checkpoint: /data1/yfzx/wangx/Code/icassp2027-experiment/checkpoints/easycom_encoder_only_1s/encoder_audio_seed0.pth
training summary: /data1/yfzx/wangx/Code/icassp2027-experiment/checkpoints/easycom_encoder_only_1s/encoder_audio_seed0.pth.json
/usr/local/bin/python /data1/yfzx/wangx/Code/icassp2027-experiment/infer_turn_head.py --dataset easycom --feature-root /data1/yfzx/wangx/Datasets/easycom/omni_features/encoder_only_audio_1s --target-root /data1/yfzx/wangx/Datasets/easyco
m/easycom_target_frame --expected-mode encoder-only-audio --device cuda:0 --checkpoint /data1/yfzx/wangx/Code/icassp2027-experiment/checkpoints/easycom_encoder_only_1s/encoder_audio_seed0.pth --output-dir /data1/yfzx/wangx/Code/icassp202
7-experiment/results/easycom_encoder_only_1s/encoder_audio_seed0 --sessions Session_3 Session_2 Session_1
/usr/local/lib/python3.11/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the
 package that installed pynvml for you.
  import pynvml  # type: ignore[import]
device: cuda:0
loss: lstr-mce; output_space: three-class-softmax
sessions: Session_3, Session_2, Session_1
infer Session_3: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 21.47batch/s]
Session_3: saved pred (8400, 3, 10), causal-valid counts=[8399, 8398, 8397, 8396, 8395, 8394, 8393, 8392, 8391, 8390]
infer Session_2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 1441.65batch/s]
Session_2: saved pred (7706, 3, 10), causal-valid counts=[7705, 7704, 7703, 7702, 7701, 7700, 7699, 7698, 7697, 7696]
infer Session_1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<00:00, 1407.96batch/s]
Session_1: saved pred (6942, 3, 10), causal-valid counts=[6941, 6940, 6939, 6938, 6937, 6936, 6935, 6934, 6933, 6932]

egospeak_full_timeline:
Action anticipation (0.20s) perframe mAP: 79.68981
  AP: background=63.69477, camera_wearer_speaking=84.09982, other_people_speaking=91.27483; n=23048
Action anticipation (0.40s) perframe mAP: 72.41576
  AP: background=52.47780, camera_wearer_speaking=77.43606, other_people_speaking=87.33340; n=23048
Action anticipation (0.60s) perframe mAP: 66.33666
  AP: background=44.20066, camera_wearer_speaking=71.41221, other_people_speaking=83.39710; n=23048
Action anticipation (0.80s) perframe mAP: 61.60584
  AP: background=38.60179, camera_wearer_speaking=66.08811, other_people_speaking=80.12762; n=23048
Action anticipation (1.00s) perframe mAP: 58.28586
  AP: background=34.75843, camera_wearer_speaking=62.53734, other_people_speaking=77.56181; n=23048
Action anticipation (1.20s) perframe mAP: 55.93708
  AP: background=32.93325, camera_wearer_speaking=59.29096, other_people_speaking=75.58705; n=23048
Action anticipation (1.40s) perframe mAP: 54.22585
  AP: background=31.56977, camera_wearer_speaking=56.79707, other_people_speaking=74.31072; n=23048
Action anticipation (1.60s) perframe mAP: 52.83014
  AP: background=30.67884, camera_wearer_speaking=54.61175, other_people_speaking=73.19983; n=23048
Action anticipation (1.80s) perframe mAP: 51.97218
  AP: background=30.37030, camera_wearer_speaking=52.80894, other_people_speaking=72.73728; n=23048
Action anticipation (2.00s) perframe mAP: 51.66256
  AP: background=30.86998, camera_wearer_speaking=51.49671, other_people_speaking=72.62098; n=23048
Action anticipation (mean) perframe mAP: 60.49617
