Initial commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+2025-11-25 16:29:16,708 INFO    MainThread:1841156 [wandb_init.py:init():838] wandb.init called with sweep_config: {}
+config: {'dataset': {'repo_id': '.', 'root': '/projects/extern/kisski/kisski-umg-fairpact-2/dir.project/VLA/binh/libero_lerobot_openpi/libero', 'episodes': None, 'image_transforms': {'enable': True, 'max_num_transforms': 3, 'random_order': False, 'image_tfs': {'hue': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'hue': [-0.05, 0.05]}}, 'contrast': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'contrast': [0.8, 1.2]}}, 'sharpness': {'weight': 1.0, 'type': 'SharpnessJitter', 'kwargs': {'sharpness': [0.5, 1.5]}}, 'brightness': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'brightness': [0.8, 1.2]}}, 'saturation': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'saturation': [0.5, 1.5]}}, 'crop_resize': {'weight': 1.0, 'type': 'RandomResizedCrop', 'kwargs': {'size': [256, 256], 'ratio': [1, 1], 'scale': [0.9, 0.95]}}, 'rotate': {'weight': 1.0, 'type': 'RandomRotate', 'kwargs': {'degrees': [-5, 5]}}}, 'wrist_tfs': {'hue': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'hue': [-0.05, 0.05]}}, 'contrast': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'contrast': [0.8, 1.2]}}, 'sharpness': {'weight': 1.0, 'type': 'SharpnessJitter', 'kwargs': {'sharpness': [0.5, 1.5]}}, 'brightness': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'brightness': [0.8, 1.2]}}, 'saturation': {'weight': 1.0, 'type': 'ColorJitter', 'kwargs': {'saturation': [0.5, 1.5]}}}}, 'revision': None, 'use_imagenet_stats': True, 'video_backend': 'torchcodec', 'vqa_data_path': None}, 'env': None, 'policy': {'type': 'smolvla', 'n_obs_steps': 1, 'normalization_mapping': {'VISUAL': <NormalizationMode.IDENTITY: 'IDENTITY'>, 'STATE': <NormalizationMode.MEAN_STD: 'MEAN_STD'>, 'ACTION': <NormalizationMode.MEAN_STD: 'MEAN_STD'>}, 'input_features': {'image': {'type': <FeatureType.VISUAL: 'VISUAL'>, 'shape': [3, 256, 256]}, 'wrist_image': {'type': <FeatureType.VISUAL: 'VISUAL'>, 'shape': [3, 256, 256]}, 'state': {'type': <FeatureType.STATE: 'STATE'>, 'shape': [8]}}, 'output_features': {'actions': {'type': <FeatureType.ACTION: 'ACTION'>, 'shape': [7]}}, 'device': 'cuda', 'use_amp': False, 'gradient_accumulation_steps': 1, 'chunk_size': 50, 'n_action_steps': 50, 'max_state_dim': 32, 'max_action_dim': 32, 'resize_imgs_with_padding': [512, 512], 'empty_cameras': 0, 'adapt_to_pi_aloha': False, 'use_delta_joint_actions_aloha': False, 'tokenizer_max_length': 48, 'num_steps': 10, 'use_cache': True, 'freeze_vision_encoder': True, 'train_expert_only': True, 'train_state_proj': True, 'optimizer_lr': 0.0001, 'optimizer_betas': [0.9, 0.95], 'optimizer_eps': 1e-08, 'optimizer_weight_decay': 1e-10, 'optimizer_grad_clip_norm': 10.0, 'scheduler_warmup_steps': 1000, 'scheduler_decay_steps': 30000, 'scheduler_decay_lr': 2.5e-06, 'vlm_model_name': 'HuggingFaceTB/SmolVLM2-500M-Video-Instruct', 'load_vlm_weights': True, 'add_image_special_tokens': False, 'attention_mode': 'cross_attn', 'prefix_length': 0, 'pad_language_to': 'max_length', 'num_expert_layers': 0, 'num_vlm_layers': 16, 'self_attn_every_n_layers': 2, 'expert_width_multiplier': 0.75, 'min_period': 0.004, 'max_period': 4.0}, 'output_dir': 'outputs/train/2025-11-25/16-28-58_libero_100%_base_openpi_data_correct_16_layers', 'job_name': 'libero_100%_base_openpi_data_correct_16_layers', 'resume': False, 'seed': 42, 'num_workers': 8, 'batch_size': 64, 'steps': 100000, 'eval_freq': 20000, 'log_freq': 10, 'save_checkpoint': True, 'save_freq': 10000, 'use_policy_training_preset': True, 'optimizer': {'type': 'adamw', 'lr': 0.0001, 'weight_decay': 1e-10, 'grad_clip_norm': 10.0, 'betas': [0.9, 0.95], 'eps': 1e-08}, 'scheduler': {'type': 'cosine_decay_with_warmup', 'num_warmup_steps': 1000, 'num_decay_steps': 30000, 'peak_lr': 0.0001, 'decay_lr': 2.5e-06}, 'eval': {'n_episodes': 50, 'batch_size': 50, 'use_async_envs': False}, 'wandb': {'enable': True, 'disable_artifact': True, 'project': 'smolvla', 'entity': 'Robotics_VLA', 'notes': None, 'run_id': None, 'mode': 'offline'}, '_wandb': {}}
+2025-11-25 16:29:16,708 INFO    MainThread:1841156 [wandb_init.py:init():881] starting backend
+2025-11-25 16:29:16,919 INFO    MainThread:1841156 [wandb_init.py:init():884] sending inform_init request
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+2025-11-25 16:29:16,926 INFO    MainThread:1841156 [wandb_init.py:init():962] updated telemetry
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wandb/offline-run-20251125_162916-byac7ckw/files/config.yaml

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+wandb_version: 1
+
+_wandb:
+  desc: null
+  value:
+    python_version: 3.10.19
+    cli_version: 0.22.3
+    framework: huggingface
+    is_jupyter_run: false
+    is_kaggle_kernel: false
+    start_time: 1764084556
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+      4: 3.10.19
+      5: 0.22.3
+      13: linux-x86_64
+    e:
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+        os: Linux-4.18.0-553.77.1.el8_10.x86_64-x86_64-with-glibc2.28
+        python: CPython 3.10.19
+        started_at: '2025-11-25T15:29:16.660354Z'
+        args:
+        - --policy.path=/mnt/vast-kisski/projects/kisski-umg-fairpact-2/VLA/binh/smol_pretrained/smolvla_base
+        - --dataset.root=/projects/extern/kisski/kisski-umg-fairpact-2/dir.project/VLA/binh/libero_lerobot_openpi/libero
+        - --output_dir=outputs/train/2025-11-25/16-28-58_libero_100%_base_openpi_data_correct_16_layers
+        - --job_name=libero_100%_base_openpi_data_correct_16_layers
+        - --config_path=configs/smolvla_config/other_default.json
+        - --batch_size=64
+        - --steps=100000
+        - --policy.gradient_accumulation_steps=1
+        - --wandb.mode=offline
+        - --log_freq=10
+        program: /mnt/vast-kisski/projects/kisski-umg-fairpact-2/VLA/binh/Smolvla_capstone_project/lerobot/scripts/train_accelerate.py
+        code_path: lerobot/scripts/train_accelerate.py
+        code_path_local: lerobot/scripts/train_accelerate.py
+        git:
+          remote_url: git@github-binh:jibby2803/Smolvla_capstone_project.git
+          commit: 32545bcbb3c2e3fe36ad7028bb607ff767450563
+        root: outputs/train/2025-11-25/16-28-58_libero_100%_base_openpi_data_correct_16_layers
+        host: ggpu192
+        executable: /projects/extern/kisski/kisski-umg-fairpact-2/dir.project/miniconda3/envs/smol/bin/python3.10
+        cpu_count: 64
+        cpu_count_logical: 128
+        disk:
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+            used: '7019024384'
+        memory:
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+        slurm:
+          mpi_type: pmi2
+          job_id: '11843069'
+        writer_id: 32ejr8bggruwdcbdkjpn95qlev429qjv
+dataset:
+  desc: null
+  value:
+    repo_id: .
+    root: /projects/extern/kisski/kisski-umg-fairpact-2/dir.project/VLA/binh/libero_lerobot_openpi/libero
+    episodes: null
+    image_transforms:
+      enable: true
+      max_num_transforms: 3
+      random_order: false
+      image_tfs:
+        hue:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            hue:
+            - -0.05
+            - 0.05
+        contrast:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            contrast:
+            - 0.8
+            - 1.2
+        sharpness:
+          weight: 1.0
+          type: SharpnessJitter
+          kwargs:
+            sharpness:
+            - 0.5
+            - 1.5
+        brightness:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            brightness:
+            - 0.8
+            - 1.2
+        saturation:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            saturation:
+            - 0.5
+            - 1.5
+        crop_resize:
+          weight: 1.0
+          type: RandomResizedCrop
+          kwargs:
+            size:
+            - 256
+            - 256
+            ratio:
+            - 1
+            - 1
+            scale:
+            - 0.9
+            - 0.95
+        rotate:
+          weight: 1.0
+          type: RandomRotate
+          kwargs:
+            degrees:
+            - -5
+            - 5
+      wrist_tfs:
+        hue:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            hue:
+            - -0.05
+            - 0.05
+        contrast:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            contrast:
+            - 0.8
+            - 1.2
+        sharpness:
+          weight: 1.0
+          type: SharpnessJitter
+          kwargs:
+            sharpness:
+            - 0.5
+            - 1.5
+        brightness:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            brightness:
+            - 0.8
+            - 1.2
+        saturation:
+          weight: 1.0
+          type: ColorJitter
+          kwargs:
+            saturation:
+            - 0.5
+            - 1.5
+    revision: null
+    use_imagenet_stats: true
+    video_backend: torchcodec
+    vqa_data_path: null
+env:
+  desc: null
+  value: null
+policy:
+  desc: null
+  value:
+    type: smolvla
+    n_obs_steps: 1
+    normalization_mapping:
+      VISUAL: IDENTITY
+      STATE: MEAN_STD
+      ACTION: MEAN_STD
+    input_features:
+      image:
+        type: VISUAL
+        shape:
+        - 3
+        - 256
+        - 256
+      wrist_image:
+        type: VISUAL
+        shape:
+        - 3
+        - 256
+        - 256
+      state:
+        type: STATE
+        shape:
+        - 8
+    output_features:
+      actions:
+        type: ACTION
+        shape:
+        - 7
+    device: cuda
+    use_amp: false
+    gradient_accumulation_steps: 1
+    chunk_size: 50
+    n_action_steps: 50
+    max_state_dim: 32
+    max_action_dim: 32
+    resize_imgs_with_padding:
+    - 512
+    - 512
+    empty_cameras: 0
+    adapt_to_pi_aloha: false
+    use_delta_joint_actions_aloha: false
+    tokenizer_max_length: 48
+    num_steps: 10
+    use_cache: true
+    freeze_vision_encoder: true
+    train_expert_only: true
+    train_state_proj: true
+    optimizer_lr: 0.0001
+    optimizer_betas:
+    - 0.9
+    - 0.95
+    optimizer_eps: 1.0e-08
+    optimizer_weight_decay: 1.0e-10
+    optimizer_grad_clip_norm: 10.0
+    scheduler_warmup_steps: 1000
+    scheduler_decay_steps: 30000
+    scheduler_decay_lr: 2.5e-06
+    vlm_model_name: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
+    load_vlm_weights: true
+    add_image_special_tokens: false
+    attention_mode: cross_attn
+    prefix_length: 0
+    pad_language_to: max_length
+    num_expert_layers: 0
+    num_vlm_layers: 16
+    self_attn_every_n_layers: 2
+    expert_width_multiplier: 0.75
+    min_period: 0.004
+    max_period: 4.0
+output_dir:
+  desc: null
+  value: outputs/train/2025-11-25/16-28-58_libero_100%_base_openpi_data_correct_16_layers
+job_name:
+  desc: null
+  value: libero_100%_base_openpi_data_correct_16_layers
+resume:
+  desc: null
+  value: false
+seed:
+  desc: null
+  value: 42
+num_workers:
+  desc: null
+  value: 8
+batch_size:
+  desc: null
+  value: 64
+steps:
+  desc: null
+  value: 100000
+eval_freq:
+  desc: null
+  value: 20000
+log_freq:
+  desc: null
+  value: 10
+save_checkpoint:
+  desc: null
+  value: true
+save_freq:
+  desc: null
+  value: 10000
+use_policy_training_preset:
+  desc: null
+  value: true
+optimizer:
+  desc: null
+  value:
+    type: adamw
+    lr: 0.0001
+    weight_decay: 1.0e-10
+    grad_clip_norm: 10.0
+    betas:
+    - 0.9
+    - 0.95
+    eps: 1.0e-08
+scheduler:
+  desc: null
+  value:
+    type: cosine_decay_with_warmup
+    num_warmup_steps: 1000
+    num_decay_steps: 30000
+    peak_lr: 0.0001
+    decay_lr: 2.5e-06
+eval:
+  desc: null
+  value:
+    n_episodes: 50
+    batch_size: 50
+    use_async_envs: false
+wandb:
+  desc: null
+  value:
+    enable: true
+    disable_artifact: true
+    project: smolvla
+    entity: Robotics_VLA
+    notes: null
+    run_id: null
+    mode: offline

wandb/offline-run-20251125_162916-byac7ckw/files/output.log

@@ -0,0 +1,101 @@
+INFO 2025-11-25 16:29:17 celerate.py:159 Creating dataset
+WARNING 2025-11-25 16:29:17 ts/utils.py:303
+The dataset you requested (.) is in 2.0 format.
+While current version of LeRobot is backward-compatible with it, the version of your dataset still uses global
+stats instead of per-episode stats. Update your dataset stats to the new format using this command:
+```
+python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py --repo-id=.
+```
+If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)
+or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose).
+WARNING 2025-11-25 16:29:17 ts/utils.py:303
+The dataset you requested (.) is in 2.0 format.
+While current version of LeRobot is backward-compatible with it, the version of your dataset still uses global
+stats instead of per-episode stats. Update your dataset stats to the new format using this command:
+```
+python lerobot/common/datasets/v21/convert_dataset_v20_to_v21.py --repo-id=.
+```
+If you encounter a problem, contact LeRobot maintainers on [Discord](https://discord.com/invite/s3KuuzsPFb)
+or open an [issue on GitHub](https://github.com/huggingface/lerobot/issues/new/choose).
+Resolving data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1693/1693 [00:00<00:00, 21061.53it/s]
+Loading dataset shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 70/70 [00:00<00:00, 2841.17it/s]
+INFO 2025-11-25 16:29:21 celerate.py:170 Creating policy
+`torch_dtype` is deprecated! Use `dtype` instead!
+INFO 2025-11-25 16:29:33 celerate.py:181 Creating optimizer and scheduler
+INFO 2025-11-25 16:29:33 celerate.py:221 Output dir: outputs/train/2025-11-25/16-28-58_libero_100%_base_openpi_data_correct_16_layers
+INFO 2025-11-25 16:29:33 celerate.py:224 cfg.steps=100000 (100K)
+INFO 2025-11-25 16:29:33 celerate.py:225 dataset.num_frames=273465 (273K)
+INFO 2025-11-25 16:29:33 celerate.py:226 dataset.num_episodes=1693
+INFO 2025-11-25 16:29:33 celerate.py:227 num_learnable_params=99880992 (100M)
+INFO 2025-11-25 16:29:33 celerate.py:228 num_total_params=450046220 (450M)
+INFO 2025-11-25 16:29:33 celerate.py:229 Number of processes: 1
+INFO 2025-11-25 16:29:33 celerate.py:230 Device: cuda
+INFO 2025-11-25 16:29:33 celerate.py:231 Mixed precision: no
+INFO 2025-11-25 16:29:33 celerate.py:253 Start offline training on a fixed dataset
+INFO 2025-11-25 16:29:58 celerate.py:303 step:10 smpl:640 ep:4 epch:0.00 loss:2.536 grdn:10.515 lr:6.5e-07 updt_s:1.872 data_s:0.435
+INFO 2025-11-25 16:30:19 celerate.py:303 step:20 smpl:1K ep:8 epch:0.00 loss:2.618 grdn:10.851 lr:1.6e-06 updt_s:1.858 data_s:0.019
+INFO 2025-11-25 16:30:39 celerate.py:303 step:30 smpl:2K ep:12 epch:0.01 loss:2.427 grdn:9.828 lr:2.6e-06 updt_s:1.811 data_s:0.020
+INFO 2025-11-25 16:30:59 celerate.py:303 step:40 smpl:3K ep:16 epch:0.01 loss:2.387 grdn:9.435 lr:3.6e-06 updt_s:1.765 data_s:0.022
+INFO 2025-11-25 16:31:20 celerate.py:303 step:50 smpl:3K ep:20 epch:0.01 loss:2.122 grdn:7.784 lr:4.6e-06 updt_s:1.828 data_s:0.020
+INFO 2025-11-25 16:31:40 celerate.py:303 step:60 smpl:4K ep:24 epch:0.01 loss:1.895 grdn:7.057 lr:5.6e-06 updt_s:1.839 data_s:0.021
+INFO 2025-11-25 16:32:00 celerate.py:303 step:70 smpl:4K ep:28 epch:0.02 loss:1.873 grdn:6.428 lr:6.6e-06 updt_s:1.753 data_s:0.024
+INFO 2025-11-25 16:32:21 celerate.py:303 step:80 smpl:5K ep:32 epch:0.02 loss:1.753 grdn:5.622 lr:7.6e-06 updt_s:1.850 data_s:0.019
+INFO 2025-11-25 16:32:42 celerate.py:303 step:90 smpl:6K ep:36 epch:0.02 loss:1.530 grdn:4.238 lr:8.6e-06 updt_s:1.823 data_s:0.024
+INFO 2025-11-25 16:33:02 celerate.py:303 step:100 smpl:6K ep:40 epch:0.02 loss:1.454 grdn:3.849 lr:9.6e-06 updt_s:1.755 data_s:0.024
+INFO 2025-11-25 16:33:22 celerate.py:303 step:110 smpl:7K ep:44 epch:0.03 loss:1.352 grdn:3.377 lr:1.1e-05 updt_s:1.845 data_s:0.018
+INFO 2025-11-25 16:33:42 celerate.py:303 step:120 smpl:8K ep:48 epch:0.03 loss:1.266 grdn:2.853 lr:1.2e-05 updt_s:1.778 data_s:0.022
+INFO 2025-11-25 16:34:03 celerate.py:303 step:130 smpl:8K ep:52 epch:0.03 loss:1.193 grdn:2.585 lr:1.3e-05 updt_s:1.833 data_s:0.023
+INFO 2025-11-25 16:34:23 celerate.py:303 step:140 smpl:9K ep:55 epch:0.03 loss:1.169 grdn:2.323 lr:1.4e-05 updt_s:1.775 data_s:0.020
+INFO 2025-11-25 16:34:43 celerate.py:303 step:150 smpl:10K ep:59 epch:0.04 loss:1.131 grdn:2.376 lr:1.5e-05 updt_s:1.867 data_s:0.020
+INFO 2025-11-25 16:35:03 celerate.py:303 step:160 smpl:10K ep:63 epch:0.04 loss:1.076 grdn:2.225 lr:1.6e-05 updt_s:1.773 data_s:0.023
+INFO 2025-11-25 16:35:23 celerate.py:303 step:170 smpl:11K ep:67 epch:0.04 loss:1.046 grdn:1.904 lr:1.7e-05 updt_s:1.742 data_s:0.022
+INFO 2025-11-25 16:35:43 celerate.py:303 step:180 smpl:12K ep:71 epch:0.04 loss:1.039 grdn:2.086 lr:1.8e-05 updt_s:1.824 data_s:0.017
+INFO 2025-11-25 16:36:04 celerate.py:303 step:190 smpl:12K ep:75 epch:0.04 loss:0.999 grdn:2.033 lr:1.9e-05 updt_s:1.807 data_s:0.020
+INFO 2025-11-25 16:36:24 celerate.py:303 step:200 smpl:13K ep:79 epch:0.05 loss:0.984 grdn:1.979 lr:2.0e-05 updt_s:1.758 data_s:0.023
+INFO 2025-11-25 16:36:45 celerate.py:303 step:210 smpl:13K ep:83 epch:0.05 loss:0.966 grdn:1.676 lr:2.1e-05 updt_s:1.863 data_s:0.019
+load pretrained policy
+Loading  HuggingFaceTB/SmolVLM2-500M-Video-Instruct weights ...
+Load pretrained VLM successfully!
+Reducing the number of VLM layers to 16 ...
+Loading weights from local directory
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wandb/offline-run-20251125_162916-byac7ckw/files/requirements.txt

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wandb/offline-run-20251125_162916-byac7ckw/files/wandb-metadata.json

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