Add DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4 from 8b1bacebd36d
Browse files- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/pretrained_model/config.json +94 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/pretrained_model/model.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/pretrained_model/train_config.json +204 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/optimizer_param_groups.json +331 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/optimizer_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/rng_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/scheduler_state.json +15 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/training_step.json +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/pretrained_model/config.json +94 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/pretrained_model/model.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/pretrained_model/train_config.json +204 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/optimizer_param_groups.json +331 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/optimizer_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/rng_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/scheduler_state.json +15 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/training_step.json +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/pretrained_model/config.json +94 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/pretrained_model/model.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/pretrained_model/train_config.json +204 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/optimizer_param_groups.json +331 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/optimizer_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/rng_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/scheduler_state.json +15 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/training_step.json +3 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/debug-internal.log +6 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/run-20250502_093744-xsemtuwn/files/output.log +646 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/run-20250502_093744-xsemtuwn/logs/debug-internal.log +6 -0
- DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/run-20250502_093744-xsemtuwn/run-xsemtuwn.wandb +2 -2
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/pretrained_model/config.json
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"type": "diffusion",
|
| 3 |
+
"n_obs_steps": 2,
|
| 4 |
+
"normalization_mapping": {
|
| 5 |
+
"VISUAL": "MEAN_STD",
|
| 6 |
+
"STATE": "MIN_MAX",
|
| 7 |
+
"ACTION": "MIN_MAX"
|
| 8 |
+
},
|
| 9 |
+
"input_features": {
|
| 10 |
+
"observation.state": {
|
| 11 |
+
"type": "STATE",
|
| 12 |
+
"shape": [
|
| 13 |
+
6
|
| 14 |
+
]
|
| 15 |
+
},
|
| 16 |
+
"observation.images.FrontCam": {
|
| 17 |
+
"type": "VISUAL",
|
| 18 |
+
"shape": [
|
| 19 |
+
3,
|
| 20 |
+
480,
|
| 21 |
+
640
|
| 22 |
+
]
|
| 23 |
+
},
|
| 24 |
+
"observation.images.TopCam": {
|
| 25 |
+
"type": "VISUAL",
|
| 26 |
+
"shape": [
|
| 27 |
+
3,
|
| 28 |
+
480,
|
| 29 |
+
640
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
"observation.images.WristCam": {
|
| 33 |
+
"type": "VISUAL",
|
| 34 |
+
"shape": [
|
| 35 |
+
3,
|
| 36 |
+
480,
|
| 37 |
+
640
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"output_features": {
|
| 42 |
+
"action": {
|
| 43 |
+
"type": "ACTION",
|
| 44 |
+
"shape": [
|
| 45 |
+
6
|
| 46 |
+
]
|
| 47 |
+
}
|
| 48 |
+
},
|
| 49 |
+
"device": "cuda",
|
| 50 |
+
"use_amp": false,
|
| 51 |
+
"horizon": 64,
|
| 52 |
+
"n_action_steps": 64,
|
| 53 |
+
"drop_n_last_frames": 7,
|
| 54 |
+
"vision_backbone": "resnet50",
|
| 55 |
+
"crop_shape": [
|
| 56 |
+
480,
|
| 57 |
+
640
|
| 58 |
+
],
|
| 59 |
+
"crop_is_random": false,
|
| 60 |
+
"pretrained_backbone_weights": "ResNet50_Weights.IMAGENET1K_V1",
|
| 61 |
+
"use_group_norm": false,
|
| 62 |
+
"spatial_softmax_num_keypoints": 32,
|
| 63 |
+
"use_separate_rgb_encoder_per_camera": false,
|
| 64 |
+
"down_dims": [
|
| 65 |
+
256,
|
| 66 |
+
512,
|
| 67 |
+
1024
|
| 68 |
+
],
|
| 69 |
+
"kernel_size": 5,
|
| 70 |
+
"n_groups": 8,
|
| 71 |
+
"diffusion_step_embed_dim": 128,
|
| 72 |
+
"use_film_scale_modulation": true,
|
| 73 |
+
"noise_scheduler_type": "DDIM",
|
| 74 |
+
"num_train_timesteps": 100,
|
| 75 |
+
"beta_schedule": "squaredcos_cap_v2",
|
| 76 |
+
"beta_start": 0.0001,
|
| 77 |
+
"beta_end": 0.02,
|
| 78 |
+
"prediction_type": "epsilon",
|
| 79 |
+
"clip_sample": true,
|
| 80 |
+
"clip_sample_range": 1.0,
|
| 81 |
+
"num_inference_steps": 10,
|
| 82 |
+
"do_mask_loss_for_padding": false,
|
| 83 |
+
"optimizer_lr": 0.0001,
|
| 84 |
+
"optimizer_betas": [
|
| 85 |
+
0.95,
|
| 86 |
+
0.999
|
| 87 |
+
],
|
| 88 |
+
"optimizer_eps": 1e-08,
|
| 89 |
+
"optimizer_weight_decay": 1e-06,
|
| 90 |
+
"scheduler_name": "cosine",
|
| 91 |
+
"scheduler_warmup_steps": 500,
|
| 92 |
+
"pre_resize_shape": null,
|
| 93 |
+
"freeze_vision_backbone": false
|
| 94 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/pretrained_model/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9d920a356efe57558fa0da051fe71ad17e91eae43b8099ac0711940fbde0b1fd
|
| 3 |
+
size 369243880
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/pretrained_model/train_config.json
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"dataset": {
|
| 3 |
+
"repo_id": "shylee/so100_cube",
|
| 4 |
+
"root": "/SSD/LSY/lerobot",
|
| 5 |
+
"episodes": null,
|
| 6 |
+
"image_transforms": {
|
| 7 |
+
"enable": false,
|
| 8 |
+
"max_num_transforms": 3,
|
| 9 |
+
"random_order": false,
|
| 10 |
+
"tfs": {
|
| 11 |
+
"brightness": {
|
| 12 |
+
"weight": 1.0,
|
| 13 |
+
"type": "ColorJitter",
|
| 14 |
+
"kwargs": {
|
| 15 |
+
"brightness": [
|
| 16 |
+
0.8,
|
| 17 |
+
1.2
|
| 18 |
+
]
|
| 19 |
+
}
|
| 20 |
+
},
|
| 21 |
+
"contrast": {
|
| 22 |
+
"weight": 1.0,
|
| 23 |
+
"type": "ColorJitter",
|
| 24 |
+
"kwargs": {
|
| 25 |
+
"contrast": [
|
| 26 |
+
0.8,
|
| 27 |
+
1.2
|
| 28 |
+
]
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
"saturation": {
|
| 32 |
+
"weight": 1.0,
|
| 33 |
+
"type": "ColorJitter",
|
| 34 |
+
"kwargs": {
|
| 35 |
+
"saturation": [
|
| 36 |
+
0.5,
|
| 37 |
+
1.5
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"hue": {
|
| 42 |
+
"weight": 1.0,
|
| 43 |
+
"type": "ColorJitter",
|
| 44 |
+
"kwargs": {
|
| 45 |
+
"hue": [
|
| 46 |
+
-0.05,
|
| 47 |
+
0.05
|
| 48 |
+
]
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"sharpness": {
|
| 52 |
+
"weight": 1.0,
|
| 53 |
+
"type": "SharpnessJitter",
|
| 54 |
+
"kwargs": {
|
| 55 |
+
"sharpness": [
|
| 56 |
+
0.5,
|
| 57 |
+
1.5
|
| 58 |
+
]
|
| 59 |
+
}
|
| 60 |
+
}
|
| 61 |
+
}
|
| 62 |
+
},
|
| 63 |
+
"revision": null,
|
| 64 |
+
"use_imagenet_stats": true,
|
| 65 |
+
"video_backend": "torchcodec"
|
| 66 |
+
},
|
| 67 |
+
"env": null,
|
| 68 |
+
"policy": {
|
| 69 |
+
"type": "diffusion",
|
| 70 |
+
"n_obs_steps": 2,
|
| 71 |
+
"normalization_mapping": {
|
| 72 |
+
"VISUAL": "MEAN_STD",
|
| 73 |
+
"STATE": "MIN_MAX",
|
| 74 |
+
"ACTION": "MIN_MAX"
|
| 75 |
+
},
|
| 76 |
+
"input_features": {
|
| 77 |
+
"observation.state": {
|
| 78 |
+
"type": "STATE",
|
| 79 |
+
"shape": [
|
| 80 |
+
6
|
| 81 |
+
]
|
| 82 |
+
},
|
| 83 |
+
"observation.images.FrontCam": {
|
| 84 |
+
"type": "VISUAL",
|
| 85 |
+
"shape": [
|
| 86 |
+
3,
|
| 87 |
+
480,
|
| 88 |
+
640
|
| 89 |
+
]
|
| 90 |
+
},
|
| 91 |
+
"observation.images.TopCam": {
|
| 92 |
+
"type": "VISUAL",
|
| 93 |
+
"shape": [
|
| 94 |
+
3,
|
| 95 |
+
480,
|
| 96 |
+
640
|
| 97 |
+
]
|
| 98 |
+
},
|
| 99 |
+
"observation.images.WristCam": {
|
| 100 |
+
"type": "VISUAL",
|
| 101 |
+
"shape": [
|
| 102 |
+
3,
|
| 103 |
+
480,
|
| 104 |
+
640
|
| 105 |
+
]
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
"output_features": {
|
| 109 |
+
"action": {
|
| 110 |
+
"type": "ACTION",
|
| 111 |
+
"shape": [
|
| 112 |
+
6
|
| 113 |
+
]
|
| 114 |
+
}
|
| 115 |
+
},
|
| 116 |
+
"device": "cuda",
|
| 117 |
+
"use_amp": false,
|
| 118 |
+
"horizon": 64,
|
| 119 |
+
"n_action_steps": 64,
|
| 120 |
+
"drop_n_last_frames": 7,
|
| 121 |
+
"vision_backbone": "resnet50",
|
| 122 |
+
"crop_shape": [
|
| 123 |
+
480,
|
| 124 |
+
640
|
| 125 |
+
],
|
| 126 |
+
"crop_is_random": false,
|
| 127 |
+
"pretrained_backbone_weights": "ResNet50_Weights.IMAGENET1K_V1",
|
| 128 |
+
"use_group_norm": false,
|
| 129 |
+
"spatial_softmax_num_keypoints": 32,
|
| 130 |
+
"use_separate_rgb_encoder_per_camera": false,
|
| 131 |
+
"down_dims": [
|
| 132 |
+
256,
|
| 133 |
+
512,
|
| 134 |
+
1024
|
| 135 |
+
],
|
| 136 |
+
"kernel_size": 5,
|
| 137 |
+
"n_groups": 8,
|
| 138 |
+
"diffusion_step_embed_dim": 128,
|
| 139 |
+
"use_film_scale_modulation": true,
|
| 140 |
+
"noise_scheduler_type": "DDIM",
|
| 141 |
+
"num_train_timesteps": 100,
|
| 142 |
+
"beta_schedule": "squaredcos_cap_v2",
|
| 143 |
+
"beta_start": 0.0001,
|
| 144 |
+
"beta_end": 0.02,
|
| 145 |
+
"prediction_type": "epsilon",
|
| 146 |
+
"clip_sample": true,
|
| 147 |
+
"clip_sample_range": 1.0,
|
| 148 |
+
"num_inference_steps": 10,
|
| 149 |
+
"do_mask_loss_for_padding": false,
|
| 150 |
+
"optimizer_lr": 0.0001,
|
| 151 |
+
"optimizer_betas": [
|
| 152 |
+
0.95,
|
| 153 |
+
0.999
|
| 154 |
+
],
|
| 155 |
+
"optimizer_eps": 1e-08,
|
| 156 |
+
"optimizer_weight_decay": 1e-06,
|
| 157 |
+
"scheduler_name": "cosine",
|
| 158 |
+
"scheduler_warmup_steps": 500,
|
| 159 |
+
"pre_resize_shape": null,
|
| 160 |
+
"freeze_vision_backbone": false
|
| 161 |
+
},
|
| 162 |
+
"output_dir": "/SSD/LSY/lerobot_model/DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4",
|
| 163 |
+
"job_name": "DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4",
|
| 164 |
+
"resume": true,
|
| 165 |
+
"seed": 1000,
|
| 166 |
+
"num_workers": 4,
|
| 167 |
+
"batch_size": 8,
|
| 168 |
+
"steps": 480000,
|
| 169 |
+
"eval_freq": 20000,
|
| 170 |
+
"log_freq": 200,
|
| 171 |
+
"save_checkpoint": true,
|
| 172 |
+
"save_freq": 60000,
|
| 173 |
+
"use_policy_training_preset": true,
|
| 174 |
+
"optimizer": {
|
| 175 |
+
"type": "adam",
|
| 176 |
+
"lr": 0.0001,
|
| 177 |
+
"weight_decay": 1e-06,
|
| 178 |
+
"grad_clip_norm": 10.0,
|
| 179 |
+
"betas": [
|
| 180 |
+
0.95,
|
| 181 |
+
0.999
|
| 182 |
+
],
|
| 183 |
+
"eps": 1e-08
|
| 184 |
+
},
|
| 185 |
+
"scheduler": {
|
| 186 |
+
"type": "diffuser",
|
| 187 |
+
"num_warmup_steps": 500,
|
| 188 |
+
"name": "cosine"
|
| 189 |
+
},
|
| 190 |
+
"eval": {
|
| 191 |
+
"n_episodes": 50,
|
| 192 |
+
"batch_size": 50,
|
| 193 |
+
"use_async_envs": false
|
| 194 |
+
},
|
| 195 |
+
"wandb": {
|
| 196 |
+
"enable": true,
|
| 197 |
+
"disable_artifact": false,
|
| 198 |
+
"project": "lerobot",
|
| 199 |
+
"entity": null,
|
| 200 |
+
"notes": null,
|
| 201 |
+
"run_id": "xsemtuwn",
|
| 202 |
+
"mode": null
|
| 203 |
+
}
|
| 204 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,331 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"lr": 6.922871086395153e-05,
|
| 4 |
+
"betas": [
|
| 5 |
+
0.95,
|
| 6 |
+
0.999
|
| 7 |
+
],
|
| 8 |
+
"eps": 1e-08,
|
| 9 |
+
"weight_decay": 1e-06,
|
| 10 |
+
"amsgrad": false,
|
| 11 |
+
"maximize": false,
|
| 12 |
+
"foreach": null,
|
| 13 |
+
"capturable": false,
|
| 14 |
+
"differentiable": false,
|
| 15 |
+
"fused": null,
|
| 16 |
+
"initial_lr": 0.0001,
|
| 17 |
+
"params": [
|
| 18 |
+
0,
|
| 19 |
+
1,
|
| 20 |
+
2,
|
| 21 |
+
3,
|
| 22 |
+
4,
|
| 23 |
+
5,
|
| 24 |
+
6,
|
| 25 |
+
7,
|
| 26 |
+
8,
|
| 27 |
+
9,
|
| 28 |
+
10,
|
| 29 |
+
11,
|
| 30 |
+
12,
|
| 31 |
+
13,
|
| 32 |
+
14,
|
| 33 |
+
15,
|
| 34 |
+
16,
|
| 35 |
+
17,
|
| 36 |
+
18,
|
| 37 |
+
19,
|
| 38 |
+
20,
|
| 39 |
+
21,
|
| 40 |
+
22,
|
| 41 |
+
23,
|
| 42 |
+
24,
|
| 43 |
+
25,
|
| 44 |
+
26,
|
| 45 |
+
27,
|
| 46 |
+
28,
|
| 47 |
+
29,
|
| 48 |
+
30,
|
| 49 |
+
31,
|
| 50 |
+
32,
|
| 51 |
+
33,
|
| 52 |
+
34,
|
| 53 |
+
35,
|
| 54 |
+
36,
|
| 55 |
+
37,
|
| 56 |
+
38,
|
| 57 |
+
39,
|
| 58 |
+
40,
|
| 59 |
+
41,
|
| 60 |
+
42,
|
| 61 |
+
43,
|
| 62 |
+
44,
|
| 63 |
+
45,
|
| 64 |
+
46,
|
| 65 |
+
47,
|
| 66 |
+
48,
|
| 67 |
+
49,
|
| 68 |
+
50,
|
| 69 |
+
51,
|
| 70 |
+
52,
|
| 71 |
+
53,
|
| 72 |
+
54,
|
| 73 |
+
55,
|
| 74 |
+
56,
|
| 75 |
+
57,
|
| 76 |
+
58,
|
| 77 |
+
59,
|
| 78 |
+
60,
|
| 79 |
+
61,
|
| 80 |
+
62,
|
| 81 |
+
63,
|
| 82 |
+
64,
|
| 83 |
+
65,
|
| 84 |
+
66,
|
| 85 |
+
67,
|
| 86 |
+
68,
|
| 87 |
+
69,
|
| 88 |
+
70,
|
| 89 |
+
71,
|
| 90 |
+
72,
|
| 91 |
+
73,
|
| 92 |
+
74,
|
| 93 |
+
75,
|
| 94 |
+
76,
|
| 95 |
+
77,
|
| 96 |
+
78,
|
| 97 |
+
79,
|
| 98 |
+
80,
|
| 99 |
+
81,
|
| 100 |
+
82,
|
| 101 |
+
83,
|
| 102 |
+
84,
|
| 103 |
+
85,
|
| 104 |
+
86,
|
| 105 |
+
87,
|
| 106 |
+
88,
|
| 107 |
+
89,
|
| 108 |
+
90,
|
| 109 |
+
91,
|
| 110 |
+
92,
|
| 111 |
+
93,
|
| 112 |
+
94,
|
| 113 |
+
95,
|
| 114 |
+
96,
|
| 115 |
+
97,
|
| 116 |
+
98,
|
| 117 |
+
99,
|
| 118 |
+
100,
|
| 119 |
+
101,
|
| 120 |
+
102,
|
| 121 |
+
103,
|
| 122 |
+
104,
|
| 123 |
+
105,
|
| 124 |
+
106,
|
| 125 |
+
107,
|
| 126 |
+
108,
|
| 127 |
+
109,
|
| 128 |
+
110,
|
| 129 |
+
111,
|
| 130 |
+
112,
|
| 131 |
+
113,
|
| 132 |
+
114,
|
| 133 |
+
115,
|
| 134 |
+
116,
|
| 135 |
+
117,
|
| 136 |
+
118,
|
| 137 |
+
119,
|
| 138 |
+
120,
|
| 139 |
+
121,
|
| 140 |
+
122,
|
| 141 |
+
123,
|
| 142 |
+
124,
|
| 143 |
+
125,
|
| 144 |
+
126,
|
| 145 |
+
127,
|
| 146 |
+
128,
|
| 147 |
+
129,
|
| 148 |
+
130,
|
| 149 |
+
131,
|
| 150 |
+
132,
|
| 151 |
+
133,
|
| 152 |
+
134,
|
| 153 |
+
135,
|
| 154 |
+
136,
|
| 155 |
+
137,
|
| 156 |
+
138,
|
| 157 |
+
139,
|
| 158 |
+
140,
|
| 159 |
+
141,
|
| 160 |
+
142,
|
| 161 |
+
143,
|
| 162 |
+
144,
|
| 163 |
+
145,
|
| 164 |
+
146,
|
| 165 |
+
147,
|
| 166 |
+
148,
|
| 167 |
+
149,
|
| 168 |
+
150,
|
| 169 |
+
151,
|
| 170 |
+
152,
|
| 171 |
+
153,
|
| 172 |
+
154,
|
| 173 |
+
155,
|
| 174 |
+
156,
|
| 175 |
+
157,
|
| 176 |
+
158,
|
| 177 |
+
159,
|
| 178 |
+
160,
|
| 179 |
+
161,
|
| 180 |
+
162,
|
| 181 |
+
163,
|
| 182 |
+
164,
|
| 183 |
+
165,
|
| 184 |
+
166,
|
| 185 |
+
167,
|
| 186 |
+
168,
|
| 187 |
+
169,
|
| 188 |
+
170,
|
| 189 |
+
171,
|
| 190 |
+
172,
|
| 191 |
+
173,
|
| 192 |
+
174,
|
| 193 |
+
175,
|
| 194 |
+
176,
|
| 195 |
+
177,
|
| 196 |
+
178,
|
| 197 |
+
179,
|
| 198 |
+
180,
|
| 199 |
+
181,
|
| 200 |
+
182,
|
| 201 |
+
183,
|
| 202 |
+
184,
|
| 203 |
+
185,
|
| 204 |
+
186,
|
| 205 |
+
187,
|
| 206 |
+
188,
|
| 207 |
+
189,
|
| 208 |
+
190,
|
| 209 |
+
191,
|
| 210 |
+
192,
|
| 211 |
+
193,
|
| 212 |
+
194,
|
| 213 |
+
195,
|
| 214 |
+
196,
|
| 215 |
+
197,
|
| 216 |
+
198,
|
| 217 |
+
199,
|
| 218 |
+
200,
|
| 219 |
+
201,
|
| 220 |
+
202,
|
| 221 |
+
203,
|
| 222 |
+
204,
|
| 223 |
+
205,
|
| 224 |
+
206,
|
| 225 |
+
207,
|
| 226 |
+
208,
|
| 227 |
+
209,
|
| 228 |
+
210,
|
| 229 |
+
211,
|
| 230 |
+
212,
|
| 231 |
+
213,
|
| 232 |
+
214,
|
| 233 |
+
215,
|
| 234 |
+
216,
|
| 235 |
+
217,
|
| 236 |
+
218,
|
| 237 |
+
219,
|
| 238 |
+
220,
|
| 239 |
+
221,
|
| 240 |
+
222,
|
| 241 |
+
223,
|
| 242 |
+
224,
|
| 243 |
+
225,
|
| 244 |
+
226,
|
| 245 |
+
227,
|
| 246 |
+
228,
|
| 247 |
+
229,
|
| 248 |
+
230,
|
| 249 |
+
231,
|
| 250 |
+
232,
|
| 251 |
+
233,
|
| 252 |
+
234,
|
| 253 |
+
235,
|
| 254 |
+
236,
|
| 255 |
+
237,
|
| 256 |
+
238,
|
| 257 |
+
239,
|
| 258 |
+
240,
|
| 259 |
+
241,
|
| 260 |
+
242,
|
| 261 |
+
243,
|
| 262 |
+
244,
|
| 263 |
+
245,
|
| 264 |
+
246,
|
| 265 |
+
247,
|
| 266 |
+
248,
|
| 267 |
+
249,
|
| 268 |
+
250,
|
| 269 |
+
251,
|
| 270 |
+
252,
|
| 271 |
+
253,
|
| 272 |
+
254,
|
| 273 |
+
255,
|
| 274 |
+
256,
|
| 275 |
+
257,
|
| 276 |
+
258,
|
| 277 |
+
259,
|
| 278 |
+
260,
|
| 279 |
+
261,
|
| 280 |
+
262,
|
| 281 |
+
263,
|
| 282 |
+
264,
|
| 283 |
+
265,
|
| 284 |
+
266,
|
| 285 |
+
267,
|
| 286 |
+
268,
|
| 287 |
+
269,
|
| 288 |
+
270,
|
| 289 |
+
271,
|
| 290 |
+
272,
|
| 291 |
+
273,
|
| 292 |
+
274,
|
| 293 |
+
275,
|
| 294 |
+
276,
|
| 295 |
+
277,
|
| 296 |
+
278,
|
| 297 |
+
279,
|
| 298 |
+
280,
|
| 299 |
+
281,
|
| 300 |
+
282,
|
| 301 |
+
283,
|
| 302 |
+
284,
|
| 303 |
+
285,
|
| 304 |
+
286,
|
| 305 |
+
287,
|
| 306 |
+
288,
|
| 307 |
+
289,
|
| 308 |
+
290,
|
| 309 |
+
291,
|
| 310 |
+
292,
|
| 311 |
+
293,
|
| 312 |
+
294,
|
| 313 |
+
295,
|
| 314 |
+
296,
|
| 315 |
+
297,
|
| 316 |
+
298,
|
| 317 |
+
299,
|
| 318 |
+
300,
|
| 319 |
+
301,
|
| 320 |
+
302,
|
| 321 |
+
303,
|
| 322 |
+
304,
|
| 323 |
+
305,
|
| 324 |
+
306,
|
| 325 |
+
307,
|
| 326 |
+
308,
|
| 327 |
+
309,
|
| 328 |
+
310
|
| 329 |
+
]
|
| 330 |
+
}
|
| 331 |
+
]
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/optimizer_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6d57fd90dc50dc378d3400c933a19298a9a4a204c513d30aba7e807784c996bf
|
| 3 |
+
size 738026076
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/rng_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f75052e9d9145d65e4dca13280a2c5deb34c920ce228ec82a1c74a23a4d3340d
|
| 3 |
+
size 15708
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/scheduler_state.json
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"base_lrs": [
|
| 3 |
+
0.0001
|
| 4 |
+
],
|
| 5 |
+
"last_epoch": 180000,
|
| 6 |
+
"verbose": false,
|
| 7 |
+
"_step_count": 180001,
|
| 8 |
+
"_get_lr_called_within_step": false,
|
| 9 |
+
"_last_lr": [
|
| 10 |
+
6.922871086395153e-05
|
| 11 |
+
],
|
| 12 |
+
"lr_lambdas": [
|
| 13 |
+
null
|
| 14 |
+
]
|
| 15 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/180000/training_state/training_step.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"step": 180000
|
| 3 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/pretrained_model/config.json
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"type": "diffusion",
|
| 3 |
+
"n_obs_steps": 2,
|
| 4 |
+
"normalization_mapping": {
|
| 5 |
+
"VISUAL": "MEAN_STD",
|
| 6 |
+
"STATE": "MIN_MAX",
|
| 7 |
+
"ACTION": "MIN_MAX"
|
| 8 |
+
},
|
| 9 |
+
"input_features": {
|
| 10 |
+
"observation.state": {
|
| 11 |
+
"type": "STATE",
|
| 12 |
+
"shape": [
|
| 13 |
+
6
|
| 14 |
+
]
|
| 15 |
+
},
|
| 16 |
+
"observation.images.FrontCam": {
|
| 17 |
+
"type": "VISUAL",
|
| 18 |
+
"shape": [
|
| 19 |
+
3,
|
| 20 |
+
480,
|
| 21 |
+
640
|
| 22 |
+
]
|
| 23 |
+
},
|
| 24 |
+
"observation.images.TopCam": {
|
| 25 |
+
"type": "VISUAL",
|
| 26 |
+
"shape": [
|
| 27 |
+
3,
|
| 28 |
+
480,
|
| 29 |
+
640
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
"observation.images.WristCam": {
|
| 33 |
+
"type": "VISUAL",
|
| 34 |
+
"shape": [
|
| 35 |
+
3,
|
| 36 |
+
480,
|
| 37 |
+
640
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"output_features": {
|
| 42 |
+
"action": {
|
| 43 |
+
"type": "ACTION",
|
| 44 |
+
"shape": [
|
| 45 |
+
6
|
| 46 |
+
]
|
| 47 |
+
}
|
| 48 |
+
},
|
| 49 |
+
"device": "cuda",
|
| 50 |
+
"use_amp": false,
|
| 51 |
+
"horizon": 64,
|
| 52 |
+
"n_action_steps": 64,
|
| 53 |
+
"drop_n_last_frames": 7,
|
| 54 |
+
"vision_backbone": "resnet50",
|
| 55 |
+
"crop_shape": [
|
| 56 |
+
480,
|
| 57 |
+
640
|
| 58 |
+
],
|
| 59 |
+
"crop_is_random": false,
|
| 60 |
+
"pretrained_backbone_weights": "ResNet50_Weights.IMAGENET1K_V1",
|
| 61 |
+
"use_group_norm": false,
|
| 62 |
+
"spatial_softmax_num_keypoints": 32,
|
| 63 |
+
"use_separate_rgb_encoder_per_camera": false,
|
| 64 |
+
"down_dims": [
|
| 65 |
+
256,
|
| 66 |
+
512,
|
| 67 |
+
1024
|
| 68 |
+
],
|
| 69 |
+
"kernel_size": 5,
|
| 70 |
+
"n_groups": 8,
|
| 71 |
+
"diffusion_step_embed_dim": 128,
|
| 72 |
+
"use_film_scale_modulation": true,
|
| 73 |
+
"noise_scheduler_type": "DDIM",
|
| 74 |
+
"num_train_timesteps": 100,
|
| 75 |
+
"beta_schedule": "squaredcos_cap_v2",
|
| 76 |
+
"beta_start": 0.0001,
|
| 77 |
+
"beta_end": 0.02,
|
| 78 |
+
"prediction_type": "epsilon",
|
| 79 |
+
"clip_sample": true,
|
| 80 |
+
"clip_sample_range": 1.0,
|
| 81 |
+
"num_inference_steps": 10,
|
| 82 |
+
"do_mask_loss_for_padding": false,
|
| 83 |
+
"optimizer_lr": 0.0001,
|
| 84 |
+
"optimizer_betas": [
|
| 85 |
+
0.95,
|
| 86 |
+
0.999
|
| 87 |
+
],
|
| 88 |
+
"optimizer_eps": 1e-08,
|
| 89 |
+
"optimizer_weight_decay": 1e-06,
|
| 90 |
+
"scheduler_name": "cosine",
|
| 91 |
+
"scheduler_warmup_steps": 500,
|
| 92 |
+
"pre_resize_shape": null,
|
| 93 |
+
"freeze_vision_backbone": false
|
| 94 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/pretrained_model/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3659b3163e198eea6345dcfe7dc415d18c504b197eb68a2e9252106b5feea62d
|
| 3 |
+
size 369243880
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/pretrained_model/train_config.json
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"dataset": {
|
| 3 |
+
"repo_id": "shylee/so100_cube",
|
| 4 |
+
"root": "/SSD/LSY/lerobot",
|
| 5 |
+
"episodes": null,
|
| 6 |
+
"image_transforms": {
|
| 7 |
+
"enable": false,
|
| 8 |
+
"max_num_transforms": 3,
|
| 9 |
+
"random_order": false,
|
| 10 |
+
"tfs": {
|
| 11 |
+
"brightness": {
|
| 12 |
+
"weight": 1.0,
|
| 13 |
+
"type": "ColorJitter",
|
| 14 |
+
"kwargs": {
|
| 15 |
+
"brightness": [
|
| 16 |
+
0.8,
|
| 17 |
+
1.2
|
| 18 |
+
]
|
| 19 |
+
}
|
| 20 |
+
},
|
| 21 |
+
"contrast": {
|
| 22 |
+
"weight": 1.0,
|
| 23 |
+
"type": "ColorJitter",
|
| 24 |
+
"kwargs": {
|
| 25 |
+
"contrast": [
|
| 26 |
+
0.8,
|
| 27 |
+
1.2
|
| 28 |
+
]
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
"saturation": {
|
| 32 |
+
"weight": 1.0,
|
| 33 |
+
"type": "ColorJitter",
|
| 34 |
+
"kwargs": {
|
| 35 |
+
"saturation": [
|
| 36 |
+
0.5,
|
| 37 |
+
1.5
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"hue": {
|
| 42 |
+
"weight": 1.0,
|
| 43 |
+
"type": "ColorJitter",
|
| 44 |
+
"kwargs": {
|
| 45 |
+
"hue": [
|
| 46 |
+
-0.05,
|
| 47 |
+
0.05
|
| 48 |
+
]
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"sharpness": {
|
| 52 |
+
"weight": 1.0,
|
| 53 |
+
"type": "SharpnessJitter",
|
| 54 |
+
"kwargs": {
|
| 55 |
+
"sharpness": [
|
| 56 |
+
0.5,
|
| 57 |
+
1.5
|
| 58 |
+
]
|
| 59 |
+
}
|
| 60 |
+
}
|
| 61 |
+
}
|
| 62 |
+
},
|
| 63 |
+
"revision": null,
|
| 64 |
+
"use_imagenet_stats": true,
|
| 65 |
+
"video_backend": "torchcodec"
|
| 66 |
+
},
|
| 67 |
+
"env": null,
|
| 68 |
+
"policy": {
|
| 69 |
+
"type": "diffusion",
|
| 70 |
+
"n_obs_steps": 2,
|
| 71 |
+
"normalization_mapping": {
|
| 72 |
+
"VISUAL": "MEAN_STD",
|
| 73 |
+
"STATE": "MIN_MAX",
|
| 74 |
+
"ACTION": "MIN_MAX"
|
| 75 |
+
},
|
| 76 |
+
"input_features": {
|
| 77 |
+
"observation.state": {
|
| 78 |
+
"type": "STATE",
|
| 79 |
+
"shape": [
|
| 80 |
+
6
|
| 81 |
+
]
|
| 82 |
+
},
|
| 83 |
+
"observation.images.FrontCam": {
|
| 84 |
+
"type": "VISUAL",
|
| 85 |
+
"shape": [
|
| 86 |
+
3,
|
| 87 |
+
480,
|
| 88 |
+
640
|
| 89 |
+
]
|
| 90 |
+
},
|
| 91 |
+
"observation.images.TopCam": {
|
| 92 |
+
"type": "VISUAL",
|
| 93 |
+
"shape": [
|
| 94 |
+
3,
|
| 95 |
+
480,
|
| 96 |
+
640
|
| 97 |
+
]
|
| 98 |
+
},
|
| 99 |
+
"observation.images.WristCam": {
|
| 100 |
+
"type": "VISUAL",
|
| 101 |
+
"shape": [
|
| 102 |
+
3,
|
| 103 |
+
480,
|
| 104 |
+
640
|
| 105 |
+
]
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
"output_features": {
|
| 109 |
+
"action": {
|
| 110 |
+
"type": "ACTION",
|
| 111 |
+
"shape": [
|
| 112 |
+
6
|
| 113 |
+
]
|
| 114 |
+
}
|
| 115 |
+
},
|
| 116 |
+
"device": "cuda",
|
| 117 |
+
"use_amp": false,
|
| 118 |
+
"horizon": 64,
|
| 119 |
+
"n_action_steps": 64,
|
| 120 |
+
"drop_n_last_frames": 7,
|
| 121 |
+
"vision_backbone": "resnet50",
|
| 122 |
+
"crop_shape": [
|
| 123 |
+
480,
|
| 124 |
+
640
|
| 125 |
+
],
|
| 126 |
+
"crop_is_random": false,
|
| 127 |
+
"pretrained_backbone_weights": "ResNet50_Weights.IMAGENET1K_V1",
|
| 128 |
+
"use_group_norm": false,
|
| 129 |
+
"spatial_softmax_num_keypoints": 32,
|
| 130 |
+
"use_separate_rgb_encoder_per_camera": false,
|
| 131 |
+
"down_dims": [
|
| 132 |
+
256,
|
| 133 |
+
512,
|
| 134 |
+
1024
|
| 135 |
+
],
|
| 136 |
+
"kernel_size": 5,
|
| 137 |
+
"n_groups": 8,
|
| 138 |
+
"diffusion_step_embed_dim": 128,
|
| 139 |
+
"use_film_scale_modulation": true,
|
| 140 |
+
"noise_scheduler_type": "DDIM",
|
| 141 |
+
"num_train_timesteps": 100,
|
| 142 |
+
"beta_schedule": "squaredcos_cap_v2",
|
| 143 |
+
"beta_start": 0.0001,
|
| 144 |
+
"beta_end": 0.02,
|
| 145 |
+
"prediction_type": "epsilon",
|
| 146 |
+
"clip_sample": true,
|
| 147 |
+
"clip_sample_range": 1.0,
|
| 148 |
+
"num_inference_steps": 10,
|
| 149 |
+
"do_mask_loss_for_padding": false,
|
| 150 |
+
"optimizer_lr": 0.0001,
|
| 151 |
+
"optimizer_betas": [
|
| 152 |
+
0.95,
|
| 153 |
+
0.999
|
| 154 |
+
],
|
| 155 |
+
"optimizer_eps": 1e-08,
|
| 156 |
+
"optimizer_weight_decay": 1e-06,
|
| 157 |
+
"scheduler_name": "cosine",
|
| 158 |
+
"scheduler_warmup_steps": 500,
|
| 159 |
+
"pre_resize_shape": null,
|
| 160 |
+
"freeze_vision_backbone": false
|
| 161 |
+
},
|
| 162 |
+
"output_dir": "/SSD/LSY/lerobot_model/DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4",
|
| 163 |
+
"job_name": "DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4",
|
| 164 |
+
"resume": true,
|
| 165 |
+
"seed": 1000,
|
| 166 |
+
"num_workers": 4,
|
| 167 |
+
"batch_size": 8,
|
| 168 |
+
"steps": 480000,
|
| 169 |
+
"eval_freq": 20000,
|
| 170 |
+
"log_freq": 200,
|
| 171 |
+
"save_checkpoint": true,
|
| 172 |
+
"save_freq": 60000,
|
| 173 |
+
"use_policy_training_preset": true,
|
| 174 |
+
"optimizer": {
|
| 175 |
+
"type": "adam",
|
| 176 |
+
"lr": 0.0001,
|
| 177 |
+
"weight_decay": 1e-06,
|
| 178 |
+
"grad_clip_norm": 10.0,
|
| 179 |
+
"betas": [
|
| 180 |
+
0.95,
|
| 181 |
+
0.999
|
| 182 |
+
],
|
| 183 |
+
"eps": 1e-08
|
| 184 |
+
},
|
| 185 |
+
"scheduler": {
|
| 186 |
+
"type": "diffuser",
|
| 187 |
+
"num_warmup_steps": 500,
|
| 188 |
+
"name": "cosine"
|
| 189 |
+
},
|
| 190 |
+
"eval": {
|
| 191 |
+
"n_episodes": 50,
|
| 192 |
+
"batch_size": 50,
|
| 193 |
+
"use_async_envs": false
|
| 194 |
+
},
|
| 195 |
+
"wandb": {
|
| 196 |
+
"enable": true,
|
| 197 |
+
"disable_artifact": false,
|
| 198 |
+
"project": "lerobot",
|
| 199 |
+
"entity": null,
|
| 200 |
+
"notes": null,
|
| 201 |
+
"run_id": "xsemtuwn",
|
| 202 |
+
"mode": null
|
| 203 |
+
}
|
| 204 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,331 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"lr": 5.0081897582086365e-05,
|
| 4 |
+
"betas": [
|
| 5 |
+
0.95,
|
| 6 |
+
0.999
|
| 7 |
+
],
|
| 8 |
+
"eps": 1e-08,
|
| 9 |
+
"weight_decay": 1e-06,
|
| 10 |
+
"amsgrad": false,
|
| 11 |
+
"maximize": false,
|
| 12 |
+
"foreach": null,
|
| 13 |
+
"capturable": false,
|
| 14 |
+
"differentiable": false,
|
| 15 |
+
"fused": null,
|
| 16 |
+
"initial_lr": 0.0001,
|
| 17 |
+
"params": [
|
| 18 |
+
0,
|
| 19 |
+
1,
|
| 20 |
+
2,
|
| 21 |
+
3,
|
| 22 |
+
4,
|
| 23 |
+
5,
|
| 24 |
+
6,
|
| 25 |
+
7,
|
| 26 |
+
8,
|
| 27 |
+
9,
|
| 28 |
+
10,
|
| 29 |
+
11,
|
| 30 |
+
12,
|
| 31 |
+
13,
|
| 32 |
+
14,
|
| 33 |
+
15,
|
| 34 |
+
16,
|
| 35 |
+
17,
|
| 36 |
+
18,
|
| 37 |
+
19,
|
| 38 |
+
20,
|
| 39 |
+
21,
|
| 40 |
+
22,
|
| 41 |
+
23,
|
| 42 |
+
24,
|
| 43 |
+
25,
|
| 44 |
+
26,
|
| 45 |
+
27,
|
| 46 |
+
28,
|
| 47 |
+
29,
|
| 48 |
+
30,
|
| 49 |
+
31,
|
| 50 |
+
32,
|
| 51 |
+
33,
|
| 52 |
+
34,
|
| 53 |
+
35,
|
| 54 |
+
36,
|
| 55 |
+
37,
|
| 56 |
+
38,
|
| 57 |
+
39,
|
| 58 |
+
40,
|
| 59 |
+
41,
|
| 60 |
+
42,
|
| 61 |
+
43,
|
| 62 |
+
44,
|
| 63 |
+
45,
|
| 64 |
+
46,
|
| 65 |
+
47,
|
| 66 |
+
48,
|
| 67 |
+
49,
|
| 68 |
+
50,
|
| 69 |
+
51,
|
| 70 |
+
52,
|
| 71 |
+
53,
|
| 72 |
+
54,
|
| 73 |
+
55,
|
| 74 |
+
56,
|
| 75 |
+
57,
|
| 76 |
+
58,
|
| 77 |
+
59,
|
| 78 |
+
60,
|
| 79 |
+
61,
|
| 80 |
+
62,
|
| 81 |
+
63,
|
| 82 |
+
64,
|
| 83 |
+
65,
|
| 84 |
+
66,
|
| 85 |
+
67,
|
| 86 |
+
68,
|
| 87 |
+
69,
|
| 88 |
+
70,
|
| 89 |
+
71,
|
| 90 |
+
72,
|
| 91 |
+
73,
|
| 92 |
+
74,
|
| 93 |
+
75,
|
| 94 |
+
76,
|
| 95 |
+
77,
|
| 96 |
+
78,
|
| 97 |
+
79,
|
| 98 |
+
80,
|
| 99 |
+
81,
|
| 100 |
+
82,
|
| 101 |
+
83,
|
| 102 |
+
84,
|
| 103 |
+
85,
|
| 104 |
+
86,
|
| 105 |
+
87,
|
| 106 |
+
88,
|
| 107 |
+
89,
|
| 108 |
+
90,
|
| 109 |
+
91,
|
| 110 |
+
92,
|
| 111 |
+
93,
|
| 112 |
+
94,
|
| 113 |
+
95,
|
| 114 |
+
96,
|
| 115 |
+
97,
|
| 116 |
+
98,
|
| 117 |
+
99,
|
| 118 |
+
100,
|
| 119 |
+
101,
|
| 120 |
+
102,
|
| 121 |
+
103,
|
| 122 |
+
104,
|
| 123 |
+
105,
|
| 124 |
+
106,
|
| 125 |
+
107,
|
| 126 |
+
108,
|
| 127 |
+
109,
|
| 128 |
+
110,
|
| 129 |
+
111,
|
| 130 |
+
112,
|
| 131 |
+
113,
|
| 132 |
+
114,
|
| 133 |
+
115,
|
| 134 |
+
116,
|
| 135 |
+
117,
|
| 136 |
+
118,
|
| 137 |
+
119,
|
| 138 |
+
120,
|
| 139 |
+
121,
|
| 140 |
+
122,
|
| 141 |
+
123,
|
| 142 |
+
124,
|
| 143 |
+
125,
|
| 144 |
+
126,
|
| 145 |
+
127,
|
| 146 |
+
128,
|
| 147 |
+
129,
|
| 148 |
+
130,
|
| 149 |
+
131,
|
| 150 |
+
132,
|
| 151 |
+
133,
|
| 152 |
+
134,
|
| 153 |
+
135,
|
| 154 |
+
136,
|
| 155 |
+
137,
|
| 156 |
+
138,
|
| 157 |
+
139,
|
| 158 |
+
140,
|
| 159 |
+
141,
|
| 160 |
+
142,
|
| 161 |
+
143,
|
| 162 |
+
144,
|
| 163 |
+
145,
|
| 164 |
+
146,
|
| 165 |
+
147,
|
| 166 |
+
148,
|
| 167 |
+
149,
|
| 168 |
+
150,
|
| 169 |
+
151,
|
| 170 |
+
152,
|
| 171 |
+
153,
|
| 172 |
+
154,
|
| 173 |
+
155,
|
| 174 |
+
156,
|
| 175 |
+
157,
|
| 176 |
+
158,
|
| 177 |
+
159,
|
| 178 |
+
160,
|
| 179 |
+
161,
|
| 180 |
+
162,
|
| 181 |
+
163,
|
| 182 |
+
164,
|
| 183 |
+
165,
|
| 184 |
+
166,
|
| 185 |
+
167,
|
| 186 |
+
168,
|
| 187 |
+
169,
|
| 188 |
+
170,
|
| 189 |
+
171,
|
| 190 |
+
172,
|
| 191 |
+
173,
|
| 192 |
+
174,
|
| 193 |
+
175,
|
| 194 |
+
176,
|
| 195 |
+
177,
|
| 196 |
+
178,
|
| 197 |
+
179,
|
| 198 |
+
180,
|
| 199 |
+
181,
|
| 200 |
+
182,
|
| 201 |
+
183,
|
| 202 |
+
184,
|
| 203 |
+
185,
|
| 204 |
+
186,
|
| 205 |
+
187,
|
| 206 |
+
188,
|
| 207 |
+
189,
|
| 208 |
+
190,
|
| 209 |
+
191,
|
| 210 |
+
192,
|
| 211 |
+
193,
|
| 212 |
+
194,
|
| 213 |
+
195,
|
| 214 |
+
196,
|
| 215 |
+
197,
|
| 216 |
+
198,
|
| 217 |
+
199,
|
| 218 |
+
200,
|
| 219 |
+
201,
|
| 220 |
+
202,
|
| 221 |
+
203,
|
| 222 |
+
204,
|
| 223 |
+
205,
|
| 224 |
+
206,
|
| 225 |
+
207,
|
| 226 |
+
208,
|
| 227 |
+
209,
|
| 228 |
+
210,
|
| 229 |
+
211,
|
| 230 |
+
212,
|
| 231 |
+
213,
|
| 232 |
+
214,
|
| 233 |
+
215,
|
| 234 |
+
216,
|
| 235 |
+
217,
|
| 236 |
+
218,
|
| 237 |
+
219,
|
| 238 |
+
220,
|
| 239 |
+
221,
|
| 240 |
+
222,
|
| 241 |
+
223,
|
| 242 |
+
224,
|
| 243 |
+
225,
|
| 244 |
+
226,
|
| 245 |
+
227,
|
| 246 |
+
228,
|
| 247 |
+
229,
|
| 248 |
+
230,
|
| 249 |
+
231,
|
| 250 |
+
232,
|
| 251 |
+
233,
|
| 252 |
+
234,
|
| 253 |
+
235,
|
| 254 |
+
236,
|
| 255 |
+
237,
|
| 256 |
+
238,
|
| 257 |
+
239,
|
| 258 |
+
240,
|
| 259 |
+
241,
|
| 260 |
+
242,
|
| 261 |
+
243,
|
| 262 |
+
244,
|
| 263 |
+
245,
|
| 264 |
+
246,
|
| 265 |
+
247,
|
| 266 |
+
248,
|
| 267 |
+
249,
|
| 268 |
+
250,
|
| 269 |
+
251,
|
| 270 |
+
252,
|
| 271 |
+
253,
|
| 272 |
+
254,
|
| 273 |
+
255,
|
| 274 |
+
256,
|
| 275 |
+
257,
|
| 276 |
+
258,
|
| 277 |
+
259,
|
| 278 |
+
260,
|
| 279 |
+
261,
|
| 280 |
+
262,
|
| 281 |
+
263,
|
| 282 |
+
264,
|
| 283 |
+
265,
|
| 284 |
+
266,
|
| 285 |
+
267,
|
| 286 |
+
268,
|
| 287 |
+
269,
|
| 288 |
+
270,
|
| 289 |
+
271,
|
| 290 |
+
272,
|
| 291 |
+
273,
|
| 292 |
+
274,
|
| 293 |
+
275,
|
| 294 |
+
276,
|
| 295 |
+
277,
|
| 296 |
+
278,
|
| 297 |
+
279,
|
| 298 |
+
280,
|
| 299 |
+
281,
|
| 300 |
+
282,
|
| 301 |
+
283,
|
| 302 |
+
284,
|
| 303 |
+
285,
|
| 304 |
+
286,
|
| 305 |
+
287,
|
| 306 |
+
288,
|
| 307 |
+
289,
|
| 308 |
+
290,
|
| 309 |
+
291,
|
| 310 |
+
292,
|
| 311 |
+
293,
|
| 312 |
+
294,
|
| 313 |
+
295,
|
| 314 |
+
296,
|
| 315 |
+
297,
|
| 316 |
+
298,
|
| 317 |
+
299,
|
| 318 |
+
300,
|
| 319 |
+
301,
|
| 320 |
+
302,
|
| 321 |
+
303,
|
| 322 |
+
304,
|
| 323 |
+
305,
|
| 324 |
+
306,
|
| 325 |
+
307,
|
| 326 |
+
308,
|
| 327 |
+
309,
|
| 328 |
+
310
|
| 329 |
+
]
|
| 330 |
+
}
|
| 331 |
+
]
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/optimizer_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:36a23a97ae4ab032c8924f7dc6ac0b2d3887e0c211a60acfc4b0c6761394d3e4
|
| 3 |
+
size 738026076
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/rng_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:819a674981c303f2a6d14e1c941eeca90546399f36b43e671dc57daedc421eb9
|
| 3 |
+
size 15708
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/scheduler_state.json
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"base_lrs": [
|
| 3 |
+
0.0001
|
| 4 |
+
],
|
| 5 |
+
"last_epoch": 240000,
|
| 6 |
+
"verbose": false,
|
| 7 |
+
"_step_count": 240001,
|
| 8 |
+
"_get_lr_called_within_step": false,
|
| 9 |
+
"_last_lr": [
|
| 10 |
+
5.0081897582086365e-05
|
| 11 |
+
],
|
| 12 |
+
"lr_lambdas": [
|
| 13 |
+
null
|
| 14 |
+
]
|
| 15 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/240000/training_state/training_step.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"step": 240000
|
| 3 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/pretrained_model/config.json
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"type": "diffusion",
|
| 3 |
+
"n_obs_steps": 2,
|
| 4 |
+
"normalization_mapping": {
|
| 5 |
+
"VISUAL": "MEAN_STD",
|
| 6 |
+
"STATE": "MIN_MAX",
|
| 7 |
+
"ACTION": "MIN_MAX"
|
| 8 |
+
},
|
| 9 |
+
"input_features": {
|
| 10 |
+
"observation.state": {
|
| 11 |
+
"type": "STATE",
|
| 12 |
+
"shape": [
|
| 13 |
+
6
|
| 14 |
+
]
|
| 15 |
+
},
|
| 16 |
+
"observation.images.FrontCam": {
|
| 17 |
+
"type": "VISUAL",
|
| 18 |
+
"shape": [
|
| 19 |
+
3,
|
| 20 |
+
480,
|
| 21 |
+
640
|
| 22 |
+
]
|
| 23 |
+
},
|
| 24 |
+
"observation.images.TopCam": {
|
| 25 |
+
"type": "VISUAL",
|
| 26 |
+
"shape": [
|
| 27 |
+
3,
|
| 28 |
+
480,
|
| 29 |
+
640
|
| 30 |
+
]
|
| 31 |
+
},
|
| 32 |
+
"observation.images.WristCam": {
|
| 33 |
+
"type": "VISUAL",
|
| 34 |
+
"shape": [
|
| 35 |
+
3,
|
| 36 |
+
480,
|
| 37 |
+
640
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"output_features": {
|
| 42 |
+
"action": {
|
| 43 |
+
"type": "ACTION",
|
| 44 |
+
"shape": [
|
| 45 |
+
6
|
| 46 |
+
]
|
| 47 |
+
}
|
| 48 |
+
},
|
| 49 |
+
"device": "cuda",
|
| 50 |
+
"use_amp": false,
|
| 51 |
+
"horizon": 64,
|
| 52 |
+
"n_action_steps": 64,
|
| 53 |
+
"drop_n_last_frames": 7,
|
| 54 |
+
"vision_backbone": "resnet50",
|
| 55 |
+
"crop_shape": [
|
| 56 |
+
480,
|
| 57 |
+
640
|
| 58 |
+
],
|
| 59 |
+
"crop_is_random": false,
|
| 60 |
+
"pretrained_backbone_weights": "ResNet50_Weights.IMAGENET1K_V1",
|
| 61 |
+
"use_group_norm": false,
|
| 62 |
+
"spatial_softmax_num_keypoints": 32,
|
| 63 |
+
"use_separate_rgb_encoder_per_camera": false,
|
| 64 |
+
"down_dims": [
|
| 65 |
+
256,
|
| 66 |
+
512,
|
| 67 |
+
1024
|
| 68 |
+
],
|
| 69 |
+
"kernel_size": 5,
|
| 70 |
+
"n_groups": 8,
|
| 71 |
+
"diffusion_step_embed_dim": 128,
|
| 72 |
+
"use_film_scale_modulation": true,
|
| 73 |
+
"noise_scheduler_type": "DDIM",
|
| 74 |
+
"num_train_timesteps": 100,
|
| 75 |
+
"beta_schedule": "squaredcos_cap_v2",
|
| 76 |
+
"beta_start": 0.0001,
|
| 77 |
+
"beta_end": 0.02,
|
| 78 |
+
"prediction_type": "epsilon",
|
| 79 |
+
"clip_sample": true,
|
| 80 |
+
"clip_sample_range": 1.0,
|
| 81 |
+
"num_inference_steps": 10,
|
| 82 |
+
"do_mask_loss_for_padding": false,
|
| 83 |
+
"optimizer_lr": 0.0001,
|
| 84 |
+
"optimizer_betas": [
|
| 85 |
+
0.95,
|
| 86 |
+
0.999
|
| 87 |
+
],
|
| 88 |
+
"optimizer_eps": 1e-08,
|
| 89 |
+
"optimizer_weight_decay": 1e-06,
|
| 90 |
+
"scheduler_name": "cosine",
|
| 91 |
+
"scheduler_warmup_steps": 500,
|
| 92 |
+
"pre_resize_shape": null,
|
| 93 |
+
"freeze_vision_backbone": false
|
| 94 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/pretrained_model/model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:86392e53e52da484ad88625a802552ddfe34ce75eccdeaa50387ef31f3ac3f0c
|
| 3 |
+
size 369243880
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/pretrained_model/train_config.json
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"dataset": {
|
| 3 |
+
"repo_id": "shylee/so100_cube",
|
| 4 |
+
"root": "/SSD/LSY/lerobot",
|
| 5 |
+
"episodes": null,
|
| 6 |
+
"image_transforms": {
|
| 7 |
+
"enable": false,
|
| 8 |
+
"max_num_transforms": 3,
|
| 9 |
+
"random_order": false,
|
| 10 |
+
"tfs": {
|
| 11 |
+
"brightness": {
|
| 12 |
+
"weight": 1.0,
|
| 13 |
+
"type": "ColorJitter",
|
| 14 |
+
"kwargs": {
|
| 15 |
+
"brightness": [
|
| 16 |
+
0.8,
|
| 17 |
+
1.2
|
| 18 |
+
]
|
| 19 |
+
}
|
| 20 |
+
},
|
| 21 |
+
"contrast": {
|
| 22 |
+
"weight": 1.0,
|
| 23 |
+
"type": "ColorJitter",
|
| 24 |
+
"kwargs": {
|
| 25 |
+
"contrast": [
|
| 26 |
+
0.8,
|
| 27 |
+
1.2
|
| 28 |
+
]
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
"saturation": {
|
| 32 |
+
"weight": 1.0,
|
| 33 |
+
"type": "ColorJitter",
|
| 34 |
+
"kwargs": {
|
| 35 |
+
"saturation": [
|
| 36 |
+
0.5,
|
| 37 |
+
1.5
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"hue": {
|
| 42 |
+
"weight": 1.0,
|
| 43 |
+
"type": "ColorJitter",
|
| 44 |
+
"kwargs": {
|
| 45 |
+
"hue": [
|
| 46 |
+
-0.05,
|
| 47 |
+
0.05
|
| 48 |
+
]
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"sharpness": {
|
| 52 |
+
"weight": 1.0,
|
| 53 |
+
"type": "SharpnessJitter",
|
| 54 |
+
"kwargs": {
|
| 55 |
+
"sharpness": [
|
| 56 |
+
0.5,
|
| 57 |
+
1.5
|
| 58 |
+
]
|
| 59 |
+
}
|
| 60 |
+
}
|
| 61 |
+
}
|
| 62 |
+
},
|
| 63 |
+
"revision": null,
|
| 64 |
+
"use_imagenet_stats": true,
|
| 65 |
+
"video_backend": "torchcodec"
|
| 66 |
+
},
|
| 67 |
+
"env": null,
|
| 68 |
+
"policy": {
|
| 69 |
+
"type": "diffusion",
|
| 70 |
+
"n_obs_steps": 2,
|
| 71 |
+
"normalization_mapping": {
|
| 72 |
+
"VISUAL": "MEAN_STD",
|
| 73 |
+
"STATE": "MIN_MAX",
|
| 74 |
+
"ACTION": "MIN_MAX"
|
| 75 |
+
},
|
| 76 |
+
"input_features": {
|
| 77 |
+
"observation.state": {
|
| 78 |
+
"type": "STATE",
|
| 79 |
+
"shape": [
|
| 80 |
+
6
|
| 81 |
+
]
|
| 82 |
+
},
|
| 83 |
+
"observation.images.FrontCam": {
|
| 84 |
+
"type": "VISUAL",
|
| 85 |
+
"shape": [
|
| 86 |
+
3,
|
| 87 |
+
480,
|
| 88 |
+
640
|
| 89 |
+
]
|
| 90 |
+
},
|
| 91 |
+
"observation.images.TopCam": {
|
| 92 |
+
"type": "VISUAL",
|
| 93 |
+
"shape": [
|
| 94 |
+
3,
|
| 95 |
+
480,
|
| 96 |
+
640
|
| 97 |
+
]
|
| 98 |
+
},
|
| 99 |
+
"observation.images.WristCam": {
|
| 100 |
+
"type": "VISUAL",
|
| 101 |
+
"shape": [
|
| 102 |
+
3,
|
| 103 |
+
480,
|
| 104 |
+
640
|
| 105 |
+
]
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
"output_features": {
|
| 109 |
+
"action": {
|
| 110 |
+
"type": "ACTION",
|
| 111 |
+
"shape": [
|
| 112 |
+
6
|
| 113 |
+
]
|
| 114 |
+
}
|
| 115 |
+
},
|
| 116 |
+
"device": "cuda",
|
| 117 |
+
"use_amp": false,
|
| 118 |
+
"horizon": 64,
|
| 119 |
+
"n_action_steps": 64,
|
| 120 |
+
"drop_n_last_frames": 7,
|
| 121 |
+
"vision_backbone": "resnet50",
|
| 122 |
+
"crop_shape": [
|
| 123 |
+
480,
|
| 124 |
+
640
|
| 125 |
+
],
|
| 126 |
+
"crop_is_random": false,
|
| 127 |
+
"pretrained_backbone_weights": "ResNet50_Weights.IMAGENET1K_V1",
|
| 128 |
+
"use_group_norm": false,
|
| 129 |
+
"spatial_softmax_num_keypoints": 32,
|
| 130 |
+
"use_separate_rgb_encoder_per_camera": false,
|
| 131 |
+
"down_dims": [
|
| 132 |
+
256,
|
| 133 |
+
512,
|
| 134 |
+
1024
|
| 135 |
+
],
|
| 136 |
+
"kernel_size": 5,
|
| 137 |
+
"n_groups": 8,
|
| 138 |
+
"diffusion_step_embed_dim": 128,
|
| 139 |
+
"use_film_scale_modulation": true,
|
| 140 |
+
"noise_scheduler_type": "DDIM",
|
| 141 |
+
"num_train_timesteps": 100,
|
| 142 |
+
"beta_schedule": "squaredcos_cap_v2",
|
| 143 |
+
"beta_start": 0.0001,
|
| 144 |
+
"beta_end": 0.02,
|
| 145 |
+
"prediction_type": "epsilon",
|
| 146 |
+
"clip_sample": true,
|
| 147 |
+
"clip_sample_range": 1.0,
|
| 148 |
+
"num_inference_steps": 10,
|
| 149 |
+
"do_mask_loss_for_padding": false,
|
| 150 |
+
"optimizer_lr": 0.0001,
|
| 151 |
+
"optimizer_betas": [
|
| 152 |
+
0.95,
|
| 153 |
+
0.999
|
| 154 |
+
],
|
| 155 |
+
"optimizer_eps": 1e-08,
|
| 156 |
+
"optimizer_weight_decay": 1e-06,
|
| 157 |
+
"scheduler_name": "cosine",
|
| 158 |
+
"scheduler_warmup_steps": 500,
|
| 159 |
+
"pre_resize_shape": null,
|
| 160 |
+
"freeze_vision_backbone": false
|
| 161 |
+
},
|
| 162 |
+
"output_dir": "/SSD/LSY/lerobot_model/DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4",
|
| 163 |
+
"job_name": "DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4",
|
| 164 |
+
"resume": true,
|
| 165 |
+
"seed": 1000,
|
| 166 |
+
"num_workers": 4,
|
| 167 |
+
"batch_size": 8,
|
| 168 |
+
"steps": 480000,
|
| 169 |
+
"eval_freq": 20000,
|
| 170 |
+
"log_freq": 200,
|
| 171 |
+
"save_checkpoint": true,
|
| 172 |
+
"save_freq": 60000,
|
| 173 |
+
"use_policy_training_preset": true,
|
| 174 |
+
"optimizer": {
|
| 175 |
+
"type": "adam",
|
| 176 |
+
"lr": 0.0001,
|
| 177 |
+
"weight_decay": 1e-06,
|
| 178 |
+
"grad_clip_norm": 10.0,
|
| 179 |
+
"betas": [
|
| 180 |
+
0.95,
|
| 181 |
+
0.999
|
| 182 |
+
],
|
| 183 |
+
"eps": 1e-08
|
| 184 |
+
},
|
| 185 |
+
"scheduler": {
|
| 186 |
+
"type": "diffuser",
|
| 187 |
+
"num_warmup_steps": 500,
|
| 188 |
+
"name": "cosine"
|
| 189 |
+
},
|
| 190 |
+
"eval": {
|
| 191 |
+
"n_episodes": 50,
|
| 192 |
+
"batch_size": 50,
|
| 193 |
+
"use_async_envs": false
|
| 194 |
+
},
|
| 195 |
+
"wandb": {
|
| 196 |
+
"enable": true,
|
| 197 |
+
"disable_artifact": false,
|
| 198 |
+
"project": "lerobot",
|
| 199 |
+
"entity": null,
|
| 200 |
+
"notes": null,
|
| 201 |
+
"run_id": "xsemtuwn",
|
| 202 |
+
"mode": null
|
| 203 |
+
}
|
| 204 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,331 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"lr": 3.092259045565738e-05,
|
| 4 |
+
"betas": [
|
| 5 |
+
0.95,
|
| 6 |
+
0.999
|
| 7 |
+
],
|
| 8 |
+
"eps": 1e-08,
|
| 9 |
+
"weight_decay": 1e-06,
|
| 10 |
+
"amsgrad": false,
|
| 11 |
+
"maximize": false,
|
| 12 |
+
"foreach": null,
|
| 13 |
+
"capturable": false,
|
| 14 |
+
"differentiable": false,
|
| 15 |
+
"fused": null,
|
| 16 |
+
"initial_lr": 0.0001,
|
| 17 |
+
"params": [
|
| 18 |
+
0,
|
| 19 |
+
1,
|
| 20 |
+
2,
|
| 21 |
+
3,
|
| 22 |
+
4,
|
| 23 |
+
5,
|
| 24 |
+
6,
|
| 25 |
+
7,
|
| 26 |
+
8,
|
| 27 |
+
9,
|
| 28 |
+
10,
|
| 29 |
+
11,
|
| 30 |
+
12,
|
| 31 |
+
13,
|
| 32 |
+
14,
|
| 33 |
+
15,
|
| 34 |
+
16,
|
| 35 |
+
17,
|
| 36 |
+
18,
|
| 37 |
+
19,
|
| 38 |
+
20,
|
| 39 |
+
21,
|
| 40 |
+
22,
|
| 41 |
+
23,
|
| 42 |
+
24,
|
| 43 |
+
25,
|
| 44 |
+
26,
|
| 45 |
+
27,
|
| 46 |
+
28,
|
| 47 |
+
29,
|
| 48 |
+
30,
|
| 49 |
+
31,
|
| 50 |
+
32,
|
| 51 |
+
33,
|
| 52 |
+
34,
|
| 53 |
+
35,
|
| 54 |
+
36,
|
| 55 |
+
37,
|
| 56 |
+
38,
|
| 57 |
+
39,
|
| 58 |
+
40,
|
| 59 |
+
41,
|
| 60 |
+
42,
|
| 61 |
+
43,
|
| 62 |
+
44,
|
| 63 |
+
45,
|
| 64 |
+
46,
|
| 65 |
+
47,
|
| 66 |
+
48,
|
| 67 |
+
49,
|
| 68 |
+
50,
|
| 69 |
+
51,
|
| 70 |
+
52,
|
| 71 |
+
53,
|
| 72 |
+
54,
|
| 73 |
+
55,
|
| 74 |
+
56,
|
| 75 |
+
57,
|
| 76 |
+
58,
|
| 77 |
+
59,
|
| 78 |
+
60,
|
| 79 |
+
61,
|
| 80 |
+
62,
|
| 81 |
+
63,
|
| 82 |
+
64,
|
| 83 |
+
65,
|
| 84 |
+
66,
|
| 85 |
+
67,
|
| 86 |
+
68,
|
| 87 |
+
69,
|
| 88 |
+
70,
|
| 89 |
+
71,
|
| 90 |
+
72,
|
| 91 |
+
73,
|
| 92 |
+
74,
|
| 93 |
+
75,
|
| 94 |
+
76,
|
| 95 |
+
77,
|
| 96 |
+
78,
|
| 97 |
+
79,
|
| 98 |
+
80,
|
| 99 |
+
81,
|
| 100 |
+
82,
|
| 101 |
+
83,
|
| 102 |
+
84,
|
| 103 |
+
85,
|
| 104 |
+
86,
|
| 105 |
+
87,
|
| 106 |
+
88,
|
| 107 |
+
89,
|
| 108 |
+
90,
|
| 109 |
+
91,
|
| 110 |
+
92,
|
| 111 |
+
93,
|
| 112 |
+
94,
|
| 113 |
+
95,
|
| 114 |
+
96,
|
| 115 |
+
97,
|
| 116 |
+
98,
|
| 117 |
+
99,
|
| 118 |
+
100,
|
| 119 |
+
101,
|
| 120 |
+
102,
|
| 121 |
+
103,
|
| 122 |
+
104,
|
| 123 |
+
105,
|
| 124 |
+
106,
|
| 125 |
+
107,
|
| 126 |
+
108,
|
| 127 |
+
109,
|
| 128 |
+
110,
|
| 129 |
+
111,
|
| 130 |
+
112,
|
| 131 |
+
113,
|
| 132 |
+
114,
|
| 133 |
+
115,
|
| 134 |
+
116,
|
| 135 |
+
117,
|
| 136 |
+
118,
|
| 137 |
+
119,
|
| 138 |
+
120,
|
| 139 |
+
121,
|
| 140 |
+
122,
|
| 141 |
+
123,
|
| 142 |
+
124,
|
| 143 |
+
125,
|
| 144 |
+
126,
|
| 145 |
+
127,
|
| 146 |
+
128,
|
| 147 |
+
129,
|
| 148 |
+
130,
|
| 149 |
+
131,
|
| 150 |
+
132,
|
| 151 |
+
133,
|
| 152 |
+
134,
|
| 153 |
+
135,
|
| 154 |
+
136,
|
| 155 |
+
137,
|
| 156 |
+
138,
|
| 157 |
+
139,
|
| 158 |
+
140,
|
| 159 |
+
141,
|
| 160 |
+
142,
|
| 161 |
+
143,
|
| 162 |
+
144,
|
| 163 |
+
145,
|
| 164 |
+
146,
|
| 165 |
+
147,
|
| 166 |
+
148,
|
| 167 |
+
149,
|
| 168 |
+
150,
|
| 169 |
+
151,
|
| 170 |
+
152,
|
| 171 |
+
153,
|
| 172 |
+
154,
|
| 173 |
+
155,
|
| 174 |
+
156,
|
| 175 |
+
157,
|
| 176 |
+
158,
|
| 177 |
+
159,
|
| 178 |
+
160,
|
| 179 |
+
161,
|
| 180 |
+
162,
|
| 181 |
+
163,
|
| 182 |
+
164,
|
| 183 |
+
165,
|
| 184 |
+
166,
|
| 185 |
+
167,
|
| 186 |
+
168,
|
| 187 |
+
169,
|
| 188 |
+
170,
|
| 189 |
+
171,
|
| 190 |
+
172,
|
| 191 |
+
173,
|
| 192 |
+
174,
|
| 193 |
+
175,
|
| 194 |
+
176,
|
| 195 |
+
177,
|
| 196 |
+
178,
|
| 197 |
+
179,
|
| 198 |
+
180,
|
| 199 |
+
181,
|
| 200 |
+
182,
|
| 201 |
+
183,
|
| 202 |
+
184,
|
| 203 |
+
185,
|
| 204 |
+
186,
|
| 205 |
+
187,
|
| 206 |
+
188,
|
| 207 |
+
189,
|
| 208 |
+
190,
|
| 209 |
+
191,
|
| 210 |
+
192,
|
| 211 |
+
193,
|
| 212 |
+
194,
|
| 213 |
+
195,
|
| 214 |
+
196,
|
| 215 |
+
197,
|
| 216 |
+
198,
|
| 217 |
+
199,
|
| 218 |
+
200,
|
| 219 |
+
201,
|
| 220 |
+
202,
|
| 221 |
+
203,
|
| 222 |
+
204,
|
| 223 |
+
205,
|
| 224 |
+
206,
|
| 225 |
+
207,
|
| 226 |
+
208,
|
| 227 |
+
209,
|
| 228 |
+
210,
|
| 229 |
+
211,
|
| 230 |
+
212,
|
| 231 |
+
213,
|
| 232 |
+
214,
|
| 233 |
+
215,
|
| 234 |
+
216,
|
| 235 |
+
217,
|
| 236 |
+
218,
|
| 237 |
+
219,
|
| 238 |
+
220,
|
| 239 |
+
221,
|
| 240 |
+
222,
|
| 241 |
+
223,
|
| 242 |
+
224,
|
| 243 |
+
225,
|
| 244 |
+
226,
|
| 245 |
+
227,
|
| 246 |
+
228,
|
| 247 |
+
229,
|
| 248 |
+
230,
|
| 249 |
+
231,
|
| 250 |
+
232,
|
| 251 |
+
233,
|
| 252 |
+
234,
|
| 253 |
+
235,
|
| 254 |
+
236,
|
| 255 |
+
237,
|
| 256 |
+
238,
|
| 257 |
+
239,
|
| 258 |
+
240,
|
| 259 |
+
241,
|
| 260 |
+
242,
|
| 261 |
+
243,
|
| 262 |
+
244,
|
| 263 |
+
245,
|
| 264 |
+
246,
|
| 265 |
+
247,
|
| 266 |
+
248,
|
| 267 |
+
249,
|
| 268 |
+
250,
|
| 269 |
+
251,
|
| 270 |
+
252,
|
| 271 |
+
253,
|
| 272 |
+
254,
|
| 273 |
+
255,
|
| 274 |
+
256,
|
| 275 |
+
257,
|
| 276 |
+
258,
|
| 277 |
+
259,
|
| 278 |
+
260,
|
| 279 |
+
261,
|
| 280 |
+
262,
|
| 281 |
+
263,
|
| 282 |
+
264,
|
| 283 |
+
265,
|
| 284 |
+
266,
|
| 285 |
+
267,
|
| 286 |
+
268,
|
| 287 |
+
269,
|
| 288 |
+
270,
|
| 289 |
+
271,
|
| 290 |
+
272,
|
| 291 |
+
273,
|
| 292 |
+
274,
|
| 293 |
+
275,
|
| 294 |
+
276,
|
| 295 |
+
277,
|
| 296 |
+
278,
|
| 297 |
+
279,
|
| 298 |
+
280,
|
| 299 |
+
281,
|
| 300 |
+
282,
|
| 301 |
+
283,
|
| 302 |
+
284,
|
| 303 |
+
285,
|
| 304 |
+
286,
|
| 305 |
+
287,
|
| 306 |
+
288,
|
| 307 |
+
289,
|
| 308 |
+
290,
|
| 309 |
+
291,
|
| 310 |
+
292,
|
| 311 |
+
293,
|
| 312 |
+
294,
|
| 313 |
+
295,
|
| 314 |
+
296,
|
| 315 |
+
297,
|
| 316 |
+
298,
|
| 317 |
+
299,
|
| 318 |
+
300,
|
| 319 |
+
301,
|
| 320 |
+
302,
|
| 321 |
+
303,
|
| 322 |
+
304,
|
| 323 |
+
305,
|
| 324 |
+
306,
|
| 325 |
+
307,
|
| 326 |
+
308,
|
| 327 |
+
309,
|
| 328 |
+
310
|
| 329 |
+
]
|
| 330 |
+
}
|
| 331 |
+
]
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/optimizer_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:145ea7cf0ca132692113ede46677dabd7407e7dd21ad8dae6853db3fe1900a56
|
| 3 |
+
size 738026076
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/rng_state.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:493ccf3c42175bdf44dba5cd3f1195f9a1a4d7c87aea6e19b15826e9e07289dd
|
| 3 |
+
size 15708
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/scheduler_state.json
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"base_lrs": [
|
| 3 |
+
0.0001
|
| 4 |
+
],
|
| 5 |
+
"last_epoch": 300000,
|
| 6 |
+
"verbose": false,
|
| 7 |
+
"_step_count": 300001,
|
| 8 |
+
"_get_lr_called_within_step": false,
|
| 9 |
+
"_last_lr": [
|
| 10 |
+
3.092259045565738e-05
|
| 11 |
+
],
|
| 12 |
+
"lr_lambdas": [
|
| 13 |
+
null
|
| 14 |
+
]
|
| 15 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/checkpoints/300000/training_state/training_step.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"step": 300000
|
| 3 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/debug-internal.log
CHANGED
|
@@ -203,3 +203,9 @@
|
|
| 203 |
{"time":"2025-05-02T10:09:20.603310421Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 204 |
{"time":"2025-05-02T10:09:20.603375451Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 205 |
{"time":"2025-05-02T10:09:20.603437341Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 203 |
{"time":"2025-05-02T10:09:20.603310421Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 204 |
{"time":"2025-05-02T10:09:20.603375451Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 205 |
{"time":"2025-05-02T10:09:20.603437341Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 206 |
+
{"time":"2025-05-02T16:46:15.908301608Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded (Client.Timeout exceeded while awaiting headers)"}
|
| 207 |
+
{"time":"2025-05-02T17:57:15.977600155Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
|
| 208 |
+
{"time":"2025-05-02T19:18:46.063853787Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
|
| 209 |
+
{"time":"2025-05-02T19:46:16.087716965Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded"}
|
| 210 |
+
{"time":"2025-05-02T19:49:31.089345225Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
|
| 211 |
+
{"time":"2025-05-02T21:32:03.806394741Z","level":"INFO","msg":"api: retrying HTTP error","status":502,"url":"https://api.wandb.ai/files/marchmelo0923-postech/lerobot/xsemtuwn/file_stream","body":"\n<html><head>\n<meta http-equiv=\"content-type\" content=\"text/html;charset=utf-8\">\n<title>502 Server Error</title>\n</head>\n<body text=#000000 bgcolor=#ffffff>\n<h1>Error: Server Error</h1>\n<h2>The server encountered a temporary error and could not complete your request.<p>Please try again in 30 seconds.</h2>\n<h2></h2>\n</body></html>\n"}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/run-20250502_093744-xsemtuwn/files/output.log
CHANGED
|
@@ -246,3 +246,649 @@ INFO 2025-05-02 15:05:30 ts/train.py:232 step:176K smpl:1M ep:5K epch:24.21 loss
|
|
| 246 |
INFO 2025-05-02 15:06:55 ts/train.py:232 step:176K smpl:1M ep:5K epch:24.24 loss:0.007 grdn:0.155 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 247 |
INFO 2025-05-02 15:08:20 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.27 loss:0.006 grdn:0.150 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 248 |
INFO 2025-05-02 15:09:45 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.30 loss:0.007 grdn:0.147 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 246 |
INFO 2025-05-02 15:06:55 ts/train.py:232 step:176K smpl:1M ep:5K epch:24.24 loss:0.007 grdn:0.155 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 247 |
INFO 2025-05-02 15:08:20 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.27 loss:0.006 grdn:0.150 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 248 |
INFO 2025-05-02 15:09:45 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.30 loss:0.007 grdn:0.147 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 249 |
+
INFO 2025-05-02 15:11:10 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.32 loss:0.007 grdn:0.155 lr:7.0e-05 updt_s:0.424 data_s:0.000
|
| 250 |
+
INFO 2025-05-02 15:12:35 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.35 loss:0.007 grdn:0.153 lr:7.0e-05 updt_s:0.424 data_s:0.000
|
| 251 |
+
INFO 2025-05-02 15:14:00 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.38 loss:0.007 grdn:0.149 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 252 |
+
INFO 2025-05-02 15:15:25 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.41 loss:0.007 grdn:0.150 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 253 |
+
INFO 2025-05-02 15:16:50 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.43 loss:0.007 grdn:0.151 lr:7.0e-05 updt_s:0.423 data_s:0.000
|
| 254 |
+
INFO 2025-05-02 15:18:15 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.46 loss:0.006 grdn:0.150 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 255 |
+
INFO 2025-05-02 15:19:40 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.49 loss:0.006 grdn:0.148 lr:7.0e-05 updt_s:0.424 data_s:0.000
|
| 256 |
+
INFO 2025-05-02 15:21:05 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.52 loss:0.007 grdn:0.148 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 257 |
+
INFO 2025-05-02 15:22:30 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.54 loss:0.006 grdn:0.140 lr:7.0e-05 updt_s:0.424 data_s:0.001
|
| 258 |
+
INFO 2025-05-02 15:23:55 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.57 loss:0.007 grdn:0.150 lr:7.0e-05 updt_s:0.423 data_s:0.001
|
| 259 |
+
INFO 2025-05-02 15:25:20 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.60 loss:0.007 grdn:0.145 lr:7.0e-05 updt_s:0.423 data_s:0.001
|
| 260 |
+
INFO 2025-05-02 15:26:45 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.63 loss:0.007 grdn:0.155 lr:7.0e-05 updt_s:0.423 data_s:0.001
|
| 261 |
+
INFO 2025-05-02 15:28:10 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.65 loss:0.007 grdn:0.162 lr:6.9e-05 updt_s:0.423 data_s:0.000
|
| 262 |
+
INFO 2025-05-02 15:29:35 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.68 loss:0.006 grdn:0.149 lr:6.9e-05 updt_s:0.423 data_s:0.001
|
| 263 |
+
INFO 2025-05-02 15:31:01 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.71 loss:0.006 grdn:0.146 lr:6.9e-05 updt_s:0.423 data_s:0.008
|
| 264 |
+
INFO 2025-05-02 15:32:26 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.74 loss:0.007 grdn:0.159 lr:6.9e-05 updt_s:0.423 data_s:0.000
|
| 265 |
+
INFO 2025-05-02 15:32:26 ts/train.py:241 Checkpoint policy after step 180000
|
| 266 |
+
INFO 2025-05-02 15:33:54 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.76 loss:0.006 grdn:0.146 lr:6.9e-05 updt_s:0.423 data_s:0.000
|
| 267 |
+
INFO 2025-05-02 15:35:19 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.79 loss:0.007 grdn:0.156 lr:6.9e-05 updt_s:0.423 data_s:0.001
|
| 268 |
+
INFO 2025-05-02 15:36:44 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.82 loss:0.007 grdn:0.156 lr:6.9e-05 updt_s:0.423 data_s:0.000
|
| 269 |
+
INFO 2025-05-02 15:38:09 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.85 loss:0.006 grdn:0.144 lr:6.9e-05 updt_s:0.424 data_s:0.001
|
| 270 |
+
INFO 2025-05-02 15:39:34 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.87 loss:0.007 grdn:0.147 lr:6.9e-05 updt_s:0.424 data_s:0.000
|
| 271 |
+
INFO 2025-05-02 15:40:59 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.90 loss:0.006 grdn:0.145 lr:6.9e-05 updt_s:0.424 data_s:0.000
|
| 272 |
+
INFO 2025-05-02 15:42:24 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.93 loss:0.007 grdn:0.159 lr:6.9e-05 updt_s:0.424 data_s:0.000
|
| 273 |
+
INFO 2025-05-02 15:43:49 ts/train.py:232 step:182K smpl:1M ep:5K epch:24.96 loss:0.007 grdn:0.145 lr:6.9e-05 updt_s:0.423 data_s:0.001
|
| 274 |
+
INFO 2025-05-02 15:45:14 ts/train.py:232 step:182K smpl:1M ep:5K epch:24.98 loss:0.007 grdn:0.149 lr:6.9e-05 updt_s:0.423 data_s:0.000
|
| 275 |
+
INFO 2025-05-02 15:46:39 ts/train.py:232 step:182K smpl:1M ep:5K epch:25.01 loss:0.006 grdn:0.144 lr:6.9e-05 updt_s:0.423 data_s:0.001
|
| 276 |
+
INFO 2025-05-02 15:48:03 ts/train.py:232 step:182K smpl:1M ep:5K epch:25.04 loss:0.007 grdn:0.161 lr:6.9e-05 updt_s:0.424 data_s:0.000
|
| 277 |
+
INFO 2025-05-02 15:49:29 ts/train.py:232 step:182K smpl:1M ep:5K epch:25.07 loss:0.006 grdn:0.143 lr:6.9e-05 updt_s:0.424 data_s:0.001
|
| 278 |
+
INFO 2025-05-02 15:50:54 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.09 loss:0.006 grdn:0.151 lr:6.8e-05 updt_s:0.424 data_s:0.000
|
| 279 |
+
INFO 2025-05-02 15:52:19 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.12 loss:0.006 grdn:0.147 lr:6.8e-05 updt_s:0.424 data_s:0.001
|
| 280 |
+
INFO 2025-05-02 15:53:44 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.15 loss:0.007 grdn:0.143 lr:6.8e-05 updt_s:0.424 data_s:0.001
|
| 281 |
+
INFO 2025-05-02 15:55:09 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.18 loss:0.007 grdn:0.153 lr:6.8e-05 updt_s:0.423 data_s:0.001
|
| 282 |
+
INFO 2025-05-02 15:56:34 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.20 loss:0.007 grdn:0.158 lr:6.8e-05 updt_s:0.423 data_s:0.001
|
| 283 |
+
INFO 2025-05-02 15:57:59 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.23 loss:0.007 grdn:0.148 lr:6.8e-05 updt_s:0.424 data_s:0.001
|
| 284 |
+
INFO 2025-05-02 15:59:24 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.26 loss:0.007 grdn:0.151 lr:6.8e-05 updt_s:0.424 data_s:0.001
|
| 285 |
+
INFO 2025-05-02 16:00:49 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.29 loss:0.007 grdn:0.149 lr:6.8e-05 updt_s:0.424 data_s:0.000
|
| 286 |
+
INFO 2025-05-02 16:02:14 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.31 loss:0.006 grdn:0.155 lr:6.8e-05 updt_s:0.424 data_s:0.000
|
| 287 |
+
INFO 2025-05-02 16:03:39 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.34 loss:0.007 grdn:0.149 lr:6.8e-05 updt_s:0.424 data_s:0.000
|
| 288 |
+
INFO 2025-05-02 16:05:03 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.37 loss:0.007 grdn:0.153 lr:6.8e-05 updt_s:0.423 data_s:0.000
|
| 289 |
+
INFO 2025-05-02 16:06:28 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.40 loss:0.006 grdn:0.140 lr:6.8e-05 updt_s:0.424 data_s:0.000
|
| 290 |
+
INFO 2025-05-02 16:07:53 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.42 loss:0.006 grdn:0.150 lr:6.8e-05 updt_s:0.424 data_s:0.001
|
| 291 |
+
INFO 2025-05-02 16:09:18 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.45 loss:0.006 grdn:0.153 lr:6.8e-05 updt_s:0.423 data_s:0.000
|
| 292 |
+
INFO 2025-05-02 16:10:43 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.48 loss:0.006 grdn:0.144 lr:6.8e-05 updt_s:0.423 data_s:0.001
|
| 293 |
+
INFO 2025-05-02 16:12:08 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.51 loss:0.007 grdn:0.148 lr:6.8e-05 updt_s:0.423 data_s:0.001
|
| 294 |
+
INFO 2025-05-02 16:13:33 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.53 loss:0.007 grdn:0.156 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 295 |
+
INFO 2025-05-02 16:14:58 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.56 loss:0.007 grdn:0.149 lr:6.7e-05 updt_s:0.423 data_s:0.000
|
| 296 |
+
INFO 2025-05-02 16:16:23 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.59 loss:0.006 grdn:0.151 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 297 |
+
INFO 2025-05-02 16:17:48 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.62 loss:0.006 grdn:0.152 lr:6.7e-05 updt_s:0.423 data_s:0.000
|
| 298 |
+
INFO 2025-05-02 16:19:13 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.64 loss:0.006 grdn:0.142 lr:6.7e-05 updt_s:0.423 data_s:0.000
|
| 299 |
+
INFO 2025-05-02 16:20:38 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.67 loss:0.006 grdn:0.141 lr:6.7e-05 updt_s:0.423 data_s:0.000
|
| 300 |
+
INFO 2025-05-02 16:22:04 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.70 loss:0.006 grdn:0.145 lr:6.7e-05 updt_s:0.423 data_s:0.010
|
| 301 |
+
INFO 2025-05-02 16:23:29 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.73 loss:0.007 grdn:0.150 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 302 |
+
INFO 2025-05-02 16:24:54 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.75 loss:0.006 grdn:0.149 lr:6.7e-05 updt_s:0.424 data_s:0.001
|
| 303 |
+
INFO 2025-05-02 16:26:19 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.78 loss:0.006 grdn:0.149 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 304 |
+
INFO 2025-05-02 16:27:44 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.81 loss:0.006 grdn:0.145 lr:6.7e-05 updt_s:0.423 data_s:0.000
|
| 305 |
+
INFO 2025-05-02 16:29:09 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.84 loss:0.006 grdn:0.141 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 306 |
+
INFO 2025-05-02 16:30:34 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.86 loss:0.007 grdn:0.153 lr:6.7e-05 updt_s:0.424 data_s:0.001
|
| 307 |
+
INFO 2025-05-02 16:31:59 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.89 loss:0.006 grdn:0.150 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 308 |
+
INFO 2025-05-02 16:33:24 ts/train.py:232 step:189K smpl:2M ep:5K epch:25.92 loss:0.006 grdn:0.143 lr:6.7e-05 updt_s:0.423 data_s:0.000
|
| 309 |
+
INFO 2025-05-02 16:34:49 ts/train.py:232 step:189K smpl:2M ep:5K epch:25.95 loss:0.007 grdn:0.163 lr:6.7e-05 updt_s:0.423 data_s:0.001
|
| 310 |
+
INFO 2025-05-02 16:36:14 ts/train.py:232 step:189K smpl:2M ep:5K epch:25.97 loss:0.007 grdn:0.150 lr:6.7e-05 updt_s:0.424 data_s:0.001
|
| 311 |
+
INFO 2025-05-02 16:37:39 ts/train.py:232 step:189K smpl:2M ep:5K epch:26.00 loss:0.006 grdn:0.152 lr:6.6e-05 updt_s:0.424 data_s:0.001
|
| 312 |
+
INFO 2025-05-02 16:39:04 ts/train.py:232 step:189K smpl:2M ep:5K epch:26.03 loss:0.007 grdn:0.160 lr:6.6e-05 updt_s:0.424 data_s:0.001
|
| 313 |
+
INFO 2025-05-02 16:40:29 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.06 loss:0.006 grdn:0.143 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 314 |
+
INFO 2025-05-02 16:41:54 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.08 loss:0.007 grdn:0.159 lr:6.6e-05 updt_s:0.423 data_s:0.000
|
| 315 |
+
INFO 2025-05-02 16:43:19 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.11 loss:0.006 grdn:0.147 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 316 |
+
INFO 2025-05-02 16:44:44 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.14 loss:0.007 grdn:0.151 lr:6.6e-05 updt_s:0.423 data_s:0.000
|
| 317 |
+
INFO 2025-05-02 16:46:09 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.17 loss:0.007 grdn:0.161 lr:6.6e-05 updt_s:0.423 data_s:0.000
|
| 318 |
+
INFO 2025-05-02 16:47:34 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.19 loss:0.006 grdn:0.145 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 319 |
+
INFO 2025-05-02 16:48:59 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.22 loss:0.006 grdn:0.140 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 320 |
+
INFO 2025-05-02 16:50:24 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.25 loss:0.006 grdn:0.146 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 321 |
+
INFO 2025-05-02 16:51:49 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.28 loss:0.006 grdn:0.153 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 322 |
+
INFO 2025-05-02 16:53:14 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.30 loss:0.007 grdn:0.159 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 323 |
+
INFO 2025-05-02 16:54:39 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.33 loss:0.006 grdn:0.153 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 324 |
+
INFO 2025-05-02 16:56:04 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.36 loss:0.006 grdn:0.142 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 325 |
+
INFO 2025-05-02 16:57:29 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.39 loss:0.006 grdn:0.143 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 326 |
+
INFO 2025-05-02 16:58:54 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.41 loss:0.007 grdn:0.155 lr:6.6e-05 updt_s:0.423 data_s:0.001
|
| 327 |
+
INFO 2025-05-02 17:00:19 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.44 loss:0.007 grdn:0.156 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 328 |
+
INFO 2025-05-02 17:01:43 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.47 loss:0.006 grdn:0.141 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 329 |
+
INFO 2025-05-02 17:03:08 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.50 loss:0.007 grdn:0.149 lr:6.5e-05 updt_s:0.423 data_s:0.000
|
| 330 |
+
INFO 2025-05-02 17:04:33 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.52 loss:0.006 grdn:0.150 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 331 |
+
INFO 2025-05-02 17:05:58 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.55 loss:0.006 grdn:0.144 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 332 |
+
INFO 2025-05-02 17:07:23 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.58 loss:0.007 grdn:0.156 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 333 |
+
INFO 2025-05-02 17:08:48 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.61 loss:0.006 grdn:0.144 lr:6.5e-05 updt_s:0.423 data_s:0.000
|
| 334 |
+
INFO 2025-05-02 17:10:13 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.63 loss:0.006 grdn:0.146 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 335 |
+
INFO 2025-05-02 17:11:39 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.66 loss:0.007 grdn:0.155 lr:6.5e-05 updt_s:0.422 data_s:0.007
|
| 336 |
+
INFO 2025-05-02 17:13:04 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.69 loss:0.007 grdn:0.155 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 337 |
+
INFO 2025-05-02 17:14:29 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.72 loss:0.007 grdn:0.154 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 338 |
+
INFO 2025-05-02 17:15:54 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.74 loss:0.007 grdn:0.160 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 339 |
+
INFO 2025-05-02 17:17:18 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.77 loss:0.006 grdn:0.148 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 340 |
+
INFO 2025-05-02 17:18:43 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.80 loss:0.006 grdn:0.151 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 341 |
+
INFO 2025-05-02 17:20:08 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.83 loss:0.006 grdn:0.146 lr:6.5e-05 updt_s:0.423 data_s:0.001
|
| 342 |
+
INFO 2025-05-02 17:21:33 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.85 loss:0.006 grdn:0.153 lr:6.5e-05 updt_s:0.423 data_s:0.000
|
| 343 |
+
INFO 2025-05-02 17:22:58 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.88 loss:0.005 grdn:0.144 lr:6.4e-05 updt_s:0.424 data_s:0.001
|
| 344 |
+
INFO 2025-05-02 17:24:23 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.91 loss:0.006 grdn:0.146 lr:6.4e-05 updt_s:0.424 data_s:0.001
|
| 345 |
+
INFO 2025-05-02 17:25:48 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.94 loss:0.007 grdn:0.162 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 346 |
+
INFO 2025-05-02 17:27:13 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.96 loss:0.006 grdn:0.148 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 347 |
+
INFO 2025-05-02 17:28:38 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.99 loss:0.006 grdn:0.153 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 348 |
+
INFO 2025-05-02 17:30:03 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.02 loss:0.006 grdn:0.147 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 349 |
+
INFO 2025-05-02 17:31:28 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.05 loss:0.007 grdn:0.160 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 350 |
+
INFO 2025-05-02 17:32:53 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.07 loss:0.007 grdn:0.155 lr:6.4e-05 updt_s:0.424 data_s:0.001
|
| 351 |
+
INFO 2025-05-02 17:34:18 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.10 loss:0.006 grdn:0.149 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 352 |
+
INFO 2025-05-02 17:35:43 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.13 loss:0.007 grdn:0.152 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 353 |
+
INFO 2025-05-02 17:37:08 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.16 loss:0.006 grdn:0.144 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 354 |
+
INFO 2025-05-02 17:38:33 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.18 loss:0.006 grdn:0.156 lr:6.4e-05 updt_s:0.424 data_s:0.001
|
| 355 |
+
INFO 2025-05-02 17:39:58 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.21 loss:0.006 grdn:0.152 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 356 |
+
INFO 2025-05-02 17:41:23 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.24 loss:0.006 grdn:0.153 lr:6.4e-05 updt_s:0.423 data_s:0.001
|
| 357 |
+
INFO 2025-05-02 17:42:48 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.27 loss:0.006 grdn:0.150 lr:6.4e-05 updt_s:0.424 data_s:0.001
|
| 358 |
+
INFO 2025-05-02 17:44:13 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.29 loss:0.006 grdn:0.149 lr:6.4e-05 updt_s:0.424 data_s:0.001
|
| 359 |
+
INFO 2025-05-02 17:45:39 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.32 loss:0.006 grdn:0.146 lr:6.3e-05 updt_s:0.424 data_s:0.001
|
| 360 |
+
INFO 2025-05-02 17:47:04 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.35 loss:0.006 grdn:0.151 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 361 |
+
INFO 2025-05-02 17:48:29 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.38 loss:0.006 grdn:0.152 lr:6.3e-05 updt_s:0.424 data_s:0.001
|
| 362 |
+
INFO 2025-05-02 17:49:54 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.40 loss:0.006 grdn:0.150 lr:6.3e-05 updt_s:0.424 data_s:0.001
|
| 363 |
+
INFO 2025-05-02 17:51:19 ts/train.py:232 step:200K smpl:2M ep:5K epch:27.43 loss:0.006 grdn:0.152 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 364 |
+
INFO 2025-05-02 17:52:44 ts/train.py:232 step:200K smpl:2M ep:5K epch:27.46 loss:0.006 grdn:0.150 lr:6.3e-05 updt_s:0.424 data_s:0.001
|
| 365 |
+
INFO 2025-05-02 17:54:09 ts/train.py:232 step:200K smpl:2M ep:5K epch:27.49 loss:0.006 grdn:0.149 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 366 |
+
INFO 2025-05-02 17:55:34 ts/train.py:232 step:200K smpl:2M ep:6K epch:27.51 loss:0.005 grdn:0.142 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 367 |
+
INFO 2025-05-02 17:56:59 ts/train.py:232 step:200K smpl:2M ep:6K epch:27.54 loss:0.006 grdn:0.145 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 368 |
+
INFO 2025-05-02 17:58:24 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.57 loss:0.006 grdn:0.145 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 369 |
+
INFO 2025-05-02 17:59:49 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.60 loss:0.007 grdn:0.165 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 370 |
+
INFO 2025-05-02 18:01:14 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.62 loss:0.006 grdn:0.151 lr:6.3e-05 updt_s:0.423 data_s:0.001
|
| 371 |
+
INFO 2025-05-02 18:02:40 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.65 loss:0.006 grdn:0.154 lr:6.3e-05 updt_s:0.423 data_s:0.007
|
| 372 |
+
INFO 2025-05-02 18:04:05 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.68 loss:0.006 grdn:0.147 lr:6.3e-05 updt_s:0.424 data_s:0.001
|
| 373 |
+
INFO 2025-05-02 18:05:30 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.71 loss:0.006 grdn:0.148 lr:6.3e-05 updt_s:0.424 data_s:0.001
|
| 374 |
+
INFO 2025-05-02 18:06:55 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.73 loss:0.006 grdn:0.155 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 375 |
+
INFO 2025-05-02 18:08:20 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.76 loss:0.006 grdn:0.148 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 376 |
+
INFO 2025-05-02 18:09:45 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.79 loss:0.006 grdn:0.155 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 377 |
+
INFO 2025-05-02 18:11:10 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.82 loss:0.007 grdn:0.154 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 378 |
+
INFO 2025-05-02 18:12:35 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.84 loss:0.006 grdn:0.150 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 379 |
+
INFO 2025-05-02 18:14:00 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.87 loss:0.006 grdn:0.151 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 380 |
+
INFO 2025-05-02 18:15:25 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.90 loss:0.006 grdn:0.152 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 381 |
+
INFO 2025-05-02 18:16:50 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.93 loss:0.006 grdn:0.151 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 382 |
+
INFO 2025-05-02 18:18:15 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.95 loss:0.007 grdn:0.156 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 383 |
+
INFO 2025-05-02 18:19:40 ts/train.py:232 step:204K smpl:2M ep:6K epch:27.98 loss:0.006 grdn:0.152 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 384 |
+
INFO 2025-05-02 18:21:05 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.01 loss:0.006 grdn:0.150 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 385 |
+
INFO 2025-05-02 18:22:31 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.03 loss:0.006 grdn:0.149 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 386 |
+
INFO 2025-05-02 18:23:56 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.06 loss:0.006 grdn:0.150 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 387 |
+
INFO 2025-05-02 18:25:21 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.09 loss:0.006 grdn:0.149 lr:6.2e-05 updt_s:0.424 data_s:0.001
|
| 388 |
+
INFO 2025-05-02 18:26:46 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.12 loss:0.006 grdn:0.152 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 389 |
+
INFO 2025-05-02 18:28:10 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.14 loss:0.006 grdn:0.150 lr:6.2e-05 updt_s:0.423 data_s:0.001
|
| 390 |
+
INFO 2025-05-02 18:29:35 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.17 loss:0.007 grdn:0.157 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 391 |
+
INFO 2025-05-02 18:31:00 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.20 loss:0.006 grdn:0.154 lr:6.1e-05 updt_s:0.424 data_s:0.001
|
| 392 |
+
INFO 2025-05-02 18:32:25 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.23 loss:0.006 grdn:0.152 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 393 |
+
INFO 2025-05-02 18:33:50 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.25 loss:0.007 grdn:0.156 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 394 |
+
INFO 2025-05-02 18:35:15 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.28 loss:0.007 grdn:0.156 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 395 |
+
INFO 2025-05-02 18:36:40 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.31 loss:0.006 grdn:0.152 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 396 |
+
INFO 2025-05-02 18:38:05 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.34 loss:0.006 grdn:0.146 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 397 |
+
INFO 2025-05-02 18:39:30 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.36 loss:0.006 grdn:0.148 lr:6.1e-05 updt_s:0.424 data_s:0.001
|
| 398 |
+
INFO 2025-05-02 18:40:55 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.39 loss:0.006 grdn:0.150 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 399 |
+
INFO 2025-05-02 18:42:20 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.42 loss:0.006 grdn:0.153 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 400 |
+
INFO 2025-05-02 18:43:45 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.45 loss:0.006 grdn:0.152 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 401 |
+
INFO 2025-05-02 18:45:10 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.47 loss:0.006 grdn:0.151 lr:6.1e-05 updt_s:0.424 data_s:0.001
|
| 402 |
+
INFO 2025-05-02 18:46:36 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.50 loss:0.006 grdn:0.138 lr:6.1e-05 updt_s:0.424 data_s:0.001
|
| 403 |
+
INFO 2025-05-02 18:48:01 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.53 loss:0.006 grdn:0.148 lr:6.1e-05 updt_s:0.424 data_s:0.001
|
| 404 |
+
INFO 2025-05-02 18:49:26 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.56 loss:0.006 grdn:0.154 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 405 |
+
INFO 2025-05-02 18:50:51 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.58 loss:0.006 grdn:0.152 lr:6.1e-05 updt_s:0.423 data_s:0.001
|
| 406 |
+
INFO 2025-05-02 18:52:17 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.61 loss:0.006 grdn:0.146 lr:6.0e-05 updt_s:0.422 data_s:0.008
|
| 407 |
+
INFO 2025-05-02 18:53:42 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.64 loss:0.006 grdn:0.148 lr:6.0e-05 updt_s:0.424 data_s:0.001
|
| 408 |
+
INFO 2025-05-02 18:55:07 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.67 loss:0.006 grdn:0.145 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 409 |
+
INFO 2025-05-02 18:56:32 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.69 loss:0.006 grdn:0.152 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 410 |
+
INFO 2025-05-02 18:57:57 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.72 loss:0.005 grdn:0.138 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 411 |
+
INFO 2025-05-02 18:59:22 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.75 loss:0.006 grdn:0.145 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 412 |
+
INFO 2025-05-02 19:00:47 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.78 loss:0.007 grdn:0.154 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 413 |
+
INFO 2025-05-02 19:02:12 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.80 loss:0.006 grdn:0.152 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 414 |
+
INFO 2025-05-02 19:03:37 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.83 loss:0.006 grdn:0.151 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 415 |
+
INFO 2025-05-02 19:05:02 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.86 loss:0.007 grdn:0.166 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 416 |
+
INFO 2025-05-02 19:06:27 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.89 loss:0.006 grdn:0.149 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 417 |
+
INFO 2025-05-02 19:07:51 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.91 loss:0.006 grdn:0.148 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 418 |
+
INFO 2025-05-02 19:09:16 ts/train.py:232 step:211K smpl:2M ep:6K epch:28.94 loss:0.006 grdn:0.155 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 419 |
+
INFO 2025-05-02 19:10:41 ts/train.py:232 step:211K smpl:2M ep:6K epch:28.97 loss:0.006 grdn:0.148 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 420 |
+
INFO 2025-05-02 19:12:06 ts/train.py:232 step:211K smpl:2M ep:6K epch:29.00 loss:0.006 grdn:0.160 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 421 |
+
INFO 2025-05-02 19:13:31 ts/train.py:232 step:211K smpl:2M ep:6K epch:29.02 loss:0.006 grdn:0.145 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 422 |
+
INFO 2025-05-02 19:14:56 ts/train.py:232 step:211K smpl:2M ep:6K epch:29.05 loss:0.007 grdn:0.162 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 423 |
+
INFO 2025-05-02 19:16:21 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.08 loss:0.006 grdn:0.143 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 424 |
+
INFO 2025-05-02 19:17:45 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.11 loss:0.005 grdn:0.144 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 425 |
+
INFO 2025-05-02 19:19:10 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.13 loss:0.006 grdn:0.156 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 426 |
+
INFO 2025-05-02 19:20:35 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.16 loss:0.006 grdn:0.159 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 427 |
+
INFO 2025-05-02 19:22:00 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.19 loss:0.006 grdn:0.149 lr:5.9e-05 updt_s:0.423 data_s:0.000
|
| 428 |
+
INFO 2025-05-02 19:23:25 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.22 loss:0.006 grdn:0.151 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 429 |
+
INFO 2025-05-02 19:24:50 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.24 loss:0.007 grdn:0.154 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 430 |
+
INFO 2025-05-02 19:26:15 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.27 loss:0.006 grdn:0.149 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 431 |
+
INFO 2025-05-02 19:27:40 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.30 loss:0.006 grdn:0.152 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 432 |
+
INFO 2025-05-02 19:29:05 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.33 loss:0.006 grdn:0.154 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 433 |
+
INFO 2025-05-02 19:30:29 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.35 loss:0.006 grdn:0.152 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 434 |
+
INFO 2025-05-02 19:31:54 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.38 loss:0.006 grdn:0.151 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 435 |
+
INFO 2025-05-02 19:33:19 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.41 loss:0.006 grdn:0.153 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 436 |
+
INFO 2025-05-02 19:34:44 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.44 loss:0.006 grdn:0.150 lr:5.9e-05 updt_s:0.423 data_s:0.001
|
| 437 |
+
INFO 2025-05-02 19:36:09 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.46 loss:0.007 grdn:0.162 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 438 |
+
INFO 2025-05-02 19:37:34 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.49 loss:0.006 grdn:0.159 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 439 |
+
INFO 2025-05-02 19:38:59 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.52 loss:0.006 grdn:0.154 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 440 |
+
INFO 2025-05-02 19:40:23 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.55 loss:0.005 grdn:0.143 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 441 |
+
INFO 2025-05-02 19:41:48 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.57 loss:0.006 grdn:0.148 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 442 |
+
INFO 2025-05-02 19:43:14 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.60 loss:0.005 grdn:0.143 lr:5.8e-05 updt_s:0.422 data_s:0.007
|
| 443 |
+
INFO 2025-05-02 19:44:39 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.63 loss:0.005 grdn:0.142 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 444 |
+
INFO 2025-05-02 19:46:04 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.66 loss:0.006 grdn:0.155 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 445 |
+
INFO 2025-05-02 19:47:29 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.68 loss:0.006 grdn:0.147 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 446 |
+
INFO 2025-05-02 19:48:54 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.71 loss:0.006 grdn:0.153 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 447 |
+
INFO 2025-05-02 19:50:18 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.74 loss:0.007 grdn:0.158 lr:5.8e-05 updt_s:0.423 data_s:0.000
|
| 448 |
+
INFO 2025-05-02 19:51:43 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.77 loss:0.006 grdn:0.145 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 449 |
+
INFO 2025-05-02 19:53:08 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.79 loss:0.006 grdn:0.152 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 450 |
+
INFO 2025-05-02 19:54:33 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.82 loss:0.006 grdn:0.148 lr:5.8e-05 updt_s:0.423 data_s:0.001
|
| 451 |
+
INFO 2025-05-02 19:55:58 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.85 loss:0.006 grdn:0.146 lr:5.8e-05 updt_s:0.423 data_s:0.000
|
| 452 |
+
INFO 2025-05-02 19:57:23 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.88 loss:0.006 grdn:0.157 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 453 |
+
INFO 2025-05-02 19:58:48 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.90 loss:0.005 grdn:0.140 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 454 |
+
INFO 2025-05-02 20:00:13 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.93 loss:0.006 grdn:0.152 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 455 |
+
INFO 2025-05-02 20:01:38 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.96 loss:0.006 grdn:0.148 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 456 |
+
INFO 2025-05-02 20:03:03 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.99 loss:0.005 grdn:0.142 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 457 |
+
INFO 2025-05-02 20:04:27 ts/train.py:232 step:218K smpl:2M ep:6K epch:30.01 loss:0.005 grdn:0.139 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 458 |
+
INFO 2025-05-02 20:05:52 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.04 loss:0.006 grdn:0.154 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 459 |
+
INFO 2025-05-02 20:07:17 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.07 loss:0.006 grdn:0.155 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 460 |
+
INFO 2025-05-02 20:08:42 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.10 loss:0.006 grdn:0.147 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 461 |
+
INFO 2025-05-02 20:10:07 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.12 loss:0.006 grdn:0.155 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 462 |
+
INFO 2025-05-02 20:11:32 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.15 loss:0.006 grdn:0.149 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 463 |
+
INFO 2025-05-02 20:12:57 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.18 loss:0.006 grdn:0.158 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 464 |
+
INFO 2025-05-02 20:14:21 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.21 loss:0.006 grdn:0.147 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 465 |
+
INFO 2025-05-02 20:15:46 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.23 loss:0.006 grdn:0.145 lr:5.7e-05 updt_s:0.423 data_s:0.000
|
| 466 |
+
INFO 2025-05-02 20:17:11 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.26 loss:0.005 grdn:0.146 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 467 |
+
INFO 2025-05-02 20:18:36 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.29 loss:0.006 grdn:0.146 lr:5.7e-05 updt_s:0.424 data_s:0.000
|
| 468 |
+
INFO 2025-05-02 20:20:01 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.32 loss:0.006 grdn:0.149 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 469 |
+
INFO 2025-05-02 20:21:26 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.34 loss:0.006 grdn:0.143 lr:5.6e-05 updt_s:0.423 data_s:0.000
|
| 470 |
+
INFO 2025-05-02 20:22:51 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.37 loss:0.005 grdn:0.142 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 471 |
+
INFO 2025-05-02 20:24:16 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.40 loss:0.006 grdn:0.156 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 472 |
+
INFO 2025-05-02 20:25:41 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.43 loss:0.006 grdn:0.151 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 473 |
+
INFO 2025-05-02 20:27:06 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.45 loss:0.006 grdn:0.151 lr:5.6e-05 updt_s:0.423 data_s:0.000
|
| 474 |
+
INFO 2025-05-02 20:28:31 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.48 loss:0.006 grdn:0.162 lr:5.6e-05 updt_s:0.423 data_s:0.000
|
| 475 |
+
INFO 2025-05-02 20:29:56 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.51 loss:0.005 grdn:0.153 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 476 |
+
INFO 2025-05-02 20:31:21 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.54 loss:0.006 grdn:0.147 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 477 |
+
INFO 2025-05-02 20:32:47 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.56 loss:0.006 grdn:0.152 lr:5.6e-05 updt_s:0.423 data_s:0.008
|
| 478 |
+
INFO 2025-05-02 20:34:12 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.59 loss:0.006 grdn:0.150 lr:5.6e-05 updt_s:0.423 data_s:0.000
|
| 479 |
+
INFO 2025-05-02 20:35:37 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.62 loss:0.005 grdn:0.138 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 480 |
+
INFO 2025-05-02 20:37:02 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.65 loss:0.006 grdn:0.154 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 481 |
+
INFO 2025-05-02 20:38:27 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.67 loss:0.006 grdn:0.149 lr:5.6e-05 updt_s:0.424 data_s:0.000
|
| 482 |
+
INFO 2025-05-02 20:39:52 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.70 loss:0.006 grdn:0.153 lr:5.6e-05 updt_s:0.423 data_s:0.000
|
| 483 |
+
INFO 2025-05-02 20:41:16 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.73 loss:0.005 grdn:0.142 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 484 |
+
INFO 2025-05-02 20:42:41 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.76 loss:0.006 grdn:0.153 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 485 |
+
INFO 2025-05-02 20:44:06 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.78 loss:0.005 grdn:0.146 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 486 |
+
INFO 2025-05-02 20:45:31 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.81 loss:0.006 grdn:0.159 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 487 |
+
INFO 2025-05-02 20:46:56 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.84 loss:0.005 grdn:0.144 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 488 |
+
INFO 2025-05-02 20:48:21 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.87 loss:0.005 grdn:0.137 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 489 |
+
INFO 2025-05-02 20:49:46 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.89 loss:0.005 grdn:0.144 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 490 |
+
INFO 2025-05-02 20:51:11 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.92 loss:0.006 grdn:0.149 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 491 |
+
INFO 2025-05-02 20:52:36 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.95 loss:0.006 grdn:0.150 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 492 |
+
INFO 2025-05-02 20:54:01 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.98 loss:0.005 grdn:0.140 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 493 |
+
INFO 2025-05-02 20:55:25 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.00 loss:0.006 grdn:0.153 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 494 |
+
INFO 2025-05-02 20:56:50 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.03 loss:0.005 grdn:0.144 lr:5.5e-05 updt_s:0.423 data_s:0.000
|
| 495 |
+
INFO 2025-05-02 20:58:15 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.06 loss:0.006 grdn:0.149 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 496 |
+
INFO 2025-05-02 20:59:40 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.09 loss:0.005 grdn:0.144 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 497 |
+
INFO 2025-05-02 21:01:05 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.11 loss:0.005 grdn:0.144 lr:5.5e-05 updt_s:0.424 data_s:0.000
|
| 498 |
+
INFO 2025-05-02 21:02:30 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.14 loss:0.006 grdn:0.149 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 499 |
+
INFO 2025-05-02 21:03:55 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.17 loss:0.005 grdn:0.144 lr:5.4e-05 updt_s:0.423 data_s:0.000
|
| 500 |
+
INFO 2025-05-02 21:05:20 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.20 loss:0.006 grdn:0.156 lr:5.4e-05 updt_s:0.423 data_s:0.000
|
| 501 |
+
INFO 2025-05-02 21:06:44 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.22 loss:0.005 grdn:0.148 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 502 |
+
INFO 2025-05-02 21:08:09 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.25 loss:0.006 grdn:0.161 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 503 |
+
INFO 2025-05-02 21:09:34 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.28 loss:0.006 grdn:0.150 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 504 |
+
INFO 2025-05-02 21:10:59 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.31 loss:0.006 grdn:0.150 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 505 |
+
INFO 2025-05-02 21:12:24 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.33 loss:0.006 grdn:0.149 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 506 |
+
INFO 2025-05-02 21:13:49 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.36 loss:0.006 grdn:0.170 lr:5.4e-05 updt_s:0.423 data_s:0.000
|
| 507 |
+
INFO 2025-05-02 21:15:14 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.39 loss:0.006 grdn:0.153 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 508 |
+
INFO 2025-05-02 21:16:39 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.42 loss:0.006 grdn:0.156 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 509 |
+
INFO 2025-05-02 21:18:04 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.44 loss:0.006 grdn:0.153 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 510 |
+
INFO 2025-05-02 21:19:29 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.47 loss:0.005 grdn:0.141 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 511 |
+
INFO 2025-05-02 21:20:54 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.50 loss:0.006 grdn:0.155 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 512 |
+
INFO 2025-05-02 21:22:19 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.53 loss:0.005 grdn:0.150 lr:5.4e-05 updt_s:0.424 data_s:0.000
|
| 513 |
+
INFO 2025-05-02 21:23:45 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.55 loss:0.006 grdn:0.155 lr:5.4e-05 updt_s:0.423 data_s:0.008
|
| 514 |
+
INFO 2025-05-02 21:25:10 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.58 loss:0.006 grdn:0.159 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 515 |
+
INFO 2025-05-02 21:26:35 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.61 loss:0.005 grdn:0.145 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 516 |
+
INFO 2025-05-02 21:28:00 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.64 loss:0.005 grdn:0.143 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 517 |
+
INFO 2025-05-02 21:29:25 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.66 loss:0.006 grdn:0.157 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 518 |
+
INFO 2025-05-02 21:30:50 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.69 loss:0.005 grdn:0.143 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 519 |
+
INFO 2025-05-02 21:32:15 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.72 loss:0.006 grdn:0.154 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 520 |
+
INFO 2025-05-02 21:33:40 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.75 loss:0.005 grdn:0.146 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 521 |
+
INFO 2025-05-02 21:35:05 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.77 loss:0.006 grdn:0.155 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 522 |
+
INFO 2025-05-02 21:36:30 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.80 loss:0.005 grdn:0.148 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 523 |
+
INFO 2025-05-02 21:37:55 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.83 loss:0.005 grdn:0.144 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 524 |
+
INFO 2025-05-02 21:39:20 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.86 loss:0.005 grdn:0.151 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 525 |
+
INFO 2025-05-02 21:40:45 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.88 loss:0.006 grdn:0.158 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 526 |
+
INFO 2025-05-02 21:42:10 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.91 loss:0.005 grdn:0.155 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 527 |
+
INFO 2025-05-02 21:43:35 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.94 loss:0.005 grdn:0.142 lr:5.3e-05 updt_s:0.423 data_s:0.000
|
| 528 |
+
INFO 2025-05-02 21:45:00 ts/train.py:232 step:233K smpl:2M ep:6K epch:31.97 loss:0.005 grdn:0.147 lr:5.3e-05 updt_s:0.424 data_s:0.000
|
| 529 |
+
INFO 2025-05-02 21:46:25 ts/train.py:232 step:233K smpl:2M ep:6K epch:31.99 loss:0.006 grdn:0.153 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 530 |
+
INFO 2025-05-02 21:47:50 ts/train.py:232 step:233K smpl:2M ep:6K epch:32.02 loss:0.006 grdn:0.152 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 531 |
+
INFO 2025-05-02 21:49:15 ts/train.py:232 step:233K smpl:2M ep:6K epch:32.05 loss:0.006 grdn:0.155 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 532 |
+
INFO 2025-05-02 21:50:40 ts/train.py:232 step:233K smpl:2M ep:6K epch:32.08 loss:0.006 grdn:0.153 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 533 |
+
INFO 2025-05-02 21:52:05 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.10 loss:0.005 grdn:0.152 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 534 |
+
INFO 2025-05-02 21:53:30 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.13 loss:0.005 grdn:0.150 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 535 |
+
INFO 2025-05-02 21:54:55 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.16 loss:0.005 grdn:0.137 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 536 |
+
INFO 2025-05-02 21:56:20 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.19 loss:0.006 grdn:0.156 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 537 |
+
INFO 2025-05-02 21:57:45 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.21 loss:0.005 grdn:0.146 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 538 |
+
INFO 2025-05-02 21:59:10 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.24 loss:0.005 grdn:0.142 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 539 |
+
INFO 2025-05-02 22:00:35 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.27 loss:0.006 grdn:0.153 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 540 |
+
INFO 2025-05-02 22:02:00 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.30 loss:0.006 grdn:0.155 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 541 |
+
INFO 2025-05-02 22:03:25 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.32 loss:0.006 grdn:0.161 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 542 |
+
INFO 2025-05-02 22:04:50 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.35 loss:0.006 grdn:0.149 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 543 |
+
INFO 2025-05-02 22:06:15 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.38 loss:0.005 grdn:0.141 lr:5.2e-05 updt_s:0.424 data_s:0.000
|
| 544 |
+
INFO 2025-05-02 22:07:40 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.41 loss:0.006 grdn:0.154 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 545 |
+
INFO 2025-05-02 22:09:05 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.43 loss:0.005 grdn:0.144 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 546 |
+
INFO 2025-05-02 22:10:30 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.46 loss:0.006 grdn:0.151 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 547 |
+
INFO 2025-05-02 22:11:55 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.49 loss:0.005 grdn:0.152 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 548 |
+
INFO 2025-05-02 22:13:21 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.52 loss:0.005 grdn:0.149 lr:5.1e-05 updt_s:0.423 data_s:0.008
|
| 549 |
+
INFO 2025-05-02 22:14:46 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.54 loss:0.005 grdn:0.148 lr:5.1e-05 updt_s:0.425 data_s:0.000
|
| 550 |
+
INFO 2025-05-02 22:16:11 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.57 loss:0.005 grdn:0.151 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 551 |
+
INFO 2025-05-02 22:17:36 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.60 loss:0.006 grdn:0.152 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 552 |
+
INFO 2025-05-02 22:19:01 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.63 loss:0.005 grdn:0.150 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 553 |
+
INFO 2025-05-02 22:20:26 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.65 loss:0.006 grdn:0.157 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 554 |
+
INFO 2025-05-02 22:21:51 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.68 loss:0.005 grdn:0.147 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 555 |
+
INFO 2025-05-02 22:23:16 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.71 loss:0.006 grdn:0.155 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 556 |
+
INFO 2025-05-02 22:24:41 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.73 loss:0.005 grdn:0.148 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 557 |
+
INFO 2025-05-02 22:26:06 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.76 loss:0.006 grdn:0.162 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 558 |
+
INFO 2025-05-02 22:27:31 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.79 loss:0.005 grdn:0.152 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 559 |
+
INFO 2025-05-02 22:28:56 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.82 loss:0.006 grdn:0.161 lr:5.1e-05 updt_s:0.424 data_s:0.000
|
| 560 |
+
INFO 2025-05-02 22:30:21 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.84 loss:0.005 grdn:0.155 lr:5.0e-05 updt_s:0.423 data_s:0.000
|
| 561 |
+
INFO 2025-05-02 22:31:46 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.87 loss:0.005 grdn:0.141 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 562 |
+
INFO 2025-05-02 22:33:11 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.90 loss:0.005 grdn:0.156 lr:5.0e-05 updt_s:0.423 data_s:0.000
|
| 563 |
+
INFO 2025-05-02 22:34:36 ts/train.py:232 step:240K smpl:2M ep:7K epch:32.93 loss:0.005 grdn:0.149 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 564 |
+
INFO 2025-05-02 22:36:01 ts/train.py:232 step:240K smpl:2M ep:7K epch:32.95 loss:0.005 grdn:0.147 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 565 |
+
INFO 2025-05-02 22:37:25 ts/train.py:232 step:240K smpl:2M ep:7K epch:32.98 loss:0.006 grdn:0.154 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 566 |
+
INFO 2025-05-02 22:37:25 ts/train.py:241 Checkpoint policy after step 240000
|
| 567 |
+
INFO 2025-05-02 22:38:53 ts/train.py:232 step:240K smpl:2M ep:7K epch:33.01 loss:0.006 grdn:0.156 lr:5.0e-05 updt_s:0.423 data_s:0.000
|
| 568 |
+
INFO 2025-05-02 22:40:18 ts/train.py:232 step:240K smpl:2M ep:7K epch:33.04 loss:0.006 grdn:0.150 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 569 |
+
INFO 2025-05-02 22:41:43 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.06 loss:0.005 grdn:0.147 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 570 |
+
INFO 2025-05-02 22:43:08 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.09 loss:0.006 grdn:0.155 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 571 |
+
INFO 2025-05-02 22:44:33 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.12 loss:0.006 grdn:0.151 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 572 |
+
INFO 2025-05-02 22:45:58 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.15 loss:0.005 grdn:0.147 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 573 |
+
INFO 2025-05-02 22:47:23 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.17 loss:0.005 grdn:0.143 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 574 |
+
INFO 2025-05-02 22:48:48 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.20 loss:0.005 grdn:0.153 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 575 |
+
INFO 2025-05-02 22:50:13 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.23 loss:0.005 grdn:0.140 lr:5.0e-05 updt_s:0.424 data_s:0.000
|
| 576 |
+
INFO 2025-05-02 22:51:38 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.26 loss:0.005 grdn:0.155 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 577 |
+
INFO 2025-05-02 22:53:03 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.28 loss:0.006 grdn:0.159 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 578 |
+
INFO 2025-05-02 22:54:28 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.31 loss:0.005 grdn:0.151 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 579 |
+
INFO 2025-05-02 22:55:53 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.34 loss:0.006 grdn:0.160 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 580 |
+
INFO 2025-05-02 22:57:18 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.37 loss:0.005 grdn:0.144 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 581 |
+
INFO 2025-05-02 22:58:43 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.39 loss:0.005 grdn:0.154 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 582 |
+
INFO 2025-05-02 23:00:08 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.42 loss:0.006 grdn:0.162 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 583 |
+
INFO 2025-05-02 23:01:33 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.45 loss:0.005 grdn:0.145 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 584 |
+
INFO 2025-05-02 23:02:57 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.48 loss:0.005 grdn:0.150 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 585 |
+
INFO 2025-05-02 23:04:24 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.50 loss:0.005 grdn:0.149 lr:4.9e-05 updt_s:0.423 data_s:0.008
|
| 586 |
+
INFO 2025-05-02 23:05:49 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.53 loss:0.005 grdn:0.143 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 587 |
+
INFO 2025-05-02 23:07:14 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.56 loss:0.005 grdn:0.152 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 588 |
+
INFO 2025-05-02 23:08:39 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.59 loss:0.005 grdn:0.144 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 589 |
+
INFO 2025-05-02 23:10:04 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.61 loss:0.005 grdn:0.154 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 590 |
+
INFO 2025-05-02 23:11:29 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.64 loss:0.006 grdn:0.165 lr:4.9e-05 updt_s:0.424 data_s:0.000
|
| 591 |
+
INFO 2025-05-02 23:12:54 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.67 loss:0.005 grdn:0.146 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 592 |
+
INFO 2025-05-02 23:14:19 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.70 loss:0.005 grdn:0.151 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 593 |
+
INFO 2025-05-02 23:15:44 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.72 loss:0.005 grdn:0.135 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 594 |
+
INFO 2025-05-02 23:17:09 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.75 loss:0.005 grdn:0.154 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 595 |
+
INFO 2025-05-02 23:18:34 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.78 loss:0.005 grdn:0.153 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 596 |
+
INFO 2025-05-02 23:19:59 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.81 loss:0.005 grdn:0.144 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 597 |
+
INFO 2025-05-02 23:21:24 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.83 loss:0.006 grdn:0.155 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 598 |
+
INFO 2025-05-02 23:22:49 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.86 loss:0.005 grdn:0.152 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 599 |
+
INFO 2025-05-02 23:24:14 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.89 loss:0.005 grdn:0.152 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 600 |
+
INFO 2025-05-02 23:25:39 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.92 loss:0.005 grdn:0.145 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 601 |
+
INFO 2025-05-02 23:27:04 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.94 loss:0.005 grdn:0.149 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 602 |
+
INFO 2025-05-02 23:28:29 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.97 loss:0.005 grdn:0.137 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 603 |
+
INFO 2025-05-02 23:29:54 ts/train.py:232 step:247K smpl:2M ep:7K epch:34.00 loss:0.005 grdn:0.146 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 604 |
+
INFO 2025-05-02 23:31:19 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.03 loss:0.005 grdn:0.147 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 605 |
+
INFO 2025-05-02 23:32:44 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.05 loss:0.006 grdn:0.159 lr:4.8e-05 updt_s:0.424 data_s:0.000
|
| 606 |
+
INFO 2025-05-02 23:34:09 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.08 loss:0.005 grdn:0.147 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 607 |
+
INFO 2025-05-02 23:35:34 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.11 loss:0.005 grdn:0.149 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 608 |
+
INFO 2025-05-02 23:36:59 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.14 loss:0.005 grdn:0.146 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 609 |
+
INFO 2025-05-02 23:38:24 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.16 loss:0.006 grdn:0.160 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 610 |
+
INFO 2025-05-02 23:39:49 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.19 loss:0.005 grdn:0.146 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 611 |
+
INFO 2025-05-02 23:41:14 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.22 loss:0.006 grdn:0.151 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 612 |
+
INFO 2025-05-02 23:42:38 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.25 loss:0.005 grdn:0.140 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 613 |
+
INFO 2025-05-02 23:44:03 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.27 loss:0.005 grdn:0.155 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 614 |
+
INFO 2025-05-02 23:45:28 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.30 loss:0.005 grdn:0.144 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 615 |
+
INFO 2025-05-02 23:46:53 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.33 loss:0.005 grdn:0.155 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 616 |
+
INFO 2025-05-02 23:48:18 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.36 loss:0.006 grdn:0.160 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 617 |
+
INFO 2025-05-02 23:49:43 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.38 loss:0.005 grdn:0.156 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 618 |
+
INFO 2025-05-02 23:51:08 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.41 loss:0.005 grdn:0.144 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 619 |
+
INFO 2025-05-02 23:52:33 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.44 loss:0.005 grdn:0.142 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 620 |
+
INFO 2025-05-02 23:54:00 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.47 loss:0.005 grdn:0.148 lr:4.7e-05 updt_s:0.423 data_s:0.007
|
| 621 |
+
INFO 2025-05-02 23:55:25 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.49 loss:0.005 grdn:0.153 lr:4.7e-05 updt_s:0.424 data_s:0.000
|
| 622 |
+
INFO 2025-05-02 23:56:50 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.52 loss:0.005 grdn:0.147 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 623 |
+
INFO 2025-05-02 23:58:15 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.55 loss:0.005 grdn:0.151 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 624 |
+
INFO 2025-05-02 23:59:39 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.58 loss:0.005 grdn:0.150 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 625 |
+
INFO 2025-05-03 00:01:04 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.60 loss:0.005 grdn:0.157 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 626 |
+
INFO 2025-05-03 00:02:29 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.63 loss:0.004 grdn:0.139 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 627 |
+
INFO 2025-05-03 00:03:54 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.66 loss:0.005 grdn:0.148 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 628 |
+
INFO 2025-05-03 00:05:19 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.69 loss:0.005 grdn:0.147 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 629 |
+
INFO 2025-05-03 00:06:44 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.71 loss:0.005 grdn:0.151 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 630 |
+
INFO 2025-05-03 00:08:09 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.74 loss:0.005 grdn:0.151 lr:4.6e-05 updt_s:0.423 data_s:0.000
|
| 631 |
+
INFO 2025-05-03 00:09:34 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.77 loss:0.005 grdn:0.151 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 632 |
+
INFO 2025-05-03 00:10:59 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.80 loss:0.005 grdn:0.159 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 633 |
+
INFO 2025-05-03 00:12:24 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.82 loss:0.005 grdn:0.154 lr:4.6e-05 updt_s:0.423 data_s:0.000
|
| 634 |
+
INFO 2025-05-03 00:13:49 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.85 loss:0.005 grdn:0.154 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 635 |
+
INFO 2025-05-03 00:15:14 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.88 loss:0.004 grdn:0.138 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 636 |
+
INFO 2025-05-03 00:16:39 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.91 loss:0.005 grdn:0.151 lr:4.6e-05 updt_s:0.424 data_s:0.000
|
| 637 |
+
INFO 2025-05-03 00:18:04 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.93 loss:0.005 grdn:0.148 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 638 |
+
INFO 2025-05-03 00:19:29 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.96 loss:0.005 grdn:0.152 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 639 |
+
INFO 2025-05-03 00:20:54 ts/train.py:232 step:255K smpl:2M ep:7K epch:34.99 loss:0.005 grdn:0.148 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 640 |
+
INFO 2025-05-03 00:22:18 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.02 loss:0.005 grdn:0.146 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 641 |
+
INFO 2025-05-03 00:23:43 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.04 loss:0.005 grdn:0.156 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 642 |
+
INFO 2025-05-03 00:25:08 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.07 loss:0.005 grdn:0.154 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 643 |
+
INFO 2025-05-03 00:26:33 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.10 loss:0.005 grdn:0.150 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 644 |
+
INFO 2025-05-03 00:27:58 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.13 loss:0.005 grdn:0.147 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 645 |
+
INFO 2025-05-03 00:29:23 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.15 loss:0.005 grdn:0.153 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 646 |
+
INFO 2025-05-03 00:30:48 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.18 loss:0.004 grdn:0.137 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 647 |
+
INFO 2025-05-03 00:32:13 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.21 loss:0.005 grdn:0.158 lr:4.5e-05 updt_s:0.423 data_s:0.000
|
| 648 |
+
INFO 2025-05-03 00:33:38 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.24 loss:0.005 grdn:0.148 lr:4.5e-05 updt_s:0.423 data_s:0.000
|
| 649 |
+
INFO 2025-05-03 00:35:03 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.26 loss:0.005 grdn:0.148 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 650 |
+
INFO 2025-05-03 00:36:28 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.29 loss:0.005 grdn:0.155 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 651 |
+
INFO 2025-05-03 00:37:53 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.32 loss:0.005 grdn:0.142 lr:4.5e-05 updt_s:0.424 data_s:0.000
|
| 652 |
+
INFO 2025-05-03 00:39:18 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.35 loss:0.005 grdn:0.148 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 653 |
+
INFO 2025-05-03 00:40:43 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.37 loss:0.005 grdn:0.152 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 654 |
+
INFO 2025-05-03 00:42:08 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.40 loss:0.005 grdn:0.141 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 655 |
+
INFO 2025-05-03 00:43:33 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.43 loss:0.004 grdn:0.138 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 656 |
+
INFO 2025-05-03 00:44:59 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.46 loss:0.005 grdn:0.147 lr:4.4e-05 updt_s:0.423 data_s:0.008
|
| 657 |
+
INFO 2025-05-03 00:46:24 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.48 loss:0.005 grdn:0.154 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 658 |
+
INFO 2025-05-03 00:47:49 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.51 loss:0.005 grdn:0.157 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 659 |
+
INFO 2025-05-03 00:49:14 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.54 loss:0.005 grdn:0.156 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 660 |
+
INFO 2025-05-03 00:50:39 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.57 loss:0.006 grdn:0.159 lr:4.4e-05 updt_s:0.423 data_s:0.000
|
| 661 |
+
INFO 2025-05-03 00:52:04 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.59 loss:0.005 grdn:0.143 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 662 |
+
INFO 2025-05-03 00:53:29 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.62 loss:0.005 grdn:0.150 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 663 |
+
INFO 2025-05-03 00:54:54 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.65 loss:0.005 grdn:0.154 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 664 |
+
INFO 2025-05-03 00:56:19 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.68 loss:0.005 grdn:0.150 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 665 |
+
INFO 2025-05-03 00:57:44 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.70 loss:0.005 grdn:0.160 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 666 |
+
INFO 2025-05-03 00:59:09 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.73 loss:0.005 grdn:0.152 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 667 |
+
INFO 2025-05-03 01:00:33 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.76 loss:0.005 grdn:0.152 lr:4.4e-05 updt_s:0.424 data_s:0.000
|
| 668 |
+
INFO 2025-05-03 01:01:58 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.79 loss:0.005 grdn:0.157 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 669 |
+
INFO 2025-05-03 01:03:23 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.81 loss:0.005 grdn:0.149 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 670 |
+
INFO 2025-05-03 01:04:48 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.84 loss:0.005 grdn:0.149 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 671 |
+
INFO 2025-05-03 01:06:13 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.87 loss:0.005 grdn:0.157 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 672 |
+
INFO 2025-05-03 01:07:38 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.90 loss:0.005 grdn:0.147 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 673 |
+
INFO 2025-05-03 01:09:03 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.92 loss:0.005 grdn:0.160 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 674 |
+
INFO 2025-05-03 01:10:28 ts/train.py:232 step:262K smpl:2M ep:7K epch:35.95 loss:0.005 grdn:0.142 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 675 |
+
INFO 2025-05-03 01:11:53 ts/train.py:232 step:262K smpl:2M ep:7K epch:35.98 loss:0.005 grdn:0.162 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 676 |
+
INFO 2025-05-03 01:13:18 ts/train.py:232 step:262K smpl:2M ep:7K epch:36.01 loss:0.005 grdn:0.146 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 677 |
+
INFO 2025-05-03 01:14:43 ts/train.py:232 step:262K smpl:2M ep:7K epch:36.03 loss:0.004 grdn:0.148 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 678 |
+
INFO 2025-05-03 01:16:08 ts/train.py:232 step:262K smpl:2M ep:7K epch:36.06 loss:0.005 grdn:0.155 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 679 |
+
INFO 2025-05-03 01:17:33 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.09 loss:0.005 grdn:0.150 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 680 |
+
INFO 2025-05-03 01:18:58 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.12 loss:0.005 grdn:0.154 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 681 |
+
INFO 2025-05-03 01:20:23 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.14 loss:0.005 grdn:0.148 lr:4.3e-05 updt_s:0.423 data_s:0.000
|
| 682 |
+
INFO 2025-05-03 01:21:48 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.17 loss:0.005 grdn:0.160 lr:4.3e-05 updt_s:0.424 data_s:0.000
|
| 683 |
+
INFO 2025-05-03 01:23:13 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.20 loss:0.005 grdn:0.142 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 684 |
+
INFO 2025-05-03 01:24:38 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.23 loss:0.005 grdn:0.158 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 685 |
+
INFO 2025-05-03 01:26:03 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.25 loss:0.004 grdn:0.142 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 686 |
+
INFO 2025-05-03 01:27:28 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.28 loss:0.005 grdn:0.149 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 687 |
+
INFO 2025-05-03 01:28:53 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.31 loss:0.004 grdn:0.144 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 688 |
+
INFO 2025-05-03 01:30:18 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.34 loss:0.005 grdn:0.147 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 689 |
+
INFO 2025-05-03 01:31:43 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.36 loss:0.004 grdn:0.139 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 690 |
+
INFO 2025-05-03 01:33:08 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.39 loss:0.005 grdn:0.153 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 691 |
+
INFO 2025-05-03 01:34:34 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.42 loss:0.005 grdn:0.153 lr:4.2e-05 updt_s:0.423 data_s:0.009
|
| 692 |
+
INFO 2025-05-03 01:35:59 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.45 loss:0.005 grdn:0.160 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 693 |
+
INFO 2025-05-03 01:37:24 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.47 loss:0.005 grdn:0.142 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 694 |
+
INFO 2025-05-03 01:38:49 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.50 loss:0.005 grdn:0.155 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 695 |
+
INFO 2025-05-03 01:40:14 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.53 loss:0.005 grdn:0.147 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 696 |
+
INFO 2025-05-03 01:41:39 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.56 loss:0.005 grdn:0.148 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 697 |
+
INFO 2025-05-03 01:43:04 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.58 loss:0.005 grdn:0.149 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 698 |
+
INFO 2025-05-03 01:44:29 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.61 loss:0.005 grdn:0.152 lr:4.2e-05 updt_s:0.424 data_s:0.000
|
| 699 |
+
INFO 2025-05-03 01:45:54 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.64 loss:0.005 grdn:0.148 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 700 |
+
INFO 2025-05-03 01:47:19 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.67 loss:0.005 grdn:0.144 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 701 |
+
INFO 2025-05-03 01:48:44 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.69 loss:0.005 grdn:0.157 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 702 |
+
INFO 2025-05-03 01:50:09 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.72 loss:0.005 grdn:0.157 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 703 |
+
INFO 2025-05-03 01:51:33 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.75 loss:0.004 grdn:0.140 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 704 |
+
INFO 2025-05-03 01:52:58 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.78 loss:0.005 grdn:0.151 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 705 |
+
INFO 2025-05-03 01:54:23 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.80 loss:0.005 grdn:0.152 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 706 |
+
INFO 2025-05-03 01:55:48 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.83 loss:0.005 grdn:0.160 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 707 |
+
INFO 2025-05-03 01:57:13 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.86 loss:0.005 grdn:0.148 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 708 |
+
INFO 2025-05-03 01:58:38 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.89 loss:0.005 grdn:0.150 lr:4.1e-05 updt_s:0.423 data_s:0.000
|
| 709 |
+
INFO 2025-05-03 02:00:03 ts/train.py:232 step:269K smpl:2M ep:7K epch:36.91 loss:0.004 grdn:0.144 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 710 |
+
INFO 2025-05-03 02:01:28 ts/train.py:232 step:269K smpl:2M ep:7K epch:36.94 loss:0.005 grdn:0.147 lr:4.1e-05 updt_s:0.423 data_s:0.000
|
| 711 |
+
INFO 2025-05-03 02:02:53 ts/train.py:232 step:269K smpl:2M ep:7K epch:36.97 loss:0.005 grdn:0.155 lr:4.1e-05 updt_s:0.423 data_s:0.000
|
| 712 |
+
INFO 2025-05-03 02:04:18 ts/train.py:232 step:269K smpl:2M ep:7K epch:37.00 loss:0.005 grdn:0.147 lr:4.1e-05 updt_s:0.424 data_s:0.000
|
| 713 |
+
INFO 2025-05-03 02:05:42 ts/train.py:232 step:269K smpl:2M ep:7K epch:37.02 loss:0.004 grdn:0.145 lr:4.1e-05 updt_s:0.423 data_s:0.000
|
| 714 |
+
INFO 2025-05-03 02:07:07 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.05 loss:0.005 grdn:0.151 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 715 |
+
INFO 2025-05-03 02:08:32 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.08 loss:0.005 grdn:0.147 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 716 |
+
INFO 2025-05-03 02:09:57 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.11 loss:0.005 grdn:0.156 lr:4.0e-05 updt_s:0.423 data_s:0.000
|
| 717 |
+
INFO 2025-05-03 02:11:22 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.13 loss:0.005 grdn:0.156 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 718 |
+
INFO 2025-05-03 02:12:47 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.16 loss:0.005 grdn:0.154 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 719 |
+
INFO 2025-05-03 02:14:12 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.19 loss:0.004 grdn:0.148 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 720 |
+
INFO 2025-05-03 02:15:37 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.22 loss:0.005 grdn:0.160 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 721 |
+
INFO 2025-05-03 02:17:02 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.24 loss:0.006 grdn:0.168 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 722 |
+
INFO 2025-05-03 02:18:27 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.27 loss:0.005 grdn:0.147 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 723 |
+
INFO 2025-05-03 02:19:51 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.30 loss:0.004 grdn:0.141 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 724 |
+
INFO 2025-05-03 02:21:16 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.32 loss:0.005 grdn:0.143 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 725 |
+
INFO 2025-05-03 02:22:41 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.35 loss:0.004 grdn:0.139 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 726 |
+
INFO 2025-05-03 02:24:06 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.38 loss:0.005 grdn:0.150 lr:4.0e-05 updt_s:0.424 data_s:0.000
|
| 727 |
+
INFO 2025-05-03 02:25:32 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.41 loss:0.005 grdn:0.150 lr:4.0e-05 updt_s:0.423 data_s:0.008
|
| 728 |
+
INFO 2025-05-03 02:26:57 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.43 loss:0.004 grdn:0.138 lr:4.0e-05 updt_s:0.423 data_s:0.000
|
| 729 |
+
INFO 2025-05-03 02:28:22 ts/train.py:232 step:273K smpl:2M ep:7K epch:37.46 loss:0.005 grdn:0.155 lr:4.0e-05 updt_s:0.423 data_s:0.000
|
| 730 |
+
INFO 2025-05-03 02:29:47 ts/train.py:232 step:273K smpl:2M ep:7K epch:37.49 loss:0.005 grdn:0.151 lr:3.9e-05 updt_s:0.424 data_s:0.000
|
| 731 |
+
INFO 2025-05-03 02:31:12 ts/train.py:232 step:273K smpl:2M ep:8K epch:37.52 loss:0.005 grdn:0.156 lr:3.9e-05 updt_s:0.424 data_s:0.000
|
| 732 |
+
INFO 2025-05-03 02:32:37 ts/train.py:232 step:273K smpl:2M ep:8K epch:37.54 loss:0.004 grdn:0.145 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 733 |
+
INFO 2025-05-03 02:34:02 ts/train.py:232 step:273K smpl:2M ep:8K epch:37.57 loss:0.004 grdn:0.143 lr:3.9e-05 updt_s:0.424 data_s:0.000
|
| 734 |
+
INFO 2025-05-03 02:35:27 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.60 loss:0.004 grdn:0.149 lr:3.9e-05 updt_s:0.424 data_s:0.000
|
| 735 |
+
INFO 2025-05-03 02:36:51 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.63 loss:0.005 grdn:0.159 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 736 |
+
INFO 2025-05-03 02:38:16 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.65 loss:0.005 grdn:0.153 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 737 |
+
INFO 2025-05-03 02:39:41 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.68 loss:0.005 grdn:0.150 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 738 |
+
INFO 2025-05-03 02:41:06 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.71 loss:0.005 grdn:0.155 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 739 |
+
INFO 2025-05-03 02:42:31 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.74 loss:0.005 grdn:0.162 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 740 |
+
INFO 2025-05-03 02:43:56 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.76 loss:0.005 grdn:0.159 lr:3.9e-05 updt_s:0.424 data_s:0.000
|
| 741 |
+
INFO 2025-05-03 02:45:21 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.79 loss:0.006 grdn:0.157 lr:3.9e-05 updt_s:0.424 data_s:0.000
|
| 742 |
+
INFO 2025-05-03 02:46:46 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.82 loss:0.004 grdn:0.142 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 743 |
+
INFO 2025-05-03 02:48:10 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.85 loss:0.005 grdn:0.150 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 744 |
+
INFO 2025-05-03 02:49:35 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.87 loss:0.005 grdn:0.153 lr:3.9e-05 updt_s:0.423 data_s:0.000
|
| 745 |
+
INFO 2025-05-03 02:51:00 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.90 loss:0.005 grdn:0.159 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 746 |
+
INFO 2025-05-03 02:52:25 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.93 loss:0.005 grdn:0.157 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 747 |
+
INFO 2025-05-03 02:53:50 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.96 loss:0.005 grdn:0.148 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 748 |
+
INFO 2025-05-03 02:55:15 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.98 loss:0.005 grdn:0.154 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 749 |
+
INFO 2025-05-03 02:56:40 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.01 loss:0.005 grdn:0.155 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 750 |
+
INFO 2025-05-03 02:58:04 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.04 loss:0.005 grdn:0.149 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 751 |
+
INFO 2025-05-03 02:59:29 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.07 loss:0.005 grdn:0.144 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 752 |
+
INFO 2025-05-03 03:00:54 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.09 loss:0.005 grdn:0.160 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 753 |
+
INFO 2025-05-03 03:02:19 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.12 loss:0.004 grdn:0.149 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 754 |
+
INFO 2025-05-03 03:03:44 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.15 loss:0.005 grdn:0.151 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 755 |
+
INFO 2025-05-03 03:05:09 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.18 loss:0.005 grdn:0.156 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 756 |
+
INFO 2025-05-03 03:06:34 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.20 loss:0.005 grdn:0.163 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 757 |
+
INFO 2025-05-03 03:07:59 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.23 loss:0.004 grdn:0.140 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 758 |
+
INFO 2025-05-03 03:09:23 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.26 loss:0.005 grdn:0.154 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 759 |
+
INFO 2025-05-03 03:10:48 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.29 loss:0.004 grdn:0.149 lr:3.8e-05 updt_s:0.424 data_s:0.000
|
| 760 |
+
INFO 2025-05-03 03:12:13 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.31 loss:0.004 grdn:0.142 lr:3.8e-05 updt_s:0.423 data_s:0.000
|
| 761 |
+
INFO 2025-05-03 03:13:38 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.34 loss:0.005 grdn:0.152 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 762 |
+
INFO 2025-05-03 03:15:05 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.37 loss:0.004 grdn:0.152 lr:3.7e-05 updt_s:0.423 data_s:0.008
|
| 763 |
+
INFO 2025-05-03 03:16:30 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.40 loss:0.005 grdn:0.153 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 764 |
+
INFO 2025-05-03 03:17:55 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.42 loss:0.005 grdn:0.146 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 765 |
+
INFO 2025-05-03 03:19:19 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.45 loss:0.004 grdn:0.137 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 766 |
+
INFO 2025-05-03 03:20:44 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.48 loss:0.005 grdn:0.149 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 767 |
+
INFO 2025-05-03 03:22:09 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.51 loss:0.005 grdn:0.152 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 768 |
+
INFO 2025-05-03 03:23:34 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.53 loss:0.005 grdn:0.151 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 769 |
+
INFO 2025-05-03 03:24:59 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.56 loss:0.004 grdn:0.145 lr:3.7e-05 updt_s:0.423 data_s:0.000
|
| 770 |
+
INFO 2025-05-03 03:26:24 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.59 loss:0.004 grdn:0.147 lr:3.7e-05 updt_s:0.423 data_s:0.000
|
| 771 |
+
INFO 2025-05-03 03:27:49 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.62 loss:0.005 grdn:0.151 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 772 |
+
INFO 2025-05-03 03:29:14 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.64 loss:0.005 grdn:0.159 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 773 |
+
INFO 2025-05-03 03:30:39 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.67 loss:0.004 grdn:0.151 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 774 |
+
INFO 2025-05-03 03:32:04 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.70 loss:0.004 grdn:0.135 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 775 |
+
INFO 2025-05-03 03:33:29 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.73 loss:0.004 grdn:0.146 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 776 |
+
INFO 2025-05-03 03:34:54 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.75 loss:0.005 grdn:0.158 lr:3.7e-05 updt_s:0.424 data_s:0.000
|
| 777 |
+
INFO 2025-05-03 03:36:19 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.78 loss:0.005 grdn:0.169 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 778 |
+
INFO 2025-05-03 03:37:44 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.81 loss:0.005 grdn:0.157 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 779 |
+
INFO 2025-05-03 03:39:09 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.84 loss:0.005 grdn:0.156 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 780 |
+
INFO 2025-05-03 03:40:33 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.86 loss:0.004 grdn:0.148 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 781 |
+
INFO 2025-05-03 03:41:58 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.89 loss:0.004 grdn:0.141 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 782 |
+
INFO 2025-05-03 03:43:23 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.92 loss:0.004 grdn:0.146 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 783 |
+
INFO 2025-05-03 03:44:48 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.95 loss:0.004 grdn:0.140 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 784 |
+
INFO 2025-05-03 03:46:13 ts/train.py:232 step:284K smpl:2M ep:8K epch:38.97 loss:0.004 grdn:0.145 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 785 |
+
INFO 2025-05-03 03:47:38 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.00 loss:0.004 grdn:0.150 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 786 |
+
INFO 2025-05-03 03:49:03 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.03 loss:0.005 grdn:0.157 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 787 |
+
INFO 2025-05-03 03:50:28 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.06 loss:0.004 grdn:0.157 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 788 |
+
INFO 2025-05-03 03:51:52 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.08 loss:0.005 grdn:0.158 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 789 |
+
INFO 2025-05-03 03:53:17 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.11 loss:0.004 grdn:0.148 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 790 |
+
INFO 2025-05-03 03:54:42 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.14 loss:0.004 grdn:0.142 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 791 |
+
INFO 2025-05-03 03:56:07 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.17 loss:0.004 grdn:0.147 lr:3.6e-05 updt_s:0.423 data_s:0.000
|
| 792 |
+
INFO 2025-05-03 03:57:32 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.19 loss:0.005 grdn:0.160 lr:3.6e-05 updt_s:0.424 data_s:0.000
|
| 793 |
+
INFO 2025-05-03 03:58:57 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.22 loss:0.005 grdn:0.160 lr:3.5e-05 updt_s:0.423 data_s:0.000
|
| 794 |
+
INFO 2025-05-03 04:00:22 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.25 loss:0.005 grdn:0.153 lr:3.5e-05 updt_s:0.423 data_s:0.000
|
| 795 |
+
INFO 2025-05-03 04:01:47 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.28 loss:0.004 grdn:0.144 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 796 |
+
INFO 2025-05-03 04:03:12 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.30 loss:0.004 grdn:0.154 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 797 |
+
INFO 2025-05-03 04:04:36 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.33 loss:0.004 grdn:0.148 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 798 |
+
INFO 2025-05-03 04:06:03 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.36 loss:0.004 grdn:0.145 lr:3.5e-05 updt_s:0.423 data_s:0.008
|
| 799 |
+
INFO 2025-05-03 04:07:28 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.39 loss:0.004 grdn:0.149 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 800 |
+
INFO 2025-05-03 04:08:53 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.41 loss:0.004 grdn:0.152 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 801 |
+
INFO 2025-05-03 04:10:18 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.44 loss:0.005 grdn:0.159 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 802 |
+
INFO 2025-05-03 04:11:43 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.47 loss:0.004 grdn:0.148 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 803 |
+
INFO 2025-05-03 04:13:08 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.50 loss:0.004 grdn:0.139 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 804 |
+
INFO 2025-05-03 04:14:32 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.52 loss:0.005 grdn:0.151 lr:3.5e-05 updt_s:0.423 data_s:0.000
|
| 805 |
+
INFO 2025-05-03 04:15:57 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.55 loss:0.004 grdn:0.146 lr:3.5e-05 updt_s:0.423 data_s:0.000
|
| 806 |
+
INFO 2025-05-03 04:17:22 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.58 loss:0.004 grdn:0.148 lr:3.5e-05 updt_s:0.423 data_s:0.000
|
| 807 |
+
INFO 2025-05-03 04:18:47 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.61 loss:0.005 grdn:0.153 lr:3.5e-05 updt_s:0.424 data_s:0.000
|
| 808 |
+
INFO 2025-05-03 04:20:12 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.63 loss:0.004 grdn:0.142 lr:3.5e-05 updt_s:0.423 data_s:0.000
|
| 809 |
+
INFO 2025-05-03 04:21:37 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.66 loss:0.004 grdn:0.153 lr:3.4e-05 updt_s:0.424 data_s:0.000
|
| 810 |
+
INFO 2025-05-03 04:23:02 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.69 loss:0.005 grdn:0.157 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 811 |
+
INFO 2025-05-03 04:24:27 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.72 loss:0.005 grdn:0.172 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 812 |
+
INFO 2025-05-03 04:25:51 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.74 loss:0.005 grdn:0.165 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 813 |
+
INFO 2025-05-03 04:27:16 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.77 loss:0.004 grdn:0.145 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 814 |
+
INFO 2025-05-03 04:28:41 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.80 loss:0.004 grdn:0.145 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 815 |
+
INFO 2025-05-03 04:30:06 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.83 loss:0.005 grdn:0.161 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 816 |
+
INFO 2025-05-03 04:31:31 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.85 loss:0.004 grdn:0.146 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 817 |
+
INFO 2025-05-03 04:32:55 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.88 loss:0.004 grdn:0.151 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 818 |
+
INFO 2025-05-03 04:34:20 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.91 loss:0.004 grdn:0.140 lr:3.4e-05 updt_s:0.424 data_s:0.000
|
| 819 |
+
INFO 2025-05-03 04:35:45 ts/train.py:232 step:291K smpl:2M ep:8K epch:39.94 loss:0.005 grdn:0.153 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 820 |
+
INFO 2025-05-03 04:37:10 ts/train.py:232 step:291K smpl:2M ep:8K epch:39.96 loss:0.005 grdn:0.160 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 821 |
+
INFO 2025-05-03 04:38:35 ts/train.py:232 step:291K smpl:2M ep:8K epch:39.99 loss:0.004 grdn:0.151 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 822 |
+
INFO 2025-05-03 04:39:59 ts/train.py:232 step:291K smpl:2M ep:8K epch:40.02 loss:0.004 grdn:0.155 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 823 |
+
INFO 2025-05-03 04:41:24 ts/train.py:232 step:291K smpl:2M ep:8K epch:40.05 loss:0.004 grdn:0.155 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 824 |
+
INFO 2025-05-03 04:42:49 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.07 loss:0.004 grdn:0.146 lr:3.4e-05 updt_s:0.423 data_s:0.000
|
| 825 |
+
INFO 2025-05-03 04:44:14 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.10 loss:0.004 grdn:0.151 lr:3.3e-05 updt_s:0.423 data_s:0.000
|
| 826 |
+
INFO 2025-05-03 04:45:39 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.13 loss:0.005 grdn:0.160 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 827 |
+
INFO 2025-05-03 04:47:04 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.16 loss:0.004 grdn:0.152 lr:3.3e-05 updt_s:0.423 data_s:0.000
|
| 828 |
+
INFO 2025-05-03 04:48:29 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.18 loss:0.005 grdn:0.160 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 829 |
+
INFO 2025-05-03 04:49:54 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.21 loss:0.004 grdn:0.146 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 830 |
+
INFO 2025-05-03 04:51:19 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.24 loss:0.004 grdn:0.163 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 831 |
+
INFO 2025-05-03 04:52:43 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.27 loss:0.004 grdn:0.147 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 832 |
+
INFO 2025-05-03 04:54:08 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.29 loss:0.004 grdn:0.141 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 833 |
+
INFO 2025-05-03 04:55:35 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.32 loss:0.005 grdn:0.167 lr:3.3e-05 updt_s:0.423 data_s:0.008
|
| 834 |
+
INFO 2025-05-03 04:57:00 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.35 loss:0.004 grdn:0.159 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 835 |
+
INFO 2025-05-03 04:58:25 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.38 loss:0.004 grdn:0.146 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 836 |
+
INFO 2025-05-03 04:59:49 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.40 loss:0.004 grdn:0.154 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 837 |
+
INFO 2025-05-03 05:01:14 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.43 loss:0.004 grdn:0.150 lr:3.3e-05 updt_s:0.423 data_s:0.000
|
| 838 |
+
INFO 2025-05-03 05:02:39 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.46 loss:0.004 grdn:0.145 lr:3.3e-05 updt_s:0.423 data_s:0.000
|
| 839 |
+
INFO 2025-05-03 05:04:04 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.49 loss:0.004 grdn:0.148 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 840 |
+
INFO 2025-05-03 05:05:29 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.51 loss:0.004 grdn:0.149 lr:3.3e-05 updt_s:0.424 data_s:0.000
|
| 841 |
+
INFO 2025-05-03 05:06:54 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.54 loss:0.004 grdn:0.157 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 842 |
+
INFO 2025-05-03 05:08:19 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.57 loss:0.004 grdn:0.143 lr:3.2e-05 updt_s:0.423 data_s:0.000
|
| 843 |
+
INFO 2025-05-03 05:09:44 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.60 loss:0.004 grdn:0.151 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 844 |
+
INFO 2025-05-03 05:11:09 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.62 loss:0.004 grdn:0.152 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 845 |
+
INFO 2025-05-03 05:12:34 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.65 loss:0.004 grdn:0.144 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 846 |
+
INFO 2025-05-03 05:13:59 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.68 loss:0.004 grdn:0.152 lr:3.2e-05 updt_s:0.423 data_s:0.000
|
| 847 |
+
INFO 2025-05-03 05:15:23 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.71 loss:0.004 grdn:0.156 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 848 |
+
INFO 2025-05-03 05:16:48 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.73 loss:0.005 grdn:0.161 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 849 |
+
INFO 2025-05-03 05:18:13 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.76 loss:0.004 grdn:0.155 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 850 |
+
INFO 2025-05-03 05:19:38 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.79 loss:0.004 grdn:0.149 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 851 |
+
INFO 2025-05-03 05:21:03 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.82 loss:0.005 grdn:0.159 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 852 |
+
INFO 2025-05-03 05:22:28 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.84 loss:0.005 grdn:0.151 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 853 |
+
INFO 2025-05-03 05:23:53 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.87 loss:0.004 grdn:0.151 lr:3.2e-05 updt_s:0.424 data_s:0.000
|
| 854 |
+
INFO 2025-05-03 05:25:18 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.90 loss:0.004 grdn:0.150 lr:3.2e-05 updt_s:0.423 data_s:0.000
|
| 855 |
+
INFO 2025-05-03 05:26:43 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.93 loss:0.004 grdn:0.144 lr:3.2e-05 updt_s:0.423 data_s:0.000
|
| 856 |
+
INFO 2025-05-03 05:28:08 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.95 loss:0.004 grdn:0.140 lr:3.2e-05 updt_s:0.423 data_s:0.000
|
| 857 |
+
INFO 2025-05-03 05:29:33 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.98 loss:0.004 grdn:0.153 lr:3.1e-05 updt_s:0.423 data_s:0.000
|
| 858 |
+
INFO 2025-05-03 05:30:58 ts/train.py:232 step:298K smpl:2M ep:8K epch:41.01 loss:0.004 grdn:0.156 lr:3.1e-05 updt_s:0.423 data_s:0.000
|
| 859 |
+
INFO 2025-05-03 05:32:22 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.04 loss:0.004 grdn:0.149 lr:3.1e-05 updt_s:0.423 data_s:0.000
|
| 860 |
+
INFO 2025-05-03 05:33:47 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.06 loss:0.004 grdn:0.143 lr:3.1e-05 updt_s:0.423 data_s:0.000
|
| 861 |
+
INFO 2025-05-03 05:35:12 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.09 loss:0.004 grdn:0.150 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 862 |
+
INFO 2025-05-03 05:36:37 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.12 loss:0.005 grdn:0.161 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 863 |
+
INFO 2025-05-03 05:38:02 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.15 loss:0.004 grdn:0.147 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 864 |
+
INFO 2025-05-03 05:39:27 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.17 loss:0.004 grdn:0.156 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 865 |
+
INFO 2025-05-03 05:40:52 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.20 loss:0.004 grdn:0.155 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 866 |
+
INFO 2025-05-03 05:42:17 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.23 loss:0.004 grdn:0.143 lr:3.1e-05 updt_s:0.423 data_s:0.000
|
| 867 |
+
INFO 2025-05-03 05:42:17 ts/train.py:241 Checkpoint policy after step 300000
|
| 868 |
+
INFO 2025-05-03 05:43:45 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.26 loss:0.004 grdn:0.144 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 869 |
+
INFO 2025-05-03 05:45:10 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.28 loss:0.004 grdn:0.152 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 870 |
+
INFO 2025-05-03 05:46:36 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.31 loss:0.004 grdn:0.150 lr:3.1e-05 updt_s:0.423 data_s:0.009
|
| 871 |
+
INFO 2025-05-03 05:48:01 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.34 loss:0.004 grdn:0.150 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 872 |
+
INFO 2025-05-03 05:49:26 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.37 loss:0.005 grdn:0.160 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 873 |
+
INFO 2025-05-03 05:50:51 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.39 loss:0.004 grdn:0.148 lr:3.1e-05 updt_s:0.423 data_s:0.000
|
| 874 |
+
INFO 2025-05-03 05:52:16 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.42 loss:0.004 grdn:0.144 lr:3.1e-05 updt_s:0.424 data_s:0.000
|
| 875 |
+
INFO 2025-05-03 05:53:41 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.45 loss:0.004 grdn:0.158 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 876 |
+
INFO 2025-05-03 05:55:06 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.48 loss:0.004 grdn:0.155 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 877 |
+
INFO 2025-05-03 05:56:31 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.50 loss:0.004 grdn:0.155 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 878 |
+
INFO 2025-05-03 05:57:56 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.53 loss:0.004 grdn:0.159 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 879 |
+
INFO 2025-05-03 05:59:21 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.56 loss:0.004 grdn:0.149 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 880 |
+
INFO 2025-05-03 06:00:45 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.59 loss:0.004 grdn:0.152 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 881 |
+
INFO 2025-05-03 06:02:10 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.61 loss:0.004 grdn:0.158 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 882 |
+
INFO 2025-05-03 06:03:35 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.64 loss:0.004 grdn:0.149 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 883 |
+
INFO 2025-05-03 06:05:00 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.67 loss:0.004 grdn:0.145 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 884 |
+
INFO 2025-05-03 06:06:25 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.70 loss:0.004 grdn:0.161 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 885 |
+
INFO 2025-05-03 06:07:50 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.72 loss:0.005 grdn:0.161 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 886 |
+
INFO 2025-05-03 06:09:15 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.75 loss:0.005 grdn:0.164 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 887 |
+
INFO 2025-05-03 06:10:39 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.78 loss:0.004 grdn:0.151 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 888 |
+
INFO 2025-05-03 06:12:04 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.81 loss:0.004 grdn:0.150 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 889 |
+
INFO 2025-05-03 06:13:29 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.83 loss:0.004 grdn:0.146 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 890 |
+
INFO 2025-05-03 06:14:54 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.86 loss:0.004 grdn:0.147 lr:3.0e-05 updt_s:0.423 data_s:0.000
|
| 891 |
+
INFO 2025-05-03 06:16:19 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.89 loss:0.003 grdn:0.144 lr:3.0e-05 updt_s:0.424 data_s:0.000
|
| 892 |
+
INFO 2025-05-03 06:17:44 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.92 loss:0.004 grdn:0.153 lr:2.9e-05 updt_s:0.424 data_s:0.000
|
| 893 |
+
INFO 2025-05-03 06:19:09 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.94 loss:0.004 grdn:0.150 lr:2.9e-05 updt_s:0.423 data_s:0.000
|
| 894 |
+
INFO 2025-05-03 06:20:34 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.97 loss:0.004 grdn:0.157 lr:2.9e-05 updt_s:0.423 data_s:0.000
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/run-20250502_093744-xsemtuwn/logs/debug-internal.log
CHANGED
|
@@ -203,3 +203,9 @@
|
|
| 203 |
{"time":"2025-05-02T10:09:20.603310421Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 204 |
{"time":"2025-05-02T10:09:20.603375451Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 205 |
{"time":"2025-05-02T10:09:20.603437341Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 203 |
{"time":"2025-05-02T10:09:20.603310421Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 204 |
{"time":"2025-05-02T10:09:20.603375451Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 205 |
{"time":"2025-05-02T10:09:20.603437341Z","level":"WARN","msg":"handler: ignoring partial history record","step":134400,"current":134401}
|
| 206 |
+
{"time":"2025-05-02T16:46:15.908301608Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded (Client.Timeout exceeded while awaiting headers)"}
|
| 207 |
+
{"time":"2025-05-02T17:57:15.977600155Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
|
| 208 |
+
{"time":"2025-05-02T19:18:46.063853787Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
|
| 209 |
+
{"time":"2025-05-02T19:46:16.087716965Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded"}
|
| 210 |
+
{"time":"2025-05-02T19:49:31.089345225Z","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
|
| 211 |
+
{"time":"2025-05-02T21:32:03.806394741Z","level":"INFO","msg":"api: retrying HTTP error","status":502,"url":"https://api.wandb.ai/files/marchmelo0923-postech/lerobot/xsemtuwn/file_stream","body":"\n<html><head>\n<meta http-equiv=\"content-type\" content=\"text/html;charset=utf-8\">\n<title>502 Server Error</title>\n</head>\n<body text=#000000 bgcolor=#ffffff>\n<h1>Error: Server Error</h1>\n<h2>The server encountered a temporary error and could not complete your request.<p>Please try again in 30 seconds.</h2>\n<h2></h2>\n</body></html>\n"}
|
DP_cube_downDims1_cropNo_freeze0_64_64_ema0_1e-4/wandb/run-20250502_093744-xsemtuwn/run-xsemtuwn.wandb
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a867c351c8079d1f07714885d44060c4cacc46f8266310a0b5956fd166ab417f
|
| 3 |
+
size 16318464
|