CUDA Available:  True
cuDNN Version:  92000
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Processing file: 235_SED_id_2_Medical_tr_2499_1st_3840.csv, Data shape: (9999, 1)
Label shape: (9999,)
Train data shape: (2499, 1), Test data shape: (7500, 1), Label test shape: (7500,)
Optimal Hyperparameters for Time_RCD: {'win_size': 5000, 'batch_size': 64}
Running Time_RCD on 235_SED_id_2_Medical_tr_2499_1st_3840.csv...
Using CUDA device: 2
Data shape: (7500, 1)
Config: TimeRCDConfig(ts_config=TimeSeriesConfig(d_model=512, d_proj=256, patch_size=16, num_query_tokens=1, num_layers=8, num_heads=8, d_ff_dropout=0.1, use_rope=True, activation='gelu', num_features=1), llm_config=LLMConfig(model_name='deepseek-ai/DeepSeek-R1-Distill-Llama-8B', max_batch_size=20, num_fixed_tokens=30, num_prototype=1000, num_heads=8), deepspeed_config_path='experiments/configs/deepspeed_config.json', accelerate_config_path='experiments/configs/accelerate_config.yaml', batch_size=64, learning_rate=0.0001, num_epochs=1000, max_seq_len=512, dropout=0.1, accumulation_steps=1, weight_decay=1e-05, enable_ts_train=False, data_path='/home3/lantian/ChatTS/data/ChatTSQA/time_rcd_dataset_20250614_133353', test_data_path='/home3/lantian/ChatTS/data/ChatTSQA/time_rcd_test_dataset_20250621_090751', pretrain_data_path='/home2/lijinbo/Projects/dataset/ChatTS/result', load_data=True, load_pretrain_path='experiments/checkpoints/pretrain/pretrain_checkpoint_latest.pth', load_path='experiments/checkpoints/checkpoint_step.pth', checkpoint_dir='experiments/checkpoints', log_freq=100, save_freq=10, save_step_freq=100, model_prefix='time_rcd_qa_by_pretrain', test_batch_limit=20, early_stopping_patience=7, seed=72, cuda_devices='2', dist_port='12355', device='cuda', use_multi_gpu=True, mixed_precision=True)
Checkpoint path: TSB_AD_Time_RCD/checkpoints/full_mask_anomaly_head_pretrain_checkpoint_best.pth
Successfully loaded checkpoint from TSB_AD_Time_RCD/checkpoints/full_mask_anomaly_head_pretrain_checkpoint_best.pth
Model loaded on device: cuda
Padding AnomalyClipDataset: original length 7500, window_size 5000, adding 2500 samples


  0%|          | 0/1 [00:00<?, ?it/s]batched_time_series shape: torch.Size([2, 5000, 1])
batched_mask shape: torch.Size([2, 5000])

100%|██████████| 1/1 [00:00<00:00,  4.13it/s]
Original shapes - Output: (10000,), Label: (7500,)
Aligned shapes - Output: (7500,), Label: (7500,)
Using 8 workers for heavy metrics, 8 for light metrics
Starting heavy computations (Affiliation and F1_T)...
Computing light metrics in parallel...
Computing Affiliation (parallel) with 8 workers
Computing standard F1 (chunked) with 4 workers, chunk_size=25
Split 500 thresholds into 20 chunks
Computing PointF1PA (chunked) with 6 workers, chunk_size=30

Processing standard F1 thresholds:   0%|          | 0/500 [00:00<?, ?threshold/s]
Split 291 thresholds into 10 chunks


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Computing affiliation metrics: 100%|██████████| 300/300 [00:00<00:00, 3985.49threshold/s]
Processing 1500 thresholds in 150 chunks of size ~10Affiliation computed in 0.19s



Processing 150 chunks (chunked parallel):   0%|          | 0/150 [00:00<?, ?it/s]  ✓ auc_roc completed
  ✓ auc_pr completed


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Processing standard F1 thresholds:  25%|██▌       | 125/500 [00:00<00:00, 428.19threshold/s, chunk=5/20, results=125]

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Processing standard F1 thresholds:  80%|████████  | 400/500 [00:00<00:00, 1012.38threshold/s, chunk=16/20, results=400]

Processing PointF1PA thresholds:  72%|███████▏  | 210/291 [00:00<00:00, 667.94threshold/s, chunk=7/10, results=210]

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Processing PointF1PA thresholds:  93%|█████████▎| 270/291 [00:00<00:00, 852.37threshold/s, chunk=9/10, results=270]

Processing PointF1PA thresholds: 100%|██████████| 291/291 [00:00<00:00, 918.49threshold/s, chunk=10/10, results=291]
Processing PointF1PA thresholds: 100%|██████████| 291/291 [00:00<00:00, 918.25threshold/s, chunk=10/10, results=291]


Processing standard F1 thresholds:  85%|████████▌ | 425/500 [00:00<00:00, 994.68threshold/s, chunk=17/20, results=425] 

Processing standard F1 thresholds:  90%|█████████ | 450/500 [00:00<00:00, 1038.71threshold/s, chunk=18/20, results=450]

Processing standard F1 thresholds:  95%|█████████▌| 475/500 [00:00<00:00, 1078.73threshold/s, chunk=19/20, results=475]

Processing standard F1 thresholds: 100%|██████████| 500/500 [00:00<00:00, 1132.65threshold/s, chunk=20/20, results=500]
Processing standard F1 thresholds: 100%|██████████| 500/500 [00:00<00:00, 1132.38threshold/s, chunk=20/20, results=500]
✓ PointF1PA computed in 0.44s
  Best F1_PA: 0.8454 at threshold 0.8711
✓ Standard F1 computed in 0.46s
  Best F1: 0.7401 at threshold 0.5180


Processing 150 chunks (chunked parallel):  91%|█████████ | 136/150 [00:01<00:00, 135.88it/s]
Processing 150 chunks (chunked parallel): 100%|██████████| 150/150 [00:01<00:00, 139.57it/s]
  ✓ vus completed
  ✓ pointf1 completed
  ✓ pointf1pa completed
  ✓ f1_t completed
Light metrics completed in 2.64s
Waiting for heavy computations...
  ✓ Affiliation completed
Heavy metrics completed in 2.69s

Total computation time: 2.69s
Evaluation Result:  {'AUC-PR': 0.6264585841089553, 'AUC-ROC': 0.9807080874086566, 'VUS-PR': 0.8476378149110302, 'VUS-ROC': 0.9930260175094193, 'Standard-F1': 0.7401197604790419, 'Standard-Precision': 0.6851441241685144, 'Standard-Recall': 0.8046875, 'PA-F1': 0.845442536327609, 'PA-Precision': 0.8579088471849866, 'PA-Recall': 0.8333333333333334, 'Affiliation-F': 0.9688647053679802, 'Affiliation-P': 0.9474215231868843, 'Affiliation-R': 0.9913011256949867, 'F1_T': 0.7380294799804688, 'Precision_T': 0.6851441264152527, 'Recall_T': 0.7997620701789856}
Results for 235_SED_id_2_Medical_tr_2499_1st_3840.csv: {'filename': '235_SED_id_2_Medical_tr_2499_1st_3840.csv', 'AD_Name': 'Time_RCD', 'sliding_window': 125, 'train_index': '2499', 'data_shape': '9999x1', 'output_length': 10000, 'label_length': 7500, 'aligned_length': 7500, 'AUC-PR': 0.6264585841089553, 'AUC-ROC': 0.9807080874086566, 'VUS-PR': 0.8476378149110302, 'VUS-ROC': 0.9930260175094193, 'Standard-F1': 0.7401197604790419, 'Standard-Precision': 0.6851441241685144, 'Standard-Recall': 0.8046875, 'PA-F1': 0.845442536327609, 'PA-Precision': 0.8579088471849866, 'PA-Recall': 0.8333333333333334, 'Affiliation-F': 0.9688647053679802, 'Affiliation-P': 0.9474215231868843, 'Affiliation-R': 0.9913011256949867, 'F1_T': 0.7380294799804688, 'Precision_T': 0.6851441264152527, 'Recall_T': 0.7997620701789856}
Traceback (most recent call last):
  File "/timercd_full_eval_work/code/Time-RCD-main/main.py", line 233, in <module>
    logit_dict = {
                 ^
TypeError: 'NoneType' object is not a mapping