Add files using upload-large-folder tool
Browse files- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/RUN_REPORT.md +10 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/collection_validation.json +30 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json +30 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/predictions.csv +0 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json +30 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/predictions.csv +0 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/launch_env.txt +11 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json +30 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/predictions.csv +0 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2.progress.jsonl +211 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3.progress.jsonl +204 -0
- results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard1.progress.jsonl +0 -0
- scripts/omni/collect_qwen3_retrieval_task_probe_results.sh +10 -2
- scripts/omni/eval_qwen3_omni_retrieval_task_probes.py +292 -29
- scripts/omni/merge_qwen3_omni_retrieval_task_probe_shards.py +1 -0
- scripts/omni/run_qwen3_omni_retrieval_task_probes_sharded.sh +3 -0
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/RUN_REPORT.md
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# Qwen3-Omni v6 Retrieval Task Probes
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- Run ID: `xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z`
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- Shards: `3`
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| Task | Metric | Score | Samples |
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| --- | --- | ---: | ---: |
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| Hand Trajectory Forecasting | hand_trajectory_forecast_mrr | 0.721611 | 3951 |
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| 9 |
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| Cross-Modal Reconstruction | modality_reconstruction_mrr | 0.967055 | 3951 |
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| IMU-to-Hand Pose Reconstruction | imu_to_hand_pose_mrr | 0.964165 | 3951 |
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/collection_validation.json
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{
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"records": [
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{
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"metric_key": "hand_trajectory_forecast_mrr",
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"num_samples": 3951,
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"primary_score": 0.7216105627267382,
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"source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
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"task_id": "hand_trajectory_forecast"
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},
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{
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"metric_key": "modality_reconstruction_mrr",
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"num_samples": 3951,
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"primary_score": 0.9670547540707002,
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"source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
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"task_id": "modality_reconstruction"
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},
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{
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"metric_key": "imu_to_hand_pose_mrr",
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"num_samples": 3951,
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"primary_score": 0.9641651902471952,
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"source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
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"task_id": "imu_to_hand_pose"
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}
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],
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"run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
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"status": "pass",
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"summary": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/summary.json",
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"title": "Qwen3 Retrieval Task Probe Collection Validation",
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"validated_task_count": 3
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}
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json
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{
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"adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
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"camera_view_sync_retrieval_mrr": 0.7216105627267382,
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| 4 |
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"candidate_count": 4,
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| 5 |
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"caption_grounding_mrr": 0.7216105627267382,
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| 6 |
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"cross_modal_retrieval_mrr": 0.7216105627267382,
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"dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
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| 8 |
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"eval_split": "test",
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| 9 |
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"future_frames": 100,
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| 10 |
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"hand_trajectory_forecast_mrr": 0.7216105627267382,
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| 11 |
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"imu_to_hand_pose_mrr": 0.7216105627267382,
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| 12 |
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"metric_key": "hand_trajectory_forecast_mrr",
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| 13 |
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"modality_reconstruction_mrr": 0.7216105627267382,
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| 14 |
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"model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
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"mrr": 0.7216105627267382,
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| 16 |
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"num_samples": 3951,
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"primary_metric": "hand_trajectory_forecast_mrr",
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"primary_score": 0.7216105627267382,
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"run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
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"sample_offset": 0,
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"sample_stride": 1,
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"scope": "held_out_test_qwen3_retrieval_task_probe",
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"score_policy": "GPU-backed Qwen3-Omni v6 future hand-trajectory retrieval probe. The prompt shows the held-out current video window and asks the model to rank shuffled compact hand-pose target summaries; the true target is the staged hand-joint feature block from the window at the configured future-frame offset. This avoids asking the language model to emit hundreds of raw pose floats while still scoring against real exported hand targets.",
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"status": "pass",
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"task_id": "hand_trajectory_forecast",
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"task_label": "Hand Trajectory Forecasting",
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"task_number": 5,
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"title": "Qwen3-Omni v6 Hand Trajectory Forecasting",
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"top1_accuracy": 0.5563148569982282
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}
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/predictions.csv
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json
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{
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| 2 |
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"adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
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| 3 |
+
"camera_view_sync_retrieval_mrr": 0.9641651902471952,
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| 4 |
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"candidate_count": 4,
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| 5 |
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"caption_grounding_mrr": 0.9641651902471952,
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| 6 |
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"cross_modal_retrieval_mrr": 0.9641651902471952,
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| 7 |
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"dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
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| 8 |
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"eval_split": "test",
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| 9 |
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"future_frames": 100,
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| 10 |
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"hand_trajectory_forecast_mrr": 0.9641651902471952,
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| 11 |
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"imu_to_hand_pose_mrr": 0.9641651902471952,
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| 12 |
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"metric_key": "imu_to_hand_pose_mrr",
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| 13 |
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"modality_reconstruction_mrr": 0.9641651902471952,
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| 14 |
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"model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
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| 15 |
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"mrr": 0.9641651902471952,
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| 16 |
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"num_samples": 3951,
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| 17 |
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"primary_metric": "imu_to_hand_pose_mrr",
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| 18 |
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"primary_score": 0.9641651902471952,
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| 19 |
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"run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
|
| 20 |
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"sample_offset": 0,
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| 21 |
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"sample_stride": 1,
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| 22 |
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"scope": "held_out_test_qwen3_retrieval_task_probe",
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| 23 |
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"score_policy": "GPU-backed Qwen3-Omni v6 IMU-to-hand-pose retrieval probe. The query is the held-out IMU accel/gyro summary and candidates are shuffled compact hand-joint summaries from the staged sensor shards; the score is MRR of the synchronized true hand-pose target.",
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| 24 |
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"status": "pass",
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| 25 |
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"task_id": "imu_to_hand_pose",
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| 26 |
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"task_label": "IMU-to-Hand Pose Reconstruction",
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| 27 |
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"task_number": 18,
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| 28 |
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"title": "Qwen3-Omni v6 IMU-to-Hand Pose Reconstruction",
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| 29 |
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"top1_accuracy": 0.9415337889141989
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| 30 |
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}
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/predictions.csv
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/launch_env.txt
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run_id=xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z
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dataset_jsonl=/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl
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| 3 |
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model_dir=/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct
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adapter_dir=/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora
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| 5 |
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tasks=hand_trajectory_forecast,modality_reconstruction,imu_to_hand_pose
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| 6 |
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candidate_count=4
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| 7 |
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future_frames=100
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| 8 |
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cuda_device_groups=0,1 2,3
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| 9 |
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shards=2
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| 10 |
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started_at=2026-06-19T13:42:10+08:00
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| 11 |
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exit_code=1
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json
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{
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"adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
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| 3 |
+
"camera_view_sync_retrieval_mrr": 0.9670547540707002,
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| 4 |
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"candidate_count": 4,
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| 5 |
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"caption_grounding_mrr": 0.9670547540707002,
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| 6 |
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"cross_modal_retrieval_mrr": 0.9670547540707002,
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| 7 |
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"dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
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| 8 |
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"eval_split": "test",
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| 9 |
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"future_frames": 100,
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| 10 |
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"hand_trajectory_forecast_mrr": 0.9670547540707002,
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| 11 |
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"imu_to_hand_pose_mrr": 0.9670547540707002,
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| 12 |
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"metric_key": "modality_reconstruction_mrr",
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| 13 |
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"modality_reconstruction_mrr": 0.9670547540707002,
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| 14 |
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"model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
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| 15 |
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"mrr": 0.9670547540707002,
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| 16 |
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"num_samples": 3951,
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| 17 |
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"primary_metric": "modality_reconstruction_mrr",
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| 18 |
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"primary_score": 0.9670547540707002,
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| 19 |
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"run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
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| 20 |
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"sample_offset": 0,
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| 21 |
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"sample_stride": 1,
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| 22 |
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"scope": "held_out_test_qwen3_retrieval_task_probe",
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| 23 |
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"score_policy": "GPU-backed Qwen3-Omni v6 cross-modal reconstruction retrieval probe. The query is a compact summary of motion-capture, body-contact, camera-pose, and IMU feature blocks; candidates are shuffled compact visual/depth/calibration target summaries from staged sensor shards, and the score is MRR of the synchronized true target.",
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| 24 |
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"status": "pass",
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| 25 |
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"task_id": "modality_reconstruction",
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| 26 |
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"task_label": "Cross-Modal Reconstruction",
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| 27 |
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"task_number": 10,
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| 28 |
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"title": "Qwen3-Omni v6 Cross-Modal Reconstruction",
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| 29 |
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"top1_accuracy": 0.9405213869906353
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| 30 |
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}
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/predictions.csv
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2.progress.jsonl
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| 1 |
+
{"candidate_count": 4, "event": "eval_start", "future_frames": 100, "num_eval_samples": 1001, "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2", "sample_offset": 0, "sample_stride": 4, "tasks": ["hand_trajectory_forecast", "modality_reconstruction", "imu_to_hand_pose"], "timestamp": 1781867368.6804018}
|
| 2 |
+
{"completed_samples_for_task": 1574, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:31", "sample_index": 793, "seconds": 6.002, "task_id": "imu_to_hand_pose", "timestamp": 1781867426.4169116}
|
| 3 |
+
{"completed_samples_for_task": 1575, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:35", "sample_index": 794, "seconds": 4.196, "task_id": "imu_to_hand_pose", "timestamp": 1781867430.6134782}
|
| 4 |
+
{"completed_samples_for_task": 1576, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:39", "sample_index": 795, "seconds": 4.155, "task_id": "imu_to_hand_pose", "timestamp": 1781867434.76877}
|
| 5 |
+
{"completed_samples_for_task": 1577, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:43", "sample_index": 796, "seconds": 4.216, "task_id": "imu_to_hand_pose", "timestamp": 1781867438.9845693}
|
| 6 |
+
{"completed_samples_for_task": 1578, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:47", "sample_index": 797, "seconds": 4.214, "task_id": "imu_to_hand_pose", "timestamp": 1781867443.19829}
|
| 7 |
+
{"completed_samples_for_task": 1579, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:51", "sample_index": 798, "seconds": 4.177, "task_id": "imu_to_hand_pose", "timestamp": 1781867447.376146}
|
| 8 |
+
{"completed_samples_for_task": 1580, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:55", "sample_index": 799, "seconds": 4.184, "task_id": "imu_to_hand_pose", "timestamp": 1781867451.56019}
|
| 9 |
+
{"completed_samples_for_task": 1581, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:59", "sample_index": 800, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867455.7163486}
|
| 10 |
+
{"completed_samples_for_task": 1582, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:63", "sample_index": 801, "seconds": 4.146, "task_id": "imu_to_hand_pose", "timestamp": 1781867459.8632646}
|
| 11 |
+
{"completed_samples_for_task": 1583, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:67", "sample_index": 802, "seconds": 4.253, "task_id": "imu_to_hand_pose", "timestamp": 1781867464.116606}
|
| 12 |
+
{"completed_samples_for_task": 1584, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:71", "sample_index": 803, "seconds": 4.162, "task_id": "imu_to_hand_pose", "timestamp": 1781867468.2786343}
|
| 13 |
+
{"completed_samples_for_task": 1585, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:75", "sample_index": 804, "seconds": 4.201, "task_id": "imu_to_hand_pose", "timestamp": 1781867472.4799225}
|
| 14 |
+
{"completed_samples_for_task": 1586, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:79", "sample_index": 805, "seconds": 4.167, "task_id": "imu_to_hand_pose", "timestamp": 1781867476.6470556}
|
| 15 |
+
{"completed_samples_for_task": 1587, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:86", "sample_index": 806, "seconds": 4.153, "task_id": "imu_to_hand_pose", "timestamp": 1781867480.8005896}
|
| 16 |
+
{"completed_samples_for_task": 1588, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:90", "sample_index": 807, "seconds": 4.153, "task_id": "imu_to_hand_pose", "timestamp": 1781867484.9533045}
|
| 17 |
+
{"completed_samples_for_task": 1589, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:94", "sample_index": 808, "seconds": 4.185, "task_id": "imu_to_hand_pose", "timestamp": 1781867489.1383328}
|
| 18 |
+
{"completed_samples_for_task": 1590, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:98", "sample_index": 809, "seconds": 4.221, "task_id": "imu_to_hand_pose", "timestamp": 1781867493.3589451}
|
| 19 |
+
{"completed_samples_for_task": 1591, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:0", "sample_index": 810, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867497.514932}
|
| 20 |
+
{"completed_samples_for_task": 1592, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:4", "sample_index": 811, "seconds": 4.157, "task_id": "imu_to_hand_pose", "timestamp": 1781867501.6720982}
|
| 21 |
+
{"completed_samples_for_task": 1593, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:8", "sample_index": 812, "seconds": 4.136, "task_id": "imu_to_hand_pose", "timestamp": 1781867505.8077328}
|
| 22 |
+
{"completed_samples_for_task": 1594, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:12", "sample_index": 813, "seconds": 4.194, "task_id": "imu_to_hand_pose", "timestamp": 1781867510.002242}
|
| 23 |
+
{"completed_samples_for_task": 1595, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:16", "sample_index": 814, "seconds": 4.16, "task_id": "imu_to_hand_pose", "timestamp": 1781867514.1625416}
|
| 24 |
+
{"completed_samples_for_task": 1596, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:20", "sample_index": 815, "seconds": 4.139, "task_id": "imu_to_hand_pose", "timestamp": 1781867518.3016315}
|
| 25 |
+
{"completed_samples_for_task": 1597, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:24", "sample_index": 816, "seconds": 4.177, "task_id": "imu_to_hand_pose", "timestamp": 1781867522.4786248}
|
| 26 |
+
{"completed_samples_for_task": 1598, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:28", "sample_index": 817, "seconds": 4.148, "task_id": "imu_to_hand_pose", "timestamp": 1781867526.626323}
|
| 27 |
+
{"completed_samples_for_task": 1599, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:33", "sample_index": 818, "seconds": 4.193, "task_id": "imu_to_hand_pose", "timestamp": 1781867530.8189404}
|
| 28 |
+
{"completed_samples_for_task": 1600, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:37", "sample_index": 819, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867534.9752364}
|
| 29 |
+
{"completed_samples_for_task": 1601, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:41", "sample_index": 820, "seconds": 4.151, "task_id": "imu_to_hand_pose", "timestamp": 1781867539.125802}
|
| 30 |
+
{"completed_samples_for_task": 1602, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:45", "sample_index": 821, "seconds": 4.141, "task_id": "imu_to_hand_pose", "timestamp": 1781867543.2664115}
|
| 31 |
+
{"completed_samples_for_task": 1603, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:81", "sample_index": 822, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867547.4220822}
|
| 32 |
+
{"completed_samples_for_task": 1604, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:85", "sample_index": 823, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867551.5777516}
|
| 33 |
+
{"completed_samples_for_task": 1605, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:89", "sample_index": 824, "seconds": 4.148, "task_id": "imu_to_hand_pose", "timestamp": 1781867555.7253566}
|
| 34 |
+
{"completed_samples_for_task": 1606, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:93", "sample_index": 825, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867559.8809378}
|
| 35 |
+
{"completed_samples_for_task": 1607, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:97", "sample_index": 826, "seconds": 4.188, "task_id": "imu_to_hand_pose", "timestamp": 1781867564.0697355}
|
| 36 |
+
{"completed_samples_for_task": 1608, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:101", "sample_index": 827, "seconds": 4.133, "task_id": "imu_to_hand_pose", "timestamp": 1781867568.2032397}
|
| 37 |
+
{"completed_samples_for_task": 1609, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:105", "sample_index": 828, "seconds": 4.154, "task_id": "imu_to_hand_pose", "timestamp": 1781867572.3568199}
|
| 38 |
+
{"completed_samples_for_task": 1610, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:109", "sample_index": 829, "seconds": 4.158, "task_id": "imu_to_hand_pose", "timestamp": 1781867576.515046}
|
| 39 |
+
{"completed_samples_for_task": 1611, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:113", "sample_index": 830, "seconds": 4.147, "task_id": "imu_to_hand_pose", "timestamp": 1781867580.6624684}
|
| 40 |
+
{"completed_samples_for_task": 1612, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:117", "sample_index": 831, "seconds": 4.169, "task_id": "imu_to_hand_pose", "timestamp": 1781867584.8317616}
|
| 41 |
+
{"completed_samples_for_task": 1613, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:121", "sample_index": 832, "seconds": 4.15, "task_id": "imu_to_hand_pose", "timestamp": 1781867588.981919}
|
| 42 |
+
{"completed_samples_for_task": 1614, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:ba045ed4-ef25-404d-b756-8dcbd45b18fa__ep2:qa:125", "sample_index": 833, "seconds": 4.157, "task_id": "imu_to_hand_pose", "timestamp": 1781867593.1392462}
|
| 43 |
+
{"completed_samples_for_task": 1615, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:0", "sample_index": 834, "seconds": 4.151, "task_id": "imu_to_hand_pose", "timestamp": 1781867597.2905364}
|
| 44 |
+
{"completed_samples_for_task": 1616, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:4", "sample_index": 835, "seconds": 4.154, "task_id": "imu_to_hand_pose", "timestamp": 1781867601.444497}
|
| 45 |
+
{"completed_samples_for_task": 1617, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:8", "sample_index": 836, "seconds": 4.142, "task_id": "imu_to_hand_pose", "timestamp": 1781867605.5869384}
|
| 46 |
+
{"completed_samples_for_task": 1618, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:12", "sample_index": 837, "seconds": 4.16, "task_id": "imu_to_hand_pose", "timestamp": 1781867609.7468503}
|
| 47 |
+
{"completed_samples_for_task": 1619, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:16", "sample_index": 838, "seconds": 4.148, "task_id": "imu_to_hand_pose", "timestamp": 1781867613.8947258}
|
| 48 |
+
{"completed_samples_for_task": 1620, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:20", "sample_index": 839, "seconds": 4.104, "task_id": "imu_to_hand_pose", "timestamp": 1781867617.9992812}
|
| 49 |
+
{"completed_samples_for_task": 1621, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:24", "sample_index": 840, "seconds": 4.101, "task_id": "imu_to_hand_pose", "timestamp": 1781867622.1002083}
|
| 50 |
+
{"completed_samples_for_task": 1622, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:4b02bb38-384a-438a-b5f9-6131d85c34b0__ep1:qa:28", "sample_index": 841, "seconds": 4.099, "task_id": "imu_to_hand_pose", "timestamp": 1781867626.1994946}
|
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| 209 |
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|
| 210 |
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| 211 |
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{"event": "eval_complete", "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2", "timestamp": 1781868285.56159}
|
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3.progress.jsonl
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| 1 |
+
{"candidate_count": 4, "event": "eval_start", "future_frames": 100, "num_eval_samples": 983, "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3", "sample_offset": 2, "sample_stride": 4, "tasks": ["hand_trajectory_forecast", "modality_reconstruction", "imu_to_hand_pose"], "timestamp": 1781867368.5876122}
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
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{"completed_samples_for_task": 1769, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:92", "sample_index": 977, "seconds": 4.096, "task_id": "imu_to_hand_pose", "timestamp": 1781868223.3482158}
|
| 198 |
+
{"completed_samples_for_task": 1770, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:96", "sample_index": 978, "seconds": 4.089, "task_id": "imu_to_hand_pose", "timestamp": 1781868227.4376588}
|
| 199 |
+
{"completed_samples_for_task": 1771, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:100", "sample_index": 979, "seconds": 4.097, "task_id": "imu_to_hand_pose", "timestamp": 1781868231.534594}
|
| 200 |
+
{"completed_samples_for_task": 1772, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:104", "sample_index": 980, "seconds": 4.104, "task_id": "imu_to_hand_pose", "timestamp": 1781868235.6389086}
|
| 201 |
+
{"completed_samples_for_task": 1773, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:108", "sample_index": 981, "seconds": 4.1, "task_id": "imu_to_hand_pose", "timestamp": 1781868239.7394118}
|
| 202 |
+
{"completed_samples_for_task": 1774, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:112", "sample_index": 982, "seconds": 4.083, "task_id": "imu_to_hand_pose", "timestamp": 1781868243.8224115}
|
| 203 |
+
{"completed_samples_for_task": 1775, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:116", "sample_index": 983, "seconds": 4.133, "task_id": "imu_to_hand_pose", "timestamp": 1781868247.9557464}
|
| 204 |
+
{"event": "eval_complete", "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3", "timestamp": 1781868248.044384}
|
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard1.progress.jsonl
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
scripts/omni/collect_qwen3_retrieval_task_probe_results.sh
CHANGED
|
@@ -7,15 +7,20 @@ PROJECT_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
|
|
| 7 |
GPU_HOST_SUFFIX="${GPU_HOST_SUFFIX:-$(printf 'A%s-80Gx4' 100)}"
|
| 8 |
REMOTE_HOST="${REMOTE_HOST:-ANGEL-${GPU_HOST_SUFFIX}}"
|
| 9 |
REMOTE_ROOT="${REMOTE_ROOT:-/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite}"
|
| 10 |
-
RUN_ID="${RUN_ID:-
|
| 11 |
RESULT_ROOT="${RESULT_ROOT:-results/omni_finetune}"
|
| 12 |
-
TASKS_CSV="${TASKS_CSV:-
|
| 13 |
|
| 14 |
REMOTE_RUN_DIR="${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}"
|
| 15 |
LOCAL_RUN_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/${RUN_ID}"
|
| 16 |
LOCAL_LAUNCHER_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/deferred_launchers"
|
| 17 |
REMOTE_LAUNCHER_LOGS=(
|
| 18 |
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.launch.log"
|
|
|
|
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|
|
|
|
|
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|
| 19 |
"${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launch.log"
|
| 20 |
"${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launcher.log"
|
| 21 |
)
|
|
@@ -46,8 +51,11 @@ run_id = sys.argv[2]
|
|
| 46 |
task_ids = [item.strip() for item in sys.argv[3].split(",") if item.strip()]
|
| 47 |
run_dir = root / "results/omni_finetune" / run_id
|
| 48 |
metric_key_by_task = {
|
|
|
|
| 49 |
"caption_grounding": "caption_grounding_mrr",
|
| 50 |
"cross_modal_retrieval": "cross_modal_retrieval_mrr",
|
|
|
|
|
|
|
| 51 |
"camera_view_sync_retrieval": "camera_view_sync_retrieval_mrr",
|
| 52 |
}
|
| 53 |
expected = {task_id: metric_key_by_task[task_id] for task_id in task_ids}
|
|
|
|
| 7 |
GPU_HOST_SUFFIX="${GPU_HOST_SUFFIX:-$(printf 'A%s-80Gx4' 100)}"
|
| 8 |
REMOTE_HOST="${REMOTE_HOST:-ANGEL-${GPU_HOST_SUFFIX}}"
|
| 9 |
REMOTE_ROOT="${REMOTE_ROOT:-/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite}"
|
| 10 |
+
RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z}"
|
| 11 |
RESULT_ROOT="${RESULT_ROOT:-results/omni_finetune}"
|
| 12 |
+
TASKS_CSV="${TASKS_CSV:-hand_trajectory_forecast,modality_reconstruction,imu_to_hand_pose}"
|
| 13 |
|
| 14 |
REMOTE_RUN_DIR="${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}"
|
| 15 |
LOCAL_RUN_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/${RUN_ID}"
|
| 16 |
LOCAL_LAUNCHER_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/deferred_launchers"
|
| 17 |
REMOTE_LAUNCHER_LOGS=(
|
| 18 |
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.launch.log"
|
| 19 |
+
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.resume_when_free.log"
|
| 20 |
+
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.resume_when_free.launch.log"
|
| 21 |
+
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.shared_vram_resume.log"
|
| 22 |
+
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.shared_vram_autoresume_guard.log"
|
| 23 |
+
"${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.autoresume_guard.launch.log"
|
| 24 |
"${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launch.log"
|
| 25 |
"${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launcher.log"
|
| 26 |
)
|
|
|
|
| 51 |
task_ids = [item.strip() for item in sys.argv[3].split(",") if item.strip()]
|
| 52 |
run_dir = root / "results/omni_finetune" / run_id
|
| 53 |
metric_key_by_task = {
|
| 54 |
+
"hand_trajectory_forecast": "hand_trajectory_forecast_mrr",
|
| 55 |
"caption_grounding": "caption_grounding_mrr",
|
| 56 |
"cross_modal_retrieval": "cross_modal_retrieval_mrr",
|
| 57 |
+
"modality_reconstruction": "modality_reconstruction_mrr",
|
| 58 |
+
"imu_to_hand_pose": "imu_to_hand_pose_mrr",
|
| 59 |
"camera_view_sync_retrieval": "camera_view_sync_retrieval_mrr",
|
| 60 |
}
|
| 61 |
expected = {task_id: metric_key_by_task[task_id] for task_id in task_ids}
|
scripts/omni/eval_qwen3_omni_retrieval_task_probes.py
CHANGED
|
@@ -2,10 +2,10 @@
|
|
| 2 |
"""Evaluate Qwen3-Omni on target-backed retrieval probes.
|
| 3 |
|
| 4 |
This runner covers model-friendly retrieval tasks whose targets can be formed
|
| 5 |
-
from the staged 128-episode JSON export
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
|
| 9 |
"""
|
| 10 |
|
| 11 |
from __future__ import annotations
|
|
@@ -31,6 +31,16 @@ from qwen3_omni_dataset_utils import has_empty_audio_items, is_empty_audio_excep
|
|
| 31 |
|
| 32 |
TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
|
| 33 |
[
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 34 |
(
|
| 35 |
"caption_grounding",
|
| 36 |
{
|
|
@@ -51,6 +61,16 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
|
|
| 51 |
"prediction_key": "ranked_candidates",
|
| 52 |
},
|
| 53 |
),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 54 |
(
|
| 55 |
"camera_view_sync_retrieval",
|
| 56 |
{
|
|
@@ -61,6 +81,16 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
|
|
| 61 |
"prediction_key": "ranked_candidates",
|
| 62 |
},
|
| 63 |
),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
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|
|
|
|
|
| 64 |
]
|
| 65 |
)
|
| 66 |
|
|
@@ -75,6 +105,29 @@ MOTION_POSE_QUERY_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
|
|
| 75 |
("imu_accel_gyro", (2205, 2247)),
|
| 76 |
]
|
| 77 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 78 |
|
| 79 |
SYSTEM_PROMPT = (
|
| 80 |
"You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
|
|
@@ -93,6 +146,7 @@ def parse_args() -> argparse.Namespace:
|
|
| 93 |
parser.add_argument("--eval-split", default="test")
|
| 94 |
parser.add_argument("--tasks", default="caption_grounding")
|
| 95 |
parser.add_argument("--candidate-count", type=int, default=4)
|
|
|
|
| 96 |
parser.add_argument("--sample-limit", type=int, default=0)
|
| 97 |
parser.add_argument("--sample-offset", type=int, default=0)
|
| 98 |
parser.add_argument("--sample-stride", type=int, default=1)
|
|
@@ -247,6 +301,27 @@ def select_eval_indices(samples: list[dict[str, Any]], args: argparse.Namespace)
|
|
| 247 |
return indices
|
| 248 |
|
| 249 |
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
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|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 250 |
def prediction_id(task_id: str, sample: dict[str, Any]) -> str:
|
| 251 |
return f"{task_id}::{sample.get('id')}"
|
| 252 |
|
|
@@ -261,15 +336,17 @@ def build_candidate_indices(
|
|
| 261 |
sample_idx: int,
|
| 262 |
task_id: str,
|
| 263 |
candidate_count: int,
|
|
|
|
| 264 |
) -> list[int]:
|
| 265 |
if candidate_count < 2 or candidate_count > 8:
|
| 266 |
raise ValueError("--candidate-count must be between 2 and 8")
|
| 267 |
sample = samples[sample_idx]
|
|
|
|
| 268 |
if task_id == "camera_view_sync_retrieval":
|
| 269 |
negatives = [
|
| 270 |
idx
|
| 271 |
for idx in eval_pool
|
| 272 |
-
if idx !=
|
| 273 |
and has_camera_view_pair(samples[idx])
|
| 274 |
and (
|
| 275 |
samples[idx].get("episode_id") != sample.get("episode_id")
|
|
@@ -277,7 +354,24 @@ def build_candidate_indices(
|
|
| 277 |
)
|
| 278 |
]
|
| 279 |
negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
|
| 280 |
-
selected = [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 281 |
selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
|
| 282 |
return selected
|
| 283 |
true_action = normalize_text(answer(sample).get("action")).casefold()
|
|
@@ -285,15 +379,15 @@ def build_candidate_indices(
|
|
| 285 |
negatives = [
|
| 286 |
idx
|
| 287 |
for idx in eval_pool
|
| 288 |
-
if idx !=
|
| 289 |
and media_video_path(samples[idx])
|
| 290 |
and samples[idx].get("episode_id") != true_episode
|
| 291 |
and normalize_text(answer(samples[idx]).get("action")).casefold() != true_action
|
| 292 |
]
|
| 293 |
if len(negatives) < candidate_count - 1:
|
| 294 |
-
negatives = [idx for idx in eval_pool if idx !=
|
| 295 |
negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
|
| 296 |
-
selected = [
|
| 297 |
selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
|
| 298 |
return selected
|
| 299 |
|
|
@@ -448,33 +542,96 @@ def summarize_vector_block(values: np.ndarray) -> dict[str, float]:
|
|
| 448 |
}
|
| 449 |
|
| 450 |
|
| 451 |
-
def
|
| 452 |
-
vector
|
| 453 |
-
|
| 454 |
-
|
| 455 |
-
|
| 456 |
-
|
| 457 |
-
|
| 458 |
-
|
| 459 |
-
for name, (start, end) in MOTION_POSE_QUERY_BLOCKS.items():
|
| 460 |
if end > vector.shape[0]:
|
| 461 |
continue
|
| 462 |
stats = summarize_vector_block(vector[start:end])
|
|
|
|
| 463 |
lines.append(
|
| 464 |
(
|
| 465 |
-
f"{
|
| 466 |
f"mean_abs={stats['mean_abs']:.5g}, l2={stats['l2']:.5g}, "
|
| 467 |
f"max_abs={stats['max_abs']:.5g}"
|
| 468 |
)
|
| 469 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 470 |
return "\n".join(lines)
|
| 471 |
|
| 472 |
|
| 473 |
-
def artifact_query_text(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 474 |
if task_id == "cross_modal_retrieval":
|
| 475 |
if sensor_cache is None:
|
| 476 |
raise ValueError("cross_modal_retrieval requires a sensor feature cache")
|
| 477 |
return sensor_query_text(sample, sensor_cache)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 478 |
if task_id == "camera_view_sync_retrieval":
|
| 479 |
ref = reference_camera_view(sample)
|
| 480 |
return "\n".join(
|
|
@@ -490,16 +647,48 @@ def artifact_query_text(task_id: str, sample: dict[str, Any], sensor_cache: Sens
|
|
| 490 |
def build_messages(
|
| 491 |
samples: list[dict[str, Any]],
|
| 492 |
sample_idx: int,
|
|
|
|
| 493 |
candidate_indices: list[int],
|
| 494 |
task_id: str,
|
| 495 |
spec: dict[str, Any],
|
| 496 |
sensor_cache: SensorFeatureCache | None = None,
|
| 497 |
camera_clip_dir: Path | None = None,
|
|
|
|
| 498 |
) -> tuple[list[dict[str, Any]], str, list[dict[str, Any]]]:
|
| 499 |
letters = [chr(ord("A") + pos) for pos in range(len(candidate_indices))]
|
| 500 |
-
true_letter = letters[candidate_indices.index(
|
| 501 |
candidate_records: list[dict[str, Any]] = []
|
| 502 |
-
if task_id == "
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 503 |
if sensor_cache is None:
|
| 504 |
raise ValueError("cross_modal_retrieval requires a sensor feature cache")
|
| 505 |
task_instruction = "Rank the candidate video windows by which one is synchronized with the sensor/motion query."
|
|
@@ -545,6 +734,8 @@ def build_messages(
|
|
| 545 |
raise ValueError("camera_view_sync_retrieval requires a camera clip directory")
|
| 546 |
ref_view = reference_camera_view(samples[sample_idx])
|
| 547 |
content.append({"type": "video", "video": camera_view_clip_path(samples[sample_idx], ref_view, camera_clip_dir)})
|
|
|
|
|
|
|
| 548 |
for letter, idx in zip(letters, candidate_indices):
|
| 549 |
sample = samples[idx]
|
| 550 |
if task_id == "camera_view_sync_retrieval":
|
|
@@ -553,9 +744,17 @@ def build_messages(
|
|
| 553 |
view = candidate_camera_view(sample)
|
| 554 |
candidate_video = camera_view_clip_path(sample, view, camera_clip_dir)
|
| 555 |
candidate_view_name = view["name"]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 556 |
else:
|
| 557 |
candidate_video = media_video_path(sample)
|
| 558 |
candidate_view_name = "mosaic"
|
|
|
|
| 559 |
candidate_records.append(
|
| 560 |
{
|
| 561 |
"letter": letter,
|
|
@@ -564,11 +763,14 @@ def build_messages(
|
|
| 564 |
"start_frame": row_start(sample),
|
| 565 |
"end_frame": row_end(sample),
|
| 566 |
"view_name": candidate_view_name,
|
| 567 |
-
"is_target": idx ==
|
| 568 |
}
|
| 569 |
)
|
| 570 |
-
|
| 571 |
-
|
|
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|
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|
|
| 572 |
return (
|
| 573 |
[
|
| 574 |
{"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
|
|
@@ -651,8 +853,11 @@ def score_retrieval(rows: list[dict[str, Any]]) -> dict[str, float]:
|
|
| 651 |
return {
|
| 652 |
"num_samples": len(rows),
|
| 653 |
"mrr": mrr,
|
|
|
|
| 654 |
"caption_grounding_mrr": mrr,
|
| 655 |
"cross_modal_retrieval_mrr": mrr,
|
|
|
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|
|
|
| 656 |
"camera_view_sync_retrieval_mrr": mrr,
|
| 657 |
"top1_accuracy": top1 / len(rows) if rows else 0.0,
|
| 658 |
}
|
|
@@ -671,6 +876,9 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
|
|
| 671 |
"split": row["split"],
|
| 672 |
"start_frame": row["start_frame"],
|
| 673 |
"end_frame": row["end_frame"],
|
|
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|
| 674 |
"true_letter": row["true_letter"],
|
| 675 |
"predicted_ranking": json.dumps(row["predicted_ranking"], ensure_ascii=False),
|
| 676 |
"reciprocal_rank": row["reciprocal_rank"],
|
|
@@ -685,6 +893,9 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
|
|
| 685 |
"split",
|
| 686 |
"start_frame",
|
| 687 |
"end_frame",
|
|
|
|
|
|
|
|
|
|
| 688 |
"true_letter",
|
| 689 |
"predicted_ranking",
|
| 690 |
"reciprocal_rank",
|
|
@@ -694,7 +905,28 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
|
|
| 694 |
)
|
| 695 |
metrics = score_retrieval(rows)
|
| 696 |
primary_score = metrics[spec["metric_key"]]
|
| 697 |
-
if task_id == "
|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
| 698 |
score_policy = (
|
| 699 |
"GPU-backed Qwen3-Omni v6 sensor-to-video retrieval probe. The query is a compact "
|
| 700 |
"summary of held-out motion-capture, body-contact, camera-pose, and IMU feature blocks; "
|
|
@@ -732,6 +964,7 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
|
|
| 732 |
"dataset_jsonl": str(args.dataset_jsonl),
|
| 733 |
"eval_split": args.eval_split,
|
| 734 |
"candidate_count": args.candidate_count,
|
|
|
|
| 735 |
"sample_offset": args.sample_offset,
|
| 736 |
"sample_stride": args.sample_stride,
|
| 737 |
"scope": "held_out_test_qwen3_retrieval_task_probe",
|
|
@@ -755,6 +988,16 @@ def main() -> int:
|
|
| 755 |
if "cross_modal_retrieval" in selected_tasks:
|
| 756 |
eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
|
| 757 |
eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 758 |
if "camera_view_sync_retrieval" in selected_tasks:
|
| 759 |
eval_indices = [idx for idx in eval_indices if has_camera_view_pair(samples[idx])]
|
| 760 |
eval_pool = [idx for idx in eval_pool if has_camera_view_pair(samples[idx])]
|
|
@@ -772,11 +1015,17 @@ def main() -> int:
|
|
| 772 |
"sample_offset": args.sample_offset,
|
| 773 |
"sample_stride": args.sample_stride,
|
| 774 |
"candidate_count": args.candidate_count,
|
|
|
|
| 775 |
},
|
| 776 |
)
|
| 777 |
|
| 778 |
model, processor = load_model_processor(args)
|
| 779 |
-
sensor_cache =
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 780 |
camera_clip_dir = args.output_dir / "camera_view_sync_clips" if "camera_view_sync_retrieval" in selected_tasks else None
|
| 781 |
partial_by_task = {
|
| 782 |
task_id: {
|
|
@@ -796,15 +1045,25 @@ def main() -> int:
|
|
| 796 |
if pred_id in partial_by_task[task_id]:
|
| 797 |
continue
|
| 798 |
started = time.time()
|
| 799 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
| 800 |
messages, true_letter, candidate_records = build_messages(
|
| 801 |
samples,
|
| 802 |
sample_idx,
|
|
|
|
| 803 |
candidate_indices,
|
| 804 |
task_id,
|
| 805 |
spec,
|
| 806 |
sensor_cache=sensor_cache,
|
| 807 |
camera_clip_dir=camera_clip_dir,
|
|
|
|
| 808 |
)
|
| 809 |
raw = generate_messages(model, processor, messages, args)
|
| 810 |
valid_letters = [record["letter"] for record in candidate_records]
|
|
@@ -819,7 +1078,10 @@ def main() -> int:
|
|
| 819 |
"episode_id": sample.get("episode_id"),
|
| 820 |
"start_frame": row_start(sample),
|
| 821 |
"end_frame": row_end(sample),
|
| 822 |
-
"query_text": artifact_query_text(task_id, sample, sensor_cache),
|
|
|
|
|
|
|
|
|
|
| 823 |
"candidates": candidate_records,
|
| 824 |
"true_letter": true_letter,
|
| 825 |
"predicted_ranking": ranking,
|
|
@@ -857,6 +1119,7 @@ def main() -> int:
|
|
| 857 |
"dataset_jsonl": str(args.dataset_jsonl),
|
| 858 |
"eval_split": args.eval_split,
|
| 859 |
"candidate_count": args.candidate_count,
|
|
|
|
| 860 |
"sample_offset": args.sample_offset,
|
| 861 |
"sample_stride": args.sample_stride,
|
| 862 |
"tasks": {
|
|
|
|
| 2 |
"""Evaluate Qwen3-Omni on target-backed retrieval probes.
|
| 3 |
|
| 4 |
This runner covers model-friendly retrieval tasks whose targets can be formed
|
| 5 |
+
from the staged 128-episode JSON export and 4430-dim sensor feature shards
|
| 6 |
+
without inventing labels. It includes text/video retrieval probes plus numeric
|
| 7 |
+
sensor-target probes where Qwen ranks compact target-block summaries instead of
|
| 8 |
+
emitting high-dimensional vectors directly.
|
| 9 |
"""
|
| 10 |
|
| 11 |
from __future__ import annotations
|
|
|
|
| 31 |
|
| 32 |
TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
|
| 33 |
[
|
| 34 |
+
(
|
| 35 |
+
"hand_trajectory_forecast",
|
| 36 |
+
{
|
| 37 |
+
"task_number": 5,
|
| 38 |
+
"label": "Hand Trajectory Forecasting",
|
| 39 |
+
"family": "sensor_target_retrieval",
|
| 40 |
+
"metric_key": "hand_trajectory_forecast_mrr",
|
| 41 |
+
"prediction_key": "ranked_candidates",
|
| 42 |
+
},
|
| 43 |
+
),
|
| 44 |
(
|
| 45 |
"caption_grounding",
|
| 46 |
{
|
|
|
|
| 61 |
"prediction_key": "ranked_candidates",
|
| 62 |
},
|
| 63 |
),
|
| 64 |
+
(
|
| 65 |
+
"modality_reconstruction",
|
| 66 |
+
{
|
| 67 |
+
"task_number": 10,
|
| 68 |
+
"label": "Cross-Modal Reconstruction",
|
| 69 |
+
"family": "sensor_target_retrieval",
|
| 70 |
+
"metric_key": "modality_reconstruction_mrr",
|
| 71 |
+
"prediction_key": "ranked_candidates",
|
| 72 |
+
},
|
| 73 |
+
),
|
| 74 |
(
|
| 75 |
"camera_view_sync_retrieval",
|
| 76 |
{
|
|
|
|
| 81 |
"prediction_key": "ranked_candidates",
|
| 82 |
},
|
| 83 |
),
|
| 84 |
+
(
|
| 85 |
+
"imu_to_hand_pose",
|
| 86 |
+
{
|
| 87 |
+
"task_number": 18,
|
| 88 |
+
"label": "IMU-to-Hand Pose Reconstruction",
|
| 89 |
+
"family": "sensor_target_retrieval",
|
| 90 |
+
"metric_key": "imu_to_hand_pose_mrr",
|
| 91 |
+
"prediction_key": "ranked_candidates",
|
| 92 |
+
},
|
| 93 |
+
),
|
| 94 |
]
|
| 95 |
)
|
| 96 |
|
|
|
|
| 105 |
("imu_accel_gyro", (2205, 2247)),
|
| 106 |
]
|
| 107 |
)
|
| 108 |
+
HAND_TARGET_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
|
| 109 |
+
[
|
| 110 |
+
("hand_left_joints", (0, 441)),
|
| 111 |
+
("hand_right_joints", (441, 882)),
|
| 112 |
+
]
|
| 113 |
+
)
|
| 114 |
+
VISUAL_TARGET_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
|
| 115 |
+
[
|
| 116 |
+
("depth_confidence", (2247, 3227)),
|
| 117 |
+
("slam_point_cloud", (4291, 4313)),
|
| 118 |
+
("calibration", (4313, 4430)),
|
| 119 |
+
]
|
| 120 |
+
)
|
| 121 |
+
IMU_QUERY_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
|
| 122 |
+
[
|
| 123 |
+
("imu_accel_gyro", (2205, 2247)),
|
| 124 |
+
]
|
| 125 |
+
)
|
| 126 |
+
SENSOR_TARGET_TASKS = {
|
| 127 |
+
"hand_trajectory_forecast",
|
| 128 |
+
"modality_reconstruction",
|
| 129 |
+
"imu_to_hand_pose",
|
| 130 |
+
}
|
| 131 |
|
| 132 |
SYSTEM_PROMPT = (
|
| 133 |
"You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
|
|
|
|
| 146 |
parser.add_argument("--eval-split", default="test")
|
| 147 |
parser.add_argument("--tasks", default="caption_grounding")
|
| 148 |
parser.add_argument("--candidate-count", type=int, default=4)
|
| 149 |
+
parser.add_argument("--future-frames", type=int, default=100)
|
| 150 |
parser.add_argument("--sample-limit", type=int, default=0)
|
| 151 |
parser.add_argument("--sample-offset", type=int, default=0)
|
| 152 |
parser.add_argument("--sample-stride", type=int, default=1)
|
|
|
|
| 301 |
return indices
|
| 302 |
|
| 303 |
|
| 304 |
+
def by_episode_sorted(samples: list[dict[str, Any]]) -> dict[str, list[int]]:
|
| 305 |
+
grouped: dict[str, list[int]] = {}
|
| 306 |
+
for idx, sample in enumerate(samples):
|
| 307 |
+
grouped.setdefault(str(sample.get("episode_id")), []).append(idx)
|
| 308 |
+
for indices in grouped.values():
|
| 309 |
+
indices.sort(key=lambda i: row_start(samples[i]))
|
| 310 |
+
return grouped
|
| 311 |
+
|
| 312 |
+
|
| 313 |
+
def future_index_map(samples: list[dict[str, Any]], frame_offset: int) -> dict[int, int]:
|
| 314 |
+
mapping: dict[int, int] = {}
|
| 315 |
+
for indices in by_episode_sorted(samples).values():
|
| 316 |
+
starts = np.asarray([row_start(samples[i]) for i in indices], dtype=np.int64)
|
| 317 |
+
for idx in indices:
|
| 318 |
+
target_start = row_start(samples[idx]) + frame_offset
|
| 319 |
+
future_pos = int(np.searchsorted(starts, target_start, side="left"))
|
| 320 |
+
if future_pos < len(indices):
|
| 321 |
+
mapping[idx] = indices[future_pos]
|
| 322 |
+
return mapping
|
| 323 |
+
|
| 324 |
+
|
| 325 |
def prediction_id(task_id: str, sample: dict[str, Any]) -> str:
|
| 326 |
return f"{task_id}::{sample.get('id')}"
|
| 327 |
|
|
|
|
| 336 |
sample_idx: int,
|
| 337 |
task_id: str,
|
| 338 |
candidate_count: int,
|
| 339 |
+
target_idx: int | None = None,
|
| 340 |
) -> list[int]:
|
| 341 |
if candidate_count < 2 or candidate_count > 8:
|
| 342 |
raise ValueError("--candidate-count must be between 2 and 8")
|
| 343 |
sample = samples[sample_idx]
|
| 344 |
+
true_idx = sample_idx if target_idx is None else target_idx
|
| 345 |
if task_id == "camera_view_sync_retrieval":
|
| 346 |
negatives = [
|
| 347 |
idx
|
| 348 |
for idx in eval_pool
|
| 349 |
+
if idx != true_idx
|
| 350 |
and has_camera_view_pair(samples[idx])
|
| 351 |
and (
|
| 352 |
samples[idx].get("episode_id") != sample.get("episode_id")
|
|
|
|
| 354 |
)
|
| 355 |
]
|
| 356 |
negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
|
| 357 |
+
selected = [true_idx] + negatives[: candidate_count - 1]
|
| 358 |
+
selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
|
| 359 |
+
return selected
|
| 360 |
+
if task_id in SENSOR_TARGET_TASKS:
|
| 361 |
+
negatives = [
|
| 362 |
+
idx
|
| 363 |
+
for idx in eval_pool
|
| 364 |
+
if idx != true_idx
|
| 365 |
+
and has_sensor_feature(samples[idx])
|
| 366 |
+
and (
|
| 367 |
+
samples[idx].get("episode_id") != samples[true_idx].get("episode_id")
|
| 368 |
+
or row_start(samples[idx]) != row_start(samples[true_idx])
|
| 369 |
+
)
|
| 370 |
+
]
|
| 371 |
+
if len(negatives) < candidate_count - 1:
|
| 372 |
+
negatives = [idx for idx in eval_pool if idx != true_idx and has_sensor_feature(samples[idx])]
|
| 373 |
+
negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
|
| 374 |
+
selected = [true_idx] + negatives[: candidate_count - 1]
|
| 375 |
selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
|
| 376 |
return selected
|
| 377 |
true_action = normalize_text(answer(sample).get("action")).casefold()
|
|
|
|
| 379 |
negatives = [
|
| 380 |
idx
|
| 381 |
for idx in eval_pool
|
| 382 |
+
if idx != true_idx
|
| 383 |
and media_video_path(samples[idx])
|
| 384 |
and samples[idx].get("episode_id") != true_episode
|
| 385 |
and normalize_text(answer(samples[idx]).get("action")).casefold() != true_action
|
| 386 |
]
|
| 387 |
if len(negatives) < candidate_count - 1:
|
| 388 |
+
negatives = [idx for idx in eval_pool if idx != true_idx and media_video_path(samples[idx])]
|
| 389 |
negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
|
| 390 |
+
selected = [true_idx] + negatives[: candidate_count - 1]
|
| 391 |
selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
|
| 392 |
return selected
|
| 393 |
|
|
|
|
| 542 |
}
|
| 543 |
|
| 544 |
|
| 545 |
+
def block_summary_lines(
|
| 546 |
+
vector: np.ndarray,
|
| 547 |
+
blocks: OrderedDict[str, tuple[int, int]],
|
| 548 |
+
*,
|
| 549 |
+
prefix: str = "",
|
| 550 |
+
) -> list[str]:
|
| 551 |
+
lines: list[str] = []
|
| 552 |
+
for name, (start, end) in blocks.items():
|
|
|
|
| 553 |
if end > vector.shape[0]:
|
| 554 |
continue
|
| 555 |
stats = summarize_vector_block(vector[start:end])
|
| 556 |
+
label = f"{prefix}{name}" if prefix else name
|
| 557 |
lines.append(
|
| 558 |
(
|
| 559 |
+
f"{label}: mean={stats['mean']:.5g}, std={stats['std']:.5g}, "
|
| 560 |
f"mean_abs={stats['mean_abs']:.5g}, l2={stats['l2']:.5g}, "
|
| 561 |
f"max_abs={stats['max_abs']:.5g}"
|
| 562 |
)
|
| 563 |
)
|
| 564 |
+
return lines
|
| 565 |
+
|
| 566 |
+
|
| 567 |
+
def sensor_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
|
| 568 |
+
vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
|
| 569 |
+
lines = [
|
| 570 |
+
"Sensor/motion query for the current 20-frame window.",
|
| 571 |
+
"Only motion capture, body contact, camera pose, and IMU blocks are summarized.",
|
| 572 |
+
"The target is the candidate depth/video window synchronized with this sensor window.",
|
| 573 |
+
f"Window frames: {row_start(sample)}-{row_end(sample)}",
|
| 574 |
+
]
|
| 575 |
+
lines.extend(block_summary_lines(vector, MOTION_POSE_QUERY_BLOCKS))
|
| 576 |
+
return "\n".join(lines)
|
| 577 |
+
|
| 578 |
+
|
| 579 |
+
def imu_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
|
| 580 |
+
vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
|
| 581 |
+
lines = [
|
| 582 |
+
"IMU query for the current 20-frame window.",
|
| 583 |
+
"The target is the synchronized hand-pose candidate summary.",
|
| 584 |
+
f"Window frames: {row_start(sample)}-{row_end(sample)}",
|
| 585 |
+
]
|
| 586 |
+
lines.extend(block_summary_lines(vector, IMU_QUERY_BLOCKS))
|
| 587 |
+
return "\n".join(lines)
|
| 588 |
+
|
| 589 |
+
|
| 590 |
+
def target_summary_text(task_id: str, sample: dict[str, Any], cache: SensorFeatureCache) -> str:
|
| 591 |
+
vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
|
| 592 |
+
if task_id in {"hand_trajectory_forecast", "imu_to_hand_pose"}:
|
| 593 |
+
blocks = HAND_TARGET_BLOCKS
|
| 594 |
+
label = "hand-pose target summary"
|
| 595 |
+
elif task_id == "modality_reconstruction":
|
| 596 |
+
blocks = VISUAL_TARGET_BLOCKS
|
| 597 |
+
label = "visual/depth target summary"
|
| 598 |
+
else:
|
| 599 |
+
raise ValueError(f"task does not use sensor target summaries: {task_id}")
|
| 600 |
+
lines = [
|
| 601 |
+
f"{label}; candidate window frames {row_start(sample)}-{row_end(sample)}",
|
| 602 |
+
f"candidate_id={sample.get('id')}",
|
| 603 |
+
]
|
| 604 |
+
lines.extend(block_summary_lines(vector, blocks))
|
| 605 |
return "\n".join(lines)
|
| 606 |
|
| 607 |
|
| 608 |
+
def artifact_query_text(
|
| 609 |
+
task_id: str,
|
| 610 |
+
sample: dict[str, Any],
|
| 611 |
+
sensor_cache: SensorFeatureCache | None,
|
| 612 |
+
*,
|
| 613 |
+
future_frames: int = 100,
|
| 614 |
+
) -> str:
|
| 615 |
if task_id == "cross_modal_retrieval":
|
| 616 |
if sensor_cache is None:
|
| 617 |
raise ValueError("cross_modal_retrieval requires a sensor feature cache")
|
| 618 |
return sensor_query_text(sample, sensor_cache)
|
| 619 |
+
if task_id == "modality_reconstruction":
|
| 620 |
+
if sensor_cache is None:
|
| 621 |
+
raise ValueError("modality_reconstruction requires a sensor feature cache")
|
| 622 |
+
return sensor_query_text(sample, sensor_cache)
|
| 623 |
+
if task_id == "imu_to_hand_pose":
|
| 624 |
+
if sensor_cache is None:
|
| 625 |
+
raise ValueError("imu_to_hand_pose requires a sensor feature cache")
|
| 626 |
+
return imu_query_text(sample, sensor_cache)
|
| 627 |
+
if task_id == "hand_trajectory_forecast":
|
| 628 |
+
return "\n".join(
|
| 629 |
+
[
|
| 630 |
+
"Current video query for future hand trajectory.",
|
| 631 |
+
f"Window frames: {row_start(sample)}-{row_end(sample)}",
|
| 632 |
+
f"Future offset: {future_frames} frames.",
|
| 633 |
+
]
|
| 634 |
+
)
|
| 635 |
if task_id == "camera_view_sync_retrieval":
|
| 636 |
ref = reference_camera_view(sample)
|
| 637 |
return "\n".join(
|
|
|
|
| 647 |
def build_messages(
|
| 648 |
samples: list[dict[str, Any]],
|
| 649 |
sample_idx: int,
|
| 650 |
+
target_idx: int,
|
| 651 |
candidate_indices: list[int],
|
| 652 |
task_id: str,
|
| 653 |
spec: dict[str, Any],
|
| 654 |
sensor_cache: SensorFeatureCache | None = None,
|
| 655 |
camera_clip_dir: Path | None = None,
|
| 656 |
+
future_frames: int = 100,
|
| 657 |
) -> tuple[list[dict[str, Any]], str, list[dict[str, Any]]]:
|
| 658 |
letters = [chr(ord("A") + pos) for pos in range(len(candidate_indices))]
|
| 659 |
+
true_letter = letters[candidate_indices.index(target_idx)]
|
| 660 |
candidate_records: list[dict[str, Any]] = []
|
| 661 |
+
if task_id == "hand_trajectory_forecast":
|
| 662 |
+
if sensor_cache is None:
|
| 663 |
+
raise ValueError("hand_trajectory_forecast requires a sensor feature cache")
|
| 664 |
+
task_instruction = (
|
| 665 |
+
f"Rank the candidate hand-pose summaries by which one best matches the likely hand trajectory "
|
| 666 |
+
f"{future_frames} frames after the query video window."
|
| 667 |
+
)
|
| 668 |
+
query = "\n".join(
|
| 669 |
+
[
|
| 670 |
+
"Current video query:",
|
| 671 |
+
f"Window frames: {row_start(samples[sample_idx])}-{row_end(samples[sample_idx])}",
|
| 672 |
+
"Use visible hand motion, object interaction, and scene context. Candidate summaries are numeric hand-pose targets.",
|
| 673 |
+
]
|
| 674 |
+
)
|
| 675 |
+
query_header = "Current video context:"
|
| 676 |
+
elif task_id == "modality_reconstruction":
|
| 677 |
+
if sensor_cache is None:
|
| 678 |
+
raise ValueError("modality_reconstruction requires a sensor feature cache")
|
| 679 |
+
task_instruction = (
|
| 680 |
+
"Rank the candidate visual/depth summaries by which one is synchronized with the sensor/motion query. "
|
| 681 |
+
"The query uses motion-capture, body-contact, camera-pose, and IMU feature summaries only."
|
| 682 |
+
)
|
| 683 |
+
query = sensor_query_text(samples[sample_idx], sensor_cache)
|
| 684 |
+
query_header = "Sensor/motion query:"
|
| 685 |
+
elif task_id == "imu_to_hand_pose":
|
| 686 |
+
if sensor_cache is None:
|
| 687 |
+
raise ValueError("imu_to_hand_pose requires a sensor feature cache")
|
| 688 |
+
task_instruction = "Rank the candidate hand-pose summaries by which one is synchronized with the IMU query."
|
| 689 |
+
query = imu_query_text(samples[sample_idx], sensor_cache)
|
| 690 |
+
query_header = "IMU query:"
|
| 691 |
+
elif task_id == "cross_modal_retrieval":
|
| 692 |
if sensor_cache is None:
|
| 693 |
raise ValueError("cross_modal_retrieval requires a sensor feature cache")
|
| 694 |
task_instruction = "Rank the candidate video windows by which one is synchronized with the sensor/motion query."
|
|
|
|
| 734 |
raise ValueError("camera_view_sync_retrieval requires a camera clip directory")
|
| 735 |
ref_view = reference_camera_view(samples[sample_idx])
|
| 736 |
content.append({"type": "video", "video": camera_view_clip_path(samples[sample_idx], ref_view, camera_clip_dir)})
|
| 737 |
+
elif task_id == "hand_trajectory_forecast":
|
| 738 |
+
content.append({"type": "video", "video": media_video_path(samples[sample_idx])})
|
| 739 |
for letter, idx in zip(letters, candidate_indices):
|
| 740 |
sample = samples[idx]
|
| 741 |
if task_id == "camera_view_sync_retrieval":
|
|
|
|
| 744 |
view = candidate_camera_view(sample)
|
| 745 |
candidate_video = camera_view_clip_path(sample, view, camera_clip_dir)
|
| 746 |
candidate_view_name = view["name"]
|
| 747 |
+
candidate_summary = None
|
| 748 |
+
elif task_id in SENSOR_TARGET_TASKS:
|
| 749 |
+
if sensor_cache is None:
|
| 750 |
+
raise ValueError(f"{task_id} requires a sensor feature cache")
|
| 751 |
+
candidate_video = None
|
| 752 |
+
candidate_view_name = "sensor_target_summary"
|
| 753 |
+
candidate_summary = target_summary_text(task_id, sample, sensor_cache)
|
| 754 |
else:
|
| 755 |
candidate_video = media_video_path(sample)
|
| 756 |
candidate_view_name = "mosaic"
|
| 757 |
+
candidate_summary = None
|
| 758 |
candidate_records.append(
|
| 759 |
{
|
| 760 |
"letter": letter,
|
|
|
|
| 763 |
"start_frame": row_start(sample),
|
| 764 |
"end_frame": row_end(sample),
|
| 765 |
"view_name": candidate_view_name,
|
| 766 |
+
"is_target": idx == target_idx,
|
| 767 |
}
|
| 768 |
)
|
| 769 |
+
if task_id in SENSOR_TARGET_TASKS:
|
| 770 |
+
content.append({"type": "text", "text": f"Candidate {letter} target summary:\n{candidate_summary}"})
|
| 771 |
+
else:
|
| 772 |
+
content.append({"type": "text", "text": f"Candidate {letter} video window:"})
|
| 773 |
+
content.append({"type": "video", "video": candidate_video})
|
| 774 |
return (
|
| 775 |
[
|
| 776 |
{"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
|
|
|
|
| 853 |
return {
|
| 854 |
"num_samples": len(rows),
|
| 855 |
"mrr": mrr,
|
| 856 |
+
"hand_trajectory_forecast_mrr": mrr,
|
| 857 |
"caption_grounding_mrr": mrr,
|
| 858 |
"cross_modal_retrieval_mrr": mrr,
|
| 859 |
+
"modality_reconstruction_mrr": mrr,
|
| 860 |
+
"imu_to_hand_pose_mrr": mrr,
|
| 861 |
"camera_view_sync_retrieval_mrr": mrr,
|
| 862 |
"top1_accuracy": top1 / len(rows) if rows else 0.0,
|
| 863 |
}
|
|
|
|
| 876 |
"split": row["split"],
|
| 877 |
"start_frame": row["start_frame"],
|
| 878 |
"end_frame": row["end_frame"],
|
| 879 |
+
"target_id": row.get("target_id"),
|
| 880 |
+
"target_start_frame": row.get("target_start_frame"),
|
| 881 |
+
"target_end_frame": row.get("target_end_frame"),
|
| 882 |
"true_letter": row["true_letter"],
|
| 883 |
"predicted_ranking": json.dumps(row["predicted_ranking"], ensure_ascii=False),
|
| 884 |
"reciprocal_rank": row["reciprocal_rank"],
|
|
|
|
| 893 |
"split",
|
| 894 |
"start_frame",
|
| 895 |
"end_frame",
|
| 896 |
+
"target_id",
|
| 897 |
+
"target_start_frame",
|
| 898 |
+
"target_end_frame",
|
| 899 |
"true_letter",
|
| 900 |
"predicted_ranking",
|
| 901 |
"reciprocal_rank",
|
|
|
|
| 905 |
)
|
| 906 |
metrics = score_retrieval(rows)
|
| 907 |
primary_score = metrics[spec["metric_key"]]
|
| 908 |
+
if task_id == "hand_trajectory_forecast":
|
| 909 |
+
score_policy = (
|
| 910 |
+
"GPU-backed Qwen3-Omni v6 future hand-trajectory retrieval probe. The prompt shows the "
|
| 911 |
+
"held-out current video window and asks the model to rank shuffled compact hand-pose "
|
| 912 |
+
"target summaries; the true target is the staged hand-joint feature block from the "
|
| 913 |
+
"window at the configured future-frame offset. This avoids asking the language model "
|
| 914 |
+
"to emit hundreds of raw pose floats while still scoring against real exported hand targets."
|
| 915 |
+
)
|
| 916 |
+
elif task_id == "modality_reconstruction":
|
| 917 |
+
score_policy = (
|
| 918 |
+
"GPU-backed Qwen3-Omni v6 cross-modal reconstruction retrieval probe. The query is a "
|
| 919 |
+
"compact summary of motion-capture, body-contact, camera-pose, and IMU feature blocks; "
|
| 920 |
+
"candidates are shuffled compact visual/depth/calibration target summaries from staged "
|
| 921 |
+
"sensor shards, and the score is MRR of the synchronized true target."
|
| 922 |
+
)
|
| 923 |
+
elif task_id == "imu_to_hand_pose":
|
| 924 |
+
score_policy = (
|
| 925 |
+
"GPU-backed Qwen3-Omni v6 IMU-to-hand-pose retrieval probe. The query is the held-out "
|
| 926 |
+
"IMU accel/gyro summary and candidates are shuffled compact hand-joint summaries from "
|
| 927 |
+
"the staged sensor shards; the score is MRR of the synchronized true hand-pose target."
|
| 928 |
+
)
|
| 929 |
+
elif task_id == "cross_modal_retrieval":
|
| 930 |
score_policy = (
|
| 931 |
"GPU-backed Qwen3-Omni v6 sensor-to-video retrieval probe. The query is a compact "
|
| 932 |
"summary of held-out motion-capture, body-contact, camera-pose, and IMU feature blocks; "
|
|
|
|
| 964 |
"dataset_jsonl": str(args.dataset_jsonl),
|
| 965 |
"eval_split": args.eval_split,
|
| 966 |
"candidate_count": args.candidate_count,
|
| 967 |
+
"future_frames": args.future_frames,
|
| 968 |
"sample_offset": args.sample_offset,
|
| 969 |
"sample_stride": args.sample_stride,
|
| 970 |
"scope": "held_out_test_qwen3_retrieval_task_probe",
|
|
|
|
| 988 |
if "cross_modal_retrieval" in selected_tasks:
|
| 989 |
eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
|
| 990 |
eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
|
| 991 |
+
if any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks):
|
| 992 |
+
eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
|
| 993 |
+
eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
|
| 994 |
+
future_targets = future_index_map(samples, args.future_frames) if "hand_trajectory_forecast" in selected_tasks else {}
|
| 995 |
+
if "hand_trajectory_forecast" in selected_tasks:
|
| 996 |
+
eval_indices = [
|
| 997 |
+
idx
|
| 998 |
+
for idx in eval_indices
|
| 999 |
+
if idx in future_targets and has_sensor_feature(samples[future_targets[idx]])
|
| 1000 |
+
]
|
| 1001 |
if "camera_view_sync_retrieval" in selected_tasks:
|
| 1002 |
eval_indices = [idx for idx in eval_indices if has_camera_view_pair(samples[idx])]
|
| 1003 |
eval_pool = [idx for idx in eval_pool if has_camera_view_pair(samples[idx])]
|
|
|
|
| 1015 |
"sample_offset": args.sample_offset,
|
| 1016 |
"sample_stride": args.sample_stride,
|
| 1017 |
"candidate_count": args.candidate_count,
|
| 1018 |
+
"future_frames": args.future_frames,
|
| 1019 |
},
|
| 1020 |
)
|
| 1021 |
|
| 1022 |
model, processor = load_model_processor(args)
|
| 1023 |
+
sensor_cache = (
|
| 1024 |
+
SensorFeatureCache()
|
| 1025 |
+
if "cross_modal_retrieval" in selected_tasks
|
| 1026 |
+
or any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks)
|
| 1027 |
+
else None
|
| 1028 |
+
)
|
| 1029 |
camera_clip_dir = args.output_dir / "camera_view_sync_clips" if "camera_view_sync_retrieval" in selected_tasks else None
|
| 1030 |
partial_by_task = {
|
| 1031 |
task_id: {
|
|
|
|
| 1045 |
if pred_id in partial_by_task[task_id]:
|
| 1046 |
continue
|
| 1047 |
started = time.time()
|
| 1048 |
+
target_idx = future_targets[sample_idx] if task_id == "hand_trajectory_forecast" else sample_idx
|
| 1049 |
+
candidate_indices = build_candidate_indices(
|
| 1050 |
+
samples,
|
| 1051 |
+
eval_pool,
|
| 1052 |
+
sample_idx,
|
| 1053 |
+
task_id,
|
| 1054 |
+
args.candidate_count,
|
| 1055 |
+
target_idx=target_idx,
|
| 1056 |
+
)
|
| 1057 |
messages, true_letter, candidate_records = build_messages(
|
| 1058 |
samples,
|
| 1059 |
sample_idx,
|
| 1060 |
+
target_idx,
|
| 1061 |
candidate_indices,
|
| 1062 |
task_id,
|
| 1063 |
spec,
|
| 1064 |
sensor_cache=sensor_cache,
|
| 1065 |
camera_clip_dir=camera_clip_dir,
|
| 1066 |
+
future_frames=args.future_frames,
|
| 1067 |
)
|
| 1068 |
raw = generate_messages(model, processor, messages, args)
|
| 1069 |
valid_letters = [record["letter"] for record in candidate_records]
|
|
|
|
| 1078 |
"episode_id": sample.get("episode_id"),
|
| 1079 |
"start_frame": row_start(sample),
|
| 1080 |
"end_frame": row_end(sample),
|
| 1081 |
+
"query_text": artifact_query_text(task_id, sample, sensor_cache, future_frames=args.future_frames),
|
| 1082 |
+
"target_id": samples[target_idx].get("id"),
|
| 1083 |
+
"target_start_frame": row_start(samples[target_idx]),
|
| 1084 |
+
"target_end_frame": row_end(samples[target_idx]),
|
| 1085 |
"candidates": candidate_records,
|
| 1086 |
"true_letter": true_letter,
|
| 1087 |
"predicted_ranking": ranking,
|
|
|
|
| 1119 |
"dataset_jsonl": str(args.dataset_jsonl),
|
| 1120 |
"eval_split": args.eval_split,
|
| 1121 |
"candidate_count": args.candidate_count,
|
| 1122 |
+
"future_frames": args.future_frames,
|
| 1123 |
"sample_offset": args.sample_offset,
|
| 1124 |
"sample_stride": args.sample_stride,
|
| 1125 |
"tasks": {
|
scripts/omni/merge_qwen3_omni_retrieval_task_probe_shards.py
CHANGED
|
@@ -44,6 +44,7 @@ def fake_args(run_id: str, first_metrics: dict[str, Any]) -> argparse.Namespace:
|
|
| 44 |
dataset_jsonl=Path(first_metrics.get("dataset_jsonl", "")),
|
| 45 |
eval_split=first_metrics.get("eval_split", "test"),
|
| 46 |
candidate_count=int(first_metrics.get("candidate_count", 4) or 4),
|
|
|
|
| 47 |
sample_offset=0,
|
| 48 |
sample_stride=1,
|
| 49 |
)
|
|
|
|
| 44 |
dataset_jsonl=Path(first_metrics.get("dataset_jsonl", "")),
|
| 45 |
eval_split=first_metrics.get("eval_split", "test"),
|
| 46 |
candidate_count=int(first_metrics.get("candidate_count", 4) or 4),
|
| 47 |
+
future_frames=int(first_metrics.get("future_frames", 100) or 100),
|
| 48 |
sample_offset=0,
|
| 49 |
sample_stride=1,
|
| 50 |
)
|
scripts/omni/run_qwen3_omni_retrieval_task_probes_sharded.sh
CHANGED
|
@@ -13,6 +13,7 @@ RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_retrieval_task_probes_$(date -u +%Y
|
|
| 13 |
TASKS="${TASKS:-caption_grounding}"
|
| 14 |
EVAL_SPLIT="${EVAL_SPLIT:-test}"
|
| 15 |
CANDIDATE_COUNT="${CANDIDATE_COUNT:-4}"
|
|
|
|
| 16 |
MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-64}"
|
| 17 |
SAMPLE_LIMIT="${SAMPLE_LIMIT:-0}"
|
| 18 |
DEVICE_MAP="${DEVICE_MAP:-auto}"
|
|
@@ -49,6 +50,7 @@ mkdir -p "$OUT_DIR"
|
|
| 49 |
echo "adapter_dir=$ADAPTER_DIR"
|
| 50 |
echo "tasks=$TASKS"
|
| 51 |
echo "candidate_count=$CANDIDATE_COUNT"
|
|
|
|
| 52 |
echo "cuda_device_groups=$CUDA_DEVICE_GROUPS"
|
| 53 |
echo "shards=$SHARDS"
|
| 54 |
echo "started_at=$(date -Is)"
|
|
@@ -61,6 +63,7 @@ COMMON_ARGS=(
|
|
| 61 |
--tasks "$TASKS"
|
| 62 |
--eval-split "$EVAL_SPLIT"
|
| 63 |
--candidate-count "$CANDIDATE_COUNT"
|
|
|
|
| 64 |
--sample-limit "$SAMPLE_LIMIT"
|
| 65 |
--max-new-tokens "$MAX_NEW_TOKENS"
|
| 66 |
--device-map "$DEVICE_MAP"
|
|
|
|
| 13 |
TASKS="${TASKS:-caption_grounding}"
|
| 14 |
EVAL_SPLIT="${EVAL_SPLIT:-test}"
|
| 15 |
CANDIDATE_COUNT="${CANDIDATE_COUNT:-4}"
|
| 16 |
+
FUTURE_FRAMES="${FUTURE_FRAMES:-100}"
|
| 17 |
MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-64}"
|
| 18 |
SAMPLE_LIMIT="${SAMPLE_LIMIT:-0}"
|
| 19 |
DEVICE_MAP="${DEVICE_MAP:-auto}"
|
|
|
|
| 50 |
echo "adapter_dir=$ADAPTER_DIR"
|
| 51 |
echo "tasks=$TASKS"
|
| 52 |
echo "candidate_count=$CANDIDATE_COUNT"
|
| 53 |
+
echo "future_frames=$FUTURE_FRAMES"
|
| 54 |
echo "cuda_device_groups=$CUDA_DEVICE_GROUPS"
|
| 55 |
echo "shards=$SHARDS"
|
| 56 |
echo "started_at=$(date -Is)"
|
|
|
|
| 63 |
--tasks "$TASKS"
|
| 64 |
--eval-split "$EVAL_SPLIT"
|
| 65 |
--candidate-count "$CANDIDATE_COUNT"
|
| 66 |
+
--future-frames "$FUTURE_FRAMES"
|
| 67 |
--sample-limit "$SAMPLE_LIMIT"
|
| 68 |
--max-new-tokens "$MAX_NEW_TOKENS"
|
| 69 |
--device-map "$DEVICE_MAP"
|