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n_vae_encoder_layers: 4 + normalization_mapping: + ACTION: MEAN_STD + STATE: MEAN_STD + VISUAL: MEAN_STD + optimizer_lr: 1e-05 + optimizer_lr_backbone: 1e-05 + optimizer_weight_decay: 0.0001 + pre_norm: false + pretrained_backbone_weights: IMAGENET1K_V1 + replace_final_stride_with_dilation: false + temporal_ensemble_coeff: null + type: act + use_amp: true + use_vae: true + vision_backbone: resnet18 +resume: + value: false +save_checkpoint: + value: true +save_freq: + value: 1000 +scheduler: + value: null +seed: + value: 1000 +steps: + value: 10000 +use_policy_training_preset: + value: true +wandb: + value: + disable_artifact: false + enable: true + entity: null + mode: null + notes: null + project: lerobot + run_id: null diff --git a/wandb/run-20250615_121800-2ags28sq/files/output.log b/wandb/run-20250615_121800-2ags28sq/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..373b565e29a9c4fd08ecba6dbe3c86cd8404d9fb --- /dev/null +++ b/wandb/run-20250615_121800-2ags28sq/files/output.log @@ -0,0 +1,74 @@ +Logs will be synced with wandb. +INFO 2025-06-15 12:18:01 db_utils.py:103 Track this run --> https://wandb.ai/ranegray-university-of-colorado-boulder/lerobot/runs/2ags28sq +INFO 2025-06-15 12:18:01 ts/train.py:127 Creating dataset +Resolving data files: 100%|████████████████████████████████████████████████████████████████████████| 51/51 [00:00<00:00, 474300.45it/s] +INFO 2025-06-15 12:18:01 ts/train.py:138 Creating policy +INFO 2025-06-15 12:18:02 ts/train.py:144 Creating optimizer and scheduler +INFO 2025-06-15 12:18:02 ts/train.py:156 Output dir: outputs/train/act_drawing_final_safe +INFO 2025-06-15 12:18:02 ts/train.py:159 cfg.steps=10000 (10K) +INFO 2025-06-15 12:18:02 ts/train.py:160 dataset.num_frames=20173 (20K) +INFO 2025-06-15 12:18:02 ts/train.py:161 dataset.num_episodes=51 +INFO 2025-06-15 12:18:02 ts/train.py:162 num_learnable_params=51597190 (52M) +INFO 2025-06-15 12:18:02 ts/train.py:163 num_total_params=51597232 (52M) +INFO 2025-06-15 12:18:02 ts/train.py:202 Start offline training on a fixed dataset +INFO 2025-06-15 12:18:40 ts/train.py:232 step:200 smpl:800 ep:2 epch:0.04 loss:10.088 grdn:nan lr:1.0e-05 updt_s:0.189 data_s:0.004 +INFO 2025-06-15 12:19:17 ts/train.py:232 step:400 smpl:2K ep:4 epch:0.08 loss:3.533 grdn:129.701 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:19:53 ts/train.py:232 step:600 smpl:2K ep:6 epch:0.12 loss:2.893 grdn:109.399 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:20:29 ts/train.py:232 step:800 smpl:3K ep:8 epch:0.16 loss:2.576 grdn:100.179 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:21:05 ts/train.py:232 step:1K smpl:4K ep:10 epch:0.20 loss:2.379 grdn:95.215 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:21:05 ts/train.py:241 Checkpoint policy after step 1000 +INFO 2025-06-15 12:21:41 ts/train.py:232 step:1K smpl:5K ep:12 epch:0.24 loss:2.178 grdn:90.239 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:22:17 ts/train.py:232 step:1K smpl:6K ep:14 epch:0.28 loss:2.020 grdn:85.371 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:22:53 ts/train.py:232 step:2K smpl:6K ep:16 epch:0.32 loss:1.859 grdn:82.080 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:23:29 ts/train.py:232 step:2K smpl:7K ep:18 epch:0.36 loss:1.772 grdn:79.082 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:24:05 ts/train.py:232 step:2K smpl:8K ep:20 epch:0.40 loss:1.641 grdn:75.191 lr:1.0e-05 updt_s:0.180 data_s:0.000 +INFO 2025-06-15 12:24:05 ts/train.py:241 Checkpoint policy after step 2000 +INFO 2025-06-15 12:24:42 ts/train.py:232 step:2K smpl:9K ep:22 epch:0.44 loss:1.534 grdn:72.340 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:25:18 ts/train.py:232 step:2K smpl:10K ep:24 epch:0.48 loss:1.435 grdn:69.562 lr:1.0e-05 updt_s:0.180 data_s:0.000 +INFO 2025-06-15 12:25:54 ts/train.py:232 step:3K smpl:10K ep:26 epch:0.52 loss:1.343 grdn:66.370 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:26:30 ts/train.py:232 step:3K smpl:11K ep:28 epch:0.56 loss:1.264 grdn:64.371 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:27:06 ts/train.py:232 step:3K smpl:12K ep:30 epch:0.59 loss:1.199 grdn:61.544 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:27:06 ts/train.py:241 Checkpoint policy after step 3000 +INFO 2025-06-15 12:27:43 ts/train.py:232 step:3K smpl:13K ep:32 epch:0.63 loss:1.131 grdn:60.585 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:28:19 ts/train.py:232 step:3K smpl:14K ep:34 epch:0.67 loss:1.044 grdn:56.530 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:28:55 ts/train.py:232 step:4K smpl:14K ep:36 epch:0.71 loss:0.957 grdn:53.974 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:29:31 ts/train.py:232 step:4K smpl:15K ep:38 epch:0.75 loss:0.898 grdn:51.801 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:30:07 ts/train.py:232 step:4K smpl:16K ep:40 epch:0.79 loss:0.865 grdn:50.380 lr:1.0e-05 updt_s:0.182 data_s:0.000 +INFO 2025-06-15 12:30:07 ts/train.py:241 Checkpoint policy after step 4000 +INFO 2025-06-15 12:30:44 ts/train.py:232 step:4K smpl:17K ep:42 epch:0.83 loss:0.802 grdn:48.013 lr:1.0e-05 updt_s:0.181 data_s:0.000 +INFO 2025-06-15 12:31:21 ts/train.py:232 step:4K smpl:18K ep:44 epch:0.87 loss:0.765 grdn:46.175 lr:1.0e-05 updt_s:0.181 data_s:0.000 +INFO 2025-06-15 12:31:57 ts/train.py:232 step:5K smpl:18K ep:47 epch:0.91 loss:0.710 grdn:44.360 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:32:33 ts/train.py:232 step:5K smpl:19K ep:49 epch:0.95 loss:0.670 grdn:42.274 lr:1.0e-05 updt_s:0.182 data_s:0.000 +INFO 2025-06-15 12:33:10 ts/train.py:232 step:5K smpl:20K ep:51 epch:0.99 loss:0.634 grdn:40.912 lr:1.0e-05 updt_s:0.185 data_s:0.000 +INFO 2025-06-15 12:33:10 ts/train.py:241 Checkpoint policy after step 5000 +INFO 2025-06-15 12:33:49 ts/train.py:232 step:5K smpl:21K ep:53 epch:1.03 loss:0.620 grdn:40.844 lr:1.0e-05 updt_s:0.186 data_s:0.005 +INFO 2025-06-15 12:34:26 ts/train.py:232 step:5K smpl:22K ep:55 epch:1.07 loss:0.589 grdn:38.850 lr:1.0e-05 updt_s:0.185 data_s:0.000 +INFO 2025-06-15 12:35:03 ts/train.py:232 step:6K smpl:22K ep:57 epch:1.11 loss:0.539 grdn:36.311 lr:1.0e-05 updt_s:0.181 data_s:0.000 +INFO 2025-06-15 12:35:40 ts/train.py:232 step:6K smpl:23K ep:59 epch:1.15 loss:0.543 grdn:35.618 lr:1.0e-05 updt_s:0.183 data_s:0.000 +INFO 2025-06-15 12:36:17 ts/train.py:232 step:6K smpl:24K ep:61 epch:1.19 loss:0.519 grdn:34.616 lr:1.0e-05 updt_s:0.187 data_s:0.000 +INFO 2025-06-15 12:36:17 ts/train.py:241 Checkpoint policy after step 6000 +INFO 2025-06-15 12:36:55 ts/train.py:232 step:6K smpl:25K ep:63 epch:1.23 loss:0.487 grdn:33.947 lr:1.0e-05 updt_s:0.183 data_s:0.000 +INFO 2025-06-15 12:37:31 ts/train.py:232 step:6K smpl:26K ep:65 epch:1.27 loss:0.475 grdn:32.496 lr:1.0e-05 updt_s:0.181 data_s:0.000 +INFO 2025-06-15 12:38:09 ts/train.py:232 step:7K smpl:26K ep:67 epch:1.31 loss:0.455 grdn:31.783 lr:1.0e-05 updt_s:0.187 data_s:0.000 +INFO 2025-06-15 12:38:46 ts/train.py:232 step:7K smpl:27K ep:69 epch:1.35 loss:0.427 grdn:30.679 lr:1.0e-05 updt_s:0.187 data_s:0.000 +INFO 2025-06-15 12:39:24 ts/train.py:232 step:7K smpl:28K ep:71 epch:1.39 loss:0.421 grdn:29.561 lr:1.0e-05 updt_s:0.187 data_s:0.000 +INFO 2025-06-15 12:39:24 ts/train.py:241 Checkpoint policy after step 7000 +INFO 2025-06-15 12:40:01 ts/train.py:232 step:7K smpl:29K ep:73 epch:1.43 loss:0.417 grdn:29.377 lr:1.0e-05 updt_s:0.184 data_s:0.000 +INFO 2025-06-15 12:40:37 ts/train.py:232 step:7K smpl:30K ep:75 epch:1.47 loss:0.408 grdn:28.740 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:41:13 ts/train.py:232 step:8K smpl:30K ep:77 epch:1.51 loss:0.399 grdn:28.245 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:41:49 ts/train.py:232 step:8K smpl:31K ep:79 epch:1.55 loss:0.366 grdn:26.960 lr:1.0e-05 updt_s:0.180 data_s:0.000 +INFO 2025-06-15 12:42:25 ts/train.py:232 step:8K smpl:32K ep:81 epch:1.59 loss:0.371 grdn:27.288 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:42:25 ts/train.py:241 Checkpoint policy after step 8000 +INFO 2025-06-15 12:43:02 ts/train.py:232 step:8K smpl:33K ep:83 epch:1.63 loss:0.384 grdn:inf lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:43:38 ts/train.py:232 step:8K smpl:34K ep:85 epch:1.67 loss:0.367 grdn:26.289 lr:1.0e-05 updt_s:0.180 data_s:0.000 +INFO 2025-06-15 12:44:14 ts/train.py:232 step:9K smpl:34K ep:87 epch:1.71 loss:0.350 grdn:25.129 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:44:50 ts/train.py:232 step:9K smpl:35K ep:89 epch:1.74 loss:0.341 grdn:24.841 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:45:26 ts/train.py:232 step:9K smpl:36K ep:91 epch:1.78 loss:0.329 grdn:24.428 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:45:26 ts/train.py:241 Checkpoint policy after step 9000 +INFO 2025-06-15 12:46:03 ts/train.py:232 step:9K smpl:37K ep:93 epch:1.82 loss:0.335 grdn:24.384 lr:1.0e-05 updt_s:0.180 data_s:0.000 +INFO 2025-06-15 12:46:39 ts/train.py:232 step:9K smpl:38K ep:95 epch:1.86 loss:0.333 grdn:24.107 lr:1.0e-05 updt_s:0.181 data_s:0.000 +INFO 2025-06-15 12:47:15 ts/train.py:232 step:10K smpl:38K ep:97 epch:1.90 loss:0.318 grdn:23.308 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:47:51 ts/train.py:232 step:10K smpl:39K ep:99 epch:1.94 loss:0.307 grdn:23.128 lr:1.0e-05 updt_s:0.179 data_s:0.000 +INFO 2025-06-15 12:48:27 ts/train.py:232 step:10K smpl:40K ep:101 epch:1.98 loss:0.300 grdn:22.946 lr:1.0e-05 updt_s:0.180 data_s:0.000 +INFO 2025-06-15 12:48:27 ts/train.py:241 Checkpoint policy after step 10000 +INFO 2025-06-15 12:48:28 ts/train.py:283 End of training diff --git a/wandb/run-20250615_121800-2ags28sq/files/requirements.txt b/wandb/run-20250615_121800-2ags28sq/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..b639a6d1833d80c2f77db60676e56ebf99c215fe --- /dev/null +++ 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