) total: 4,690 correct: 4,196 acc: 89.47% -* class: 3 (car) total: 10,401 correct: 7,943 acc: 76.37% -* class: 4 (horse) total: 4,691 correct: 4,589 acc: 97.83% -* class: 5 (knife) total: 2,075 correct: 1,861 acc: 89.69% -* class: 6 (motorcycle) total: 5,796 correct: 5,531 acc: 95.43% -* class: 7 (person) total: 4,000 correct: 3,002 acc: 75.05% -* class: 8 (plant) total: 4,549 correct: 3,898 acc: 85.69% -* class: 9 (skateboard) total: 2,281 correct: 2,074 acc: 90.93% -* class: 10 (train) total: 4,236 correct: 3,956 acc: 93.39% -* class: 11 (truck) total: 5,548 correct: 3,431 acc: 61.84% -* average: 86.40% -epoch [11/25][100/4762] time 0.159 (0.153) data 0.001 (0.003) eta 3:01:20 loss 0.4721 (0.3577) loss_x 0.4359 (0.3423) loss_u 0.0362 (0.0154) acc_x 90.6250 (88.2500) lr 4.065471e-04 -epoch [11/25][200/4762] time 0.137 (0.151) data 0.001 (0.002) eta 2:59:09 loss 0.2121 (0.3685) loss_x 0.2055 (0.3553) loss_u 0.0066 (0.0132) acc_x 87.5000 (87.4375) lr 4.065471e-04 -epoch [11/25][300/4762] time 0.175 (0.151) data 0.003 (0.002) eta 2:59:06 loss 0.3049 (0.3693) loss_x 0.3002 (0.3568) loss_u 0.0047 (0.0125) acc_x 90.6250 (87.4271) lr 4.065471e-04 -epoch [11/25][400/4762] time 0.152 (0.151) data 0.001 (0.001) eta 2:59:09 loss 0.3848 (0.3662) loss_x 0.3727 (0.3541) loss_u 0.0121 (0.0122) acc_x 84.3750 (87.6406) lr 4.065471e-04 -epoch [11/25][500/4762] time 0.139 (0.152) data 0.001 (0.001) eta 2:59:53 loss 0.6027 (0.3698) loss_x 0.5940 (0.3579) loss_u 0.0087 (0.0119) acc_x 84.3750 (87.4250) lr 4.065471e-04 -epoch [11/25][600/4762] time 0.159 (0.153) data 0.001 (0.001) eta 3:00:09 loss 0.3996 (0.3716) loss_x 0.3917 (0.3601) loss_u 0.0079 (0.0115) acc_x 90.6250 (87.3438) lr 4.065471e-04 -epoch [11/25][700/4762] time 0.145 (0.152) data 0.001 (0.001) eta 2:59:27 loss 0.5209 (0.3726) loss_x 0.5118 (0.3610) loss_u 0.0091 (0.0116) acc_x 84.3750 (87.3661) lr 4.065471e-04 -epoch [11/25][800/4762] time 0.142 (0.152) data 0.001 (0.001) eta 2:58:54 loss 0.3085 (0.3727) loss_x 0.3062 (0.3613) loss_u 0.0024 (0.0114) acc_x 87.5000 (87.3594) lr 4.065471e-04 -epoch [11/25][900/4762] time 0.202 (0.152) data 0.001 (0.001) eta 2:58:22 loss 0.4866 (0.3740) loss_x 0.4806 (0.3627) loss_u 0.0059 (0.0113) acc_x 87.5000 (87.3194) lr 4.065471e-04 -epoch [11/25][1000/4762] time 0.138 (0.152) data 0.001 (0.001) eta 2:58:21 loss 0.5662 (0.3762) loss_x 0.5649 (0.3649) loss_u 0.0013 (0.0113) acc_x 81.2500 (87.2500) lr 4.065471e-04 -epoch [11/25][1100/4762] time 0.137 (0.152) data 0.001 (0.001) eta 2:58:25 loss 0.6221 (0.3753) loss_x 0.6141 (0.3639) loss_u 0.0080 (0.0114) acc_x 75.0000 (87.2443) lr 4.065471e-04 -epoch [11/25][1200/4762] time 0.143 (0.153) data 0.001 (0.001) eta 2:58:34 loss 0.3159 (0.3761) loss_x 0.3076 (0.3650) loss_u 0.0083 (0.0111) acc_x 87.5000 (87.2240) lr 4.065471e-04 -epoch [11/25][1300/4762] time 0.155 (0.153) data 0.001 (0.001) eta 2:58:22 loss 0.2432 (0.3755) loss_x 0.2276 (0.3643) loss_u 0.0157 (0.0113) acc_x 90.6250 (87.1947) lr 4.065471e-04 -epoch [11/25][1400/4762] time 0.148 (0.153) data 0.001 (0.001) eta 2:58:17 loss 0.4514 (0.3750) loss_x 0.4440 (0.3637) loss_u 0.0074 (0.0113) acc_x 84.3750 (87.2299) lr 4.065471e-04 -epoch [11/25][1500/4762] time 0.138 (0.153) data 0.001 (0.001) eta 2:58:12 loss 0.3561 (0.3760) loss_x 0.3548 (0.3649) loss_u 0.0013 (0.0112) acc_x 84.3750 (87.1646) lr 4.065471e-04 -epoch [11/25][1600/4762] time 0.150 (0.153) data 0.001 (0.001) eta 2:58:07 loss 0.2142 (0.3757) loss_x 0.2067 (0.3646) loss_u 0.0076 (0.0112) acc_x 90.6250 (87.1992) lr 4.065471e-04 -epoch [11/25][1700/4762] time 0.139 (0.153) data 0.001 (0.001) eta 2:57:43 loss 0.5383 (0.3748) loss_x 0.5017 (0.3637) loss_u 0.0366 (0.0111) acc_x 81.2500 (87.2040) lr 4.065471e-04 -epoch [11/25][1800/4762] time 0.136 (0.153) data 0.001 (0.001) eta 2:57:58 loss 0.2722 (0.3746) loss_x 0.2706 (0.3636) loss_u 0.0016 (0.0109) acc_x 96.8750 (87.2118) lr 4.065471e-04 -epoch [11/25][1900/4762] time 0.185 (0.154) data 0.001 (0.001) eta 2:57:54 loss 0.5667 (0.3748) loss_x 0.5574 (0.3640) loss_u 0.0093 (0.0108) acc_x 78.1250 (87.2171) lr 4.065471e-04 -epoch [11/25][2000/4762] time 0.180 (0.154) data 0.001 (0.001) eta 2:57:45 loss 0.5173 (0.3754) loss_x 0.5142 (0.3646) loss_u 0.0031 (0.0108) acc_x 84.3750 (87.1906) lr 4.065471e-04 -epoch [11/25][2100/4762] time 0.200 (0.154) data 0.001 (0.001) eta 2:57:40 loss 0.3792 (0.3747) loss_x 0.3765 (0.3640) loss_u 0.0028 (0.0107) acc_x 90.6250 (87.1964) lr 4.065471e-04 -epoch [11/25][2200/4762] time 0.154 (0.154) data 0.001 (0.001) eta 2:57:31 loss 0.4199 (0.3745) loss_x 0.4112 (0.3638) loss_u 0.0087 (0.0106) acc_x 90.6250 (87.2060) lr 4.065471e-04 -epoch [11/25][2300/4762] time 0.165 (0.154) data 0.001 (0.001) eta 2:57:09 loss 0.2830 (0.3738) loss_x 0.2600 (0.3632) loss_u 0.0230 (0.0107) acc_x 84.3750 (87.2174) lr 4.065471e-04 -epoch [11/25][2400/4762] time 0.182 (0.154) data 0.001 (0.001) eta 2:56:49 loss 0.2815 (0.3746) loss_x 0.2708 (0.3640) loss_u 0.0108 (0.0106) acc_x 87.5000 (87.1953) lr 4.065471e-04 -epoch [11/25][2500/4762] time 0.155 (0.154) data 0.001 (0.001) eta 2:56:32 loss 0.2876 (0.3745) loss_x 0.2759 (0.3638) loss_u 0.0117 (0.0107) acc_x 87.5000 (87.2175) lr 4.065471e-04 -epoch [11/25][2600/4762] time 0.149 (0.154) data 0.001 (0.001) eta 2:56:14 loss 0.2290 (0.3750) loss_x 0.2185 (0.3643) loss_u 0.0106 (0.0107) acc_x 93.7500 (87.1935) lr 4.065471e-04 -epoch [11/25][2700/4762] time 0.157 (0.153) data 0.001 (0.001) eta 2:55:45 loss 0.4862 (0.3762) loss_x 0.4744 (0.3656) loss_u 0.0119 (0.0107) acc_x 81.2500 (87.1285) lr 4.065471e-04 -epoch [11/25][2800/4762] time 0.141 (0.153) data 0.001 (0.001) eta 2:55:25 loss 0.2436 (0.3757) loss_x 0.2398 (0.3651) loss_u 0.0037 (0.0106) acc_x 90.6250 (87.1596) lr 4.065471e-04 -epoch [11/25][2900/4762] time 0.138 (0.153) data 0.001 (0.001) eta 2:55:02 loss 0.5457 (0.3750) loss_x 0.5399 (0.3644) loss_u 0.0059 (0.0106) acc_x 87.5000 (87.1886) lr 4.065471e-04 -epoch [11/25][3000/4762] time 0.181 (0.153) data 0.001 (0.001) eta 2:54:47 loss 0.3446 (0.3753) loss_x 0.3421 (0.3647) loss_u 0.0024 (0.0106) acc_x 90.6250 (87.1792) lr 4.065471e-04 -epoch [11/25][3100/4762] time 0.143 (0.153) data 0.001 (0.001) eta 2:54:30 loss 0.4490 (0.3755) loss_x 0.4370 (0.3650) loss_u 0.0119 (0.0106) acc_x 81.2500 (87.1774) lr 4.065471e-04 -epoch [11/25][3200/4762] time 0.167 (0.153) data 0.001 (0.001) eta 2:54:15 loss 0.2178 (0.3763) loss_x 0.2125 (0.3657) loss_u 0.0053 (0.0106) acc_x 93.7500 (87.1533) lr 4.065471e-04 -epoch [11/25][3300/4762] time 0.155 (0.153) data 0.001 (0.001) eta 2:53:54 loss 0.4086 (0.3761) loss_x 0.4013 (0.3655) loss_u 0.0072 (0.0106) acc_x 81.2500 (87.1439) lr 4.065471e-04 -epoch [11/25][3400/4762] time 0.151 (0.153) data 0.002 (0.001) eta 2:53:37 loss 0.4721 (0.3759) loss_x 0.4593 (0.3654) loss_u 0.0128 (0.0106) acc_x 84.3750 (87.1471) lr 4.065471e-04 -epoch [11/25][3500/4762] time 0.164 (0.153) data 0.001 (0.001) eta 2:53:31 loss 0.2535 (0.3761) loss_x 0.2510 (0.3655) loss_u 0.0025 (0.0106) acc_x 93.7500 (87.1357) lr 4.065471e-04 -epoch [11/25][3600/4762] time 0.178 (0.153) data 0.001 (0.001) eta 2:53:15 loss 0.3430 (0.3761) loss_x 0.3224 (0.3655) loss_u 0.0206 (0.0106) acc_x 90.6250 (87.1380) lr 4.065471e-04 -epoch [11/25][3700/4762] time 0.165 (0.153) data 0.001 (0.001) eta 2:52:57 loss 0.3010 (0.3758) loss_x 0.2959 (0.3652) loss_u 0.0051 (0.0106) acc_x 84.3750 (87.1512) lr 4.065471e-04 -epoch [11/25][3800/4762] time 0.172 (0.153) data 0.001 (0.001) eta 2:52:38 loss 0.3967 (0.3757) loss_x 0.3808 (0.3652) loss_u 0.0159 (0.0106) acc_x 81.2500 (87.1612) lr 4.065471e-04 -epoch [11/25][3900/4762] time 0.145 (0.153) data 0.001 (0.001) eta 2:52:15 loss 0.1801 (0.3754) loss_x 0.1746 (0.3649) loss_u 0.0056 (0.0105) acc_x 93.7500 (87.1482) lr 4.065471e-04 -epoch [11/25][4000/4762] time 0.141 (0.153) data 0.001 (0.001) eta 2:52:00 loss 0.3041 (0.3755) loss_x 0.2943 (0.3650) loss_u 0.0098 (0.0105) acc_x 90.6250 (87.1297) lr 4.065471e-04 -epoch [11/25][4100/4762] time 0.137 (0.153) data 0.001 (0.001) eta 2:51:46 loss 0.2716 (0.3749) loss_x 0.2649 (0.3644) loss_u 0.0067 (0.0105) acc_x 87.5000 (87.1410) lr 4.065471e-04 -epoch [11/25][4200/4762] time 0.158 (0.153) data 0.001 (0.001) eta 2:51:27 loss 0.5674 (0.3749) loss_x 0.5477 (0.3644) loss_u 0.0196 (0.0105) acc_x 78.1250 (87.1458) lr 4.065471e-04 -epoch [11/25][4300/4762] time 0.139 (0.153) data 0.001 (0.001) eta 2:51:09 loss 0.6607 (0.3752) loss_x 0.6535 (0.3647) loss_u 0.0073 (0.0105) acc_x 71.8750 (87.1410) lr 4.065471e-04 -epoch [11/25][4400/4762] time 0.156 (0.153) data 0.003 (0.001) eta 2:50:52 loss 0.4028 (0.3757) loss_x 0.4022 (0.3652) loss_u 0.0006 (0.0105) acc_x 84.3750 (87.1477) lr 4.065471e-04 -epoch [11/25][4500/4762] time 0.174 (0.153) data 0.001 (0.001) eta 2:50:35 loss 0.4567 (0.3755) loss_x 0.4553 (0.3650) loss_u 0.0014 (0.0105) acc_x 87.5000 (87.1556) lr 4.065471e-04 -epoch [11/25][4600/4762] time 0.141 (0.153) data 0.001 (0.001) eta 2:50:19 loss 0.4989 (0.3752) loss_x 0.4944 (0.3647) loss_u 0.0045 (0.0106) acc_x 78.1250 (87.1739) lr 4.065471e-04 -epoch [11/25][4700/4762] time 0.137 (0.153) data 0.001 (0.001) eta 2:49:58 loss 0.6014 (0.3757) loss_x 0.5996 (0.3652) loss_u 0.0018 (0.0105) acc_x 78.1250 (87.1549) lr 4.065471e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,762 -* accuracy: 84.43% -* error: 15.57% -* macro_f1: 84.47% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,589 acc: 98.44% -* class: 1 (bicycle) total: 3,475 correct: 2,924 acc: 84.14% -* class: 2 (bus) total: 4,690 correct: 4,266 acc: 90.96% -* class: 3 (car) total: 10,401 correct: 7,830 acc: 75.28% -* class: 4 (horse) total: 4,691 correct: 4,579 acc: 97.61% -* class: 5 (knife) total: 2,075 correct: 1,896 acc: 91.37% -* class: 6 (motorcycle) total: 5,796 correct: 5,497 acc: 94.84% -* class: 7 (person) total: 4,000 correct: 2,840 acc: 71.00% -* class: 8 (plant) total: 4,549 correct: 3,910 acc: 85.95% -* class: 9 (skateboard) total: 2,281 correct: 2,056 acc: 90.14% -* class: 10 (train) total: 4,236 correct: 3,929 acc: 92.75% -* class: 11 (truck) total: 5,548 correct: 3,446 acc: 62.11% -* average: 86.22% -epoch [12/25][100/4762] time 0.137 (0.152) data 0.001 (0.003) eta 2:49:08 loss 0.2919 (0.3835) loss_x 0.2777 (0.3726) loss_u 0.0143 (0.0109) acc_x 90.6250 (87.4062) lr 4.065471e-04 -epoch [12/25][200/4762] time 0.144 (0.152) data 0.001 (0.002) eta 2:47:53 loss 0.1499 (0.3813) loss_x 0.1451 (0.3712) loss_u 0.0047 (0.0101) acc_x 96.8750 (87.0625) lr 4.065471e-04 -epoch [12/25][300/4762] time 0.138 (0.153) data 0.001 (0.002) eta 2:48:47 loss 0.4329 (0.3772) loss_x 0.4237 (0.3670) loss_u 0.0092 (0.0102) acc_x 87.5000 (87.2500) lr 4.065471e-04 -epoch [12/25][400/4762] time 0.157 (0.153) data 0.001 (0.002) eta 2:48:54 loss 0.6530 (0.3765) loss_x 0.5300 (0.3657) loss_u 0.1230 (0.0109) acc_x 87.5000 (87.3672) lr 4.065471e-04 -epoch [12/25][500/4762] time 0.137 (0.152) data 0.001 (0.001) eta 2:47:58 loss 0.2707 (0.3771) loss_x 0.2667 (0.3658) loss_u 0.0040 (0.0113) acc_x 84.3750 (87.2687) lr 4.065471e-04 -epoch [12/25][600/4762] time 0.142 (0.152) data 0.001 (0.001) eta 2:47:26 loss 0.1010 (0.3787) loss_x 0.0905 (0.3673) loss_u 0.0104 (0.0114) acc_x 96.8750 (87.2500) lr 4.065471e-04 -epoch [12/25][700/4762] time 0.189 (0.152) data 0.001 (0.001) eta 2:47:26 loss 0.6520 (0.3773) loss_x 0.6374 (0.3660) loss_u 0.0146 (0.0114) acc_x 78.1250 (87.1920) lr 4.065471e-04 -epoch [12/25][800/4762] time 0.150 (0.153) data 0.001 (0.001) eta 2:47:33 loss 0.2168 (0.3770) loss_x 0.2067 (0.3656) loss_u 0.0101 (0.0114) acc_x 87.5000 (87.2031) lr 4.065471e-04 -epoch [12/25][900/4762] time 0.153 (0.152) data 0.015 (0.001) eta 2:46:58 loss 0.3730 (0.3761) loss_x 0.3687 (0.3648) loss_u 0.0043 (0.0114) acc_x 84.3750 (87.1910) lr 4.065471e-04 -epoch [12/25][1000/4762] time 0.170 (0.152) data 0.001 (0.001) eta 2:46:50 loss 0.5263 (0.3776) loss_x 0.5163 (0.3662) loss_u 0.0100 (0.0114) acc_x 75.0000 (87.0563) lr 4.065471e-04 -epoch [12/25][1100/4762] time 0.169 (0.153) data 0.001 (0.001) eta 2:47:08 loss 0.2635 (0.3777) loss_x 0.2438 (0.3663) loss_u 0.0197 (0.0114) acc_x 90.6250 (87.0483) lr 4.065471e-04 -epoch [12/25][1200/4762] time 0.190 (0.153) data 0.001 (0.001) eta 2:47:14 loss 0.4155 (0.3781) loss_x 0.4086 (0.3668) loss_u 0.0069 (0.0113) acc_x 81.2500 (87.0755) lr 4.065471e-04 -epoch [12/25][1300/4762] time 0.165 (0.153) data 0.001 (0.001) eta 2:47:09 loss 0.5391 (0.3780) loss_x 0.5330 (0.3668) loss_u 0.0062 (0.0112) acc_x 75.0000 (87.0721) lr 4.065471e-04 -epoch [12/25][1400/4762] time 0.137 (0.154) data 0.001 (0.001) eta 2:47:03 loss 0.1818 (0.3775) loss_x 0.1726 (0.3663) loss_u 0.0092 (0.0112) acc_x 93.7500 (87.0915) lr 4.065471e-04 -epoch [12/25][1500/4762] time 0.141 (0.154) data 0.001 (0.001) eta 2:46:49 loss 0.6925 (0.3792) loss_x 0.6828 (0.3680) loss_u 0.0097 (0.0112) acc_x 75.0000 (87.0500) lr 4.065471e-04 -epoch [12/25][1600/4762] time 0.140 (0.153) data 0.001 (0.001) eta 2:46:21 loss 0.2845 (0.3791) loss_x 0.2741 (0.3680) loss_u 0.0105 (0.0111) acc_x 90.6250 (87.0801) lr 4.065471e-04 -epoch [12/25][1700/4762] time 0.165 (0.153) data 0.001 (0.001) eta 2:45:57 loss 0.2402 (0.3785) loss_x 0.2236 (0.3675) loss_u 0.0166 (0.0110) acc_x 90.6250 (87.1103) lr 4.065471e-04 -epoch [12/25][1800/4762] time 0.152 (0.153) data 0.001 (0.001) eta 2:45:45 loss 0.4990 (0.3793) loss_x 0.4574 (0.3683) loss_u 0.0416 (0.0110) acc_x 84.3750 (87.1007) lr 4.065471e-04 -epoch [12/25][1900/4762] time 0.152 (0.153) data 0.001 (0.001) eta 2:45:09 loss 0.2946 (0.3779) loss_x 0.2883 (0.3670) loss_u 0.0063 (0.0109) acc_x 84.3750 (87.1299) lr 4.065471e-04 -epoch [12/25][2000/4762] time 0.145 (0.153) data 0.001 (0.001) eta 2:44:35 loss 0.4003 (0.3773) loss_x 0.3896 (0.3664) loss_u 0.0107 (0.0109) acc_x 84.3750 (87.1578) lr 4.065471e-04 -epoch [12/25][2100/4762] time 0.164 (0.153) data 0.001 (0.001) eta 2:44:21 loss 0.3753 (0.3774) loss_x 0.3700 (0.3666) loss_u 0.0053 (0.0108) acc_x 84.3750 (87.1637) lr 4.065471e-04 -epoch [12/25][2200/4762] time 0.153 (0.153) data 0.001 (0.001) eta 2:44:06 loss 0.1885 (0.3766) loss_x 0.1830 (0.3658) loss_u 0.0055 (0.0108) acc_x 90.6250 (87.1662) lr 4.065471e-04 -epoch [12/25][2300/4762] time 0.179 (0.153) data 0.001 (0.001) eta 2:43:54 loss 0.3063 (0.3758) loss_x 0.2938 (0.3651) loss_u 0.0125 (0.0107) acc_x 87.5000 (87.1929) lr 4.065471e-04 -epoch [12/25][2400/4762] time 0.160 (0.153) data 0.001 (0.001) eta 2:43:52 loss 0.4125 (0.3757) loss_x 0.4078 (0.3651) loss_u 0.0047 (0.0106) acc_x 81.2500 (87.1940) lr 4.065471e-04 -epoch [12/25][2500/4762] time 0.149 (0.153) data 0.001 (0.001) eta 2:43:45 loss 0.4285 (0.3748) loss_x 0.3972 (0.3640) loss_u 0.0313 (0.0108) acc_x 84.3750 (87.2262) lr 4.065471e-04 -epoch [12/25][2600/4762] time 0.144 (0.153) data 0.001 (0.001) eta 2:43:29 loss 0.7214 (0.3747) loss_x 0.7156 (0.3639) loss_u 0.0058 (0.0108) acc_x 81.2500 (87.2356) lr 4.065471e-04 -epoch [12/25][2700/4762] time 0.140 (0.153) data 0.001 (0.001) eta 2:43:12 loss 0.4044 (0.3751) loss_x 0.3901 (0.3644) loss_u 0.0143 (0.0107) acc_x 84.3750 (87.2269) lr 4.065471e-04 -epoch [12/25][2800/4762] time 0.137 (0.153) data 0.001 (0.001) eta 2:42:47 loss 0.5395 (0.3745) loss_x 0.5265 (0.3639) loss_u 0.0130 (0.0107) acc_x 75.0000 (87.2444) lr 4.065471e-04 -epoch [12/25][2900/4762] time 0.140 (0.153) data 0.001 (0.001) eta 2:42:28 loss 0.3449 (0.3744) loss_x 0.3443 (0.3637) loss_u 0.0006 (0.0106) acc_x 84.3750 (87.2381) lr 4.065471e-04 -epoch [12/25][3000/4762] time 0.173 (0.153) data 0.001 (0.001) eta 2:42:07 loss 0.4793 (0.3750) loss_x 0.4679 (0.3644) loss_u 0.0114 (0.0106) acc_x 78.1250 (87.2083) lr 4.065471e-04 -epoch [12/25][3100/4762] time 0.136 (0.153) data 0.001 (0.001) eta 2:41:47 loss 0.4486 (0.3748) loss_x 0.4422 (0.3643) loss_u 0.0064 (0.0105) acc_x 81.2500 (87.2228) lr 4.065471e-04 -epoch [12/25][3200/4762] time 0.158 (0.153) data 0.001 (0.001) eta 2:41:28 loss 0.3887 (0.3745) loss_x 0.3710 (0.3640) loss_u 0.0178 (0.0105) acc_x 90.6250 (87.2246) lr 4.065471e-04 -epoch [12/25][3300/4762] time 0.139 (0.153) data 0.001 (0.001) eta 2:41:11 loss 0.3037 (0.3743) loss_x 0.2991 (0.3638) loss_u 0.0046 (0.0105) acc_x 90.6250 (87.2424) lr 4.065471e-04 -epoch [12/25][3400/4762] time 0.142 (0.153) data 0.001 (0.001) eta 2:40:53 loss 0.3673 (0.3744) loss_x 0.3585 (0.3640) loss_u 0.0088 (0.0105) acc_x 84.3750 (87.2316) lr 4.065471e-04 -epoch [12/25][3500/4762] time 0.175 (0.153) data 0.001 (0.001) eta 2:40:46 loss 0.4685 (0.3744) loss_x 0.4651 (0.3639) loss_u 0.0034 (0.0105) acc_x 90.6250 (87.2152) lr 4.065471e-04 -epoch [12/25][3600/4762] time 0.141 (0.153) data 0.001 (0.001) eta 2:40:29 loss 0.4722 (0.3749) loss_x 0.4682 (0.3644) loss_u 0.0040 (0.0105) acc_x 84.3750 (87.1832) lr 4.065471e-04 -epoch [12/25][3700/4762] time 0.203 (0.153) data 0.001 (0.001) eta 2:40:08 loss 0.2689 (0.3741) loss_x 0.2376 (0.3636) loss_u 0.0313 (0.0105) acc_x 90.6250 (87.2086) lr 4.065471e-04 -epoch [12/25][3800/4762] time 0.145 (0.153) data 0.001 (0.001) eta 2:39:48 loss 0.5058 (0.3744) loss_x 0.5006 (0.3639) loss_u 0.0052 (0.0105) acc_x 71.8750 (87.2056) lr 4.065471e-04 -epoch [12/25][3900/4762] time 0.143 (0.152) data 0.001 (0.001) eta 2:39:28 loss 0.6496 (0.3750) loss_x 0.6405 (0.3645) loss_u 0.0091 (0.0105) acc_x 75.0000 (87.1883) lr 4.065471e-04 -epoch [12/25][4000/4762] time 0.140 (0.152) data 0.001 (0.001) eta 2:39:14 loss 0.3046 (0.3752) loss_x 0.2931 (0.3647) loss_u 0.0115 (0.0104) acc_x 90.6250 (87.1805) lr 4.065471e-04 -epoch [12/25][4100/4762] time 0.155 (0.153) data 0.001 (0.001) eta 2:39:01 loss 0.5497 (0.3751) loss_x 0.5367 (0.3647) loss_u 0.0130 (0.0104) acc_x 81.2500 (87.1867) lr 4.065471e-04 -epoch [12/25][4200/4762] time 0.158 (0.152) data 0.001 (0.001) eta 2:38:42 loss 0.2018 (0.3755) loss_x 0.1975 (0.3651) loss_u 0.0043 (0.0104) acc_x 93.7500 (87.1741) lr 4.065471e-04 -epoch [12/25][4300/4762] time 0.143 (0.152) data 0.001 (0.001) eta 2:38:25 loss 0.5080 (0.3764) loss_x 0.5052 (0.3659) loss_u 0.0028 (0.0105) acc_x 84.3750 (87.1468) lr 4.065471e-04 -epoch [12/25][4400/4762] time 0.154 (0.152) data 0.001 (0.001) eta 2:38:09 loss 0.6047 (0.3759) loss_x 0.5976 (0.3654) loss_u 0.0071 (0.0105) acc_x 81.2500 (87.1648) lr 4.065471e-04 -epoch [12/25][4500/4762] time 0.164 (0.152) data 0.001 (0.001) eta 2:37:51 loss 0.2925 (0.3753) loss_x 0.2893 (0.3649) loss_u 0.0032 (0.0105) acc_x 90.6250 (87.1792) lr 4.065471e-04 -epoch [12/25][4600/4762] time 0.164 (0.152) data 0.002 (0.001) eta 2:37:33 loss 0.3363 (0.3754) loss_x 0.3301 (0.3649) loss_u 0.0062 (0.0105) acc_x 87.5000 (87.1726) lr 4.065471e-04 -epoch [12/25][4700/4762] time 0.156 (0.152) data 0.001 (0.001) eta 2:37:20 loss 0.5374 (0.3755) loss_x 0.5280 (0.3650) loss_u 0.0094 (0.0105) acc_x 78.1250 (87.1609) lr 4.065471e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,747 -* accuracy: 84.40% -* error: 15.60% -* macro_f1: 84.56% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,590 acc: 98.46% -* class: 1 (bicycle) total: 3,475 correct: 2,926 acc: 84.20% -* class: 2 (bus) total: 4,690 correct: 4,235 acc: 90.30% -* class: 3 (car) total: 10,401 correct: 7,724 acc: 74.26% -* class: 4 (horse) total: 4,691 correct: 4,583 acc: 97.70% -* class: 5 (knife) total: 2,075 correct: 1,886 acc: 90.89% -* class: 6 (motorcycle) total: 5,796 correct: 5,520 acc: 95.24% -* class: 7 (person) total: 4,000 correct: 3,051 acc: 76.28% -* class: 8 (plant) total: 4,549 correct: 3,829 acc: 84.17% -* class: 9 (skateboard) total: 2,281 correct: 2,036 acc: 89.26% -* class: 10 (train) total: 4,236 correct: 3,923 acc: 92.61% -* class: 11 (truck) total: 5,548 correct: 3,444 acc: 62.08% -* average: 86.29% -epoch [13/25][100/4762] time 0.145 (0.155) data 0.001 (0.003) eta 2:39:34 loss 0.2692 (0.3742) loss_x 0.2552 (0.3638) loss_u 0.0139 (0.0104) acc_x 93.7500 (87.5312) lr 2.456136e-03 -epoch [13/25][200/4762] time 0.172 (0.153) data 0.001 (0.002) eta 2:37:24 loss 0.5457 (0.3885) loss_x 0.5294 (0.3781) loss_u 0.0163 (0.0104) acc_x 81.2500 (86.7656) lr 2.456136e-03 -epoch [13/25][300/4762] time 0.166 (0.153) data 0.004 (0.002) eta 2:37:01 loss 0.4971 (0.3864) loss_x 0.4841 (0.3758) loss_u 0.0130 (0.0105) acc_x 84.3750 (86.9167) lr 2.456136e-03 -epoch [13/25][400/4762] time 0.140 (0.153) data 0.001 (0.001) eta 2:36:21 loss 0.3630 (0.3867) loss_x 0.3615 (0.3761) loss_u 0.0015 (0.0105) acc_x 90.6250 (86.8359) lr 2.456136e-03 -epoch [13/25][500/4762] time 0.154 (0.153) data 0.001 (0.001) eta 2:36:47 loss 0.1951 (0.3800) loss_x 0.1691 (0.3697) loss_u 0.0260 (0.0103) acc_x 93.7500 (87.1063) lr 2.456136e-03 -epoch [13/25][600/4762] time 0.141 (0.153) data 0.001 (0.001) eta 2:36:04 loss 0.4118 (0.3793) loss_x 0.3981 (0.3690) loss_u 0.0137 (0.0103) acc_x 75.0000 (86.9896) lr 2.456136e-03 -epoch [13/25][700/4762] time 0.159 (0.153) data 0.001 (0.001) eta 2:35:49 loss 0.3261 (0.3808) loss_x 0.2886 (0.3704) loss_u 0.0375 (0.0104) acc_x 93.7500 (86.9330) lr 2.456136e-03 -epoch [13/25][800/4762] time 0.147 (0.153) data 0.001 (0.001) eta 2:35:37 loss 0.1827 (0.3779) loss_x 0.1675 (0.3678) loss_u 0.0151 (0.0102) acc_x 87.5000 (87.0312) lr 2.456136e-03 -epoch [13/25][900/4762] time 0.139 (0.153) data 0.001 (0.001) eta 2:35:11 loss 0.3868 (0.3767) loss_x 0.3669 (0.3666) loss_u 0.0199 (0.0101) acc_x 87.5000 (87.0139) lr 2.456136e-03 -epoch [13/25][1000/4762] time 0.153 (0.153) data 0.001 (0.001) eta 2:34:49 loss 0.1879 (0.3766) loss_x 0.1847 (0.3664) loss_u 0.0032 (0.0101) acc_x 96.8750 (86.9406) lr 2.456136e-03 -epoch [13/25][1100/4762] time 0.156 (0.153) data 0.001 (0.001) eta 2:34:46 loss 0.2606 (0.3779) loss_x 0.2519 (0.3674) loss_u 0.0087 (0.0105) acc_x 93.7500 (86.8665) lr 2.456136e-03 -epoch [13/25][1200/4762] time 0.137 (0.153) data 0.001 (0.001) eta 2:34:38 loss 0.6650 (0.3762) loss_x 0.6601 (0.3657) loss_u 0.0048 (0.0105) acc_x 78.1250 (86.9479) lr 2.456136e-03 -epoch [13/25][1300/4762] time 0.156 (0.153) data 0.001 (0.001) eta 2:34:26 loss 0.5969 (0.3761) loss_x 0.5679 (0.3656) loss_u 0.0290 (0.0105) acc_x 71.8750 (86.9423) lr 2.456136e-03 -epoch [13/25][1400/4762] time 0.141 (0.153) data 0.001 (0.001) eta 2:34:04 loss 0.3565 (0.3767) loss_x 0.3524 (0.3661) loss_u 0.0041 (0.0106) acc_x 84.3750 (86.9330) lr 2.456136e-03 -epoch [13/25][1500/4762] time 0.137 (0.153) data 0.001 (0.001) eta 2:33:50 loss 0.3252 (0.3759) loss_x 0.3119 (0.3651) loss_u 0.0132 (0.0108) acc_x 90.6250 (86.9708) lr 2.456136e-03 -epoch [13/25][1600/4762] time 0.173 (0.153) data 0.001 (0.001) eta 2:33:50 loss 0.2080 (0.3762) loss_x 0.1780 (0.3655) loss_u 0.0300 (0.0107) acc_x 93.7500 (86.9512) lr 2.456136e-03 -epoch [13/25][1700/4762] time 0.187 (0.153) data 0.001 (0.001) eta 2:33:28 loss 0.1603 (0.3746) loss_x 0.1532 (0.3639) loss_u 0.0071 (0.0107) acc_x 96.8750 (87.0294) lr 2.456136e-03 -epoch [13/25][1800/4762] time 0.159 (0.153) data 0.001 (0.001) eta 2:33:33 loss 0.5611 (0.3749) loss_x 0.5599 (0.3642) loss_u 0.0012 (0.0107) acc_x 81.2500 (87.0451) lr 2.456136e-03 -epoch [13/25][1900/4762] time 0.142 (0.153) data 0.001 (0.001) eta 2:33:15 loss 0.3121 (0.3742) loss_x 0.3043 (0.3636) loss_u 0.0078 (0.0107) acc_x 87.5000 (87.0658) lr 2.456136e-03 -epoch [13/25][2000/4762] time 0.199 (0.153) data 0.002 (0.001) eta 2:32:59 loss 0.5819 (0.3750) loss_x 0.5680 (0.3643) loss_u 0.0139 (0.0106) acc_x 81.2500 (87.0469) lr 2.456136e-03 -epoch [13/25][2100/4762] time 0.175 (0.153) data 0.001 (0.001) eta 2:32:50 loss 0.4656 (0.3759) loss_x 0.4529 (0.3653) loss_u 0.0126 (0.0106) acc_x 84.3750 (87.0134) lr 2.456136e-03 -epoch [13/25][2200/4762] time 0.150 (0.153) data 0.001 (0.001) eta 2:32:38 loss 0.3079 (0.3753) loss_x 0.3044 (0.3647) loss_u 0.0035 (0.0106) acc_x 87.5000 (87.0710) lr 2.456136e-03 -epoch [13/25][2300/4762] time 0.136 (0.153) data 0.001 (0.001) eta 2:32:19 loss 0.2289 (0.3737) loss_x 0.2250 (0.3631) loss_u 0.0038 (0.0106) acc_x 90.6250 (87.1332) lr 2.456136e-03 -epoch [13/25][2400/4762] time 0.153 (0.153) data 0.001 (0.001) eta 2:32:13 loss 0.2516 (0.3735) loss_x 0.2471 (0.3629) loss_u 0.0045 (0.0106) acc_x 90.6250 (87.1458) lr 2.456136e-03 -epoch [13/25][2500/4762] time 0.137 (0.154) data 0.001 (0.001) eta 2:32:07 loss 0.2090 (0.3736) loss_x 0.2015 (0.3630) loss_u 0.0075 (0.0106) acc_x 93.7500 (87.1350) lr 2.456136e-03 -epoch [13/25][2600/4762] time 0.167 (0.154) data 0.001 (0.001) eta 2:31:56 loss 0.2876 (0.3739) loss_x 0.2587 (0.3634) loss_u 0.0289 (0.0105) acc_x 93.7500 (87.1190) lr 2.456136e-03 -epoch [13/25][2700/4762] time 0.165 (0.154) data 0.001 (0.001) eta 2:31:36 loss 0.3993 (0.3745) loss_x 0.3787 (0.3639) loss_u 0.0207 (0.0105) acc_x 87.5000 (87.1065) lr 2.456136e-03 -epoch [13/25][2800/4762] time 0.141 (0.154) data 0.001 (0.001) eta 2:31:19 loss 0.5496 (0.3747) loss_x 0.5412 (0.3643) loss_u 0.0084 (0.0105) acc_x 84.3750 (87.1105) lr 2.456136e-03 -epoch [13/25][2900/4762] time 0.137 (0.154) data 0.001 (0.001) eta 2:31:03 loss 0.3784 (0.3746) loss_x 0.3723 (0.3642) loss_u 0.0061 (0.0105) acc_x 87.5000 (87.1293) lr 2.456136e-03 -epoch [13/25][3000/4762] time 0.150 (0.153) data 0.001 (0.001) eta 2:30:33 loss 0.2245 (0.3746) loss_x 0.2160 (0.3640) loss_u 0.0085 (0.0105) acc_x 93.7500 (87.1396) lr 2.456136e-03 -epoch [13/25][3100/4762] time 0.166 (0.153) data 0.001 (0.001) eta 2:30:13 loss 0.3569 (0.3744) loss_x 0.3508 (0.3638) loss_u 0.0061 (0.0106) acc_x 84.3750 (87.1623) lr 2.456136e-03 -epoch [13/25][3200/4762] time 0.146 (0.153) data 0.001 (0.001) eta 2:29:59 loss 0.2549 (0.3745) loss_x 0.2518 (0.3639) loss_u 0.0031 (0.0106) acc_x 90.6250 (87.1582) lr 2.456136e-03 -epoch [13/25][3300/4762] time 0.169 (0.153) data 0.001 (0.001) eta 2:29:43 loss 0.4623 (0.3742) loss_x 0.4462 (0.3637) loss_u 0.0161 (0.0105) acc_x 78.1250 (87.1771) lr 2.456136e-03 -epoch [13/25][3400/4762] time 0.176 (0.153) data 0.001 (0.001) eta 2:29:28 loss 0.2627 (0.3737) loss_x 0.2596 (0.3632) loss_u 0.0031 (0.0105) acc_x 93.7500 (87.2086) lr 2.456136e-03 -epoch [13/25][3500/4762] time 0.151 (0.153) data 0.001 (0.001) eta 2:29:17 loss 0.6631 (0.3739) loss_x 0.6404 (0.3634) loss_u 0.0227 (0.0105) acc_x 75.0000 (87.2054) lr 2.456136e-03 -epoch [13/25][3600/4762] time 0.155 (0.153) data 0.001 (0.001) eta 2:29:00 loss 0.1835 (0.3739) loss_x 0.1726 (0.3633) loss_u 0.0109 (0.0106) acc_x 93.7500 (87.2205) lr 2.456136e-03 -epoch [13/25][3700/4762] time 0.162 (0.153) data 0.001 (0.001) eta 2:28:43 loss 0.3092 (0.3747) loss_x 0.3043 (0.3642) loss_u 0.0049 (0.0105) acc_x 96.8750 (87.1858) lr 2.456136e-03 -epoch [13/25][3800/4762] time 0.166 (0.153) data 0.001 (0.001) eta 2:28:25 loss 0.2919 (0.3744) loss_x 0.2859 (0.3639) loss_u 0.0060 (0.0106) acc_x 90.6250 (87.1842) lr 2.456136e-03 -epoch [13/25][3900/4762] time 0.146 (0.153) data 0.001 (0.001) eta 2:28:11 loss 0.3486 (0.3745) loss_x 0.3379 (0.3640) loss_u 0.0107 (0.0105) acc_x 87.5000 (87.1643) lr 2.456136e-03 -epoch [13/25][4000/4762] time 0.138 (0.153) data 0.001 (0.001) eta 2:27:54 loss 0.3619 (0.3747) loss_x 0.3522 (0.3642) loss_u 0.0097 (0.0105) acc_x 90.6250 (87.1570) lr 2.456136e-03 -epoch [13/25][4100/4762] time 0.143 (0.153) data 0.001 (0.001) eta 2:27:40 loss 0.6101 (0.3750) loss_x 0.6026 (0.3646) loss_u 0.0075 (0.0104) acc_x 75.0000 (87.1448) lr 2.456136e-03 -epoch [13/25][4200/4762] time 0.144 (0.153) data 0.001 (0.001) eta 2:27:21 loss 0.2918 (0.3754) loss_x 0.2870 (0.3649) loss_u 0.0048 (0.0106) acc_x 87.5000 (87.1362) lr 2.456136e-03 -epoch [13/25][4300/4762] time 0.138 (0.153) data 0.001 (0.001) eta 2:27:05 loss 0.5251 (0.3760) loss_x 0.5190 (0.3654) loss_u 0.0062 (0.0106) acc_x 81.2500 (87.1192) lr 2.456136e-03 -epoch [13/25][4400/4762] time 0.137 (0.153) data 0.001 (0.001) eta 2:26:47 loss 0.4186 (0.3751) loss_x 0.4166 (0.3645) loss_u 0.0019 (0.0106) acc_x 87.5000 (87.1435) lr 2.456136e-03 -epoch [13/25][4500/4762] time 0.139 (0.153) data 0.001 (0.001) eta 2:26:33 loss 0.5014 (0.3754) loss_x 0.4889 (0.3647) loss_u 0.0125 (0.0106) acc_x 87.5000 (87.1278) lr 2.456136e-03 -epoch [13/25][4600/4762] time 0.139 (0.153) data 0.001 (0.001) eta 2:26:16 loss 0.3717 (0.3752) loss_x 0.3643 (0.3645) loss_u 0.0073 (0.0106) acc_x 90.6250 (87.1359) lr 2.456136e-03 -epoch [13/25][4700/4762] time 0.151 (0.153) data 0.001 (0.001) eta 2:25:58 loss 0.3462 (0.3754) loss_x 0.3303 (0.3648) loss_u 0.0158 (0.0106) acc_x 87.5000 (87.1390) lr 2.456136e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,922 -* accuracy: 84.72% -* error: 15.28% -* macro_f1: 84.96% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,580 acc: 98.19% -* class: 1 (bicycle) total: 3,475 correct: 2,900 acc: 83.45% -* class: 2 (bus) total: 4,690 correct: 4,209 acc: 89.74% -* class: 3 (car) total: 10,401 correct: 7,896 acc: 75.92% -* class: 4 (horse) total: 4,691 correct: 4,589 acc: 97.83% -* class: 5 (knife) total: 2,075 correct: 1,824 acc: 87.90% -* class: 6 (motorcycle) total: 5,796 correct: 5,519 acc: 95.22% -* class: 7 (person) total: 4,000 correct: 3,127 acc: 78.17% -* class: 8 (plant) total: 4,549 correct: 3,881 acc: 85.32% -* class: 9 (skateboard) total: 2,281 correct: 2,073 acc: 90.88% -* class: 10 (train) total: 4,236 correct: 3,916 acc: 92.45% -* class: 11 (truck) total: 5,548 correct: 3,408 acc: 61.43% -* average: 86.37% -epoch [14/25][100/4762] time 0.147 (0.153) data 0.001 (0.003) eta 2:25:03 loss 0.5222 (0.3809) loss_x 0.5202 (0.3720) loss_u 0.0020 (0.0090) acc_x 84.3750 (86.4688) lr 2.713525e-03 -epoch [14/25][200/4762] time 0.141 (0.152) data 0.001 (0.002) eta 2:24:18 loss 0.3801 (0.3688) loss_x 0.3648 (0.3601) loss_u 0.0153 (0.0087) acc_x 87.5000 (87.2969) lr 2.713525e-03 -epoch [14/25][300/4762] time 0.172 (0.151) data 0.001 (0.001) eta 2:23:26 loss 0.4660 (0.3755) loss_x 0.4518 (0.3667) loss_u 0.0142 (0.0088) acc_x 78.1250 (87.1354) lr 2.713525e-03 -epoch [14/25][400/4762] time 0.176 (0.152) data 0.001 (0.001) eta 2:23:17 loss 0.1988 (0.3751) loss_x 0.1972 (0.3660) loss_u 0.0016 (0.0091) acc_x 93.7500 (87.2500) lr 2.713525e-03 -epoch [14/25][500/4762] time 0.138 (0.151) data 0.001 (0.001) eta 2:22:35 loss 0.7119 (0.3726) loss_x 0.7079 (0.3637) loss_u 0.0040 (0.0090) acc_x 78.1250 (87.3187) lr 2.713525e-03 -epoch [14/25][600/4762] time 0.137 (0.151) data 0.001 (0.001) eta 2:22:33 loss 0.2891 (0.3706) loss_x 0.2766 (0.3615) loss_u 0.0125 (0.0091) acc_x 93.7500 (87.3490) lr 2.713525e-03 -epoch [14/25][700/4762] time 0.148 (0.151) data 0.006 (0.001) eta 2:22:14 loss 0.2023 (0.3711) loss_x 0.1959 (0.3618) loss_u 0.0064 (0.0093) acc_x 93.7500 (87.3973) lr 2.713525e-03 -epoch [14/25][800/4762] time 0.138 (0.151) data 0.001 (0.001) eta 2:21:57 loss 0.2009 (0.3716) loss_x 0.1909 (0.3624) loss_u 0.0100 (0.0093) acc_x 93.7500 (87.3359) lr 2.713525e-03 -epoch [14/25][900/4762] time 0.148 (0.151) data 0.001 (0.001) eta 2:21:47 loss 0.3761 (0.3721) loss_x 0.3757 (0.3629) loss_u 0.0004 (0.0092) acc_x 90.6250 (87.3229) lr 2.713525e-03 -epoch [14/25][1000/4762] time 0.136 (0.151) data 0.001 (0.001) eta 2:21:19 loss 0.6745 (0.3706) loss_x 0.6678 (0.3612) loss_u 0.0067 (0.0094) acc_x 78.1250 (87.3906) lr 2.713525e-03 -epoch [14/25][1100/4762] time 0.157 (0.151) data 0.001 (0.001) eta 2:21:00 loss 0.4282 (0.3711) loss_x 0.3904 (0.3617) loss_u 0.0378 (0.0094) acc_x 90.6250 (87.3409) lr 2.713525e-03 -epoch [14/25][1200/4762] time 0.148 (0.151) data 0.001 (0.001) eta 2:21:08 loss 0.2170 (0.3695) loss_x 0.2120 (0.3602) loss_u 0.0050 (0.0092) acc_x 90.6250 (87.3672) lr 2.713525e-03 -epoch [14/25][1300/4762] time 0.153 (0.152) data 0.001 (0.001) eta 2:21:09 loss 0.5255 (0.3712) loss_x 0.5163 (0.3618) loss_u 0.0092 (0.0094) acc_x 90.6250 (87.2812) lr 2.713525e-03 -epoch [14/25][1400/4762] time 0.150 (0.152) data 0.001 (0.001) eta 2:21:04 loss 0.4874 (0.3701) loss_x 0.4868 (0.3606) loss_u 0.0006 (0.0094) acc_x 84.3750 (87.3371) lr 2.713525e-03 -epoch [14/25][1500/4762] time 0.139 (0.152) data 0.001 (0.001) eta 2:20:51 loss 0.4755 (0.3708) loss_x 0.4643 (0.3613) loss_u 0.0112 (0.0096) acc_x 84.3750 (87.3063) lr 2.713525e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,881 -* accuracy: 84.64% -* error: 15.36% -* macro_f1: 84.83% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,586 acc: 98.35% -* class: 1 (bicycle) total: 3,475 correct: 2,867 acc: 82.50% -* class: 2 (bus) total: 4,690 correct: 4,248 acc: 90.58% -* class: 3 (car) total: 10,401 correct: 7,878 acc: 75.74% -* class: 4 (horse) total: 4,691 correct: 4,575 acc: 97.53% -* class: 5 (knife) total: 2,075 correct: 1,860 acc: 89.64% -* class: 6 (motorcycle) total: 5,796 correct: 5,513 acc: 95.12% -* class: 7 (person) total: 4,000 correct: 3,058 acc: 76.45% -* class: 8 (plant) total: 4,549 correct: 3,901 acc: 85.76% -* class: 9 (skateboard) total: 2,281 correct: 2,066 acc: 90.57% -* class: 10 (train) total: 4,236 correct: 3,943 acc: 93.08% -* class: 11 (truck) total: 5,548 correct: 3,386 acc: 61.03% -* average: 86.36% -epoch [14/25][1600/4762] time 0.158 (0.152) data 0.001 (0.001) eta 2:20:41 loss 0.2530 (0.3717) loss_x 0.2497 (0.3622) loss_u 0.0033 (0.0096) acc_x 90.6250 (87.2500) lr 2.713525e-03 -epoch [14/25][1700/4762] time 0.137 (0.152) data 0.001 (0.001) eta 2:20:23 loss 0.2936 (0.3711) loss_x 0.2754 (0.3614) loss_u 0.0182 (0.0097) acc_x 87.5000 (87.2684) lr 2.713525e-03 -epoch [14/25][1800/4762] time 0.136 (0.152) data 0.001 (0.001) eta 2:20:14 loss 0.4576 (0.3722) loss_x 0.4521 (0.3625) loss_u 0.0055 (0.0097) acc_x 84.3750 (87.2205) lr 2.713525e-03 -epoch [14/25][1900/4762] time 0.141 (0.152) data 0.001 (0.001) eta 2:19:54 loss 0.4175 (0.3721) loss_x 0.4143 (0.3624) loss_u 0.0032 (0.0097) acc_x 87.5000 (87.2418) lr 2.713525e-03 -epoch [14/25][2000/4762] time 0.143 (0.152) data 0.001 (0.001) eta 2:19:36 loss 0.4482 (0.3719) loss_x 0.4424 (0.3621) loss_u 0.0058 (0.0098) acc_x 81.2500 (87.2438) lr 2.713525e-03 -epoch [14/25][2100/4762] time 0.137 (0.152) data 0.001 (0.001) eta 2:19:24 loss 0.2593 (0.3729) loss_x 0.2396 (0.3631) loss_u 0.0197 (0.0098) acc_x 90.6250 (87.1935) lr 2.713525e-03 -epoch [14/25][2200/4762] time 0.139 (0.152) data 0.001 (0.001) eta 2:19:12 loss 0.6269 (0.3730) loss_x 0.6048 (0.3633) loss_u 0.0221 (0.0097) acc_x 81.2500 (87.1832) lr 2.713525e-03 -epoch [14/25][2300/4762] time 0.189 (0.152) data 0.001 (0.001) eta 2:19:02 loss 0.7326 (0.3732) loss_x 0.7190 (0.3634) loss_u 0.0136 (0.0098) acc_x 78.1250 (87.2065) lr 2.713525e-03 -epoch [14/25][2400/4762] time 0.151 (0.152) data 0.001 (0.001) eta 2:18:50 loss 0.4498 (0.3737) loss_x 0.4459 (0.3638) loss_u 0.0040 (0.0098) acc_x 87.5000 (87.1927) lr 2.713525e-03 -epoch [14/25][2500/4762] time 0.137 (0.152) data 0.001 (0.001) eta 2:18:37 loss 0.5332 (0.3746) loss_x 0.5306 (0.3647) loss_u 0.0025 (0.0098) acc_x 81.2500 (87.1637) lr 2.713525e-03 -epoch [14/25][2600/4762] time 0.166 (0.152) data 0.001 (0.001) eta 2:18:27 loss 0.3145 (0.3737) loss_x 0.3026 (0.3637) loss_u 0.0119 (0.0100) acc_x 93.7500 (87.2200) lr 2.713525e-03 -epoch [14/25][2700/4762] time 0.137 (0.152) data 0.001 (0.001) eta 2:18:13 loss 0.2259 (0.3740) loss_x 0.2216 (0.3640) loss_u 0.0043 (0.0099) acc_x 93.7500 (87.1991) lr 2.713525e-03 -epoch [14/25][2800/4762] time 0.138 (0.152) data 0.001 (0.001) eta 2:17:57 loss 0.3622 (0.3737) loss_x 0.3614 (0.3637) loss_u 0.0009 (0.0100) acc_x 87.5000 (87.2109) lr 2.713525e-03 -epoch [14/25][2900/4762] time 0.136 (0.152) data 0.001 (0.001) eta 2:17:40 loss 0.4889 (0.3736) loss_x 0.4591 (0.3635) loss_u 0.0298 (0.0100) acc_x 87.5000 (87.2188) lr 2.713525e-03 -epoch [14/25][3000/4762] time 0.139 (0.152) data 0.000 (0.001) eta 2:17:21 loss 0.4183 (0.3736) loss_x 0.4121 (0.3635) loss_u 0.0061 (0.0101) acc_x 84.3750 (87.2260) lr 2.713525e-03 -epoch [14/25][3100/4762] time 0.148 (0.152) data 0.001 (0.001) eta 2:17:02 loss 0.2606 (0.3737) loss_x 0.2490 (0.3636) loss_u 0.0116 (0.0101) acc_x 93.7500 (87.2208) lr 2.713525e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,668 -* accuracy: 84.26% -* error: 15.74% -* macro_f1: 84.27% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,575 acc: 98.05% -* class: 1 (bicycle) total: 3,475 correct: 2,892 acc: 83.22% -* class: 2 (bus) total: 4,690 correct: 4,266 acc: 90.96% -* class: 3 (car) total: 10,401 correct: 7,745 acc: 74.46% -* class: 4 (horse) total: 4,691 correct: 4,568 acc: 97.38% -* class: 5 (knife) total: 2,075 correct: 1,855 acc: 89.40% -* class: 6 (motorcycle) total: 5,796 correct: 5,522 acc: 95.27% -* class: 7 (person) total: 4,000 correct: 2,917 acc: 72.92% -* class: 8 (plant) total: 4,549 correct: 3,916 acc: 86.08% -* class: 9 (skateboard) total: 2,281 correct: 2,072 acc: 90.84% -* class: 10 (train) total: 4,236 correct: 3,902 acc: 92.12% -* class: 11 (truck) total: 5,548 correct: 3,438 acc: 61.97% -* average: 86.06% -epoch [14/25][3200/4762] time 0.149 (0.152) data 0.001 (0.001) eta 2:16:43 loss 0.4938 (0.3735) loss_x 0.4919 (0.3634) loss_u 0.0019 (0.0101) acc_x 84.3750 (87.2236) lr 2.713525e-03 -epoch [14/25][3300/4762] time 0.147 (0.152) data 0.001 (0.001) eta 2:16:31 loss 0.4574 (0.3731) loss_x 0.4548 (0.3630) loss_u 0.0025 (0.0101) acc_x 81.2500 (87.2424) lr 2.713525e-03 -epoch [14/25][3400/4762] time 0.150 (0.152) data 0.001 (0.001) eta 2:16:13 loss 0.2105 (0.3735) loss_x 0.1988 (0.3635) loss_u 0.0116 (0.0100) acc_x 93.7500 (87.2270) lr 2.713525e-03 -epoch [14/25][3500/4762] time 0.143 (0.152) data 0.001 (0.001) eta 2:16:10 loss 0.3790 (0.3738) loss_x 0.3533 (0.3638) loss_u 0.0256 (0.0100) acc_x 84.3750 (87.1929) lr 2.713525e-03 -epoch [14/25][3600/4762] time 0.142 (0.152) data 0.001 (0.001) eta 2:16:00 loss 0.4496 (0.3739) loss_x 0.4235 (0.3639) loss_u 0.0261 (0.0100) acc_x 81.2500 (87.2031) lr 2.713525e-03 -epoch [14/25][3700/4762] time 0.149 (0.152) data 0.001 (0.001) eta 2:15:48 loss 0.2979 (0.3733) loss_x 0.2974 (0.3633) loss_u 0.0005 (0.0100) acc_x 90.6250 (87.2289) lr 2.713525e-03 -epoch [14/25][3800/4762] time 0.160 (0.153) data 0.001 (0.001) eta 2:15:45 loss 0.3231 (0.3729) loss_x 0.3190 (0.3629) loss_u 0.0041 (0.0100) acc_x 87.5000 (87.2442) lr 2.713525e-03 -epoch [14/25][3900/4762] time 0.186 (0.153) data 0.001 (0.001) eta 2:15:35 loss 0.6642 (0.3727) loss_x 0.6618 (0.3628) loss_u 0.0024 (0.0100) acc_x 78.1250 (87.2596) lr 2.713525e-03 -epoch [14/25][4000/4762] time 0.155 (0.153) data 0.001 (0.001) eta 2:15:24 loss 0.4039 (0.3731) loss_x 0.4020 (0.3632) loss_u 0.0019 (0.0100) acc_x 90.6250 (87.2500) lr 2.713525e-03 -epoch [14/25][4100/4762] time 0.149 (0.153) data 0.001 (0.001) eta 2:15:09 loss 0.2841 (0.3728) loss_x 0.2717 (0.3629) loss_u 0.0124 (0.0099) acc_x 93.7500 (87.2713) lr 2.713525e-03 -epoch [14/25][4200/4762] time 0.140 (0.153) data 0.001 (0.001) eta 2:14:53 loss 0.4332 (0.3728) loss_x 0.4294 (0.3629) loss_u 0.0038 (0.0099) acc_x 90.6250 (87.2842) lr 2.713525e-03 -epoch [14/25][4300/4762] time 0.138 (0.153) data 0.001 (0.001) eta 2:14:37 loss 0.1941 (0.3729) loss_x 0.1910 (0.3629) loss_u 0.0031 (0.0099) acc_x 93.7500 (87.2863) lr 2.713525e-03 -epoch [14/25][4400/4762] time 0.152 (0.153) data 0.001 (0.001) eta 2:14:21 loss 0.3828 (0.3727) loss_x 0.3768 (0.3628) loss_u 0.0061 (0.0099) acc_x 87.5000 (87.2891) lr 2.713525e-03 -epoch [14/25][4500/4762] time 0.136 (0.153) data 0.001 (0.001) eta 2:14:07 loss 0.4371 (0.3722) loss_x 0.4262 (0.3623) loss_u 0.0109 (0.0099) acc_x 87.5000 (87.3118) lr 2.713525e-03 -epoch [14/25][4600/4762] time 0.142 (0.153) data 0.001 (0.001) eta 2:13:50 loss 0.3226 (0.3720) loss_x 0.3079 (0.3620) loss_u 0.0147 (0.0099) acc_x 90.6250 (87.3118) lr 2.713525e-03 -epoch [14/25][4700/4762] time 0.174 (0.153) data 0.001 (0.001) eta 2:13:38 loss 0.2577 (0.3722) loss_x 0.2373 (0.3623) loss_u 0.0203 (0.0099) acc_x 90.6250 (87.2959) lr 2.713525e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,889 -* accuracy: 84.66% -* error: 15.34% -* macro_f1: 84.75% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,580 acc: 98.19% -* class: 1 (bicycle) total: 3,475 correct: 2,930 acc: 84.32% -* class: 2 (bus) total: 4,690 correct: 4,237 acc: 90.34% -* class: 3 (car) total: 10,401 correct: 7,818 acc: 75.17% -* class: 4 (horse) total: 4,691 correct: 4,585 acc: 97.74% -* class: 5 (knife) total: 2,075 correct: 1,867 acc: 89.98% -* class: 6 (motorcycle) total: 5,796 correct: 5,533 acc: 95.46% -* class: 7 (person) total: 4,000 correct: 2,913 acc: 72.83% -* class: 8 (plant) total: 4,549 correct: 3,975 acc: 87.38% -* class: 9 (skateboard) total: 2,281 correct: 2,060 acc: 90.31% -* class: 10 (train) total: 4,236 correct: 3,937 acc: 92.94% -* class: 11 (truck) total: 5,548 correct: 3,454 acc: 62.26% -* average: 86.41% -epoch [15/25][100/4762] time 0.157 (0.156) data 0.001 (0.003) eta 2:15:34 loss 0.4049 (0.3675) loss_x 0.4025 (0.3585) loss_u 0.0023 (0.0089) acc_x 90.6250 (87.6250) lr 6.962598e-04 -epoch [15/25][200/4762] time 0.174 (0.154) data 0.001 (0.002) eta 2:14:07 loss 0.2228 (0.3740) loss_x 0.2052 (0.3652) loss_u 0.0176 (0.0087) acc_x 90.6250 (87.1719) lr 6.962598e-04 -epoch [15/25][300/4762] time 0.166 (0.154) data 0.000 (0.002) eta 2:13:45 loss 0.2647 (0.3765) loss_x 0.2560 (0.3679) loss_u 0.0087 (0.0085) acc_x 90.6250 (87.0208) lr 6.962598e-04 -epoch [15/25][400/4762] time 0.153 (0.153) data 0.001 (0.001) eta 2:12:55 loss 0.2312 (0.3669) loss_x 0.2288 (0.3582) loss_u 0.0024 (0.0087) acc_x 90.6250 (87.4062) lr 6.962598e-04 -epoch [15/25][500/4762] time 0.179 (0.154) data 0.001 (0.001) eta 2:13:22 loss 0.4416 (0.3640) loss_x 0.4346 (0.3554) loss_u 0.0069 (0.0086) acc_x 90.6250 (87.5438) lr 6.962598e-04 -epoch [15/25][600/4762] time 0.145 (0.154) data 0.001 (0.001) eta 2:13:12 loss 0.4993 (0.3641) loss_x 0.4867 (0.3552) loss_u 0.0125 (0.0090) acc_x 78.1250 (87.4583) lr 6.962598e-04 -epoch [15/25][700/4762] time 0.206 (0.155) data 0.001 (0.001) eta 2:13:13 loss 0.3519 (0.3637) loss_x 0.3289 (0.3544) loss_u 0.0229 (0.0093) acc_x 87.5000 (87.5000) lr 6.962598e-04 -epoch [15/25][800/4762] time 0.176 (0.156) data 0.001 (0.001) eta 2:14:11 loss 0.4390 (0.3651) loss_x 0.4304 (0.3559) loss_u 0.0086 (0.0092) acc_x 84.3750 (87.3906) lr 6.962598e-04 -epoch [15/25][900/4762] time 0.141 (0.157) data 0.001 (0.001) eta 2:14:38 loss 0.3241 (0.3655) loss_x 0.3212 (0.3563) loss_u 0.0029 (0.0092) acc_x 93.7500 (87.4271) lr 6.962598e-04 -epoch [15/25][1000/4762] time 0.141 (0.156) data 0.001 (0.001) eta 2:13:46 loss 0.2132 (0.3645) loss_x 0.2046 (0.3552) loss_u 0.0086 (0.0093) acc_x 96.8750 (87.4938) lr 6.962598e-04 -epoch [15/25][1100/4762] time 0.159 (0.156) data 0.001 (0.001) eta 2:13:24 loss 0.3808 (0.3650) loss_x 0.3572 (0.3556) loss_u 0.0236 (0.0095) acc_x 90.6250 (87.5028) lr 6.962598e-04 -epoch [15/25][1200/4762] time 0.143 (0.156) data 0.002 (0.001) eta 2:13:06 loss 0.6147 (0.3667) loss_x 0.6028 (0.3571) loss_u 0.0118 (0.0095) acc_x 78.1250 (87.4219) lr 6.962598e-04 -epoch [15/25][1300/4762] time 0.138 (0.156) data 0.001 (0.001) eta 2:12:41 loss 0.3283 (0.3647) loss_x 0.3168 (0.3550) loss_u 0.0115 (0.0097) acc_x 87.5000 (87.4760) lr 6.962598e-04 -epoch [15/25][1400/4762] time 0.141 (0.156) data 0.001 (0.001) eta 2:12:16 loss 0.6021 (0.3652) loss_x 0.5994 (0.3555) loss_u 0.0027 (0.0097) acc_x 81.2500 (87.4777) lr 6.962598e-04 -epoch [15/25][1500/4762] time 0.137 (0.155) data 0.001 (0.001) eta 2:11:49 loss 0.3187 (0.3664) loss_x 0.3143 (0.3567) loss_u 0.0044 (0.0097) acc_x 93.7500 (87.4562) lr 6.962598e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,760 -* accuracy: 84.42% -* error: 15.58% -* macro_f1: 84.45% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,585 acc: 98.33% -* class: 1 (bicycle) total: 3,475 correct: 2,903 acc: 83.54% -* class: 2 (bus) total: 4,690 correct: 4,260 acc: 90.83% -* class: 3 (car) total: 10,401 correct: 7,903 acc: 75.98% -* class: 4 (horse) total: 4,691 correct: 4,581 acc: 97.66% -* class: 5 (knife) total: 2,075 correct: 1,906 acc: 91.86% -* class: 6 (motorcycle) total: 5,796 correct: 5,518 acc: 95.20% -* class: 7 (person) total: 4,000 correct: 2,907 acc: 72.67% -* class: 8 (plant) total: 4,549 correct: 3,939 acc: 86.59% -* class: 9 (skateboard) total: 2,281 correct: 2,041 acc: 89.48% -* class: 10 (train) total: 4,236 correct: 3,950 acc: 93.25% -* class: 11 (truck) total: 5,548 correct: 3,267 acc: 58.89% -* average: 86.19% -epoch [15/25][1600/4762] time 0.149 (0.155) data 0.001 (0.001) eta 2:11:17 loss 0.1855 (0.3660) loss_x 0.1638 (0.3563) loss_u 0.0217 (0.0097) acc_x 100.0000 (87.4609) lr 6.962598e-04 -epoch [15/25][1700/4762] time 0.183 (0.155) data 0.001 (0.001) eta 2:10:48 loss 0.4830 (0.3664) loss_x 0.4750 (0.3568) loss_u 0.0080 (0.0096) acc_x 81.2500 (87.4393) lr 6.962598e-04 -epoch [15/25][1800/4762] time 0.209 (0.155) data 0.001 (0.001) eta 2:10:40 loss 0.3001 (0.3665) loss_x 0.2899 (0.3569) loss_u 0.0102 (0.0096) acc_x 90.6250 (87.4323) lr 6.962598e-04 -epoch [15/25][1900/4762] time 0.144 (0.155) data 0.001 (0.001) eta 2:10:21 loss 0.3290 (0.3669) loss_x 0.3239 (0.3572) loss_u 0.0051 (0.0096) acc_x 87.5000 (87.4112) lr 6.962598e-04 -epoch [15/25][2000/4762] time 0.147 (0.155) data 0.001 (0.001) eta 2:10:04 loss 0.5742 (0.3677) loss_x 0.5572 (0.3581) loss_u 0.0170 (0.0095) acc_x 84.3750 (87.3922) lr 6.962598e-04 -epoch [15/25][2100/4762] time 0.142 (0.155) data 0.001 (0.001) eta 2:09:42 loss 0.3416 (0.3672) loss_x 0.3394 (0.3577) loss_u 0.0022 (0.0095) acc_x 87.5000 (87.4241) lr 6.962598e-04 -epoch [15/25][2200/4762] time 0.146 (0.155) data 0.001 (0.001) eta 2:09:26 loss 0.3776 (0.3679) loss_x 0.3761 (0.3584) loss_u 0.0016 (0.0096) acc_x 93.7500 (87.4261) lr 6.962598e-04 -epoch [15/25][2300/4762] time 0.154 (0.155) data 0.001 (0.001) eta 2:09:11 loss 0.3567 (0.3675) loss_x 0.3558 (0.3580) loss_u 0.0008 (0.0095) acc_x 93.7500 (87.4361) lr 6.962598e-04 -epoch [15/25][2400/4762] time 0.142 (0.155) data 0.001 (0.001) eta 2:08:54 loss 0.5756 (0.3676) loss_x 0.5601 (0.3581) loss_u 0.0155 (0.0095) acc_x 81.2500 (87.4362) lr 6.962598e-04 -epoch [15/25][2500/4762] time 0.154 (0.155) data 0.001 (0.001) eta 2:08:49 loss 0.2395 (0.3684) loss_x 0.2387 (0.3587) loss_u 0.0008 (0.0096) acc_x 90.6250 (87.4100) lr 6.962598e-04 -epoch [15/25][2600/4762] time 0.147 (0.155) data 0.001 (0.001) eta 2:08:32 loss 0.1408 (0.3686) loss_x 0.1379 (0.3589) loss_u 0.0029 (0.0096) acc_x 100.0000 (87.4038) lr 6.962598e-04 -epoch [15/25][2700/4762] time 0.153 (0.155) data 0.001 (0.001) eta 2:08:09 loss 0.2969 (0.3682) loss_x 0.2879 (0.3586) loss_u 0.0090 (0.0096) acc_x 93.7500 (87.4236) lr 6.962598e-04 -epoch [15/25][2800/4762] time 0.154 (0.155) data 0.001 (0.001) eta 2:07:52 loss 0.2602 (0.3682) loss_x 0.2591 (0.3584) loss_u 0.0011 (0.0097) acc_x 90.6250 (87.4163) lr 6.962598e-04 -epoch [15/25][2900/4762] time 0.139 (0.155) data 0.001 (0.001) eta 2:07:39 loss 0.2045 (0.3693) loss_x 0.1999 (0.3595) loss_u 0.0046 (0.0098) acc_x 93.7500 (87.3815) lr 6.962598e-04 -epoch [15/25][3000/4762] time 0.164 (0.155) data 0.001 (0.001) eta 2:07:24 loss 0.3040 (0.3694) loss_x 0.2707 (0.3596) loss_u 0.0333 (0.0098) acc_x 96.8750 (87.3844) lr 6.962598e-04 -epoch [15/25][3100/4762] time 0.164 (0.155) data 0.001 (0.001) eta 2:07:11 loss 0.4927 (0.3692) loss_x 0.4924 (0.3595) loss_u 0.0003 (0.0098) acc_x 87.5000 (87.4022) lr 6.962598e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,848 -* accuracy: 84.58% -* error: 15.42% -* macro_f1: 84.85% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,589 acc: 98.44% -* class: 1 (bicycle) total: 3,475 correct: 2,905 acc: 83.60% -* class: 2 (bus) total: 4,690 correct: 4,246 acc: 90.53% -* class: 3 (car) total: 10,401 correct: 7,721 acc: 74.23% -* class: 4 (horse) total: 4,691 correct: 4,554 acc: 97.08% -* class: 5 (knife) total: 2,075 correct: 1,877 acc: 90.46% -* class: 6 (motorcycle) total: 5,796 correct: 5,542 acc: 95.62% -* class: 7 (person) total: 4,000 correct: 3,059 acc: 76.47% -* class: 8 (plant) total: 4,549 correct: 3,941 acc: 86.63% -* class: 9 (skateboard) total: 2,281 correct: 2,038 acc: 89.35% -* class: 10 (train) total: 4,236 correct: 3,949 acc: 93.22% -* class: 11 (truck) total: 5,548 correct: 3,427 acc: 61.77% -* average: 86.45% -epoch [15/25][3200/4762] time 0.146 (0.155) data 0.001 (0.001) eta 2:06:50 loss 0.3071 (0.3693) loss_x 0.2943 (0.3595) loss_u 0.0128 (0.0098) acc_x 90.6250 (87.3867) lr 6.962598e-04 -epoch [15/25][3300/4762] time 0.141 (0.155) data 0.001 (0.001) eta 2:06:33 loss 0.3665 (0.3697) loss_x 0.3651 (0.3600) loss_u 0.0013 (0.0097) acc_x 90.6250 (87.3655) lr 6.962598e-04 -epoch [15/25][3400/4762] time 0.143 (0.155) data 0.001 (0.001) eta 2:06:19 loss 0.4157 (0.3697) loss_x 0.4100 (0.3600) loss_u 0.0056 (0.0098) acc_x 87.5000 (87.3585) lr 6.962598e-04 -epoch [15/25][3500/4762] time 0.141 (0.155) data 0.001 (0.001) eta 2:06:06 loss 0.0947 (0.3697) loss_x 0.0936 (0.3599) loss_u 0.0011 (0.0097) acc_x 100.0000 (87.3536) lr 6.962598e-04 -epoch [15/25][3600/4762] time 0.145 (0.155) data 0.001 (0.001) eta 2:05:45 loss 0.5174 (0.3702) loss_x 0.5126 (0.3605) loss_u 0.0048 (0.0098) acc_x 81.2500 (87.3255) lr 6.962598e-04 -epoch [15/25][3700/4762] time 0.153 (0.155) data 0.001 (0.001) eta 2:05:32 loss 0.1207 (0.3707) loss_x 0.1173 (0.3609) loss_u 0.0034 (0.0098) acc_x 96.8750 (87.3041) lr 6.962598e-04 -epoch [15/25][3800/4762] time 0.137 (0.155) data 0.001 (0.001) eta 2:05:13 loss 0.1840 (0.3705) loss_x 0.1696 (0.3608) loss_u 0.0144 (0.0097) acc_x 100.0000 (87.3059) lr 6.962598e-04 -epoch [15/25][3900/4762] time 0.150 (0.155) data 0.001 (0.001) eta 2:05:02 loss 0.1859 (0.3706) loss_x 0.1722 (0.3609) loss_u 0.0137 (0.0097) acc_x 96.8750 (87.3101) lr 6.962598e-04 -epoch [15/25][4000/4762] time 0.177 (0.155) data 0.001 (0.001) eta 2:04:51 loss 0.7205 (0.3699) loss_x 0.7169 (0.3602) loss_u 0.0036 (0.0097) acc_x 75.0000 (87.3242) lr 6.962598e-04 -epoch [15/25][4100/4762] time 0.150 (0.155) data 0.001 (0.001) eta 2:04:47 loss 0.2853 (0.3696) loss_x 0.2813 (0.3599) loss_u 0.0040 (0.0097) acc_x 87.5000 (87.3407) lr 6.962598e-04 -epoch [15/25][4200/4762] time 0.157 (0.155) data 0.001 (0.001) eta 2:04:37 loss 0.6747 (0.3700) loss_x 0.6736 (0.3604) loss_u 0.0011 (0.0097) acc_x 78.1250 (87.3185) lr 6.962598e-04 -epoch [15/25][4300/4762] time 0.147 (0.155) data 0.001 (0.001) eta 2:04:20 loss 0.3492 (0.3706) loss_x 0.3446 (0.3610) loss_u 0.0046 (0.0096) acc_x 81.2500 (87.3016) lr 6.962598e-04 -epoch [15/25][4400/4762] time 0.138 (0.155) data 0.001 (0.001) eta 2:04:02 loss 0.4103 (0.3707) loss_x 0.4001 (0.3610) loss_u 0.0103 (0.0097) acc_x 87.5000 (87.2955) lr 6.962598e-04 -epoch [15/25][4500/4762] time 0.171 (0.155) data 0.001 (0.001) eta 2:03:48 loss 0.1588 (0.3705) loss_x 0.1582 (0.3608) loss_u 0.0005 (0.0097) acc_x 96.8750 (87.3076) lr 6.962598e-04 -epoch [15/25][4600/4762] time 0.148 (0.155) data 0.001 (0.001) eta 2:03:32 loss 0.3671 (0.3707) loss_x 0.3587 (0.3609) loss_u 0.0084 (0.0097) acc_x 81.2500 (87.2948) lr 6.962598e-04 -epoch [15/25][4700/4762] time 0.141 (0.155) data 0.001 (0.001) eta 2:03:12 loss 0.1415 (0.3710) loss_x 0.1260 (0.3612) loss_u 0.0155 (0.0098) acc_x 96.8750 (87.2945) lr 6.962598e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,606 -* accuracy: 84.14% -* error: 15.86% -* macro_f1: 84.45% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,587 acc: 98.38% -* class: 1 (bicycle) total: 3,475 correct: 2,950 acc: 84.89% -* class: 2 (bus) total: 4,690 correct: 4,303 acc: 91.75% -* class: 3 (car) total: 10,401 correct: 7,581 acc: 72.89% -* class: 4 (horse) total: 4,691 correct: 4,575 acc: 97.53% -* class: 5 (knife) total: 2,075 correct: 1,876 acc: 90.41% -* class: 6 (motorcycle) total: 5,796 correct: 5,504 acc: 94.96% -* class: 7 (person) total: 4,000 correct: 3,074 acc: 76.85% -* class: 8 (plant) total: 4,549 correct: 3,856 acc: 84.77% -* class: 9 (skateboard) total: 2,281 correct: 2,047 acc: 89.74% -* class: 10 (train) total: 4,236 correct: 3,901 acc: 92.09% -* class: 11 (truck) total: 5,548 correct: 3,352 acc: 60.42% -* average: 86.22% -epoch [16/25][100/4762] time 0.142 (0.154) data 0.001 (0.003) eta 2:02:05 loss 0.2853 (0.3540) loss_x 0.2809 (0.3438) loss_u 0.0045 (0.0103) acc_x 87.5000 (88.1250) lr 1.855400e-04 -epoch [16/25][200/4762] time 0.142 (0.154) data 0.001 (0.002) eta 2:01:57 loss 0.5277 (0.3613) loss_x 0.5267 (0.3510) loss_u 0.0009 (0.0103) acc_x 75.0000 (87.8281) lr 1.855400e-04 -epoch [16/25][300/4762] time 0.148 (0.153) data 0.001 (0.002) eta 2:00:47 loss 0.3606 (0.3704) loss_x 0.3544 (0.3608) loss_u 0.0062 (0.0096) acc_x 87.5000 (87.3438) lr 1.855400e-04 -epoch [16/25][400/4762] time 0.156 (0.152) data 0.001 (0.001) eta 1:59:52 loss 0.2283 (0.3731) loss_x 0.2252 (0.3635) loss_u 0.0031 (0.0096) acc_x 96.8750 (87.3203) lr 1.855400e-04 -epoch [16/25][500/4762] time 0.148 (0.153) data 0.001 (0.001) eta 1:59:48 loss 0.1949 (0.3716) loss_x 0.1272 (0.3615) loss_u 0.0677 (0.0101) acc_x 96.8750 (87.4313) lr 1.855400e-04 -epoch [16/25][600/4762] time 0.161 (0.153) data 0.001 (0.001) eta 1:59:44 loss 0.3768 (0.3744) loss_x 0.3762 (0.3644) loss_u 0.0006 (0.0100) acc_x 84.3750 (87.4531) lr 1.855400e-04 -epoch [16/25][700/4762] time 0.151 (0.154) data 0.001 (0.001) eta 2:00:12 loss 0.3196 (0.3754) loss_x 0.3089 (0.3654) loss_u 0.0107 (0.0100) acc_x 93.7500 (87.4107) lr 1.855400e-04 -epoch [16/25][800/4762] time 0.145 (0.154) data 0.001 (0.001) eta 2:00:00 loss 0.2573 (0.3753) loss_x 0.2554 (0.3654) loss_u 0.0019 (0.0100) acc_x 87.5000 (87.4414) lr 1.855400e-04 -epoch [16/25][900/4762] time 0.148 (0.154) data 0.001 (0.001) eta 2:00:08 loss 0.5155 (0.3738) loss_x 0.4980 (0.3639) loss_u 0.0176 (0.0099) acc_x 81.2500 (87.4236) lr 1.855400e-04 -epoch [16/25][1000/4762] time 0.143 (0.155) data 0.001 (0.001) eta 2:00:18 loss 0.3309 (0.3734) loss_x 0.3270 (0.3633) loss_u 0.0039 (0.0101) acc_x 84.3750 (87.4000) lr 1.855400e-04 -epoch [16/25][1100/4762] time 0.161 (0.155) data 0.001 (0.001) eta 2:00:06 loss 0.2381 (0.3739) loss_x 0.1935 (0.3639) loss_u 0.0445 (0.0100) acc_x 93.7500 (87.3267) lr 1.855400e-04 -epoch [16/25][1200/4762] time 0.168 (0.155) data 0.001 (0.001) eta 1:59:49 loss 0.3184 (0.3737) loss_x 0.3170 (0.3636) loss_u 0.0014 (0.0101) acc_x 90.6250 (87.3490) lr 1.855400e-04 -epoch [16/25][1300/4762] time 0.138 (0.155) data 0.001 (0.001) eta 1:59:36 loss 0.4963 (0.3741) loss_x 0.4784 (0.3640) loss_u 0.0179 (0.0101) acc_x 78.1250 (87.3101) lr 1.855400e-04 -epoch [16/25][1400/4762] time 0.182 (0.155) data 0.001 (0.001) eta 1:59:29 loss 0.3737 (0.3735) loss_x 0.3733 (0.3633) loss_u 0.0005 (0.0101) acc_x 78.1250 (87.3058) lr 1.855400e-04 -epoch [16/25][1500/4762] time 0.164 (0.155) data 0.000 (0.001) eta 1:59:20 loss 0.3882 (0.3737) loss_x 0.3872 (0.3637) loss_u 0.0010 (0.0100) acc_x 90.6250 (87.3354) lr 1.855400e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,698 -* accuracy: 84.31% -* error: 15.69% -* macro_f1: 84.48% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,582 acc: 98.24% -* class: 1 (bicycle) total: 3,475 correct: 2,947 acc: 84.81% -* class: 2 (bus) total: 4,690 correct: 4,274 acc: 91.13% -* class: 3 (car) total: 10,401 correct: 7,653 acc: 73.58% -* class: 4 (horse) total: 4,691 correct: 4,576 acc: 97.55% -* class: 5 (knife) total: 2,075 correct: 1,821 acc: 87.76% -* class: 6 (motorcycle) total: 5,796 correct: 5,505 acc: 94.98% -* class: 7 (person) total: 4,000 correct: 3,029 acc: 75.72% -* class: 8 (plant) total: 4,549 correct: 3,945 acc: 86.72% -* class: 9 (skateboard) total: 2,281 correct: 2,078 acc: 91.10% -* class: 10 (train) total: 4,236 correct: 3,920 acc: 92.54% -* class: 11 (truck) total: 5,548 correct: 3,368 acc: 60.71% -* average: 86.24% -epoch [16/25][1600/4762] time 0.170 (0.155) data 0.001 (0.001) eta 1:59:06 loss 0.1354 (0.3735) loss_x 0.1338 (0.3634) loss_u 0.0016 (0.0101) acc_x 93.7500 (87.3223) lr 1.855400e-04 -epoch [16/25][1700/4762] time 0.163 (0.155) data 0.001 (0.001) eta 1:58:37 loss 0.2654 (0.3732) loss_x 0.2605 (0.3631) loss_u 0.0048 (0.0101) acc_x 84.3750 (87.3364) lr 1.855400e-04 -epoch [16/25][1800/4762] time 0.138 (0.155) data 0.001 (0.001) eta 1:58:18 loss 0.7268 (0.3742) loss_x 0.7237 (0.3640) loss_u 0.0031 (0.0101) acc_x 75.0000 (87.2882) lr 1.855400e-04 -epoch [16/25][1900/4762] time 0.172 (0.155) data 0.001 (0.001) eta 1:57:44 loss 0.4070 (0.3735) loss_x 0.3628 (0.3634) loss_u 0.0443 (0.0101) acc_x 84.3750 (87.2944) lr 1.855400e-04 -epoch [16/25][2000/4762] time 0.139 (0.154) data 0.001 (0.001) eta 1:57:17 loss 0.1211 (0.3741) loss_x 0.1077 (0.3640) loss_u 0.0134 (0.0102) acc_x 100.0000 (87.2797) lr 1.855400e-04 -epoch [16/25][2100/4762] time 0.173 (0.154) data 0.001 (0.001) eta 1:56:47 loss 0.4994 (0.3745) loss_x 0.4784 (0.3643) loss_u 0.0210 (0.0102) acc_x 84.3750 (87.2693) lr 1.855400e-04 -epoch [16/25][2200/4762] time 0.179 (0.154) data 0.002 (0.001) eta 1:56:29 loss 0.4644 (0.3746) loss_x 0.4597 (0.3644) loss_u 0.0048 (0.0102) acc_x 81.2500 (87.2429) lr 1.855400e-04 -epoch [16/25][2300/4762] time 0.158 (0.154) data 0.001 (0.001) eta 1:56:18 loss 0.5191 (0.3740) loss_x 0.5178 (0.3639) loss_u 0.0013 (0.0101) acc_x 81.2500 (87.2323) lr 1.855400e-04 -epoch [16/25][2400/4762] time 0.147 (0.154) data 0.001 (0.001) eta 1:56:03 loss 0.4518 (0.3730) loss_x 0.4332 (0.3629) loss_u 0.0186 (0.0101) acc_x 84.3750 (87.2734) lr 1.855400e-04 -epoch [16/25][2500/4762] time 0.137 (0.154) data 0.001 (0.001) eta 1:55:52 loss 0.4152 (0.3735) loss_x 0.4088 (0.3635) loss_u 0.0065 (0.0100) acc_x 84.3750 (87.2750) lr 1.855400e-04 -epoch [16/25][2600/4762] time 0.149 (0.154) data 0.001 (0.001) eta 1:55:40 loss 0.4111 (0.3736) loss_x 0.4071 (0.3636) loss_u 0.0039 (0.0101) acc_x 84.3750 (87.2716) lr 1.855400e-04 -epoch [16/25][2700/4762] time 0.152 (0.154) data 0.001 (0.001) eta 1:55:24 loss 0.4423 (0.3735) loss_x 0.4306 (0.3634) loss_u 0.0116 (0.0101) acc_x 81.2500 (87.2801) lr 1.855400e-04 -epoch [16/25][2800/4762] time 0.138 (0.154) data 0.001 (0.001) eta 1:55:05 loss 0.5317 (0.3737) loss_x 0.5266 (0.3636) loss_u 0.0051 (0.0100) acc_x 84.3750 (87.2533) lr 1.855400e-04 -epoch [16/25][2900/4762] time 0.148 (0.154) data 0.001 (0.001) eta 1:54:51 loss 0.2172 (0.3735) loss_x 0.2162 (0.3635) loss_u 0.0010 (0.0100) acc_x 93.7500 (87.2349) lr 1.855400e-04 -epoch [16/25][3000/4762] time 0.181 (0.154) data 0.001 (0.001) eta 1:54:43 loss 0.2011 (0.3731) loss_x 0.2007 (0.3632) loss_u 0.0004 (0.0100) acc_x 90.6250 (87.2500) lr 1.855400e-04 -epoch [16/25][3100/4762] time 0.169 (0.155) data 0.001 (0.001) eta 1:54:38 loss 0.4296 (0.3728) loss_x 0.4276 (0.3628) loss_u 0.0020 (0.0100) acc_x 81.2500 (87.2339) lr 1.855400e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,815 -* accuracy: 84.52% -* error: 15.48% -* macro_f1: 84.70% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,585 acc: 98.33% -* class: 1 (bicycle) total: 3,475 correct: 2,883 acc: 82.96% -* class: 2 (bus) total: 4,690 correct: 4,283 acc: 91.32% -* class: 3 (car) total: 10,401 correct: 7,908 acc: 76.03% -* class: 4 (horse) total: 4,691 correct: 4,585 acc: 97.74% -* class: 5 (knife) total: 2,075 correct: 1,914 acc: 92.24% -* class: 6 (motorcycle) total: 5,796 correct: 5,506 acc: 95.00% -* class: 7 (person) total: 4,000 correct: 2,986 acc: 74.65% -* class: 8 (plant) total: 4,549 correct: 3,885 acc: 85.40% -* class: 9 (skateboard) total: 2,281 correct: 2,027 acc: 88.86% -* class: 10 (train) total: 4,236 correct: 3,909 acc: 92.28% -* class: 11 (truck) total: 5,548 correct: 3,344 acc: 60.27% -* average: 86.26% -epoch [16/25][3200/4762] time 0.163 (0.154) data 0.001 (0.001) eta 1:54:21 loss 0.1985 (0.3726) loss_x 0.1970 (0.3627) loss_u 0.0015 (0.0099) acc_x 90.6250 (87.2402) lr 1.855400e-04 -epoch [16/25][3300/4762] time 0.187 (0.155) data 0.001 (0.001) eta 1:54:09 loss 0.2307 (0.3723) loss_x 0.2293 (0.3624) loss_u 0.0014 (0.0099) acc_x 90.6250 (87.2614) lr 1.855400e-04 -epoch [16/25][3400/4762] time 0.141 (0.155) data 0.001 (0.001) eta 1:53:54 loss 0.1855 (0.3723) loss_x 0.1841 (0.3624) loss_u 0.0015 (0.0099) acc_x 96.8750 (87.2776) lr 1.855400e-04 -epoch [16/25][3500/4762] time 0.143 (0.155) data 0.001 (0.001) eta 1:53:42 loss 0.4152 (0.3728) loss_x 0.4103 (0.3628) loss_u 0.0049 (0.0100) acc_x 90.6250 (87.2571) lr 1.855400e-04 -epoch [16/25][3600/4762] time 0.160 (0.155) data 0.001 (0.001) eta 1:53:26 loss 0.3482 (0.3729) loss_x 0.3398 (0.3629) loss_u 0.0084 (0.0100) acc_x 90.6250 (87.2569) lr 1.855400e-04 -epoch [16/25][3700/4762] time 0.145 (0.155) data 0.001 (0.001) eta 1:53:09 loss 0.3910 (0.3728) loss_x 0.3874 (0.3628) loss_u 0.0036 (0.0100) acc_x 84.3750 (87.2449) lr 1.855400e-04 -epoch [16/25][3800/4762] time 0.148 (0.155) data 0.001 (0.001) eta 1:52:57 loss 0.5364 (0.3725) loss_x 0.4797 (0.3625) loss_u 0.0567 (0.0100) acc_x 84.3750 (87.2664) lr 1.855400e-04 -epoch [16/25][3900/4762] time 0.144 (0.155) data 0.001 (0.001) eta 1:52:47 loss 0.1238 (0.3718) loss_x 0.1195 (0.3619) loss_u 0.0043 (0.0099) acc_x 93.7500 (87.2989) lr 1.855400e-04 -epoch [16/25][4000/4762] time 0.181 (0.155) data 0.001 (0.001) eta 1:52:43 loss 0.4109 (0.3717) loss_x 0.3456 (0.3618) loss_u 0.0653 (0.0099) acc_x 90.6250 (87.2914) lr 1.855400e-04 -epoch [16/25][4100/4762] time 0.145 (0.155) data 0.001 (0.001) eta 1:52:38 loss 0.6872 (0.3718) loss_x 0.6777 (0.3620) loss_u 0.0094 (0.0099) acc_x 78.1250 (87.2889) lr 1.855400e-04 -epoch [16/25][4200/4762] time 0.149 (0.155) data 0.001 (0.001) eta 1:52:27 loss 0.3083 (0.3715) loss_x 0.3058 (0.3616) loss_u 0.0025 (0.0099) acc_x 90.6250 (87.2991) lr 1.855400e-04 -epoch [16/25][4300/4762] time 0.147 (0.156) data 0.001 (0.001) eta 1:52:19 loss 0.3187 (0.3716) loss_x 0.3024 (0.3617) loss_u 0.0163 (0.0099) acc_x 90.6250 (87.2980) lr 1.855400e-04 -epoch [16/25][4400/4762] time 0.166 (0.155) data 0.001 (0.001) eta 1:51:59 loss 0.3079 (0.3715) loss_x 0.3042 (0.3616) loss_u 0.0037 (0.0099) acc_x 90.6250 (87.3004) lr 1.855400e-04 -epoch [16/25][4500/4762] time 0.163 (0.155) data 0.001 (0.001) eta 1:51:44 loss 0.5888 (0.3717) loss_x 0.5870 (0.3618) loss_u 0.0018 (0.0099) acc_x 75.0000 (87.2958) lr 1.855400e-04 -epoch [16/25][4600/4762] time 0.172 (0.156) data 0.001 (0.001) eta 1:51:38 loss 0.4124 (0.3718) loss_x 0.4040 (0.3619) loss_u 0.0085 (0.0098) acc_x 81.2500 (87.3057) lr 1.855400e-04 -epoch [16/25][4700/4762] time 0.159 (0.156) data 0.001 (0.001) eta 1:51:24 loss 0.3584 (0.3719) loss_x 0.3004 (0.3621) loss_u 0.0579 (0.0098) acc_x 84.3750 (87.2859) lr 1.855400e-04 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,868 -* accuracy: 84.62% -* error: 15.38% -* macro_f1: 84.64% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,583 acc: 98.27% -* class: 1 (bicycle) total: 3,475 correct: 2,934 acc: 84.43% -* class: 2 (bus) total: 4,690 correct: 4,236 acc: 90.32% -* class: 3 (car) total: 10,401 correct: 7,899 acc: 75.94% -* class: 4 (horse) total: 4,691 correct: 4,575 acc: 97.53% -* class: 5 (knife) total: 2,075 correct: 1,888 acc: 90.99% -* class: 6 (motorcycle) total: 5,796 correct: 5,529 acc: 95.39% -* class: 7 (person) total: 4,000 correct: 2,921 acc: 73.03% -* class: 8 (plant) total: 4,549 correct: 3,913 acc: 86.02% -* class: 9 (skateboard) total: 2,281 correct: 2,043 acc: 89.57% -* class: 10 (train) total: 4,236 correct: 3,947 acc: 93.18% -* class: 11 (truck) total: 5,548 correct: 3,400 acc: 61.28% -* average: 86.33% -epoch [17/25][100/4762] time 0.140 (0.155) data 0.001 (0.003) eta 1:50:40 loss 0.4387 (0.3859) loss_x 0.4317 (0.3741) loss_u 0.0070 (0.0118) acc_x 81.2500 (86.5938) lr 2.138669e-03 -epoch [17/25][200/4762] time 0.171 (0.154) data 0.001 (0.002) eta 1:49:37 loss 0.5689 (0.3777) loss_x 0.5440 (0.3676) loss_u 0.0250 (0.0100) acc_x 78.1250 (86.7656) lr 2.138669e-03 -epoch [17/25][300/4762] time 0.171 (0.153) data 0.006 (0.002) eta 1:48:50 loss 0.4559 (0.3649) loss_x 0.4416 (0.3552) loss_u 0.0143 (0.0097) acc_x 87.5000 (87.2812) lr 2.138669e-03 -epoch [17/25][400/4762] time 0.174 (0.154) data 0.001 (0.002) eta 1:48:39 loss 0.2353 (0.3715) loss_x 0.2321 (0.3622) loss_u 0.0032 (0.0093) acc_x 93.7500 (87.0938) lr 2.138669e-03 -epoch [17/25][500/4762] time 0.160 (0.153) data 0.001 (0.001) eta 1:48:21 loss 0.3260 (0.3755) loss_x 0.3055 (0.3665) loss_u 0.0205 (0.0089) acc_x 87.5000 (86.9313) lr 2.138669e-03 -epoch [17/25][600/4762] time 0.151 (0.154) data 0.001 (0.001) eta 1:48:08 loss 0.2653 (0.3726) loss_x 0.2532 (0.3635) loss_u 0.0122 (0.0090) acc_x 87.5000 (87.2188) lr 2.138669e-03 -epoch [17/25][700/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:48:09 loss 0.3301 (0.3698) loss_x 0.3239 (0.3606) loss_u 0.0063 (0.0091) acc_x 84.3750 (87.3348) lr 2.138669e-03 -epoch [17/25][800/4762] time 0.141 (0.154) data 0.001 (0.001) eta 1:47:54 loss 0.7600 (0.3673) loss_x 0.7270 (0.3581) loss_u 0.0330 (0.0091) acc_x 75.0000 (87.4141) lr 2.138669e-03 -epoch [17/25][900/4762] time 0.174 (0.154) data 0.001 (0.001) eta 1:47:56 loss 0.4324 (0.3662) loss_x 0.4171 (0.3568) loss_u 0.0152 (0.0094) acc_x 87.5000 (87.4479) lr 2.138669e-03 -epoch [17/25][1000/4762] time 0.169 (0.154) data 0.001 (0.001) eta 1:47:41 loss 0.2339 (0.3647) loss_x 0.2103 (0.3552) loss_u 0.0237 (0.0095) acc_x 93.7500 (87.5031) lr 2.138669e-03 -epoch [17/25][1100/4762] time 0.162 (0.154) data 0.001 (0.001) eta 1:47:30 loss 0.2111 (0.3674) loss_x 0.1980 (0.3579) loss_u 0.0131 (0.0095) acc_x 93.7500 (87.3494) lr 2.138669e-03 -epoch [17/25][1200/4762] time 0.141 (0.155) data 0.001 (0.001) eta 1:47:17 loss 0.6001 (0.3686) loss_x 0.5945 (0.3590) loss_u 0.0056 (0.0096) acc_x 75.0000 (87.3099) lr 2.138669e-03 -epoch [17/25][1300/4762] time 0.171 (0.154) data 0.001 (0.001) eta 1:46:50 loss 0.6117 (0.3699) loss_x 0.6080 (0.3603) loss_u 0.0037 (0.0096) acc_x 75.0000 (87.3149) lr 2.138669e-03 -epoch [17/25][1400/4762] time 0.166 (0.154) data 0.001 (0.001) eta 1:46:30 loss 0.2190 (0.3698) loss_x 0.1921 (0.3602) loss_u 0.0269 (0.0095) acc_x 87.5000 (87.3348) lr 2.138669e-03 -epoch [17/25][1500/4762] time 0.137 (0.154) data 0.001 (0.001) eta 1:46:24 loss 0.2397 (0.3698) loss_x 0.2337 (0.3603) loss_u 0.0059 (0.0095) acc_x 87.5000 (87.3125) lr 2.138669e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,925 -* accuracy: 84.72% -* error: 15.28% -* macro_f1: 84.92% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,584 acc: 98.30% -* class: 1 (bicycle) total: 3,475 correct: 2,936 acc: 84.49% -* class: 2 (bus) total: 4,690 correct: 4,262 acc: 90.87% -* class: 3 (car) total: 10,401 correct: 7,827 acc: 75.25% -* class: 4 (horse) total: 4,691 correct: 4,579 acc: 97.61% -* class: 5 (knife) total: 2,075 correct: 1,863 acc: 89.78% -* class: 6 (motorcycle) total: 5,796 correct: 5,520 acc: 95.24% -* class: 7 (person) total: 4,000 correct: 3,014 acc: 75.35% -* class: 8 (plant) total: 4,549 correct: 3,940 acc: 86.61% -* class: 9 (skateboard) total: 2,281 correct: 2,063 acc: 90.44% -* class: 10 (train) total: 4,236 correct: 3,944 acc: 93.11% -* class: 11 (truck) total: 5,548 correct: 3,393 acc: 61.16% -* average: 86.52% -epoch [17/25][1600/4762] time 0.136 (0.154) data 0.000 (0.001) eta 1:46:02 loss 0.6813 (0.3716) loss_x 0.6780 (0.3622) loss_u 0.0033 (0.0093) acc_x 78.1250 (87.2363) lr 2.138669e-03 -epoch [17/25][1700/4762] time 0.161 (0.154) data 0.001 (0.001) eta 1:45:33 loss 0.3342 (0.3712) loss_x 0.3282 (0.3619) loss_u 0.0060 (0.0093) acc_x 84.3750 (87.2647) lr 2.138669e-03 -epoch [17/25][1800/4762] time 0.138 (0.154) data 0.001 (0.001) eta 1:45:24 loss 0.2858 (0.3716) loss_x 0.2787 (0.3623) loss_u 0.0071 (0.0093) acc_x 90.6250 (87.2309) lr 2.138669e-03 -epoch [17/25][1900/4762] time 0.142 (0.154) data 0.001 (0.001) eta 1:45:09 loss 0.3711 (0.3722) loss_x 0.3679 (0.3629) loss_u 0.0032 (0.0093) acc_x 84.3750 (87.1809) lr 2.138669e-03 -epoch [17/25][2000/4762] time 0.154 (0.154) data 0.001 (0.001) eta 1:44:47 loss 0.4138 (0.3723) loss_x 0.3920 (0.3630) loss_u 0.0219 (0.0092) acc_x 87.5000 (87.1891) lr 2.138669e-03 -epoch [17/25][2100/4762] time 0.172 (0.154) data 0.001 (0.001) eta 1:44:28 loss 0.3720 (0.3724) loss_x 0.3618 (0.3631) loss_u 0.0102 (0.0093) acc_x 84.3750 (87.1786) lr 2.138669e-03 -epoch [17/25][2200/4762] time 0.139 (0.154) data 0.001 (0.001) eta 1:44:17 loss 0.1576 (0.3718) loss_x 0.1291 (0.3625) loss_u 0.0285 (0.0093) acc_x 96.8750 (87.2017) lr 2.138669e-03 -epoch [17/25][2300/4762] time 0.145 (0.154) data 0.001 (0.001) eta 1:44:01 loss 0.2116 (0.3719) loss_x 0.2107 (0.3626) loss_u 0.0009 (0.0093) acc_x 96.8750 (87.1821) lr 2.138669e-03 -epoch [17/25][2400/4762] time 0.159 (0.154) data 0.004 (0.001) eta 1:43:55 loss 0.1336 (0.3728) loss_x 0.1303 (0.3635) loss_u 0.0033 (0.0093) acc_x 96.8750 (87.1484) lr 2.138669e-03 -epoch [17/25][2500/4762] time 0.179 (0.154) data 0.001 (0.001) eta 1:43:46 loss 0.2232 (0.3728) loss_x 0.1957 (0.3635) loss_u 0.0276 (0.0093) acc_x 96.8750 (87.1700) lr 2.138669e-03 -epoch [17/25][2600/4762] time 0.139 (0.154) data 0.001 (0.001) eta 1:43:25 loss 0.6333 (0.3728) loss_x 0.5955 (0.3634) loss_u 0.0378 (0.0093) acc_x 78.1250 (87.1755) lr 2.138669e-03 -epoch [17/25][2700/4762] time 0.147 (0.154) data 0.001 (0.001) eta 1:43:07 loss 0.5295 (0.3737) loss_x 0.5216 (0.3642) loss_u 0.0079 (0.0095) acc_x 75.0000 (87.1412) lr 2.138669e-03 -epoch [17/25][2800/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:42:49 loss 0.2368 (0.3741) loss_x 0.1922 (0.3646) loss_u 0.0446 (0.0095) acc_x 90.6250 (87.1083) lr 2.138669e-03 -epoch [17/25][2900/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:42:26 loss 0.2507 (0.3739) loss_x 0.2469 (0.3644) loss_u 0.0038 (0.0095) acc_x 96.8750 (87.1304) lr 2.138669e-03 -epoch [17/25][3000/4762] time 0.140 (0.154) data 0.001 (0.001) eta 1:42:13 loss 0.3861 (0.3742) loss_x 0.3756 (0.3647) loss_u 0.0104 (0.0095) acc_x 90.6250 (87.1094) lr 2.138669e-03 -epoch [17/25][3100/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:42:00 loss 0.3587 (0.3744) loss_x 0.3543 (0.3649) loss_u 0.0043 (0.0095) acc_x 87.5000 (87.0998) lr 2.138669e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,695 -* accuracy: 84.31% -* error: 15.69% -* macro_f1: 84.44% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,591 acc: 98.49% -* class: 1 (bicycle) total: 3,475 correct: 2,914 acc: 83.86% -* class: 2 (bus) total: 4,690 correct: 4,308 acc: 91.86% -* class: 3 (car) total: 10,401 correct: 7,717 acc: 74.19% -* class: 4 (horse) total: 4,691 correct: 4,578 acc: 97.59% -* class: 5 (knife) total: 2,075 correct: 1,847 acc: 89.01% -* class: 6 (motorcycle) total: 5,796 correct: 5,506 acc: 95.00% -* class: 7 (person) total: 4,000 correct: 2,931 acc: 73.28% -* class: 8 (plant) total: 4,549 correct: 3,882 acc: 85.34% -* class: 9 (skateboard) total: 2,281 correct: 2,080 acc: 91.19% -* class: 10 (train) total: 4,236 correct: 3,900 acc: 92.07% -* class: 11 (truck) total: 5,548 correct: 3,441 acc: 62.02% -* average: 86.16% -epoch [17/25][3200/4762] time 0.164 (0.154) data 0.001 (0.001) eta 1:41:51 loss 0.2427 (0.3740) loss_x 0.2408 (0.3645) loss_u 0.0018 (0.0095) acc_x 90.6250 (87.1104) lr 2.138669e-03 -epoch [17/25][3300/4762] time 0.143 (0.154) data 0.001 (0.001) eta 1:41:31 loss 0.2161 (0.3745) loss_x 0.2112 (0.3649) loss_u 0.0050 (0.0096) acc_x 87.5000 (87.0956) lr 2.138669e-03 -epoch [17/25][3400/4762] time 0.137 (0.154) data 0.001 (0.001) eta 1:41:09 loss 0.3076 (0.3741) loss_x 0.3037 (0.3645) loss_u 0.0039 (0.0096) acc_x 87.5000 (87.1029) lr 2.138669e-03 -epoch [17/25][3500/4762] time 0.149 (0.154) data 0.001 (0.001) eta 1:40:54 loss 0.3728 (0.3737) loss_x 0.3656 (0.3640) loss_u 0.0072 (0.0096) acc_x 87.5000 (87.1205) lr 2.138669e-03 -epoch [17/25][3600/4762] time 0.166 (0.154) data 0.001 (0.001) eta 1:40:37 loss 0.5596 (0.3741) loss_x 0.5479 (0.3645) loss_u 0.0117 (0.0096) acc_x 78.1250 (87.1024) lr 2.138669e-03 -epoch [17/25][3700/4762] time 0.182 (0.154) data 0.001 (0.001) eta 1:40:21 loss 0.3010 (0.3736) loss_x 0.3005 (0.3639) loss_u 0.0005 (0.0097) acc_x 87.5000 (87.1275) lr 2.138669e-03 -epoch [17/25][3800/4762] time 0.155 (0.154) data 0.001 (0.001) eta 1:40:05 loss 0.4579 (0.3734) loss_x 0.4421 (0.3637) loss_u 0.0158 (0.0097) acc_x 78.1250 (87.1373) lr 2.138669e-03 -epoch [17/25][3900/4762] time 0.145 (0.154) data 0.000 (0.001) eta 1:39:47 loss 0.4276 (0.3729) loss_x 0.4150 (0.3632) loss_u 0.0126 (0.0097) acc_x 81.2500 (87.1611) lr 2.138669e-03 -epoch [17/25][4000/4762] time 0.136 (0.154) data 0.001 (0.001) eta 1:39:30 loss 0.3935 (0.3732) loss_x 0.3843 (0.3635) loss_u 0.0091 (0.0097) acc_x 87.5000 (87.1570) lr 2.138669e-03 -epoch [17/25][4100/4762] time 0.157 (0.154) data 0.001 (0.001) eta 1:39:17 loss 0.4936 (0.3735) loss_x 0.4759 (0.3638) loss_u 0.0177 (0.0097) acc_x 87.5000 (87.1448) lr 2.138669e-03 -epoch [17/25][4200/4762] time 0.161 (0.154) data 0.001 (0.001) eta 1:39:02 loss 0.4381 (0.3735) loss_x 0.4353 (0.3638) loss_u 0.0028 (0.0097) acc_x 84.3750 (87.1362) lr 2.138669e-03 -epoch [17/25][4300/4762] time 0.156 (0.154) data 0.001 (0.001) eta 1:38:49 loss 0.5233 (0.3733) loss_x 0.5040 (0.3635) loss_u 0.0194 (0.0098) acc_x 84.3750 (87.1453) lr 2.138669e-03 -epoch [17/25][4400/4762] time 0.141 (0.154) data 0.001 (0.001) eta 1:38:33 loss 0.3552 (0.3737) loss_x 0.3466 (0.3640) loss_u 0.0086 (0.0098) acc_x 87.5000 (87.1243) lr 2.138669e-03 -epoch [17/25][4500/4762] time 0.148 (0.154) data 0.001 (0.001) eta 1:38:18 loss 0.3774 (0.3739) loss_x 0.3742 (0.3641) loss_u 0.0031 (0.0098) acc_x 78.1250 (87.1160) lr 2.138669e-03 -epoch [17/25][4600/4762] time 0.177 (0.154) data 0.000 (0.001) eta 1:38:08 loss 1.0307 (0.3740) loss_x 0.9121 (0.3642) loss_u 0.1186 (0.0098) acc_x 68.7500 (87.1121) lr 2.138669e-03 -epoch [17/25][4700/4762] time 0.150 (0.154) data 0.001 (0.001) eta 1:37:57 loss 0.1811 (0.3738) loss_x 0.1795 (0.3640) loss_u 0.0016 (0.0098) acc_x 87.5000 (87.1330) lr 2.138669e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,624 -* accuracy: 84.18% -* error: 15.82% -* macro_f1: 84.32% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,590 acc: 98.46% -* class: 1 (bicycle) total: 3,475 correct: 2,922 acc: 84.09% -* class: 2 (bus) total: 4,690 correct: 4,272 acc: 91.09% -* class: 3 (car) total: 10,401 correct: 7,768 acc: 74.69% -* class: 4 (horse) total: 4,691 correct: 4,588 acc: 97.80% -* class: 5 (knife) total: 2,075 correct: 1,883 acc: 90.75% -* class: 6 (motorcycle) total: 5,796 correct: 5,525 acc: 95.32% -* class: 7 (person) total: 4,000 correct: 2,926 acc: 73.15% -* class: 8 (plant) total: 4,549 correct: 3,805 acc: 83.64% -* class: 9 (skateboard) total: 2,281 correct: 2,051 acc: 89.92% -* class: 10 (train) total: 4,236 correct: 3,909 acc: 92.28% -* class: 11 (truck) total: 5,548 correct: 3,385 acc: 61.01% -* average: 86.02% -epoch [18/25][100/4762] time 0.143 (0.158) data 0.001 (0.003) eta 1:39:45 loss 0.3457 (0.3692) loss_x 0.3340 (0.3609) loss_u 0.0117 (0.0083) acc_x 87.5000 (87.5312) lr 2.894665e-03 -epoch [18/25][200/4762] time 0.137 (0.156) data 0.001 (0.002) eta 1:38:21 loss 0.3815 (0.3778) loss_x 0.3757 (0.3682) loss_u 0.0058 (0.0096) acc_x 87.5000 (86.8906) lr 2.894665e-03 -epoch [18/25][300/4762] time 0.152 (0.154) data 0.001 (0.002) eta 1:37:00 loss 0.6685 (0.3816) loss_x 0.6640 (0.3721) loss_u 0.0045 (0.0095) acc_x 78.1250 (86.6354) lr 2.894665e-03 -epoch [18/25][400/4762] time 0.144 (0.153) data 0.001 (0.001) eta 1:36:11 loss 0.4484 (0.3858) loss_x 0.4432 (0.3763) loss_u 0.0052 (0.0096) acc_x 84.3750 (86.3906) lr 2.894665e-03 -epoch [18/25][500/4762] time 0.139 (0.153) data 0.001 (0.001) eta 1:35:51 loss 0.3438 (0.3848) loss_x 0.3377 (0.3749) loss_u 0.0060 (0.0099) acc_x 81.2500 (86.5438) lr 2.894665e-03 -epoch [18/25][600/4762] time 0.145 (0.153) data 0.001 (0.001) eta 1:35:36 loss 0.3684 (0.3847) loss_x 0.3662 (0.3749) loss_u 0.0022 (0.0098) acc_x 84.3750 (86.5521) lr 2.894665e-03 -epoch [18/25][700/4762] time 0.153 (0.153) data 0.000 (0.001) eta 1:35:32 loss 0.1791 (0.3825) loss_x 0.1737 (0.3723) loss_u 0.0054 (0.0102) acc_x 96.8750 (86.7500) lr 2.894665e-03 -epoch [18/25][800/4762] time 0.145 (0.153) data 0.001 (0.001) eta 1:35:13 loss 0.3600 (0.3777) loss_x 0.3551 (0.3676) loss_u 0.0049 (0.0101) acc_x 87.5000 (86.8906) lr 2.894665e-03 -epoch [18/25][900/4762] time 0.149 (0.153) data 0.001 (0.001) eta 1:35:08 loss 0.4249 (0.3740) loss_x 0.4225 (0.3640) loss_u 0.0024 (0.0100) acc_x 84.3750 (86.9618) lr 2.894665e-03 -epoch [18/25][1000/4762] time 0.152 (0.153) data 0.001 (0.001) eta 1:34:47 loss 0.3959 (0.3747) loss_x 0.3721 (0.3649) loss_u 0.0239 (0.0099) acc_x 81.2500 (86.9594) lr 2.894665e-03 -epoch [18/25][1100/4762] time 0.145 (0.154) data 0.001 (0.001) eta 1:34:46 loss 0.3126 (0.3724) loss_x 0.3087 (0.3626) loss_u 0.0039 (0.0099) acc_x 87.5000 (87.0483) lr 2.894665e-03 -epoch [18/25][1200/4762] time 0.148 (0.154) data 0.001 (0.001) eta 1:34:39 loss 0.1976 (0.3721) loss_x 0.1909 (0.3623) loss_u 0.0067 (0.0097) acc_x 90.6250 (87.1224) lr 2.894665e-03 -epoch [18/25][1300/4762] time 0.163 (0.154) data 0.001 (0.001) eta 1:34:23 loss 0.3107 (0.3720) loss_x 0.3099 (0.3623) loss_u 0.0008 (0.0097) acc_x 87.5000 (87.0962) lr 2.894665e-03 -epoch [18/25][1400/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:34:08 loss 0.1557 (0.3721) loss_x 0.1537 (0.3624) loss_u 0.0021 (0.0097) acc_x 96.8750 (87.0960) lr 2.894665e-03 -epoch [18/25][1500/4762] time 0.154 (0.154) data 0.001 (0.001) eta 1:34:05 loss 0.3921 (0.3735) loss_x 0.3765 (0.3637) loss_u 0.0156 (0.0098) acc_x 87.5000 (87.0625) lr 2.894665e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,455 -* accuracy: 83.87% -* error: 16.13% -* macro_f1: 83.91% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,579 acc: 98.16% -* class: 1 (bicycle) total: 3,475 correct: 2,940 acc: 84.60% -* class: 2 (bus) total: 4,690 correct: 4,260 acc: 90.83% -* class: 3 (car) total: 10,401 correct: 7,674 acc: 73.78% -* class: 4 (horse) total: 4,691 correct: 4,595 acc: 97.95% -* class: 5 (knife) total: 2,075 correct: 1,896 acc: 91.37% -* class: 6 (motorcycle) total: 5,796 correct: 5,543 acc: 95.63% -* class: 7 (person) total: 4,000 correct: 2,663 acc: 66.58% -* class: 8 (plant) total: 4,549 correct: 3,886 acc: 85.43% -* class: 9 (skateboard) total: 2,281 correct: 2,055 acc: 90.09% -* class: 10 (train) total: 4,236 correct: 3,963 acc: 93.56% -* class: 11 (truck) total: 5,548 correct: 3,401 acc: 61.30% -* average: 85.77% -epoch [18/25][1600/4762] time 0.160 (0.154) data 0.001 (0.001) eta 1:33:54 loss 0.0930 (0.3738) loss_x 0.0797 (0.3640) loss_u 0.0133 (0.0098) acc_x 100.0000 (87.0742) lr 2.894665e-03 -epoch [18/25][1700/4762] time 0.185 (0.154) data 0.001 (0.001) eta 1:33:38 loss 0.1910 (0.3746) loss_x 0.1881 (0.3646) loss_u 0.0028 (0.0100) acc_x 93.7500 (87.0478) lr 2.894665e-03 -epoch [18/25][1800/4762] time 0.137 (0.155) data 0.001 (0.001) eta 1:33:29 loss 0.4256 (0.3737) loss_x 0.4139 (0.3637) loss_u 0.0117 (0.0100) acc_x 84.3750 (87.0764) lr 2.894665e-03 -epoch [18/25][1900/4762] time 0.140 (0.154) data 0.001 (0.001) eta 1:33:08 loss 0.3529 (0.3736) loss_x 0.3513 (0.3636) loss_u 0.0016 (0.0100) acc_x 90.6250 (87.0674) lr 2.894665e-03 -epoch [18/25][2000/4762] time 0.145 (0.154) data 0.001 (0.001) eta 1:32:47 loss 0.2340 (0.3722) loss_x 0.2119 (0.3623) loss_u 0.0221 (0.0099) acc_x 90.6250 (87.1219) lr 2.894665e-03 -epoch [18/25][2100/4762] time 0.143 (0.154) data 0.001 (0.001) eta 1:32:27 loss 0.4073 (0.3719) loss_x 0.4026 (0.3620) loss_u 0.0047 (0.0099) acc_x 81.2500 (87.1473) lr 2.894665e-03 -epoch [18/25][2200/4762] time 0.150 (0.154) data 0.001 (0.001) eta 1:32:07 loss 0.7093 (0.3720) loss_x 0.7077 (0.3621) loss_u 0.0016 (0.0099) acc_x 71.8750 (87.1506) lr 2.894665e-03 -epoch [18/25][2300/4762] time 0.157 (0.154) data 0.001 (0.001) eta 1:31:48 loss 0.3941 (0.3720) loss_x 0.3869 (0.3622) loss_u 0.0072 (0.0098) acc_x 84.3750 (87.1549) lr 2.894665e-03 -epoch [18/25][2400/4762] time 0.139 (0.154) data 0.001 (0.001) eta 1:31:31 loss 0.3998 (0.3727) loss_x 0.3971 (0.3629) loss_u 0.0027 (0.0098) acc_x 81.2500 (87.1302) lr 2.894665e-03 -epoch [18/25][2500/4762] time 0.148 (0.154) data 0.001 (0.001) eta 1:31:15 loss 0.6106 (0.3721) loss_x 0.5938 (0.3622) loss_u 0.0168 (0.0098) acc_x 87.5000 (87.1675) lr 2.894665e-03 -epoch [18/25][2600/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:31:01 loss 0.5945 (0.3719) loss_x 0.5892 (0.3621) loss_u 0.0053 (0.0098) acc_x 75.0000 (87.1743) lr 2.894665e-03 -epoch [18/25][2700/4762] time 0.154 (0.154) data 0.002 (0.001) eta 1:30:54 loss 0.6995 (0.3724) loss_x 0.6847 (0.3626) loss_u 0.0148 (0.0098) acc_x 78.1250 (87.1644) lr 2.894665e-03 -epoch [18/25][2800/4762] time 0.192 (0.154) data 0.001 (0.001) eta 1:30:42 loss 0.3798 (0.3720) loss_x 0.3771 (0.3623) loss_u 0.0027 (0.0098) acc_x 90.6250 (87.1853) lr 2.894665e-03 -epoch [18/25][2900/4762] time 0.152 (0.154) data 0.001 (0.001) eta 1:30:29 loss 0.4174 (0.3723) loss_x 0.3989 (0.3625) loss_u 0.0186 (0.0098) acc_x 84.3750 (87.1810) lr 2.894665e-03 -epoch [18/25][3000/4762] time 0.139 (0.154) data 0.001 (0.001) eta 1:30:16 loss 0.4045 (0.3731) loss_x 0.4012 (0.3633) loss_u 0.0033 (0.0098) acc_x 84.3750 (87.1531) lr 2.894665e-03 -epoch [18/25][3100/4762] time 0.168 (0.155) data 0.001 (0.001) eta 1:30:08 loss 0.3544 (0.3728) loss_x 0.3495 (0.3630) loss_u 0.0049 (0.0098) acc_x 90.6250 (87.1613) lr 2.894665e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,844 -* accuracy: 84.57% -* error: 15.43% -* macro_f1: 84.82% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,578 acc: 98.13% -* class: 1 (bicycle) total: 3,475 correct: 2,959 acc: 85.15% -* class: 2 (bus) total: 4,690 correct: 4,221 acc: 90.00% -* class: 3 (car) total: 10,401 correct: 7,661 acc: 73.66% -* class: 4 (horse) total: 4,691 correct: 4,582 acc: 97.68% -* class: 5 (knife) total: 2,075 correct: 1,894 acc: 91.28% -* class: 6 (motorcycle) total: 5,796 correct: 5,503 acc: 94.94% -* class: 7 (person) total: 4,000 correct: 2,979 acc: 74.47% -* class: 8 (plant) total: 4,549 correct: 3,911 acc: 85.97% -* class: 9 (skateboard) total: 2,281 correct: 2,061 acc: 90.36% -* class: 10 (train) total: 4,236 correct: 3,952 acc: 93.30% -* class: 11 (truck) total: 5,548 correct: 3,543 acc: 63.86% -* average: 86.57% -epoch [18/25][3200/4762] time 0.137 (0.154) data 0.001 (0.001) eta 1:29:51 loss 0.3098 (0.3731) loss_x 0.3079 (0.3634) loss_u 0.0019 (0.0098) acc_x 93.7500 (87.1436) lr 2.894665e-03 -epoch [18/25][3300/4762] time 0.145 (0.154) data 0.001 (0.001) eta 1:29:33 loss 0.2441 (0.3726) loss_x 0.2299 (0.3628) loss_u 0.0142 (0.0098) acc_x 90.6250 (87.1657) lr 2.894665e-03 -epoch [18/25][3400/4762] time 0.147 (0.154) data 0.000 (0.001) eta 1:29:13 loss 0.2469 (0.3728) loss_x 0.2421 (0.3630) loss_u 0.0048 (0.0098) acc_x 90.6250 (87.1480) lr 2.894665e-03 -epoch [18/25][3500/4762] time 0.145 (0.154) data 0.001 (0.001) eta 1:28:59 loss 0.4132 (0.3734) loss_x 0.4069 (0.3637) loss_u 0.0063 (0.0098) acc_x 81.2500 (87.1312) lr 2.894665e-03 -epoch [18/25][3600/4762] time 0.152 (0.154) data 0.001 (0.001) eta 1:28:42 loss 0.0999 (0.3736) loss_x 0.0939 (0.3639) loss_u 0.0060 (0.0097) acc_x 100.0000 (87.1137) lr 2.894665e-03 -epoch [18/25][3700/4762] time 0.141 (0.154) data 0.001 (0.001) eta 1:28:24 loss 0.3949 (0.3739) loss_x 0.3911 (0.3642) loss_u 0.0037 (0.0097) acc_x 84.3750 (87.1022) lr 2.894665e-03 -epoch [18/25][3800/4762] time 0.151 (0.154) data 0.001 (0.001) eta 1:28:07 loss 0.0913 (0.3740) loss_x 0.0798 (0.3643) loss_u 0.0115 (0.0097) acc_x 100.0000 (87.1151) lr 2.894665e-03 -epoch [18/25][3900/4762] time 0.156 (0.154) data 0.001 (0.001) eta 1:27:53 loss 0.3091 (0.3743) loss_x 0.3078 (0.3645) loss_u 0.0013 (0.0097) acc_x 87.5000 (87.1042) lr 2.894665e-03 -epoch [18/25][4000/4762] time 0.180 (0.154) data 0.001 (0.001) eta 1:27:41 loss 0.4154 (0.3743) loss_x 0.4105 (0.3646) loss_u 0.0049 (0.0097) acc_x 84.3750 (87.1172) lr 2.894665e-03 -epoch [18/25][4100/4762] time 0.151 (0.154) data 0.001 (0.001) eta 1:27:25 loss 0.3350 (0.3742) loss_x 0.3217 (0.3645) loss_u 0.0134 (0.0097) acc_x 84.3750 (87.1265) lr 2.894665e-03 -epoch [18/25][4200/4762] time 0.140 (0.154) data 0.001 (0.001) eta 1:27:08 loss 0.5136 (0.3738) loss_x 0.5047 (0.3641) loss_u 0.0089 (0.0097) acc_x 84.3750 (87.1414) lr 2.894665e-03 -epoch [18/25][4300/4762] time 0.181 (0.154) data 0.001 (0.001) eta 1:26:54 loss 0.5091 (0.3737) loss_x 0.4746 (0.3640) loss_u 0.0345 (0.0097) acc_x 84.3750 (87.1446) lr 2.894665e-03 -epoch [18/25][4400/4762] time 0.145 (0.154) data 0.001 (0.001) eta 1:26:38 loss 0.4945 (0.3735) loss_x 0.4823 (0.3639) loss_u 0.0123 (0.0097) acc_x 87.5000 (87.1612) lr 2.894665e-03 -epoch [18/25][4500/4762] time 0.141 (0.154) data 0.001 (0.001) eta 1:26:22 loss 0.2111 (0.3732) loss_x 0.2028 (0.3635) loss_u 0.0083 (0.0096) acc_x 90.6250 (87.1694) lr 2.894665e-03 -epoch [18/25][4600/4762] time 0.142 (0.154) data 0.002 (0.001) eta 1:26:04 loss 0.3421 (0.3733) loss_x 0.3362 (0.3636) loss_u 0.0059 (0.0097) acc_x 87.5000 (87.1692) lr 2.894665e-03 -epoch [18/25][4700/4762] time 0.154 (0.154) data 0.001 (0.001) eta 1:25:49 loss 0.4996 (0.3730) loss_x 0.4971 (0.3634) loss_u 0.0025 (0.0096) acc_x 81.2500 (87.1922) lr 2.894665e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,873 -* accuracy: 84.63% -* error: 15.37% -* macro_f1: 84.82% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,572 acc: 97.97% -* class: 1 (bicycle) total: 3,475 correct: 2,942 acc: 84.66% -* class: 2 (bus) total: 4,690 correct: 4,286 acc: 91.39% -* class: 3 (car) total: 10,401 correct: 7,744 acc: 74.45% -* class: 4 (horse) total: 4,691 correct: 4,570 acc: 97.42% -* class: 5 (knife) total: 2,075 correct: 1,855 acc: 89.40% -* class: 6 (motorcycle) total: 5,796 correct: 5,514 acc: 95.13% -* class: 7 (person) total: 4,000 correct: 2,977 acc: 74.42% -* class: 8 (plant) total: 4,549 correct: 3,973 acc: 87.34% -* class: 9 (skateboard) total: 2,281 correct: 2,067 acc: 90.62% -* class: 10 (train) total: 4,236 correct: 3,878 acc: 91.55% -* class: 11 (truck) total: 5,548 correct: 3,495 acc: 63.00% -* average: 86.45% -epoch [19/25][100/4762] time 0.156 (0.157) data 0.001 (0.003) eta 1:26:45 loss 0.5941 (0.3674) loss_x 0.5829 (0.3570) loss_u 0.0112 (0.0103) acc_x 75.0000 (87.5625) lr 1.036475e-03 -epoch [19/25][200/4762] time 0.145 (0.156) data 0.001 (0.002) eta 1:26:16 loss 0.6807 (0.3621) loss_x 0.6657 (0.3523) loss_u 0.0150 (0.0098) acc_x 78.1250 (87.6094) lr 1.036475e-03 -epoch [19/25][300/4762] time 0.168 (0.155) data 0.001 (0.002) eta 1:25:36 loss 0.3925 (0.3642) loss_x 0.3836 (0.3547) loss_u 0.0089 (0.0095) acc_x 87.5000 (87.6042) lr 1.036475e-03 -epoch [19/25][400/4762] time 0.148 (0.155) data 0.001 (0.001) eta 1:25:16 loss 0.4282 (0.3653) loss_x 0.4231 (0.3560) loss_u 0.0051 (0.0093) acc_x 90.6250 (87.6797) lr 1.036475e-03 -epoch [19/25][500/4762] time 0.147 (0.155) data 0.001 (0.001) eta 1:24:49 loss 0.3076 (0.3665) loss_x 0.3034 (0.3571) loss_u 0.0042 (0.0095) acc_x 90.6250 (87.4813) lr 1.036475e-03 -epoch [19/25][600/4762] time 0.154 (0.155) data 0.001 (0.001) eta 1:24:37 loss 0.3758 (0.3681) loss_x 0.3713 (0.3586) loss_u 0.0045 (0.0095) acc_x 84.3750 (87.4219) lr 1.036475e-03 -epoch [19/25][700/4762] time 0.161 (0.155) data 0.001 (0.001) eta 1:24:27 loss 0.4282 (0.3698) loss_x 0.3794 (0.3602) loss_u 0.0488 (0.0096) acc_x 87.5000 (87.3839) lr 1.036475e-03 -epoch [19/25][800/4762] time 0.185 (0.155) data 0.001 (0.001) eta 1:24:12 loss 0.2489 (0.3679) loss_x 0.2414 (0.3585) loss_u 0.0075 (0.0094) acc_x 93.7500 (87.4375) lr 1.036475e-03 -epoch [19/25][900/4762] time 0.166 (0.155) data 0.001 (0.001) eta 1:23:52 loss 0.4217 (0.3688) loss_x 0.4139 (0.3594) loss_u 0.0078 (0.0093) acc_x 87.5000 (87.4201) lr 1.036475e-03 -epoch [19/25][1000/4762] time 0.183 (0.155) data 0.001 (0.001) eta 1:23:41 loss 0.4827 (0.3685) loss_x 0.4818 (0.3590) loss_u 0.0009 (0.0095) acc_x 75.0000 (87.3937) lr 1.036475e-03 -epoch [19/25][1100/4762] time 0.166 (0.156) data 0.001 (0.001) eta 1:23:41 loss 0.3894 (0.3673) loss_x 0.3729 (0.3578) loss_u 0.0165 (0.0095) acc_x 90.6250 (87.4688) lr 1.036475e-03 -epoch [19/25][1200/4762] time 0.148 (0.156) data 0.001 (0.001) eta 1:23:31 loss 0.6783 (0.3682) loss_x 0.6781 (0.3589) loss_u 0.0002 (0.0093) acc_x 71.8750 (87.4427) lr 1.036475e-03 -epoch [19/25][1300/4762] time 0.184 (0.156) data 0.001 (0.001) eta 1:23:20 loss 0.3368 (0.3676) loss_x 0.3278 (0.3584) loss_u 0.0091 (0.0092) acc_x 90.6250 (87.4207) lr 1.036475e-03 -epoch [19/25][1400/4762] time 0.165 (0.156) data 0.001 (0.001) eta 1:22:55 loss 0.4064 (0.3685) loss_x 0.3950 (0.3590) loss_u 0.0114 (0.0094) acc_x 90.6250 (87.3728) lr 1.036475e-03 -epoch [19/25][1500/4762] time 0.140 (0.156) data 0.001 (0.001) eta 1:22:31 loss 0.6010 (0.3682) loss_x 0.5986 (0.3588) loss_u 0.0024 (0.0095) acc_x 81.2500 (87.3708) lr 1.036475e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,656 -* accuracy: 84.23% -* error: 15.77% -* macro_f1: 84.19% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,584 acc: 98.30% -* class: 1 (bicycle) total: 3,475 correct: 2,917 acc: 83.94% -* class: 2 (bus) total: 4,690 correct: 4,273 acc: 91.11% -* class: 3 (car) total: 10,401 correct: 7,803 acc: 75.02% -* class: 4 (horse) total: 4,691 correct: 4,578 acc: 97.59% -* class: 5 (knife) total: 2,075 correct: 1,903 acc: 91.71% -* class: 6 (motorcycle) total: 5,796 correct: 5,512 acc: 95.10% -* class: 7 (person) total: 4,000 correct: 2,856 acc: 71.40% -* class: 8 (plant) total: 4,549 correct: 3,902 acc: 85.78% -* class: 9 (skateboard) total: 2,281 correct: 2,036 acc: 89.26% -* class: 10 (train) total: 4,236 correct: 3,925 acc: 92.66% -* class: 11 (truck) total: 5,548 correct: 3,367 acc: 60.69% -* average: 86.05% -epoch [19/25][1600/4762] time 0.157 (0.156) data 0.001 (0.001) eta 1:22:23 loss 0.3131 (0.3693) loss_x 0.3072 (0.3597) loss_u 0.0059 (0.0096) acc_x 90.6250 (87.3457) lr 1.036475e-03 -epoch [19/25][1700/4762] time 0.148 (0.156) data 0.001 (0.001) eta 1:22:07 loss 0.4876 (0.3693) loss_x 0.4831 (0.3598) loss_u 0.0045 (0.0095) acc_x 81.2500 (87.3603) lr 1.036475e-03 -epoch [19/25][1800/4762] time 0.147 (0.156) data 0.001 (0.001) eta 1:21:53 loss 0.4265 (0.3689) loss_x 0.4198 (0.3593) loss_u 0.0066 (0.0095) acc_x 87.5000 (87.3628) lr 1.036475e-03 -epoch [19/25][1900/4762] time 0.136 (0.156) data 0.001 (0.001) eta 1:21:33 loss 0.3787 (0.3702) loss_x 0.3781 (0.3605) loss_u 0.0006 (0.0097) acc_x 90.6250 (87.3355) lr 1.036475e-03 -epoch [19/25][2000/4762] time 0.143 (0.156) data 0.001 (0.001) eta 1:21:13 loss 0.2918 (0.3712) loss_x 0.2811 (0.3616) loss_u 0.0107 (0.0097) acc_x 87.5000 (87.3312) lr 1.036475e-03 -epoch [19/25][2100/4762] time 0.151 (0.155) data 0.001 (0.001) eta 1:20:53 loss 0.2781 (0.3715) loss_x 0.2712 (0.3619) loss_u 0.0069 (0.0096) acc_x 93.7500 (87.3155) lr 1.036475e-03 -epoch [19/25][2200/4762] time 0.173 (0.155) data 0.001 (0.001) eta 1:20:33 loss 0.4797 (0.3714) loss_x 0.4598 (0.3619) loss_u 0.0199 (0.0095) acc_x 84.3750 (87.3153) lr 1.036475e-03 -epoch [19/25][2300/4762] time 0.142 (0.155) data 0.001 (0.001) eta 1:20:18 loss 0.4363 (0.3718) loss_x 0.4349 (0.3624) loss_u 0.0014 (0.0094) acc_x 84.3750 (87.3030) lr 1.036475e-03 -epoch [19/25][2400/4762] time 0.165 (0.155) data 0.001 (0.001) eta 1:19:57 loss 0.4227 (0.3720) loss_x 0.4191 (0.3626) loss_u 0.0035 (0.0095) acc_x 81.2500 (87.2747) lr 1.036475e-03 -epoch [19/25][2500/4762] time 0.143 (0.155) data 0.001 (0.001) eta 1:19:34 loss 0.4441 (0.3724) loss_x 0.4415 (0.3629) loss_u 0.0026 (0.0095) acc_x 90.6250 (87.2763) lr 1.036475e-03 -epoch [19/25][2600/4762] time 0.143 (0.155) data 0.001 (0.001) eta 1:19:13 loss 0.3786 (0.3716) loss_x 0.3781 (0.3621) loss_u 0.0005 (0.0095) acc_x 87.5000 (87.3137) lr 1.036475e-03 -epoch [19/25][2700/4762] time 0.139 (0.155) data 0.001 (0.001) eta 1:18:55 loss 0.2968 (0.3711) loss_x 0.2839 (0.3617) loss_u 0.0129 (0.0095) acc_x 84.3750 (87.3137) lr 1.036475e-03 -epoch [19/25][2800/4762] time 0.155 (0.155) data 0.001 (0.001) eta 1:18:40 loss 0.3143 (0.3716) loss_x 0.3125 (0.3621) loss_u 0.0018 (0.0095) acc_x 81.2500 (87.2958) lr 1.036475e-03 -epoch [19/25][2900/4762] time 0.142 (0.155) data 0.001 (0.001) eta 1:18:27 loss 0.1566 (0.3713) loss_x 0.1549 (0.3618) loss_u 0.0017 (0.0095) acc_x 93.7500 (87.3050) lr 1.036475e-03 -epoch [19/25][3000/4762] time 0.158 (0.155) data 0.002 (0.001) eta 1:18:08 loss 0.1419 (0.3709) loss_x 0.1395 (0.3615) loss_u 0.0024 (0.0094) acc_x 96.8750 (87.3156) lr 1.036475e-03 -epoch [19/25][3100/4762] time 0.158 (0.155) data 0.001 (0.001) eta 1:17:54 loss 0.1364 (0.3706) loss_x 0.1341 (0.3613) loss_u 0.0023 (0.0093) acc_x 96.8750 (87.3226) lr 1.036475e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,927 -* accuracy: 84.72% -* error: 15.28% -* macro_f1: 84.95% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,579 acc: 98.16% -* class: 1 (bicycle) total: 3,475 correct: 2,945 acc: 84.75% -* class: 2 (bus) total: 4,690 correct: 4,268 acc: 91.00% -* class: 3 (car) total: 10,401 correct: 7,812 acc: 75.11% -* class: 4 (horse) total: 4,691 correct: 4,578 acc: 97.59% -* class: 5 (knife) total: 2,075 correct: 1,884 acc: 90.80% -* class: 6 (motorcycle) total: 5,796 correct: 5,515 acc: 95.15% -* class: 7 (person) total: 4,000 correct: 3,068 acc: 76.70% -* class: 8 (plant) total: 4,549 correct: 3,936 acc: 86.52% -* class: 9 (skateboard) total: 2,281 correct: 2,054 acc: 90.05% -* class: 10 (train) total: 4,236 correct: 3,931 acc: 92.80% -* class: 11 (truck) total: 5,548 correct: 3,357 acc: 60.51% -* average: 86.59% -epoch [19/25][3200/4762] time 0.156 (0.155) data 0.001 (0.001) eta 1:17:39 loss 0.3327 (0.3707) loss_x 0.3276 (0.3614) loss_u 0.0052 (0.0093) acc_x 90.6250 (87.3145) lr 1.036475e-03 -epoch [19/25][3300/4762] time 0.175 (0.155) data 0.001 (0.001) eta 1:17:20 loss 0.3472 (0.3710) loss_x 0.3469 (0.3617) loss_u 0.0003 (0.0093) acc_x 87.5000 (87.2955) lr 1.036475e-03 -epoch [19/25][3400/4762] time 0.167 (0.154) data 0.001 (0.001) eta 1:17:03 loss 0.3911 (0.3713) loss_x 0.3856 (0.3621) loss_u 0.0056 (0.0092) acc_x 87.5000 (87.2950) lr 1.036475e-03 -epoch [19/25][3500/4762] time 0.154 (0.155) data 0.001 (0.001) eta 1:16:51 loss 0.5402 (0.3708) loss_x 0.5341 (0.3616) loss_u 0.0061 (0.0092) acc_x 81.2500 (87.2964) lr 1.036475e-03 -epoch [19/25][3600/4762] time 0.158 (0.155) data 0.001 (0.001) eta 1:16:34 loss 0.2480 (0.3709) loss_x 0.2279 (0.3616) loss_u 0.0201 (0.0093) acc_x 93.7500 (87.2934) lr 1.036475e-03 -epoch [19/25][3700/4762] time 0.157 (0.154) data 0.001 (0.001) eta 1:16:16 loss 0.4852 (0.3705) loss_x 0.4561 (0.3613) loss_u 0.0291 (0.0093) acc_x 81.2500 (87.3049) lr 1.036475e-03 -epoch [19/25][3800/4762] time 0.151 (0.154) data 0.001 (0.001) eta 1:15:58 loss 0.1682 (0.3706) loss_x 0.1562 (0.3613) loss_u 0.0120 (0.0093) acc_x 93.7500 (87.3117) lr 1.036475e-03 -epoch [19/25][3900/4762] time 0.162 (0.154) data 0.001 (0.001) eta 1:15:39 loss 0.6588 (0.3705) loss_x 0.6267 (0.3612) loss_u 0.0321 (0.0093) acc_x 71.8750 (87.3069) lr 1.036475e-03 -epoch [19/25][4000/4762] time 0.143 (0.154) data 0.001 (0.001) eta 1:15:22 loss 0.5177 (0.3710) loss_x 0.5116 (0.3617) loss_u 0.0061 (0.0093) acc_x 81.2500 (87.2828) lr 1.036475e-03 -epoch [19/25][4100/4762] time 0.173 (0.154) data 0.001 (0.001) eta 1:15:04 loss 0.3497 (0.3708) loss_x 0.3423 (0.3615) loss_u 0.0073 (0.0093) acc_x 87.5000 (87.2980) lr 1.036475e-03 -epoch [19/25][4200/4762] time 0.162 (0.154) data 0.001 (0.001) eta 1:14:49 loss 0.4347 (0.3708) loss_x 0.4316 (0.3615) loss_u 0.0030 (0.0093) acc_x 81.2500 (87.2984) lr 1.036475e-03 -epoch [19/25][4300/4762] time 0.149 (0.154) data 0.001 (0.001) eta 1:14:33 loss 0.2211 (0.3704) loss_x 0.1845 (0.3611) loss_u 0.0366 (0.0093) acc_x 93.7500 (87.3052) lr 1.036475e-03 -epoch [19/25][4400/4762] time 0.142 (0.154) data 0.001 (0.001) eta 1:14:17 loss 0.6102 (0.3704) loss_x 0.6016 (0.3611) loss_u 0.0086 (0.0093) acc_x 75.0000 (87.3196) lr 1.036475e-03 -epoch [19/25][4500/4762] time 0.141 (0.154) data 0.001 (0.001) eta 1:14:01 loss 0.3258 (0.3703) loss_x 0.3174 (0.3609) loss_u 0.0084 (0.0094) acc_x 87.5000 (87.3236) lr 1.036475e-03 -epoch [19/25][4600/4762] time 0.144 (0.154) data 0.001 (0.001) eta 1:13:46 loss 0.4367 (0.3704) loss_x 0.4350 (0.3610) loss_u 0.0017 (0.0094) acc_x 81.2500 (87.3179) lr 1.036475e-03 -epoch [19/25][4700/4762] time 0.159 (0.154) data 0.001 (0.001) eta 1:13:32 loss 0.4834 (0.3706) loss_x 0.4771 (0.3612) loss_u 0.0062 (0.0094) acc_x 81.2500 (87.2999) lr 1.036475e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,704 -* accuracy: 84.32% -* error: 15.68% -* macro_f1: 84.34% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,580 acc: 98.19% -* class: 1 (bicycle) total: 3,475 correct: 2,906 acc: 83.63% -* class: 2 (bus) total: 4,690 correct: 4,259 acc: 90.81% -* class: 3 (car) total: 10,401 correct: 7,787 acc: 74.87% -* class: 4 (horse) total: 4,691 correct: 4,555 acc: 97.10% -* class: 5 (knife) total: 2,075 correct: 1,781 acc: 85.83% -* class: 6 (motorcycle) total: 5,796 correct: 5,534 acc: 95.48% -* class: 7 (person) total: 4,000 correct: 2,983 acc: 74.58% -* class: 8 (plant) total: 4,549 correct: 3,866 acc: 84.99% -* class: 9 (skateboard) total: 2,281 correct: 2,076 acc: 91.01% -* class: 10 (train) total: 4,236 correct: 3,909 acc: 92.28% -* class: 11 (truck) total: 5,548 correct: 3,468 acc: 62.51% -* average: 85.94% -epoch [20/25][100/4762] time 0.163 (0.153) data 0.001 (0.003) eta 1:12:40 loss 0.3925 (0.3548) loss_x 0.3815 (0.3448) loss_u 0.0110 (0.0100) acc_x 87.5000 (88.1875) lr 4.712526e-05 -epoch [20/25][200/4762] time 0.140 (0.152) data 0.001 (0.002) eta 1:11:56 loss 0.2972 (0.3626) loss_x 0.2941 (0.3527) loss_u 0.0030 (0.0099) acc_x 90.6250 (87.3750) lr 4.712526e-05 -epoch [20/25][300/4762] time 0.136 (0.151) data 0.001 (0.002) eta 1:11:14 loss 0.5046 (0.3665) loss_x 0.4882 (0.3570) loss_u 0.0164 (0.0095) acc_x 81.2500 (87.2500) lr 4.712526e-05 -epoch [20/25][400/4762] time 0.159 (0.151) data 0.001 (0.001) eta 1:10:53 loss 0.4444 (0.3727) loss_x 0.3869 (0.3636) loss_u 0.0575 (0.0090) acc_x 84.3750 (87.0312) lr 4.712526e-05 -epoch [20/25][500/4762] time 0.151 (0.151) data 0.001 (0.001) eta 1:10:24 loss 0.4057 (0.3768) loss_x 0.4024 (0.3679) loss_u 0.0033 (0.0089) acc_x 84.3750 (86.8812) lr 4.712526e-05 -epoch [20/25][600/4762] time 0.137 (0.150) data 0.001 (0.001) eta 1:10:08 loss 0.4186 (0.3776) loss_x 0.4102 (0.3684) loss_u 0.0084 (0.0092) acc_x 87.5000 (87.0052) lr 4.712526e-05 -epoch [20/25][700/4762] time 0.146 (0.150) data 0.001 (0.001) eta 1:09:54 loss 0.5079 (0.3729) loss_x 0.5027 (0.3639) loss_u 0.0052 (0.0090) acc_x 81.2500 (87.2143) lr 4.712526e-05 -epoch [20/25][800/4762] time 0.165 (0.151) data 0.001 (0.001) eta 1:09:39 loss 0.6022 (0.3755) loss_x 0.5923 (0.3662) loss_u 0.0098 (0.0093) acc_x 78.1250 (87.1836) lr 4.712526e-05 -epoch [20/25][900/4762] time 0.144 (0.150) data 0.001 (0.001) eta 1:09:24 loss 0.2230 (0.3745) loss_x 0.2173 (0.3652) loss_u 0.0057 (0.0093) acc_x 90.6250 (87.2049) lr 4.712526e-05 -epoch [20/25][1000/4762] time 0.158 (0.151) data 0.001 (0.001) eta 1:09:12 loss 0.4465 (0.3744) loss_x 0.4420 (0.3649) loss_u 0.0045 (0.0094) acc_x 84.3750 (87.2188) lr 4.712526e-05 -epoch [20/25][1100/4762] time 0.137 (0.151) data 0.001 (0.001) eta 1:09:05 loss 0.3746 (0.3750) loss_x 0.3665 (0.3655) loss_u 0.0081 (0.0095) acc_x 87.5000 (87.2074) lr 4.712526e-05 -epoch [20/25][1200/4762] time 0.139 (0.151) data 0.001 (0.001) eta 1:09:06 loss 0.4445 (0.3746) loss_x 0.4335 (0.3652) loss_u 0.0110 (0.0094) acc_x 90.6250 (87.2161) lr 4.712526e-05 -epoch [20/25][1300/4762] time 0.170 (0.152) data 0.001 (0.001) eta 1:08:57 loss 0.1945 (0.3749) loss_x 0.1894 (0.3656) loss_u 0.0051 (0.0093) acc_x 93.7500 (87.2067) lr 4.712526e-05 -epoch [20/25][1400/4762] time 0.158 (0.152) data 0.001 (0.001) eta 1:08:43 loss 0.3554 (0.3742) loss_x 0.3239 (0.3648) loss_u 0.0315 (0.0094) acc_x 93.7500 (87.2143) lr 4.712526e-05 -epoch [20/25][1500/4762] time 0.142 (0.152) data 0.001 (0.001) eta 1:08:30 loss 0.4771 (0.3752) loss_x 0.4756 (0.3658) loss_u 0.0015 (0.0094) acc_x 84.3750 (87.1917) lr 4.712526e-05 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,595 -* accuracy: 84.12% -* error: 15.88% -* macro_f1: 84.21% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,586 acc: 98.35% -* class: 1 (bicycle) total: 3,475 correct: 2,911 acc: 83.77% -* class: 2 (bus) total: 4,690 correct: 4,310 acc: 91.90% -* class: 3 (car) total: 10,401 correct: 7,809 acc: 75.08% -* class: 4 (horse) total: 4,691 correct: 4,572 acc: 97.46% -* class: 5 (knife) total: 2,075 correct: 1,878 acc: 90.51% -* class: 6 (motorcycle) total: 5,796 correct: 5,539 acc: 95.57% -* class: 7 (person) total: 4,000 correct: 2,893 acc: 72.33% -* class: 8 (plant) total: 4,549 correct: 3,901 acc: 85.76% -* class: 9 (skateboard) total: 2,281 correct: 2,058 acc: 90.22% -* class: 10 (train) total: 4,236 correct: 3,906 acc: 92.21% -* class: 11 (truck) total: 5,548 correct: 3,232 acc: 58.26% -* average: 85.95% -epoch [20/25][1600/4762] time 0.171 (0.152) data 0.001 (0.001) eta 1:08:18 loss 0.4580 (0.3753) loss_x 0.4525 (0.3658) loss_u 0.0056 (0.0095) acc_x 81.2500 (87.1699) lr 4.712526e-05 -epoch [20/25][1700/4762] time 0.161 (0.152) data 0.001 (0.001) eta 1:08:00 loss 0.4054 (0.3749) loss_x 0.4017 (0.3654) loss_u 0.0037 (0.0095) acc_x 90.6250 (87.2206) lr 4.712526e-05 -epoch [20/25][1800/4762] time 0.142 (0.152) data 0.001 (0.001) eta 1:07:51 loss 0.3432 (0.3756) loss_x 0.3116 (0.3662) loss_u 0.0315 (0.0094) acc_x 93.7500 (87.1476) lr 4.712526e-05 -epoch [20/25][1900/4762] time 0.138 (0.152) data 0.001 (0.001) eta 1:07:35 loss 0.3689 (0.3762) loss_x 0.3551 (0.3669) loss_u 0.0138 (0.0094) acc_x 84.3750 (87.1168) lr 4.712526e-05 -epoch [20/25][2000/4762] time 0.163 (0.152) data 0.001 (0.001) eta 1:07:20 loss 0.5415 (0.3755) loss_x 0.5388 (0.3660) loss_u 0.0028 (0.0095) acc_x 81.2500 (87.1469) lr 4.712526e-05 -epoch [20/25][2100/4762] time 0.146 (0.152) data 0.001 (0.001) eta 1:07:03 loss 0.5233 (0.3762) loss_x 0.5124 (0.3666) loss_u 0.0109 (0.0096) acc_x 84.3750 (87.1265) lr 4.712526e-05 -epoch [20/25][2200/4762] time 0.143 (0.152) data 0.001 (0.001) eta 1:06:50 loss 0.2690 (0.3763) loss_x 0.2596 (0.3666) loss_u 0.0093 (0.0097) acc_x 90.6250 (87.1236) lr 4.712526e-05 -epoch [20/25][2300/4762] time 0.138 (0.152) data 0.001 (0.001) eta 1:06:38 loss 0.4447 (0.3764) loss_x 0.4421 (0.3668) loss_u 0.0026 (0.0097) acc_x 78.1250 (87.1291) lr 4.712526e-05 -epoch [20/25][2400/4762] time 0.142 (0.152) data 0.001 (0.001) eta 1:06:24 loss 0.5679 (0.3765) loss_x 0.5590 (0.3668) loss_u 0.0090 (0.0097) acc_x 75.0000 (87.1185) lr 4.712526e-05 -epoch [20/25][2500/4762] time 0.144 (0.152) data 0.001 (0.001) eta 1:06:08 loss 0.1402 (0.3755) loss_x 0.1389 (0.3660) loss_u 0.0014 (0.0096) acc_x 93.7500 (87.1550) lr 4.712526e-05 -epoch [20/25][2600/4762] time 0.141 (0.152) data 0.001 (0.001) eta 1:05:51 loss 0.6648 (0.3757) loss_x 0.6437 (0.3661) loss_u 0.0211 (0.0095) acc_x 84.3750 (87.1418) lr 4.712526e-05 -epoch [20/25][2700/4762] time 0.149 (0.152) data 0.001 (0.001) eta 1:05:35 loss 0.3604 (0.3757) loss_x 0.3315 (0.3661) loss_u 0.0289 (0.0095) acc_x 87.5000 (87.1516) lr 4.712526e-05 -epoch [20/25][2800/4762] time 0.168 (0.152) data 0.001 (0.001) eta 1:05:19 loss 0.4464 (0.3754) loss_x 0.4390 (0.3659) loss_u 0.0074 (0.0095) acc_x 87.5000 (87.1652) lr 4.712526e-05 -epoch [20/25][2900/4762] time 0.161 (0.152) data 0.001 (0.001) eta 1:05:06 loss 0.2896 (0.3751) loss_x 0.2805 (0.3655) loss_u 0.0091 (0.0095) acc_x 90.6250 (87.1670) lr 4.712526e-05 -epoch [20/25][3000/4762] time 0.137 (0.152) data 0.001 (0.001) eta 1:04:51 loss 0.2841 (0.3743) loss_x 0.2619 (0.3648) loss_u 0.0222 (0.0095) acc_x 90.6250 (87.1823) lr 4.712526e-05 -epoch [20/25][3100/4762] time 0.144 (0.152) data 0.001 (0.001) eta 1:04:36 loss 0.3925 (0.3741) loss_x 0.3877 (0.3647) loss_u 0.0048 (0.0094) acc_x 90.6250 (87.1815) lr 4.712526e-05 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,714 -* accuracy: 84.34% -* error: 15.66% -* macro_f1: 84.55% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,582 acc: 98.24% -* class: 1 (bicycle) total: 3,475 correct: 2,965 acc: 85.32% -* class: 2 (bus) total: 4,690 correct: 4,258 acc: 90.79% -* class: 3 (car) total: 10,401 correct: 7,536 acc: 72.45% -* class: 4 (horse) total: 4,691 correct: 4,581 acc: 97.66% -* class: 5 (knife) total: 2,075 correct: 1,844 acc: 88.87% -* class: 6 (motorcycle) total: 5,796 correct: 5,496 acc: 94.82% -* class: 7 (person) total: 4,000 correct: 2,953 acc: 73.83% -* class: 8 (plant) total: 4,549 correct: 3,934 acc: 86.48% -* class: 9 (skateboard) total: 2,281 correct: 2,085 acc: 91.41% -* class: 10 (train) total: 4,236 correct: 3,929 acc: 92.75% -* class: 11 (truck) total: 5,548 correct: 3,551 acc: 64.01% -* average: 86.39% -epoch [20/25][3200/4762] time 0.154 (0.152) data 0.001 (0.001) eta 1:04:21 loss 0.4712 (0.3734) loss_x 0.4675 (0.3640) loss_u 0.0037 (0.0094) acc_x 78.1250 (87.2207) lr 4.712526e-05 -epoch [20/25][3300/4762] time 0.172 (0.152) data 0.014 (0.001) eta 1:04:06 loss 0.3231 (0.3732) loss_x 0.3199 (0.3638) loss_u 0.0031 (0.0094) acc_x 84.3750 (87.2216) lr 4.712526e-05 -epoch [20/25][3400/4762] time 0.154 (0.152) data 0.002 (0.001) eta 1:03:52 loss 0.3362 (0.3736) loss_x 0.3268 (0.3642) loss_u 0.0094 (0.0094) acc_x 93.7500 (87.2169) lr 4.712526e-05 -epoch [20/25][3500/4762] time 0.144 (0.152) data 0.001 (0.001) eta 1:03:41 loss 0.3964 (0.3740) loss_x 0.3935 (0.3647) loss_u 0.0029 (0.0094) acc_x 87.5000 (87.2009) lr 4.712526e-05 -epoch [20/25][3600/4762] time 0.161 (0.152) data 0.001 (0.001) eta 1:03:26 loss 0.3156 (0.3739) loss_x 0.3141 (0.3645) loss_u 0.0015 (0.0094) acc_x 87.5000 (87.2101) lr 4.712526e-05 -epoch [20/25][3700/4762] time 0.156 (0.152) data 0.001 (0.001) eta 1:03:10 loss 0.3824 (0.3739) loss_x 0.3704 (0.3645) loss_u 0.0121 (0.0094) acc_x 87.5000 (87.2137) lr 4.712526e-05 -epoch [20/25][3800/4762] time 0.151 (0.152) data 0.001 (0.001) eta 1:02:52 loss 0.2608 (0.3736) loss_x 0.2507 (0.3642) loss_u 0.0101 (0.0094) acc_x 90.6250 (87.2179) lr 4.712526e-05 -epoch [20/25][3900/4762] time 0.145 (0.152) data 0.001 (0.001) eta 1:02:35 loss 0.2974 (0.3734) loss_x 0.2903 (0.3640) loss_u 0.0071 (0.0094) acc_x 90.6250 (87.2212) lr 4.712526e-05 -epoch [20/25][4000/4762] time 0.139 (0.152) data 0.001 (0.001) eta 1:02:20 loss 0.3356 (0.3738) loss_x 0.3289 (0.3644) loss_u 0.0067 (0.0094) acc_x 81.2500 (87.2008) lr 4.712526e-05 -epoch [20/25][4100/4762] time 0.161 (0.152) data 0.001 (0.001) eta 1:02:01 loss 0.4586 (0.3739) loss_x 0.4509 (0.3645) loss_u 0.0077 (0.0094) acc_x 87.5000 (87.2035) lr 4.712526e-05 -epoch [20/25][4200/4762] time 0.149 (0.152) data 0.001 (0.001) eta 1:01:46 loss 0.5636 (0.3738) loss_x 0.5545 (0.3643) loss_u 0.0091 (0.0095) acc_x 78.1250 (87.2076) lr 4.712526e-05 -epoch [20/25][4300/4762] time 0.159 (0.152) data 0.001 (0.001) eta 1:01:31 loss 0.3820 (0.3739) loss_x 0.3606 (0.3644) loss_u 0.0214 (0.0095) acc_x 87.5000 (87.1999) lr 4.712526e-05 -epoch [20/25][4400/4762] time 0.147 (0.152) data 0.001 (0.001) eta 1:01:17 loss 0.3073 (0.3739) loss_x 0.2985 (0.3644) loss_u 0.0088 (0.0095) acc_x 93.7500 (87.2017) lr 4.712526e-05 -epoch [20/25][4500/4762] time 0.143 (0.152) data 0.001 (0.001) eta 1:01:01 loss 0.5160 (0.3737) loss_x 0.4957 (0.3642) loss_u 0.0203 (0.0095) acc_x 81.2500 (87.2083) lr 4.712526e-05 -epoch [20/25][4600/4762] time 0.158 (0.152) data 0.001 (0.001) eta 1:00:47 loss 0.3268 (0.3732) loss_x 0.3237 (0.3637) loss_u 0.0031 (0.0095) acc_x 90.6250 (87.2269) lr 4.712526e-05 -epoch [20/25][4700/4762] time 0.141 (0.152) data 0.001 (0.001) eta 1:00:32 loss 0.5196 (0.3735) loss_x 0.5120 (0.3641) loss_u 0.0076 (0.0095) acc_x 81.2500 (87.2061) lr 4.712526e-05 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,839 -* accuracy: 84.57% -* error: 15.43% -* macro_f1: 84.73% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,582 acc: 98.24% -* class: 1 (bicycle) total: 3,475 correct: 2,929 acc: 84.29% -* class: 2 (bus) total: 4,690 correct: 4,232 acc: 90.23% -* class: 3 (car) total: 10,401 correct: 7,757 acc: 74.58% -* class: 4 (horse) total: 4,691 correct: 4,599 acc: 98.04% -* class: 5 (knife) total: 2,075 correct: 1,865 acc: 89.88% -* class: 6 (motorcycle) total: 5,796 correct: 5,513 acc: 95.12% -* class: 7 (person) total: 4,000 correct: 2,922 acc: 73.05% -* class: 8 (plant) total: 4,549 correct: 3,974 acc: 87.36% -* class: 9 (skateboard) total: 2,281 correct: 2,063 acc: 90.44% -* class: 10 (train) total: 4,236 correct: 3,962 acc: 93.53% -* class: 11 (truck) total: 5,548 correct: 3,441 acc: 62.02% -* average: 86.40% -epoch [21/25][100/4762] time 0.176 (0.158) data 0.001 (0.004) eta 1:02:29 loss 0.2160 (0.3664) loss_x 0.2104 (0.3553) loss_u 0.0056 (0.0111) acc_x 96.8750 (87.4062) lr 1.781072e-03 -epoch [21/25][200/4762] time 0.160 (0.154) data 0.001 (0.002) eta 1:00:43 loss 0.2074 (0.3615) loss_x 0.1913 (0.3517) loss_u 0.0161 (0.0098) acc_x 100.0000 (87.7656) lr 1.781072e-03 -epoch [21/25][300/4762] time 0.154 (0.154) data 0.001 (0.002) eta 1:00:32 loss 0.4267 (0.3661) loss_x 0.4248 (0.3558) loss_u 0.0018 (0.0103) acc_x 81.2500 (87.4271) lr 1.781072e-03 -epoch [21/25][400/4762] time 0.188 (0.154) data 0.001 (0.002) eta 1:00:09 loss 0.6129 (0.3651) loss_x 0.6112 (0.3551) loss_u 0.0017 (0.0100) acc_x 78.1250 (87.4766) lr 1.781072e-03 -epoch [21/25][500/4762] time 0.162 (0.154) data 0.001 (0.001) eta 0:59:42 loss 0.3369 (0.3692) loss_x 0.3352 (0.3591) loss_u 0.0017 (0.0101) acc_x 87.5000 (87.4062) lr 1.781072e-03 -epoch [21/25][600/4762] time 0.137 (0.153) data 0.001 (0.001) eta 0:59:17 loss 0.2450 (0.3683) loss_x 0.2259 (0.3585) loss_u 0.0191 (0.0098) acc_x 87.5000 (87.4115) lr 1.781072e-03 -epoch [21/25][700/4762] time 0.138 (0.153) data 0.001 (0.001) eta 0:58:53 loss 0.3154 (0.3701) loss_x 0.3086 (0.3605) loss_u 0.0068 (0.0096) acc_x 87.5000 (87.4464) lr 1.781072e-03 -epoch [21/25][800/4762] time 0.150 (0.153) data 0.001 (0.001) eta 0:58:32 loss 0.4063 (0.3722) loss_x 0.3998 (0.3628) loss_u 0.0065 (0.0094) acc_x 87.5000 (87.3594) lr 1.781072e-03 -epoch [21/25][900/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:58:18 loss 0.3434 (0.3721) loss_x 0.3423 (0.3627) loss_u 0.0010 (0.0094) acc_x 87.5000 (87.3333) lr 1.781072e-03 -epoch [21/25][1000/4762] time 0.144 (0.152) data 0.001 (0.001) eta 0:57:57 loss 0.3179 (0.3716) loss_x 0.3166 (0.3622) loss_u 0.0014 (0.0094) acc_x 87.5000 (87.3219) lr 1.781072e-03 -epoch [21/25][1100/4762] time 0.153 (0.153) data 0.001 (0.001) eta 0:57:45 loss 0.3060 (0.3734) loss_x 0.3030 (0.3641) loss_u 0.0030 (0.0093) acc_x 87.5000 (87.2585) lr 1.781072e-03 -epoch [21/25][1200/4762] time 0.143 (0.152) data 0.001 (0.001) eta 0:57:27 loss 0.4092 (0.3721) loss_x 0.4056 (0.3629) loss_u 0.0036 (0.0092) acc_x 78.1250 (87.3177) lr 1.781072e-03 -epoch [21/25][1300/4762] time 0.144 (0.152) data 0.001 (0.001) eta 0:57:09 loss 0.4223 (0.3726) loss_x 0.4177 (0.3636) loss_u 0.0046 (0.0090) acc_x 78.1250 (87.2740) lr 1.781072e-03 -epoch [21/25][1400/4762] time 0.145 (0.152) data 0.002 (0.001) eta 0:56:56 loss 0.3635 (0.3729) loss_x 0.3609 (0.3639) loss_u 0.0026 (0.0090) acc_x 87.5000 (87.2679) lr 1.781072e-03 -epoch [21/25][1500/4762] time 0.143 (0.153) data 0.001 (0.001) eta 0:56:43 loss 0.4329 (0.3729) loss_x 0.4257 (0.3640) loss_u 0.0073 (0.0089) acc_x 81.2500 (87.2562) lr 1.781072e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,990 -* accuracy: 84.84% -* error: 15.16% -* macro_f1: 84.91% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,589 acc: 98.44% -* class: 1 (bicycle) total: 3,475 correct: 2,912 acc: 83.80% -* class: 2 (bus) total: 4,690 correct: 4,271 acc: 91.07% -* class: 3 (car) total: 10,401 correct: 8,112 acc: 77.99% -* class: 4 (horse) total: 4,691 correct: 4,587 acc: 97.78% -* class: 5 (knife) total: 2,075 correct: 1,892 acc: 91.18% -* class: 6 (motorcycle) total: 5,796 correct: 5,498 acc: 94.86% -* class: 7 (person) total: 4,000 correct: 2,960 acc: 74.00% -* class: 8 (plant) total: 4,549 correct: 3,932 acc: 86.44% -* class: 9 (skateboard) total: 2,281 correct: 2,037 acc: 89.30% -* class: 10 (train) total: 4,236 correct: 3,895 acc: 91.95% -* class: 11 (truck) total: 5,548 correct: 3,305 acc: 59.57% -* average: 86.36% -epoch [21/25][1600/4762] time 0.207 (0.153) data 0.001 (0.001) eta 0:56:33 loss 0.4470 (0.3730) loss_x 0.4362 (0.3642) loss_u 0.0108 (0.0088) acc_x 84.3750 (87.2695) lr 1.781072e-03 -epoch [21/25][1700/4762] time 0.159 (0.153) data 0.001 (0.001) eta 0:56:21 loss 0.4451 (0.3736) loss_x 0.4285 (0.3647) loss_u 0.0167 (0.0089) acc_x 84.3750 (87.2426) lr 1.781072e-03 -epoch [21/25][1800/4762] time 0.158 (0.153) data 0.001 (0.001) eta 0:56:13 loss 0.2475 (0.3749) loss_x 0.2458 (0.3658) loss_u 0.0017 (0.0091) acc_x 90.6250 (87.1858) lr 1.781072e-03 -epoch [21/25][1900/4762] time 0.193 (0.153) data 0.001 (0.001) eta 0:55:55 loss 0.7300 (0.3745) loss_x 0.7221 (0.3656) loss_u 0.0078 (0.0089) acc_x 78.1250 (87.1645) lr 1.781072e-03 -epoch [21/25][2000/4762] time 0.168 (0.153) data 0.001 (0.001) eta 0:55:43 loss 0.1483 (0.3744) loss_x 0.1389 (0.3655) loss_u 0.0093 (0.0089) acc_x 100.0000 (87.1531) lr 1.781072e-03 -epoch [21/25][2100/4762] time 0.155 (0.153) data 0.001 (0.001) eta 0:55:23 loss 0.2593 (0.3734) loss_x 0.2521 (0.3644) loss_u 0.0072 (0.0090) acc_x 87.5000 (87.1696) lr 1.781072e-03 -epoch [21/25][2200/4762] time 0.142 (0.153) data 0.001 (0.001) eta 0:55:07 loss 1.0043 (0.3724) loss_x 0.9969 (0.3634) loss_u 0.0074 (0.0090) acc_x 62.5000 (87.2102) lr 1.781072e-03 -epoch [21/25][2300/4762] time 0.152 (0.153) data 0.001 (0.001) eta 0:54:51 loss 0.2963 (0.3721) loss_x 0.2944 (0.3631) loss_u 0.0019 (0.0090) acc_x 90.6250 (87.2147) lr 1.781072e-03 -epoch [21/25][2400/4762] time 0.158 (0.153) data 0.001 (0.001) eta 0:54:36 loss 0.4821 (0.3735) loss_x 0.4805 (0.3645) loss_u 0.0015 (0.0089) acc_x 84.3750 (87.1680) lr 1.781072e-03 -epoch [21/25][2500/4762] time 0.176 (0.153) data 0.001 (0.001) eta 0:54:23 loss 0.4140 (0.3738) loss_x 0.4111 (0.3649) loss_u 0.0029 (0.0089) acc_x 81.2500 (87.1488) lr 1.781072e-03 -epoch [21/25][2600/4762] time 0.147 (0.153) data 0.001 (0.001) eta 0:54:05 loss 0.2221 (0.3741) loss_x 0.2216 (0.3651) loss_u 0.0005 (0.0090) acc_x 93.7500 (87.1370) lr 1.781072e-03 -epoch [21/25][2700/4762] time 0.138 (0.153) data 0.001 (0.001) eta 0:53:49 loss 0.3532 (0.3740) loss_x 0.3439 (0.3650) loss_u 0.0093 (0.0090) acc_x 84.3750 (87.1586) lr 1.781072e-03 -epoch [21/25][2800/4762] time 0.147 (0.153) data 0.001 (0.001) eta 0:53:33 loss 0.3433 (0.3737) loss_x 0.3405 (0.3647) loss_u 0.0028 (0.0090) acc_x 84.3750 (87.1741) lr 1.781072e-03 -epoch [21/25][2900/4762] time 0.164 (0.153) data 0.001 (0.001) eta 0:53:15 loss 0.4939 (0.3741) loss_x 0.4571 (0.3651) loss_u 0.0368 (0.0090) acc_x 81.2500 (87.1562) lr 1.781072e-03 -epoch [21/25][3000/4762] time 0.138 (0.153) data 0.001 (0.001) eta 0:53:02 loss 0.3187 (0.3744) loss_x 0.3150 (0.3653) loss_u 0.0037 (0.0090) acc_x 81.2500 (87.1229) lr 1.781072e-03 -epoch [21/25][3100/4762] time 0.150 (0.153) data 0.001 (0.001) eta 0:52:48 loss 0.2893 (0.3739) loss_x 0.2784 (0.3649) loss_u 0.0109 (0.0090) acc_x 90.6250 (87.1290) lr 1.781072e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,825 -* accuracy: 84.54% -* error: 15.46% -* macro_f1: 84.61% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,586 acc: 98.35% -* class: 1 (bicycle) total: 3,475 correct: 2,915 acc: 83.88% -* class: 2 (bus) total: 4,690 correct: 4,264 acc: 90.92% -* class: 3 (car) total: 10,401 correct: 7,902 acc: 75.97% -* class: 4 (horse) total: 4,691 correct: 4,591 acc: 97.87% -* class: 5 (knife) total: 2,075 correct: 1,836 acc: 88.48% -* class: 6 (motorcycle) total: 5,796 correct: 5,511 acc: 95.08% -* class: 7 (person) total: 4,000 correct: 2,992 acc: 74.80% -* class: 8 (plant) total: 4,549 correct: 3,951 acc: 86.85% -* class: 9 (skateboard) total: 2,281 correct: 2,063 acc: 90.44% -* class: 10 (train) total: 4,236 correct: 3,935 acc: 92.89% -* class: 11 (truck) total: 5,548 correct: 3,279 acc: 59.10% -* average: 86.22% -epoch [21/25][3200/4762] time 0.187 (0.153) data 0.001 (0.001) eta 0:52:34 loss 0.2877 (0.3739) loss_x 0.2860 (0.3649) loss_u 0.0017 (0.0090) acc_x 84.3750 (87.1475) lr 1.781072e-03 -epoch [21/25][3300/4762] time 0.138 (0.153) data 0.001 (0.001) eta 0:52:19 loss 0.2351 (0.3736) loss_x 0.2320 (0.3645) loss_u 0.0031 (0.0091) acc_x 90.6250 (87.1544) lr 1.781072e-03 -epoch [21/25][3400/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:52:03 loss 0.3153 (0.3731) loss_x 0.3074 (0.3640) loss_u 0.0079 (0.0091) acc_x 90.6250 (87.1544) lr 1.781072e-03 -epoch [21/25][3500/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:51:50 loss 0.2881 (0.3734) loss_x 0.2825 (0.3642) loss_u 0.0056 (0.0091) acc_x 87.5000 (87.1536) lr 1.781072e-03 -epoch [21/25][3600/4762] time 0.139 (0.153) data 0.001 (0.001) eta 0:51:33 loss 0.4246 (0.3739) loss_x 0.4158 (0.3648) loss_u 0.0087 (0.0092) acc_x 87.5000 (87.1328) lr 1.781072e-03 -epoch [21/25][3700/4762] time 0.147 (0.153) data 0.001 (0.001) eta 0:51:17 loss 0.5448 (0.3735) loss_x 0.5401 (0.3643) loss_u 0.0047 (0.0092) acc_x 84.3750 (87.1562) lr 1.781072e-03 -epoch [21/25][3800/4762] time 0.136 (0.153) data 0.001 (0.001) eta 0:51:01 loss 0.4927 (0.3731) loss_x 0.4894 (0.3638) loss_u 0.0033 (0.0092) acc_x 78.1250 (87.1686) lr 1.781072e-03 -epoch [21/25][3900/4762] time 0.165 (0.153) data 0.001 (0.001) eta 0:50:45 loss 0.4250 (0.3723) loss_x 0.4172 (0.3630) loss_u 0.0079 (0.0092) acc_x 84.3750 (87.1963) lr 1.781072e-03 -epoch [21/25][4000/4762] time 0.162 (0.153) data 0.001 (0.001) eta 0:50:29 loss 0.3614 (0.3716) loss_x 0.3564 (0.3623) loss_u 0.0050 (0.0093) acc_x 78.1250 (87.2070) lr 1.781072e-03 -epoch [21/25][4100/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:50:11 loss 0.3240 (0.3719) loss_x 0.3134 (0.3627) loss_u 0.0106 (0.0092) acc_x 84.3750 (87.1875) lr 1.781072e-03 -epoch [21/25][4200/4762] time 0.149 (0.153) data 0.001 (0.001) eta 0:49:54 loss 0.2558 (0.3715) loss_x 0.2548 (0.3623) loss_u 0.0010 (0.0092) acc_x 93.7500 (87.2024) lr 1.781072e-03 -epoch [21/25][4300/4762] time 0.146 (0.153) data 0.001 (0.001) eta 0:49:38 loss 0.2883 (0.3711) loss_x 0.2855 (0.3620) loss_u 0.0029 (0.0092) acc_x 90.6250 (87.2151) lr 1.781072e-03 -epoch [21/25][4400/4762] time 0.144 (0.153) data 0.001 (0.001) eta 0:49:23 loss 0.4047 (0.3710) loss_x 0.3985 (0.3618) loss_u 0.0062 (0.0092) acc_x 87.5000 (87.2131) lr 1.781072e-03 -epoch [21/25][4500/4762] time 0.149 (0.153) data 0.001 (0.001) eta 0:49:07 loss 0.3555 (0.3708) loss_x 0.3538 (0.3616) loss_u 0.0017 (0.0092) acc_x 90.6250 (87.2160) lr 1.781072e-03 -epoch [21/25][4600/4762] time 0.140 (0.153) data 0.001 (0.001) eta 0:48:51 loss 0.4223 (0.3711) loss_x 0.4100 (0.3619) loss_u 0.0123 (0.0092) acc_x 84.3750 (87.1984) lr 1.781072e-03 -epoch [21/25][4700/4762] time 0.154 (0.153) data 0.001 (0.001) eta 0:48:36 loss 0.2191 (0.3708) loss_x 0.2177 (0.3616) loss_u 0.0014 (0.0092) acc_x 90.6250 (87.2048) lr 1.781072e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,867 -* accuracy: 84.62% -* error: 15.38% -* macro_f1: 84.67% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,589 acc: 98.44% -* class: 1 (bicycle) total: 3,475 correct: 2,921 acc: 84.06% -* class: 2 (bus) total: 4,690 correct: 4,256 acc: 90.75% -* class: 3 (car) total: 10,401 correct: 7,899 acc: 75.94% -* class: 4 (horse) total: 4,691 correct: 4,591 acc: 97.87% -* class: 5 (knife) total: 2,075 correct: 1,863 acc: 89.78% -* class: 6 (motorcycle) total: 5,796 correct: 5,521 acc: 95.26% -* class: 7 (person) total: 4,000 correct: 3,011 acc: 75.28% -* class: 8 (plant) total: 4,549 correct: 3,898 acc: 85.69% -* class: 9 (skateboard) total: 2,281 correct: 2,044 acc: 89.61% -* class: 10 (train) total: 4,236 correct: 3,926 acc: 92.68% -* class: 11 (truck) total: 5,548 correct: 3,348 acc: 60.35% -* average: 86.31% -epoch [22/25][100/4762] time 0.157 (0.157) data 0.001 (0.004) eta 0:49:33 loss 0.3542 (0.3511) loss_x 0.3497 (0.3442) loss_u 0.0045 (0.0069) acc_x 84.3750 (87.9062) lr 2.988172e-03 -epoch [22/25][200/4762] time 0.184 (0.156) data 0.001 (0.002) eta 0:48:53 loss 0.3988 (0.3596) loss_x 0.3895 (0.3522) loss_u 0.0092 (0.0074) acc_x 81.2500 (87.3750) lr 2.988172e-03 -epoch [22/25][300/4762] time 0.142 (0.155) data 0.001 (0.002) eta 0:48:16 loss 0.3087 (0.3607) loss_x 0.3001 (0.3529) loss_u 0.0085 (0.0078) acc_x 87.5000 (87.4167) lr 2.988172e-03 -epoch [22/25][400/4762] time 0.138 (0.154) data 0.001 (0.001) eta 0:47:47 loss 0.5415 (0.3592) loss_x 0.5345 (0.3510) loss_u 0.0070 (0.0082) acc_x 81.2500 (87.4531) lr 2.988172e-03 -epoch [22/25][500/4762] time 0.138 (0.154) data 0.001 (0.001) eta 0:47:32 loss 0.5214 (0.3613) loss_x 0.5187 (0.3532) loss_u 0.0026 (0.0081) acc_x 84.3750 (87.3937) lr 2.988172e-03 -epoch [22/25][600/4762] time 0.164 (0.153) data 0.001 (0.001) eta 0:47:08 loss 0.4355 (0.3614) loss_x 0.4227 (0.3532) loss_u 0.0128 (0.0082) acc_x 90.6250 (87.4583) lr 2.988172e-03 -epoch [22/25][700/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:46:57 loss 0.4119 (0.3617) loss_x 0.4105 (0.3535) loss_u 0.0014 (0.0082) acc_x 84.3750 (87.3661) lr 2.988172e-03 -epoch [22/25][800/4762] time 0.137 (0.153) data 0.001 (0.001) eta 0:46:30 loss 0.4395 (0.3633) loss_x 0.4318 (0.3551) loss_u 0.0076 (0.0082) acc_x 90.6250 (87.3750) lr 2.988172e-03 -epoch [22/25][900/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:46:14 loss 0.3302 (0.3637) loss_x 0.3239 (0.3553) loss_u 0.0063 (0.0084) acc_x 84.3750 (87.3194) lr 2.988172e-03 -epoch [22/25][1000/4762] time 0.152 (0.153) data 0.001 (0.001) eta 0:45:56 loss 0.3355 (0.3647) loss_x 0.3322 (0.3562) loss_u 0.0032 (0.0085) acc_x 90.6250 (87.2406) lr 2.988172e-03 -epoch [22/25][1100/4762] time 0.140 (0.152) data 0.001 (0.001) eta 0:45:36 loss 0.6669 (0.3644) loss_x 0.6608 (0.3559) loss_u 0.0061 (0.0085) acc_x 78.1250 (87.2500) lr 2.988172e-03 -epoch [22/25][1200/4762] time 0.173 (0.152) data 0.001 (0.001) eta 0:45:19 loss 0.3057 (0.3658) loss_x 0.3000 (0.3572) loss_u 0.0058 (0.0086) acc_x 84.3750 (87.2344) lr 2.988172e-03 -epoch [22/25][1300/4762] time 0.144 (0.152) data 0.001 (0.001) eta 0:45:01 loss 0.2742 (0.3644) loss_x 0.2665 (0.3557) loss_u 0.0077 (0.0087) acc_x 90.6250 (87.3125) lr 2.988172e-03 -epoch [22/25][1400/4762] time 0.145 (0.152) data 0.001 (0.001) eta 0:44:41 loss 0.2444 (0.3646) loss_x 0.2318 (0.3558) loss_u 0.0126 (0.0088) acc_x 93.7500 (87.3304) lr 2.988172e-03 -epoch [22/25][1500/4762] time 0.152 (0.152) data 0.001 (0.001) eta 0:44:26 loss 0.5294 (0.3663) loss_x 0.5175 (0.3575) loss_u 0.0119 (0.0089) acc_x 81.2500 (87.2750) lr 2.988172e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,730 -* accuracy: 84.37% -* error: 15.63% -* macro_f1: 84.50% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,588 acc: 98.41% -* class: 1 (bicycle) total: 3,475 correct: 2,892 acc: 83.22% -* class: 2 (bus) total: 4,690 correct: 4,249 acc: 90.60% -* class: 3 (car) total: 10,401 correct: 7,670 acc: 73.74% -* class: 4 (horse) total: 4,691 correct: 4,575 acc: 97.53% -* class: 5 (knife) total: 2,075 correct: 1,868 acc: 90.02% -* class: 6 (motorcycle) total: 5,796 correct: 5,533 acc: 95.46% -* class: 7 (person) total: 4,000 correct: 2,990 acc: 74.75% -* class: 8 (plant) total: 4,549 correct: 3,909 acc: 85.93% -* class: 9 (skateboard) total: 2,281 correct: 2,078 acc: 91.10% -* class: 10 (train) total: 4,236 correct: 3,930 acc: 92.78% -* class: 11 (truck) total: 5,548 correct: 3,448 acc: 62.15% -* average: 86.31% -epoch [22/25][1600/4762] time 0.145 (0.152) data 0.001 (0.001) eta 0:44:13 loss 0.4244 (0.3667) loss_x 0.4056 (0.3579) loss_u 0.0188 (0.0088) acc_x 81.2500 (87.2637) lr 2.988172e-03 -epoch [22/25][1700/4762] time 0.149 (0.152) data 0.001 (0.001) eta 0:44:03 loss 0.6388 (0.3663) loss_x 0.6145 (0.3574) loss_u 0.0243 (0.0089) acc_x 78.1250 (87.2868) lr 2.988172e-03 -epoch [22/25][1800/4762] time 0.153 (0.153) data 0.001 (0.001) eta 0:43:55 loss 0.4847 (0.3653) loss_x 0.4799 (0.3563) loss_u 0.0048 (0.0089) acc_x 84.3750 (87.3090) lr 2.988172e-03 -epoch [22/25][1900/4762] time 0.143 (0.153) data 0.001 (0.001) eta 0:43:37 loss 0.3890 (0.3656) loss_x 0.3852 (0.3567) loss_u 0.0038 (0.0089) acc_x 81.2500 (87.3092) lr 2.988172e-03 -epoch [22/25][2000/4762] time 0.157 (0.153) data 0.001 (0.001) eta 0:43:23 loss 0.2649 (0.3662) loss_x 0.2614 (0.3573) loss_u 0.0035 (0.0089) acc_x 93.7500 (87.3297) lr 2.988172e-03 -epoch [22/25][2100/4762] time 0.146 (0.153) data 0.001 (0.001) eta 0:43:08 loss 0.3143 (0.3658) loss_x 0.3033 (0.3568) loss_u 0.0111 (0.0090) acc_x 90.6250 (87.3720) lr 2.988172e-03 -epoch [22/25][2200/4762] time 0.138 (0.153) data 0.001 (0.001) eta 0:42:55 loss 0.2849 (0.3662) loss_x 0.2800 (0.3572) loss_u 0.0048 (0.0090) acc_x 87.5000 (87.3622) lr 2.988172e-03 -epoch [22/25][2300/4762] time 0.157 (0.153) data 0.001 (0.001) eta 0:42:41 loss 0.2333 (0.3657) loss_x 0.2268 (0.3566) loss_u 0.0065 (0.0090) acc_x 90.6250 (87.3913) lr 2.988172e-03 -epoch [22/25][2400/4762] time 0.144 (0.153) data 0.001 (0.001) eta 0:42:24 loss 0.7262 (0.3660) loss_x 0.6763 (0.3570) loss_u 0.0499 (0.0090) acc_x 75.0000 (87.3711) lr 2.988172e-03 -epoch [22/25][2500/4762] time 0.166 (0.153) data 0.001 (0.001) eta 0:42:10 loss 0.4789 (0.3659) loss_x 0.4753 (0.3568) loss_u 0.0036 (0.0090) acc_x 81.2500 (87.3738) lr 2.988172e-03 -epoch [22/25][2600/4762] time 0.143 (0.153) data 0.001 (0.001) eta 0:41:56 loss 0.4154 (0.3657) loss_x 0.4118 (0.3567) loss_u 0.0036 (0.0090) acc_x 87.5000 (87.3954) lr 2.988172e-03 -epoch [22/25][2700/4762] time 0.138 (0.153) data 0.003 (0.001) eta 0:41:38 loss 0.4574 (0.3662) loss_x 0.4515 (0.3572) loss_u 0.0059 (0.0090) acc_x 81.2500 (87.3785) lr 2.988172e-03 -epoch [22/25][2800/4762] time 0.175 (0.153) data 0.001 (0.001) eta 0:41:22 loss 0.6520 (0.3669) loss_x 0.6509 (0.3579) loss_u 0.0011 (0.0090) acc_x 84.3750 (87.3415) lr 2.988172e-03 -epoch [22/25][2900/4762] time 0.167 (0.153) data 0.001 (0.001) eta 0:41:05 loss 0.4239 (0.3671) loss_x 0.4047 (0.3581) loss_u 0.0192 (0.0091) acc_x 90.6250 (87.3491) lr 2.988172e-03 -epoch [22/25][3000/4762] time 0.164 (0.153) data 0.001 (0.001) eta 0:40:50 loss 0.4254 (0.3672) loss_x 0.4242 (0.3581) loss_u 0.0013 (0.0091) acc_x 81.2500 (87.3344) lr 2.988172e-03 -epoch [22/25][3100/4762] time 0.142 (0.153) data 0.001 (0.001) eta 0:40:35 loss 0.3307 (0.3670) loss_x 0.3297 (0.3580) loss_u 0.0010 (0.0090) acc_x 90.6250 (87.3427) lr 2.988172e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,691 -* accuracy: 84.30% -* error: 15.70% -* macro_f1: 84.35% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,587 acc: 98.38% -* class: 1 (bicycle) total: 3,475 correct: 2,884 acc: 82.99% -* class: 2 (bus) total: 4,690 correct: 4,251 acc: 90.64% -* class: 3 (car) total: 10,401 correct: 7,715 acc: 74.18% -* class: 4 (horse) total: 4,691 correct: 4,580 acc: 97.63% -* class: 5 (knife) total: 2,075 correct: 1,868 acc: 90.02% -* class: 6 (motorcycle) total: 5,796 correct: 5,518 acc: 95.20% -* class: 7 (person) total: 4,000 correct: 2,796 acc: 69.90% -* class: 8 (plant) total: 4,549 correct: 3,981 acc: 87.51% -* class: 9 (skateboard) total: 2,281 correct: 2,053 acc: 90.00% -* class: 10 (train) total: 4,236 correct: 3,946 acc: 93.15% -* class: 11 (truck) total: 5,548 correct: 3,512 acc: 63.30% -* average: 86.08% -epoch [22/25][3200/4762] time 0.144 (0.153) data 0.000 (0.001) eta 0:40:22 loss 0.6205 (0.3678) loss_x 0.6200 (0.3587) loss_u 0.0005 (0.0091) acc_x 84.3750 (87.3135) lr 2.988172e-03 -epoch [22/25][3300/4762] time 0.151 (0.153) data 0.001 (0.001) eta 0:40:07 loss 0.3774 (0.3679) loss_x 0.3762 (0.3588) loss_u 0.0012 (0.0091) acc_x 87.5000 (87.3078) lr 2.988172e-03 -epoch [22/25][3400/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:39:51 loss 0.4281 (0.3679) loss_x 0.4255 (0.3588) loss_u 0.0027 (0.0091) acc_x 87.5000 (87.3263) lr 2.988172e-03 -epoch [22/25][3500/4762] time 0.145 (0.153) data 0.001 (0.001) eta 0:39:37 loss 0.1675 (0.3685) loss_x 0.1556 (0.3593) loss_u 0.0120 (0.0092) acc_x 96.8750 (87.3196) lr 2.988172e-03 -epoch [22/25][3600/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:39:22 loss 0.5780 (0.3687) loss_x 0.5648 (0.3595) loss_u 0.0132 (0.0092) acc_x 84.3750 (87.3012) lr 2.988172e-03 -epoch [22/25][3700/4762] time 0.158 (0.153) data 0.001 (0.001) eta 0:39:05 loss 0.2976 (0.3691) loss_x 0.2942 (0.3599) loss_u 0.0034 (0.0092) acc_x 87.5000 (87.2863) lr 2.988172e-03 -epoch [22/25][3800/4762] time 0.146 (0.153) data 0.001 (0.001) eta 0:38:49 loss 0.2767 (0.3691) loss_x 0.2691 (0.3599) loss_u 0.0076 (0.0092) acc_x 90.6250 (87.2887) lr 2.988172e-03 -epoch [22/25][3900/4762] time 0.160 (0.153) data 0.001 (0.001) eta 0:38:35 loss 0.3511 (0.3690) loss_x 0.3500 (0.3598) loss_u 0.0011 (0.0093) acc_x 87.5000 (87.2957) lr 2.988172e-03 -epoch [22/25][4000/4762] time 0.137 (0.153) data 0.001 (0.001) eta 0:38:21 loss 0.5896 (0.3692) loss_x 0.5821 (0.3600) loss_u 0.0075 (0.0093) acc_x 75.0000 (87.2969) lr 2.988172e-03 -epoch [22/25][4100/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:38:05 loss 0.5769 (0.3691) loss_x 0.5666 (0.3597) loss_u 0.0104 (0.0093) acc_x 87.5000 (87.3049) lr 2.988172e-03 -epoch [22/25][4200/4762] time 0.159 (0.153) data 0.001 (0.001) eta 0:37:49 loss 0.2279 (0.3693) loss_x 0.2245 (0.3600) loss_u 0.0034 (0.0093) acc_x 93.7500 (87.3132) lr 2.988172e-03 -epoch [22/25][4300/4762] time 0.139 (0.153) data 0.001 (0.001) eta 0:37:33 loss 0.2071 (0.3687) loss_x 0.1650 (0.3594) loss_u 0.0421 (0.0093) acc_x 93.7500 (87.3234) lr 2.988172e-03 -epoch [22/25][4400/4762] time 0.143 (0.153) data 0.001 (0.001) eta 0:37:17 loss 0.3467 (0.3687) loss_x 0.3415 (0.3594) loss_u 0.0051 (0.0093) acc_x 90.6250 (87.3246) lr 2.988172e-03 -epoch [22/25][4500/4762] time 0.137 (0.153) data 0.001 (0.001) eta 0:37:02 loss 0.5781 (0.3686) loss_x 0.5768 (0.3593) loss_u 0.0012 (0.0093) acc_x 81.2500 (87.3215) lr 2.988172e-03 -epoch [22/25][4600/4762] time 0.138 (0.153) data 0.001 (0.001) eta 0:36:46 loss 0.5162 (0.3686) loss_x 0.5084 (0.3593) loss_u 0.0077 (0.0093) acc_x 84.3750 (87.3193) lr 2.988172e-03 -epoch [22/25][4700/4762] time 0.141 (0.153) data 0.001 (0.001) eta 0:36:30 loss 0.5327 (0.3682) loss_x 0.5289 (0.3589) loss_u 0.0038 (0.0092) acc_x 84.3750 (87.3338) lr 2.988172e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,984 -* accuracy: 84.83% -* error: 15.17% -* macro_f1: 84.92% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,589 acc: 98.44% -* class: 1 (bicycle) total: 3,475 correct: 2,935 acc: 84.46% -* class: 2 (bus) total: 4,690 correct: 4,178 acc: 89.08% -* class: 3 (car) total: 10,401 correct: 7,812 acc: 75.11% -* class: 4 (horse) total: 4,691 correct: 4,592 acc: 97.89% -* class: 5 (knife) total: 2,075 correct: 1,894 acc: 91.28% -* class: 6 (motorcycle) total: 5,796 correct: 5,517 acc: 95.19% -* class: 7 (person) total: 4,000 correct: 2,886 acc: 72.15% -* class: 8 (plant) total: 4,549 correct: 3,982 acc: 87.54% -* class: 9 (skateboard) total: 2,281 correct: 2,035 acc: 89.22% -* class: 10 (train) total: 4,236 correct: 3,931 acc: 92.80% -* class: 11 (truck) total: 5,548 correct: 3,633 acc: 65.48% -* average: 86.55% -epoch [23/25][100/4762] time 0.144 (0.159) data 0.001 (0.003) eta 0:37:39 loss 0.5740 (0.3810) loss_x 0.5693 (0.3717) loss_u 0.0047 (0.0093) acc_x 81.2500 (86.0625) lr 1.405814e-03 -epoch [23/25][200/4762] time 0.135 (0.157) data 0.001 (0.002) eta 0:36:51 loss 0.6073 (0.3736) loss_x 0.6029 (0.3645) loss_u 0.0044 (0.0091) acc_x 81.2500 (86.9062) lr 1.405814e-03 -epoch [23/25][300/4762] time 0.146 (0.156) data 0.001 (0.002) eta 0:36:19 loss 0.2388 (0.3727) loss_x 0.2259 (0.3629) loss_u 0.0129 (0.0098) acc_x 93.7500 (87.1354) lr 1.405814e-03 -epoch [23/25][400/4762] time 0.179 (0.156) data 0.001 (0.001) eta 0:35:59 loss 0.2347 (0.3699) loss_x 0.2296 (0.3601) loss_u 0.0051 (0.0098) acc_x 90.6250 (87.3203) lr 1.405814e-03 -epoch [23/25][500/4762] time 0.156 (0.155) data 0.001 (0.001) eta 0:35:32 loss 0.2776 (0.3720) loss_x 0.2761 (0.3621) loss_u 0.0015 (0.0099) acc_x 90.6250 (87.2687) lr 1.405814e-03 -epoch [23/25][600/4762] time 0.178 (0.155) data 0.001 (0.001) eta 0:35:17 loss 0.3361 (0.3748) loss_x 0.3350 (0.3649) loss_u 0.0011 (0.0099) acc_x 87.5000 (87.1667) lr 1.405814e-03 -epoch [23/25][700/4762] time 0.156 (0.154) data 0.001 (0.001) eta 0:34:55 loss 0.2184 (0.3760) loss_x 0.1952 (0.3661) loss_u 0.0232 (0.0099) acc_x 93.7500 (87.1429) lr 1.405814e-03 -epoch [23/25][800/4762] time 0.144 (0.154) data 0.001 (0.001) eta 0:34:40 loss 0.4075 (0.3747) loss_x 0.3998 (0.3651) loss_u 0.0077 (0.0096) acc_x 90.6250 (87.1562) lr 1.405814e-03 -epoch [23/25][900/4762] time 0.151 (0.154) data 0.001 (0.001) eta 0:34:23 loss 0.3899 (0.3755) loss_x 0.3829 (0.3657) loss_u 0.0070 (0.0098) acc_x 81.2500 (87.1389) lr 1.405814e-03 -epoch [23/25][1000/4762] time 0.156 (0.154) data 0.001 (0.001) eta 0:34:05 loss 0.3147 (0.3746) loss_x 0.3124 (0.3648) loss_u 0.0023 (0.0098) acc_x 87.5000 (87.1813) lr 1.405814e-03 -epoch [23/25][1100/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:33:46 loss 0.2966 (0.3747) loss_x 0.2898 (0.3648) loss_u 0.0068 (0.0099) acc_x 93.7500 (87.1932) lr 1.405814e-03 -epoch [23/25][1200/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:33:28 loss 0.3582 (0.3761) loss_x 0.3362 (0.3661) loss_u 0.0219 (0.0100) acc_x 93.7500 (87.1667) lr 1.405814e-03 -epoch [23/25][1300/4762] time 0.169 (0.154) data 0.001 (0.001) eta 0:33:15 loss 0.4815 (0.3746) loss_x 0.4752 (0.3645) loss_u 0.0063 (0.0101) acc_x 87.5000 (87.2380) lr 1.405814e-03 -epoch [23/25][1400/4762] time 0.157 (0.154) data 0.001 (0.001) eta 0:33:01 loss 0.3466 (0.3754) loss_x 0.3291 (0.3653) loss_u 0.0175 (0.0101) acc_x 90.6250 (87.1562) lr 1.405814e-03 -epoch [23/25][1500/4762] time 0.151 (0.154) data 0.001 (0.001) eta 0:32:45 loss 0.2460 (0.3738) loss_x 0.2354 (0.3637) loss_u 0.0107 (0.0101) acc_x 93.7500 (87.2292) lr 1.405814e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,780 -* accuracy: 84.46% -* error: 15.54% -* macro_f1: 84.64% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,580 acc: 98.19% -* class: 1 (bicycle) total: 3,475 correct: 2,948 acc: 84.83% -* class: 2 (bus) total: 4,690 correct: 4,269 acc: 91.02% -* class: 3 (car) total: 10,401 correct: 7,732 acc: 74.34% -* class: 4 (horse) total: 4,691 correct: 4,587 acc: 97.78% -* class: 5 (knife) total: 2,075 correct: 1,885 acc: 90.84% -* class: 6 (motorcycle) total: 5,796 correct: 5,528 acc: 95.38% -* class: 7 (person) total: 4,000 correct: 2,982 acc: 74.55% -* class: 8 (plant) total: 4,549 correct: 3,871 acc: 85.10% -* class: 9 (skateboard) total: 2,281 correct: 2,079 acc: 91.14% -* class: 10 (train) total: 4,236 correct: 3,904 acc: 92.16% -* class: 11 (truck) total: 5,548 correct: 3,415 acc: 61.55% -* average: 86.41% -epoch [23/25][1600/4762] time 0.154 (0.154) data 0.001 (0.001) eta 0:32:29 loss 0.6733 (0.3749) loss_x 0.6679 (0.3649) loss_u 0.0054 (0.0100) acc_x 78.1250 (87.1973) lr 1.405814e-03 -epoch [23/25][1700/4762] time 0.145 (0.154) data 0.001 (0.001) eta 0:32:20 loss 0.2655 (0.3740) loss_x 0.2616 (0.3640) loss_u 0.0039 (0.0100) acc_x 93.7500 (87.2371) lr 1.405814e-03 -epoch [23/25][1800/4762] time 0.167 (0.155) data 0.001 (0.001) eta 0:32:10 loss 0.3841 (0.3731) loss_x 0.3803 (0.3631) loss_u 0.0038 (0.0100) acc_x 87.5000 (87.2517) lr 1.405814e-03 -epoch [23/25][1900/4762] time 0.170 (0.155) data 0.001 (0.001) eta 0:31:54 loss 0.2565 (0.3735) loss_x 0.2542 (0.3634) loss_u 0.0023 (0.0101) acc_x 90.6250 (87.2204) lr 1.405814e-03 -epoch [23/25][2000/4762] time 0.139 (0.154) data 0.001 (0.001) eta 0:31:35 loss 0.4738 (0.3744) loss_x 0.4733 (0.3645) loss_u 0.0006 (0.0099) acc_x 84.3750 (87.1828) lr 1.405814e-03 -epoch [23/25][2100/4762] time 0.149 (0.154) data 0.001 (0.001) eta 0:31:17 loss 0.5058 (0.3727) loss_x 0.4971 (0.3629) loss_u 0.0087 (0.0098) acc_x 78.1250 (87.2411) lr 1.405814e-03 -epoch [23/25][2200/4762] time 0.145 (0.154) data 0.001 (0.001) eta 0:31:02 loss 0.1862 (0.3726) loss_x 0.1705 (0.3628) loss_u 0.0157 (0.0098) acc_x 96.8750 (87.2614) lr 1.405814e-03 -epoch [23/25][2300/4762] time 0.138 (0.154) data 0.001 (0.001) eta 0:30:45 loss 0.3219 (0.3727) loss_x 0.3156 (0.3629) loss_u 0.0063 (0.0098) acc_x 87.5000 (87.2568) lr 1.405814e-03 -epoch [23/25][2400/4762] time 0.136 (0.154) data 0.001 (0.001) eta 0:30:29 loss 0.3860 (0.3725) loss_x 0.3855 (0.3627) loss_u 0.0005 (0.0098) acc_x 90.6250 (87.2539) lr 1.405814e-03 -epoch [23/25][2500/4762] time 0.168 (0.154) data 0.001 (0.001) eta 0:30:13 loss 0.3662 (0.3727) loss_x 0.3581 (0.3629) loss_u 0.0081 (0.0098) acc_x 84.3750 (87.2313) lr 1.405814e-03 -epoch [23/25][2600/4762] time 0.157 (0.154) data 0.001 (0.001) eta 0:29:56 loss 0.2692 (0.3725) loss_x 0.2552 (0.3626) loss_u 0.0140 (0.0098) acc_x 93.7500 (87.2668) lr 1.405814e-03 -epoch [23/25][2700/4762] time 0.156 (0.154) data 0.001 (0.001) eta 0:29:40 loss 0.5217 (0.3720) loss_x 0.5193 (0.3621) loss_u 0.0023 (0.0099) acc_x 81.2500 (87.2859) lr 1.405814e-03 -epoch [23/25][2800/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:29:25 loss 0.7129 (0.3719) loss_x 0.7086 (0.3620) loss_u 0.0043 (0.0099) acc_x 75.0000 (87.2768) lr 1.405814e-03 -epoch [23/25][2900/4762] time 0.149 (0.154) data 0.001 (0.001) eta 0:29:09 loss 0.4151 (0.3732) loss_x 0.4137 (0.3633) loss_u 0.0013 (0.0098) acc_x 90.6250 (87.2306) lr 1.405814e-03 -epoch [23/25][3000/4762] time 0.153 (0.154) data 0.001 (0.001) eta 0:28:54 loss 0.6045 (0.3722) loss_x 0.6013 (0.3624) loss_u 0.0032 (0.0098) acc_x 81.2500 (87.2750) lr 1.405814e-03 -epoch [23/25][3100/4762] time 0.149 (0.154) data 0.001 (0.001) eta 0:28:38 loss 0.4011 (0.3724) loss_x 0.3984 (0.3626) loss_u 0.0027 (0.0098) acc_x 84.3750 (87.2681) lr 1.405814e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,837 -* accuracy: 84.56% -* error: 15.44% -* macro_f1: 84.82% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,586 acc: 98.35% -* class: 1 (bicycle) total: 3,475 correct: 2,888 acc: 83.11% -* class: 2 (bus) total: 4,690 correct: 4,268 acc: 91.00% -* class: 3 (car) total: 10,401 correct: 7,788 acc: 74.88% -* class: 4 (horse) total: 4,691 correct: 4,575 acc: 97.53% -* class: 5 (knife) total: 2,075 correct: 1,817 acc: 87.57% -* class: 6 (motorcycle) total: 5,796 correct: 5,515 acc: 95.15% -* class: 7 (person) total: 4,000 correct: 3,047 acc: 76.17% -* class: 8 (plant) total: 4,549 correct: 3,951 acc: 86.85% -* class: 9 (skateboard) total: 2,281 correct: 2,064 acc: 90.49% -* class: 10 (train) total: 4,236 correct: 3,948 acc: 93.20% -* class: 11 (truck) total: 5,548 correct: 3,390 acc: 61.10% -* average: 86.28% -epoch [23/25][3200/4762] time 0.168 (0.154) data 0.001 (0.001) eta 0:28:24 loss 0.3235 (0.3720) loss_x 0.3203 (0.3621) loss_u 0.0032 (0.0098) acc_x 90.6250 (87.2773) lr 1.405814e-03 -epoch [23/25][3300/4762] time 0.154 (0.154) data 0.001 (0.001) eta 0:28:12 loss 0.2729 (0.3716) loss_x 0.2702 (0.3618) loss_u 0.0028 (0.0098) acc_x 90.6250 (87.2860) lr 1.405814e-03 -epoch [23/25][3400/4762] time 0.136 (0.154) data 0.001 (0.001) eta 0:27:55 loss 0.2278 (0.3717) loss_x 0.2152 (0.3619) loss_u 0.0126 (0.0098) acc_x 93.7500 (87.2812) lr 1.405814e-03 -epoch [23/25][3500/4762] time 0.162 (0.154) data 0.001 (0.001) eta 0:27:40 loss 0.2210 (0.3716) loss_x 0.2187 (0.3618) loss_u 0.0023 (0.0097) acc_x 90.6250 (87.2893) lr 1.405814e-03 -epoch [23/25][3600/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:27:24 loss 0.2738 (0.3711) loss_x 0.2731 (0.3613) loss_u 0.0007 (0.0097) acc_x 87.5000 (87.2995) lr 1.405814e-03 -epoch [23/25][3700/4762] time 0.137 (0.154) data 0.001 (0.001) eta 0:27:08 loss 0.4685 (0.3709) loss_x 0.4655 (0.3613) loss_u 0.0029 (0.0096) acc_x 84.3750 (87.2829) lr 1.405814e-03 -epoch [23/25][3800/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:26:52 loss 0.7744 (0.3705) loss_x 0.7667 (0.3609) loss_u 0.0078 (0.0096) acc_x 75.0000 (87.3084) lr 1.405814e-03 -epoch [23/25][3900/4762] time 0.160 (0.154) data 0.001 (0.001) eta 0:26:36 loss 0.2350 (0.3704) loss_x 0.2332 (0.3607) loss_u 0.0018 (0.0096) acc_x 93.7500 (87.3077) lr 1.405814e-03 -epoch [23/25][4000/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:26:20 loss 0.3039 (0.3700) loss_x 0.2884 (0.3604) loss_u 0.0155 (0.0096) acc_x 87.5000 (87.3289) lr 1.405814e-03 -epoch [23/25][4100/4762] time 0.137 (0.154) data 0.001 (0.001) eta 0:26:04 loss 0.5187 (0.3707) loss_x 0.5139 (0.3611) loss_u 0.0049 (0.0096) acc_x 75.0000 (87.2950) lr 1.405814e-03 -epoch [23/25][4200/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:25:48 loss 0.4041 (0.3707) loss_x 0.3798 (0.3611) loss_u 0.0243 (0.0096) acc_x 87.5000 (87.3065) lr 1.405814e-03 -epoch [23/25][4300/4762] time 0.154 (0.154) data 0.001 (0.001) eta 0:25:33 loss 0.3634 (0.3698) loss_x 0.3457 (0.3603) loss_u 0.0177 (0.0096) acc_x 87.5000 (87.3328) lr 1.405814e-03 -epoch [23/25][4400/4762] time 0.151 (0.154) data 0.001 (0.001) eta 0:25:17 loss 0.5005 (0.3693) loss_x 0.4763 (0.3598) loss_u 0.0242 (0.0095) acc_x 81.2500 (87.3494) lr 1.405814e-03 -epoch [23/25][4500/4762] time 0.163 (0.153) data 0.001 (0.001) eta 0:25:01 loss 0.4618 (0.3693) loss_x 0.4489 (0.3598) loss_u 0.0129 (0.0095) acc_x 81.2500 (87.3472) lr 1.405814e-03 -epoch [23/25][4600/4762] time 0.150 (0.153) data 0.001 (0.001) eta 0:24:45 loss 0.2546 (0.3689) loss_x 0.2370 (0.3595) loss_u 0.0177 (0.0094) acc_x 90.6250 (87.3601) lr 1.405814e-03 -epoch [23/25][4700/4762] time 0.139 (0.153) data 0.001 (0.001) eta 0:24:29 loss 0.6299 (0.3688) loss_x 0.6175 (0.3594) loss_u 0.0123 (0.0094) acc_x 75.0000 (87.3590) lr 1.405814e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,948 -* accuracy: 84.76% -* error: 15.24% -* macro_f1: 84.97% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,587 acc: 98.38% -* class: 1 (bicycle) total: 3,475 correct: 2,924 acc: 84.14% -* class: 2 (bus) total: 4,690 correct: 4,284 acc: 91.34% -* class: 3 (car) total: 10,401 correct: 7,888 acc: 75.84% -* class: 4 (horse) total: 4,691 correct: 4,574 acc: 97.51% -* class: 5 (knife) total: 2,075 correct: 1,853 acc: 89.30% -* class: 6 (motorcycle) total: 5,796 correct: 5,500 acc: 94.89% -* class: 7 (person) total: 4,000 correct: 3,101 acc: 77.53% -* class: 8 (plant) total: 4,549 correct: 3,923 acc: 86.24% -* class: 9 (skateboard) total: 2,281 correct: 2,049 acc: 89.83% -* class: 10 (train) total: 4,236 correct: 3,913 acc: 92.37% -* class: 11 (truck) total: 5,548 correct: 3,352 acc: 60.42% -* average: 86.48% -epoch [24/25][100/4762] time 0.186 (0.165) data 0.001 (0.007) eta 0:25:56 loss 0.4882 (0.3458) loss_x 0.4791 (0.3360) loss_u 0.0091 (0.0099) acc_x 84.3750 (88.4375) lr 0.000000e+00 -epoch [24/25][200/4762] time 0.151 (0.160) data 0.001 (0.004) eta 0:24:53 loss 0.2411 (0.3552) loss_x 0.2399 (0.3461) loss_u 0.0011 (0.0091) acc_x 93.7500 (88.0469) lr 0.000000e+00 -epoch [24/25][300/4762] time 0.142 (0.157) data 0.000 (0.003) eta 0:24:09 loss 0.3146 (0.3555) loss_x 0.3124 (0.3468) loss_u 0.0022 (0.0087) acc_x 87.5000 (88.1458) lr 0.000000e+00 -epoch [24/25][400/4762] time 0.175 (0.156) data 0.001 (0.002) eta 0:23:39 loss 0.3484 (0.3581) loss_x 0.3439 (0.3491) loss_u 0.0045 (0.0090) acc_x 90.6250 (88.0859) lr 0.000000e+00 -epoch [24/25][500/4762] time 0.151 (0.155) data 0.001 (0.002) eta 0:23:18 loss 0.2949 (0.3601) loss_x 0.2813 (0.3508) loss_u 0.0137 (0.0093) acc_x 93.7500 (87.9000) lr 0.000000e+00 -epoch [24/25][600/4762] time 0.173 (0.155) data 0.001 (0.002) eta 0:23:01 loss 0.3021 (0.3646) loss_x 0.2931 (0.3547) loss_u 0.0090 (0.0099) acc_x 87.5000 (87.6927) lr 0.000000e+00 -epoch [24/25][700/4762] time 0.154 (0.155) data 0.001 (0.002) eta 0:22:46 loss 0.3494 (0.3622) loss_x 0.3373 (0.3527) loss_u 0.0121 (0.0095) acc_x 87.5000 (87.8259) lr 0.000000e+00 -epoch [24/25][800/4762] time 0.160 (0.155) data 0.001 (0.002) eta 0:22:30 loss 0.4462 (0.3636) loss_x 0.4375 (0.3542) loss_u 0.0087 (0.0094) acc_x 84.3750 (87.6992) lr 0.000000e+00 -epoch [24/25][900/4762] time 0.141 (0.155) data 0.001 (0.002) eta 0:22:15 loss 0.4559 (0.3654) loss_x 0.4335 (0.3560) loss_u 0.0224 (0.0093) acc_x 78.1250 (87.4861) lr 0.000000e+00 -epoch [24/25][1000/4762] time 0.148 (0.155) data 0.001 (0.001) eta 0:22:00 loss 0.4739 (0.3667) loss_x 0.4731 (0.3574) loss_u 0.0007 (0.0093) acc_x 81.2500 (87.4188) lr 0.000000e+00 -epoch [24/25][1100/4762] time 0.153 (0.155) data 0.001 (0.001) eta 0:21:42 loss 0.4081 (0.3674) loss_x 0.4024 (0.3580) loss_u 0.0057 (0.0094) acc_x 87.5000 (87.3835) lr 0.000000e+00 -epoch [24/25][1200/4762] time 0.190 (0.155) data 0.001 (0.001) eta 0:21:26 loss 0.2890 (0.3695) loss_x 0.2885 (0.3601) loss_u 0.0005 (0.0094) acc_x 90.6250 (87.3776) lr 0.000000e+00 -epoch [24/25][1300/4762] time 0.153 (0.154) data 0.001 (0.001) eta 0:21:07 loss 0.3085 (0.3705) loss_x 0.3074 (0.3612) loss_u 0.0011 (0.0092) acc_x 93.7500 (87.3486) lr 0.000000e+00 -epoch [24/25][1400/4762] time 0.140 (0.154) data 0.001 (0.001) eta 0:20:49 loss 0.2515 (0.3699) loss_x 0.2490 (0.3606) loss_u 0.0024 (0.0092) acc_x 90.6250 (87.3415) lr 0.000000e+00 -epoch [24/25][1500/4762] time 0.165 (0.154) data 0.001 (0.001) eta 0:20:33 loss 0.4599 (0.3705) loss_x 0.4526 (0.3612) loss_u 0.0072 (0.0093) acc_x 81.2500 (87.3229) lr 0.000000e+00 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,809 -* accuracy: 84.51% -* error: 15.49% -* macro_f1: 84.69% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,586 acc: 98.35% -* class: 1 (bicycle) total: 3,475 correct: 2,909 acc: 83.71% -* class: 2 (bus) total: 4,690 correct: 4,260 acc: 90.83% -* class: 3 (car) total: 10,401 correct: 7,790 acc: 74.90% -* class: 4 (horse) total: 4,691 correct: 4,585 acc: 97.74% -* class: 5 (knife) total: 2,075 correct: 1,875 acc: 90.36% -* class: 6 (motorcycle) total: 5,796 correct: 5,510 acc: 95.07% -* class: 7 (person) total: 4,000 correct: 3,055 acc: 76.38% -* class: 8 (plant) total: 4,549 correct: 3,847 acc: 84.57% -* class: 9 (skateboard) total: 2,281 correct: 2,050 acc: 89.87% -* class: 10 (train) total: 4,236 correct: 3,919 acc: 92.52% -* class: 11 (truck) total: 5,548 correct: 3,423 acc: 61.70% -* average: 86.33% -epoch [24/25][1600/4762] time 0.149 (0.154) data 0.001 (0.001) eta 0:20:19 loss 0.5697 (0.3707) loss_x 0.5364 (0.3614) loss_u 0.0333 (0.0092) acc_x 78.1250 (87.3281) lr 0.000000e+00 -epoch [24/25][1700/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:20:05 loss 0.4631 (0.3702) loss_x 0.4551 (0.3610) loss_u 0.0080 (0.0092) acc_x 90.6250 (87.3125) lr 0.000000e+00 -epoch [24/25][1800/4762] time 0.179 (0.154) data 0.001 (0.002) eta 0:19:53 loss 0.4887 (0.3694) loss_x 0.4853 (0.3603) loss_u 0.0034 (0.0091) acc_x 84.3750 (87.3438) lr 0.000000e+00 -epoch [24/25][1900/4762] time 0.136 (0.154) data 0.001 (0.001) eta 0:19:35 loss 0.3139 (0.3698) loss_x 0.3126 (0.3608) loss_u 0.0013 (0.0090) acc_x 90.6250 (87.3109) lr 0.000000e+00 -epoch [24/25][2000/4762] time 0.160 (0.154) data 0.001 (0.001) eta 0:19:18 loss 0.4226 (0.3708) loss_x 0.4212 (0.3618) loss_u 0.0014 (0.0090) acc_x 84.3750 (87.2641) lr 0.000000e+00 -epoch [24/25][2100/4762] time 0.140 (0.154) data 0.001 (0.001) eta 0:19:02 loss 0.6173 (0.3706) loss_x 0.6164 (0.3615) loss_u 0.0009 (0.0091) acc_x 71.8750 (87.2946) lr 0.000000e+00 -epoch [24/25][2200/4762] time 0.148 (0.154) data 0.001 (0.001) eta 0:18:45 loss 0.4609 (0.3701) loss_x 0.4551 (0.3611) loss_u 0.0058 (0.0091) acc_x 87.5000 (87.2997) lr 0.000000e+00 -epoch [24/25][2300/4762] time 0.163 (0.154) data 0.001 (0.001) eta 0:18:31 loss 0.2761 (0.3691) loss_x 0.2723 (0.3600) loss_u 0.0038 (0.0091) acc_x 87.5000 (87.3247) lr 0.000000e+00 -epoch [24/25][2400/4762] time 0.171 (0.154) data 0.001 (0.001) eta 0:18:15 loss 0.4849 (0.3702) loss_x 0.4830 (0.3612) loss_u 0.0020 (0.0090) acc_x 78.1250 (87.2839) lr 0.000000e+00 -epoch [24/25][2500/4762] time 0.144 (0.154) data 0.001 (0.001) eta 0:18:00 loss 0.3318 (0.3710) loss_x 0.3188 (0.3620) loss_u 0.0130 (0.0090) acc_x 87.5000 (87.2463) lr 0.000000e+00 -epoch [24/25][2600/4762] time 0.156 (0.154) data 0.001 (0.001) eta 0:17:45 loss 0.3685 (0.3706) loss_x 0.3355 (0.3616) loss_u 0.0330 (0.0090) acc_x 87.5000 (87.2656) lr 0.000000e+00 -epoch [24/25][2700/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:17:30 loss 0.4421 (0.3713) loss_x 0.4365 (0.3623) loss_u 0.0057 (0.0090) acc_x 87.5000 (87.2373) lr 0.000000e+00 -epoch [24/25][2800/4762] time 0.160 (0.154) data 0.001 (0.001) eta 0:17:15 loss 0.0768 (0.3712) loss_x 0.0742 (0.3622) loss_u 0.0026 (0.0090) acc_x 100.0000 (87.2522) lr 0.000000e+00 -epoch [24/25][2900/4762] time 0.158 (0.154) data 0.001 (0.001) eta 0:16:59 loss 0.4780 (0.3717) loss_x 0.4717 (0.3627) loss_u 0.0064 (0.0089) acc_x 84.3750 (87.2392) lr 0.000000e+00 -epoch [24/25][3000/4762] time 0.144 (0.154) data 0.001 (0.001) eta 0:16:43 loss 0.6090 (0.3721) loss_x 0.5907 (0.3632) loss_u 0.0183 (0.0089) acc_x 81.2500 (87.2229) lr 0.000000e+00 -epoch [24/25][3100/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:16:27 loss 0.3698 (0.3716) loss_x 0.3689 (0.3627) loss_u 0.0009 (0.0089) acc_x 87.5000 (87.2450) lr 0.000000e+00 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,595 -* accuracy: 84.12% -* error: 15.88% -* macro_f1: 84.20% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,584 acc: 98.30% -* class: 1 (bicycle) total: 3,475 correct: 2,914 acc: 83.86% -* class: 2 (bus) total: 4,690 correct: 4,252 acc: 90.66% -* class: 3 (car) total: 10,401 correct: 7,655 acc: 73.60% -* class: 4 (horse) total: 4,691 correct: 4,591 acc: 97.87% -* class: 5 (knife) total: 2,075 correct: 1,839 acc: 88.63% -* class: 6 (motorcycle) total: 5,796 correct: 5,529 acc: 95.39% -* class: 7 (person) total: 4,000 correct: 2,836 acc: 70.90% -* class: 8 (plant) total: 4,549 correct: 3,896 acc: 85.65% -* class: 9 (skateboard) total: 2,281 correct: 2,076 acc: 91.01% -* class: 10 (train) total: 4,236 correct: 3,927 acc: 92.71% -* class: 11 (truck) total: 5,548 correct: 3,496 acc: 63.01% -* average: 85.97% -epoch [24/25][3200/4762] time 0.208 (0.154) data 0.001 (0.001) eta 0:16:13 loss 0.4673 (0.3710) loss_x 0.4659 (0.3621) loss_u 0.0014 (0.0089) acc_x 87.5000 (87.2549) lr 0.000000e+00 -epoch [24/25][3300/4762] time 0.166 (0.154) data 0.001 (0.001) eta 0:15:59 loss 0.4091 (0.3710) loss_x 0.3989 (0.3621) loss_u 0.0102 (0.0089) acc_x 87.5000 (87.2462) lr 0.000000e+00 -epoch [24/25][3400/4762] time 0.138 (0.154) data 0.001 (0.001) eta 0:15:44 loss 0.7328 (0.3711) loss_x 0.6750 (0.3622) loss_u 0.0578 (0.0089) acc_x 78.1250 (87.2390) lr 0.000000e+00 -epoch [24/25][3500/4762] time 0.169 (0.154) data 0.001 (0.001) eta 0:15:29 loss 0.4523 (0.3707) loss_x 0.4487 (0.3618) loss_u 0.0036 (0.0089) acc_x 81.2500 (87.2420) lr 0.000000e+00 -epoch [24/25][3600/4762] time 0.159 (0.154) data 0.001 (0.001) eta 0:15:13 loss 0.5497 (0.3705) loss_x 0.5474 (0.3616) loss_u 0.0024 (0.0089) acc_x 81.2500 (87.2483) lr 0.000000e+00 -epoch [24/25][3700/4762] time 0.153 (0.154) data 0.001 (0.001) eta 0:14:57 loss 0.3097 (0.3705) loss_x 0.3078 (0.3616) loss_u 0.0019 (0.0089) acc_x 90.6250 (87.2399) lr 0.000000e+00 -epoch [24/25][3800/4762] time 0.137 (0.154) data 0.001 (0.001) eta 0:14:41 loss 0.6476 (0.3706) loss_x 0.6271 (0.3617) loss_u 0.0205 (0.0089) acc_x 81.2500 (87.2533) lr 0.000000e+00 -epoch [24/25][3900/4762] time 0.150 (0.154) data 0.001 (0.001) eta 0:14:26 loss 0.3090 (0.3709) loss_x 0.2889 (0.3620) loss_u 0.0201 (0.0089) acc_x 90.6250 (87.2428) lr 0.000000e+00 -epoch [24/25][4000/4762] time 0.142 (0.154) data 0.001 (0.001) eta 0:14:11 loss 0.6461 (0.3710) loss_x 0.6447 (0.3620) loss_u 0.0015 (0.0090) acc_x 78.1250 (87.2523) lr 0.000000e+00 -epoch [24/25][4100/4762] time 0.136 (0.154) data 0.001 (0.001) eta 0:13:56 loss 0.4640 (0.3710) loss_x 0.3443 (0.3621) loss_u 0.1197 (0.0089) acc_x 90.6250 (87.2576) lr 0.000000e+00 -epoch [24/25][4200/4762] time 0.158 (0.154) data 0.001 (0.001) eta 0:13:40 loss 0.3819 (0.3705) loss_x 0.3761 (0.3615) loss_u 0.0058 (0.0090) acc_x 84.3750 (87.2783) lr 0.000000e+00 -epoch [24/25][4300/4762] time 0.160 (0.154) data 0.003 (0.001) eta 0:13:24 loss 0.4955 (0.3704) loss_x 0.4943 (0.3614) loss_u 0.0012 (0.0090) acc_x 84.3750 (87.2907) lr 0.000000e+00 -epoch [24/25][4400/4762] time 0.144 (0.154) data 0.001 (0.001) eta 0:13:08 loss 0.1985 (0.3699) loss_x 0.1956 (0.3609) loss_u 0.0029 (0.0090) acc_x 90.6250 (87.3161) lr 0.000000e+00 -epoch [24/25][4500/4762] time 0.139 (0.154) data 0.002 (0.001) eta 0:12:53 loss 0.3040 (0.3698) loss_x 0.2963 (0.3609) loss_u 0.0078 (0.0090) acc_x 90.6250 (87.3174) lr 0.000000e+00 -epoch [24/25][4600/4762] time 0.151 (0.154) data 0.001 (0.001) eta 0:12:37 loss 0.3236 (0.3694) loss_x 0.3190 (0.3605) loss_u 0.0046 (0.0090) acc_x 84.3750 (87.3186) lr 0.000000e+00 -epoch [24/25][4700/4762] time 0.169 (0.154) data 0.001 (0.001) eta 0:12:21 loss 0.3758 (0.3692) loss_x 0.3707 (0.3602) loss_u 0.0051 (0.0090) acc_x 90.6250 (87.3265) lr 0.000000e+00 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,667 -* accuracy: 84.25% -* error: 15.75% -* macro_f1: 84.28% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,580 acc: 98.19% -* class: 1 (bicycle) total: 3,475 correct: 2,896 acc: 83.34% -* class: 2 (bus) total: 4,690 correct: 4,252 acc: 90.66% -* class: 3 (car) total: 10,401 correct: 7,822 acc: 75.20% -* class: 4 (horse) total: 4,691 correct: 4,581 acc: 97.66% -* class: 5 (knife) total: 2,075 correct: 1,878 acc: 90.51% -* class: 6 (motorcycle) total: 5,796 correct: 5,521 acc: 95.26% -* class: 7 (person) total: 4,000 correct: 2,904 acc: 72.60% -* class: 8 (plant) total: 4,549 correct: 3,888 acc: 85.47% -* class: 9 (skateboard) total: 2,281 correct: 2,059 acc: 90.27% -* class: 10 (train) total: 4,236 correct: 3,936 acc: 92.92% -* class: 11 (truck) total: 5,548 correct: 3,350 acc: 60.38% -* average: 86.04% -epoch [25/25][100/4762] time 0.177 (0.165) data 0.001 (0.004) eta 0:12:49 loss 0.1916 (0.3680) loss_x 0.1902 (0.3569) loss_u 0.0014 (0.0111) acc_x 93.7500 (88.5312) lr 1.405814e-03 -epoch [25/25][200/4762] time 0.179 (0.161) data 0.001 (0.002) eta 0:12:13 loss 0.2227 (0.3723) loss_x 0.2151 (0.3627) loss_u 0.0075 (0.0096) acc_x 96.8750 (87.6719) lr 1.405814e-03 -epoch [25/25][300/4762] time 0.137 (0.160) data 0.000 (0.002) eta 0:11:53 loss 0.3026 (0.3614) loss_x 0.3015 (0.3526) loss_u 0.0011 (0.0089) acc_x 90.6250 (87.8438) lr 1.405814e-03 -epoch [25/25][400/4762] time 0.147 (0.158) data 0.001 (0.002) eta 0:11:30 loss 0.4072 (0.3645) loss_x 0.4071 (0.3560) loss_u 0.0001 (0.0085) acc_x 90.6250 (87.5859) lr 1.405814e-03 -epoch [25/25][500/4762] time 0.174 (0.157) data 0.001 (0.001) eta 0:11:09 loss 0.5133 (0.3654) loss_x 0.5010 (0.3569) loss_u 0.0123 (0.0084) acc_x 78.1250 (87.5375) lr 1.405814e-03 -epoch [25/25][600/4762] time 0.169 (0.156) data 0.001 (0.001) eta 0:10:50 loss 0.3301 (0.3686) loss_x 0.3237 (0.3600) loss_u 0.0065 (0.0086) acc_x 90.6250 (87.4167) lr 1.405814e-03 -epoch [25/25][700/4762] time 0.175 (0.156) data 0.001 (0.001) eta 0:10:32 loss 0.2742 (0.3701) loss_x 0.2736 (0.3617) loss_u 0.0005 (0.0085) acc_x 93.7500 (87.3884) lr 1.405814e-03 -epoch [25/25][800/4762] time 0.159 (0.156) data 0.002 (0.001) eta 0:10:16 loss 0.4696 (0.3700) loss_x 0.4574 (0.3616) loss_u 0.0121 (0.0084) acc_x 81.2500 (87.3516) lr 1.405814e-03 -epoch [25/25][900/4762] time 0.157 (0.155) data 0.001 (0.001) eta 0:10:00 loss 0.3124 (0.3705) loss_x 0.3035 (0.3621) loss_u 0.0089 (0.0084) acc_x 84.3750 (87.3090) lr 1.405814e-03 -epoch [25/25][1000/4762] time 0.144 (0.155) data 0.001 (0.001) eta 0:09:43 loss 0.3293 (0.3711) loss_x 0.3268 (0.3624) loss_u 0.0025 (0.0087) acc_x 87.5000 (87.2594) lr 1.405814e-03 -epoch [25/25][1100/4762] time 0.145 (0.155) data 0.001 (0.001) eta 0:09:27 loss 0.6497 (0.3741) loss_x 0.5847 (0.3653) loss_u 0.0651 (0.0088) acc_x 68.7500 (87.1676) lr 1.405814e-03 -epoch [25/25][1200/4762] time 0.156 (0.155) data 0.001 (0.001) eta 0:09:10 loss 0.1938 (0.3732) loss_x 0.1718 (0.3643) loss_u 0.0220 (0.0089) acc_x 93.7500 (87.2396) lr 1.405814e-03 -epoch [25/25][1300/4762] time 0.141 (0.154) data 0.001 (0.001) eta 0:08:54 loss 0.4009 (0.3736) loss_x 0.3950 (0.3643) loss_u 0.0059 (0.0093) acc_x 84.3750 (87.1995) lr 1.405814e-03 -epoch [25/25][1400/4762] time 0.141 (0.154) data 0.001 (0.001) eta 0:08:38 loss 0.4662 (0.3730) loss_x 0.4657 (0.3636) loss_u 0.0005 (0.0094) acc_x 90.6250 (87.2076) lr 1.405814e-03 -epoch [25/25][1500/4762] time 0.142 (0.154) data 0.001 (0.001) eta 0:08:23 loss 0.5211 (0.3732) loss_x 0.5192 (0.3637) loss_u 0.0019 (0.0095) acc_x 81.2500 (87.1833) lr 1.405814e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 47,028 -* accuracy: 84.91% -* error: 15.09% -* macro_f1: 85.04% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,582 acc: 98.24% -* class: 1 (bicycle) total: 3,475 correct: 2,929 acc: 84.29% -* class: 2 (bus) total: 4,690 correct: 4,227 acc: 90.13% -* class: 3 (car) total: 10,401 correct: 8,020 acc: 77.11% -* class: 4 (horse) total: 4,691 correct: 4,579 acc: 97.61% -* class: 5 (knife) total: 2,075 correct: 1,848 acc: 89.06% -* class: 6 (motorcycle) total: 5,796 correct: 5,510 acc: 95.07% -* class: 7 (person) total: 4,000 correct: 3,076 acc: 76.90% -* class: 8 (plant) total: 4,549 correct: 3,931 acc: 86.41% -* class: 9 (skateboard) total: 2,281 correct: 2,067 acc: 90.62% -* class: 10 (train) total: 4,236 correct: 3,922 acc: 92.59% -* class: 11 (truck) total: 5,548 correct: 3,337 acc: 60.15% -* average: 86.51% -epoch [25/25][1600/4762] time 0.153 (0.154) data 0.001 (0.001) eta 0:08:07 loss 0.5102 (0.3713) loss_x 0.4976 (0.3617) loss_u 0.0126 (0.0095) acc_x 78.1250 (87.2617) lr 1.405814e-03 -epoch [25/25][1700/4762] time 0.165 (0.155) data 0.001 (0.001) eta 0:07:53 loss 0.3860 (0.3725) loss_x 0.3786 (0.3630) loss_u 0.0074 (0.0095) acc_x 90.6250 (87.2224) lr 1.405814e-03 -epoch [25/25][1800/4762] time 0.150 (0.155) data 0.001 (0.001) eta 0:07:39 loss 0.1830 (0.3719) loss_x 0.1798 (0.3624) loss_u 0.0032 (0.0095) acc_x 90.6250 (87.2240) lr 1.405814e-03 -epoch [25/25][1900/4762] time 0.142 (0.155) data 0.001 (0.001) eta 0:07:23 loss 0.3627 (0.3713) loss_x 0.3605 (0.3618) loss_u 0.0022 (0.0095) acc_x 87.5000 (87.2352) lr 1.405814e-03 -epoch [25/25][2000/4762] time 0.157 (0.155) data 0.001 (0.001) eta 0:07:07 loss 0.4061 (0.3719) loss_x 0.4050 (0.3625) loss_u 0.0011 (0.0094) acc_x 87.5000 (87.2250) lr 1.405814e-03 -epoch [25/25][2100/4762] time 0.163 (0.155) data 0.001 (0.001) eta 0:06:51 loss 0.4499 (0.3718) loss_x 0.4333 (0.3624) loss_u 0.0166 (0.0094) acc_x 87.5000 (87.2485) lr 1.405814e-03 -epoch [25/25][2200/4762] time 0.137 (0.155) data 0.001 (0.001) eta 0:06:36 loss 0.2057 (0.3713) loss_x 0.2046 (0.3620) loss_u 0.0012 (0.0093) acc_x 96.8750 (87.2614) lr 1.405814e-03 -epoch [25/25][2300/4762] time 0.145 (0.155) data 0.001 (0.001) eta 0:06:20 loss 0.1412 (0.3711) loss_x 0.1313 (0.3618) loss_u 0.0099 (0.0094) acc_x 93.7500 (87.2745) lr 1.405814e-03 -epoch [25/25][2400/4762] time 0.149 (0.154) data 0.001 (0.001) eta 0:06:04 loss 0.4385 (0.3709) loss_x 0.4215 (0.3615) loss_u 0.0170 (0.0094) acc_x 81.2500 (87.2943) lr 1.405814e-03 -epoch [25/25][2500/4762] time 0.154 (0.154) data 0.001 (0.001) eta 0:05:49 loss 0.2024 (0.3707) loss_x 0.1985 (0.3613) loss_u 0.0040 (0.0094) acc_x 93.7500 (87.3125) lr 1.405814e-03 -epoch [25/25][2600/4762] time 0.140 (0.154) data 0.001 (0.001) eta 0:05:33 loss 0.2446 (0.3699) loss_x 0.2382 (0.3604) loss_u 0.0064 (0.0095) acc_x 96.8750 (87.3389) lr 1.405814e-03 -epoch [25/25][2700/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:05:17 loss 0.3085 (0.3698) loss_x 0.2983 (0.3603) loss_u 0.0102 (0.0095) acc_x 90.6250 (87.3472) lr 1.405814e-03 -epoch [25/25][2800/4762] time 0.140 (0.154) data 0.001 (0.001) eta 0:05:02 loss 0.3642 (0.3694) loss_x 0.3608 (0.3599) loss_u 0.0034 (0.0095) acc_x 90.6250 (87.3728) lr 1.405814e-03 -epoch [25/25][2900/4762] time 0.156 (0.154) data 0.001 (0.001) eta 0:04:46 loss 0.5581 (0.3698) loss_x 0.5410 (0.3603) loss_u 0.0171 (0.0095) acc_x 78.1250 (87.3761) lr 1.405814e-03 -epoch [25/25][3000/4762] time 0.143 (0.154) data 0.001 (0.001) eta 0:04:30 loss 0.2971 (0.3693) loss_x 0.2798 (0.3599) loss_u 0.0173 (0.0095) acc_x 87.5000 (87.4062) lr 1.405814e-03 -epoch [25/25][3100/4762] time 0.159 (0.154) data 0.001 (0.001) eta 0:04:15 loss 0.2581 (0.3692) loss_x 0.2490 (0.3598) loss_u 0.0091 (0.0094) acc_x 90.6250 (87.4032) lr 1.405814e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,780 -* accuracy: 84.46% -* error: 15.54% -* macro_f1: 84.52% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,592 acc: 98.52% -* class: 1 (bicycle) total: 3,475 correct: 2,927 acc: 84.23% -* class: 2 (bus) total: 4,690 correct: 4,228 acc: 90.15% -* class: 3 (car) total: 10,401 correct: 7,849 acc: 75.46% -* class: 4 (horse) total: 4,691 correct: 4,598 acc: 98.02% -* class: 5 (knife) total: 2,075 correct: 1,884 acc: 90.80% -* class: 6 (motorcycle) total: 5,796 correct: 5,512 acc: 95.10% -* class: 7 (person) total: 4,000 correct: 2,829 acc: 70.72% -* class: 8 (plant) total: 4,549 correct: 3,888 acc: 85.47% -* class: 9 (skateboard) total: 2,281 correct: 2,065 acc: 90.53% -* class: 10 (train) total: 4,236 correct: 3,946 acc: 93.15% -* class: 11 (truck) total: 5,548 correct: 3,462 acc: 62.40% -* average: 86.21% -epoch [25/25][3200/4762] time 0.147 (0.154) data 0.001 (0.001) eta 0:04:00 loss 0.2721 (0.3688) loss_x 0.2615 (0.3594) loss_u 0.0106 (0.0094) acc_x 87.5000 (87.4043) lr 1.405814e-03 -epoch [25/25][3300/4762] time 0.178 (0.154) data 0.001 (0.001) eta 0:03:45 loss 0.4526 (0.3688) loss_x 0.4172 (0.3595) loss_u 0.0354 (0.0094) acc_x 87.5000 (87.4271) lr 1.405814e-03 -epoch [25/25][3400/4762] time 0.139 (0.154) data 0.001 (0.001) eta 0:03:29 loss 0.5220 (0.3684) loss_x 0.5198 (0.3591) loss_u 0.0022 (0.0093) acc_x 84.3750 (87.4393) lr 1.405814e-03 -epoch [25/25][3500/4762] time 0.158 (0.154) data 0.001 (0.001) eta 0:03:14 loss 0.4385 (0.3682) loss_x 0.4380 (0.3588) loss_u 0.0004 (0.0093) acc_x 78.1250 (87.4339) lr 1.405814e-03 -epoch [25/25][3600/4762] time 0.148 (0.154) data 0.001 (0.001) eta 0:02:58 loss 0.2919 (0.3695) loss_x 0.2701 (0.3602) loss_u 0.0218 (0.0093) acc_x 87.5000 (87.4028) lr 1.405814e-03 -epoch [25/25][3700/4762] time 0.166 (0.154) data 0.001 (0.001) eta 0:02:43 loss 0.4517 (0.3691) loss_x 0.4495 (0.3598) loss_u 0.0022 (0.0093) acc_x 84.3750 (87.4139) lr 1.405814e-03 -epoch [25/25][3800/4762] time 0.166 (0.154) data 0.001 (0.001) eta 0:02:27 loss 0.4140 (0.3693) loss_x 0.3993 (0.3600) loss_u 0.0147 (0.0092) acc_x 84.3750 (87.4137) lr 1.405814e-03 -epoch [25/25][3900/4762] time 0.149 (0.154) data 0.001 (0.001) eta 0:02:12 loss 0.2221 (0.3689) loss_x 0.2211 (0.3597) loss_u 0.0010 (0.0092) acc_x 93.7500 (87.4175) lr 1.405814e-03 -epoch [25/25][4000/4762] time 0.188 (0.154) data 0.001 (0.001) eta 0:01:57 loss 0.1965 (0.3688) loss_x 0.1958 (0.3595) loss_u 0.0007 (0.0092) acc_x 90.6250 (87.4227) lr 1.405814e-03 -epoch [25/25][4100/4762] time 0.137 (0.154) data 0.001 (0.001) eta 0:01:41 loss 0.2630 (0.3691) loss_x 0.2611 (0.3599) loss_u 0.0019 (0.0092) acc_x 90.6250 (87.4040) lr 1.405814e-03 -epoch [25/25][4200/4762] time 0.142 (0.154) data 0.001 (0.001) eta 0:01:26 loss 0.4092 (0.3694) loss_x 0.4079 (0.3602) loss_u 0.0013 (0.0092) acc_x 84.3750 (87.3884) lr 1.405814e-03 -epoch [25/25][4300/4762] time 0.139 (0.154) data 0.001 (0.001) eta 0:01:10 loss 0.2963 (0.3698) loss_x 0.2858 (0.3606) loss_u 0.0105 (0.0092) acc_x 93.7500 (87.3677) lr 1.405814e-03 -epoch [25/25][4400/4762] time 0.164 (0.153) data 0.001 (0.001) eta 0:00:55 loss 0.3587 (0.3702) loss_x 0.3583 (0.3609) loss_u 0.0004 (0.0092) acc_x 84.3750 (87.3601) lr 1.405814e-03 -epoch [25/25][4500/4762] time 0.140 (0.153) data 0.001 (0.001) eta 0:00:40 loss 0.3928 (0.3708) loss_x 0.3920 (0.3616) loss_u 0.0007 (0.0092) acc_x 87.5000 (87.3410) lr 1.405814e-03 -epoch [25/25][4600/4762] time 0.172 (0.153) data 0.001 (0.001) eta 0:00:24 loss 0.2821 (0.3707) loss_x 0.2805 (0.3616) loss_u 0.0016 (0.0092) acc_x 84.3750 (87.3512) lr 1.405814e-03 -epoch [25/25][4700/4762] time 0.154 (0.153) data 0.001 (0.001) eta 0:00:09 loss 0.4385 (0.3707) loss_x 0.4301 (0.3616) loss_u 0.0084 (0.0091) acc_x 87.5000 (87.3531) lr 1.405814e-03 -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,787 -* accuracy: 84.47% -* error: 15.53% -* macro_f1: 84.66% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,568 acc: 97.86% -* class: 1 (bicycle) total: 3,475 correct: 2,934 acc: 84.43% -* class: 2 (bus) total: 4,690 correct: 4,247 acc: 90.55% -* class: 3 (car) total: 10,401 correct: 7,977 acc: 76.69% -* class: 4 (horse) total: 4,691 correct: 4,596 acc: 97.97% -* class: 5 (knife) total: 2,075 correct: 1,854 acc: 89.35% -* class: 6 (motorcycle) total: 5,796 correct: 5,514 acc: 95.13% -* class: 7 (person) total: 4,000 correct: 2,972 acc: 74.30% -* class: 8 (plant) total: 4,549 correct: 3,833 acc: 84.26% -* class: 9 (skateboard) total: 2,281 correct: 2,064 acc: 90.49% -* class: 10 (train) total: 4,236 correct: 3,893 acc: 91.90% -* class: 11 (truck) total: 5,548 correct: 3,335 acc: 60.11% -* average: 86.09% -Checkpoint saved to "output/visda17/DAPL/ep25-32-v1/1.0_0.6_1.0_t0/seed_2/prompt_learner/model.pth.tar-25" -Finished training -Do evaluation on test set -=> result -* total: 55,388 -* correct: 46,787 -* accuracy: 84.47% -* error: 15.53% -* macro_f1: 84.66% -=> per-class result -* class: 0 (aeroplane) total: 3,646 correct: 3,568 acc: 97.86% -* class: 1 (bicycle) total: 3,475 correct: 2,934 acc: 84.43% -* class: 2 (bus) total: 4,690 correct: 4,247 acc: 90.55% -* class: 3 (car) total: 10,401 correct: 7,977 acc: 76.69% -* class: 4 (horse) total: 4,691 correct: 4,596 acc: 97.97% -* class: 5 (knife) total: 2,075 correct: 1,854 acc: 89.35% -* class: 6 (motorcycle) total: 5,796 correct: 5,514 acc: 95.13% -* class: 7 (person) total: 4,000 correct: 2,972 acc: 74.30% -* class: 8 (plant) total: 4,549 correct: 3,833 acc: 84.26% -* class: 9 (skateboard) total: 2,281 correct: 2,064 acc: 90.49% -* class: 10 (train) total: 4,236 correct: 3,893 acc: 91.90% -* class: 11 (truck) total: 5,548 correct: 3,335 acc: 60.11% -* average: 86.09% -Elapsed: 6:10:27