checkpoint: /data1/yfzx/wangx/Code/icassp2027-experiment/checkpoints/easycom_encoder_only_1s/encoder_visual_seed0.pth
training summary: /data1/yfzx/wangx/Code/icassp2027-experiment/checkpoints/easycom_encoder_only_1s/encoder_visual_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_visual_1s --target-root /data1/yfzx/wangx/Datasets/easyc
om/easycom_target_frame --expected-mode encoder-only-visual --device cuda:0 --checkpoint /data1/yfzx/wangx/Code/icassp2027-experiment/checkpoints/easycom_encoder_only_1s/encoder_visual_seed0.pth --output-dir /data1/yfzx/wangx/Code/icassp
2027-experiment/results/easycom_encoder_only_1s/encoder_visual_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, 71.22batch/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, 1487.47batch/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, 1409.24batch/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: 48.38326
  AP: background=30.16284, camera_wearer_speaking=41.68834, other_people_speaking=73.29862; n=23048
Action anticipation (0.40s) perframe mAP: 47.68478
  AP: background=29.82215, camera_wearer_speaking=40.98323, other_people_speaking=72.24896; n=23048
Action anticipation (0.60s) perframe mAP: 47.26191
  AP: background=29.28129, camera_wearer_speaking=40.74974, other_people_speaking=71.75469; n=23048
Action anticipation (0.80s) perframe mAP: 46.62600
  AP: background=28.24103, camera_wearer_speaking=40.47370, other_people_speaking=71.16328; n=23048
Action anticipation (1.00s) perframe mAP: 46.00582
  AP: background=27.73638, camera_wearer_speaking=39.50369, other_people_speaking=70.77740; n=23048
Action anticipation (1.20s) perframe mAP: 45.75002
  AP: background=27.39577, camera_wearer_speaking=39.53354, other_people_speaking=70.32075; n=23048
Action anticipation (1.40s) perframe mAP: 45.41812
  AP: background=27.04920, camera_wearer_speaking=39.35560, other_people_speaking=69.84957; n=23048
Action anticipation (1.60s) perframe mAP: 45.56259
  AP: background=27.36515, camera_wearer_speaking=39.16056, other_people_speaking=70.16205; n=23048
Action anticipation (1.80s) perframe mAP: 44.79454
  AP: background=26.64061, camera_wearer_speaking=38.50155, other_people_speaking=69.24145; n=23048
Action anticipation (2.00s) perframe mAP: 44.97339
  AP: background=27.34810, camera_wearer_speaking=37.96646, other_people_speaking=69.60560; n=23048
Action anticipation (mean) perframe mAP: 46.24604
