Add DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4 from 8b1bacebd36d
Browse files- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/pretrained_model/config.json +94 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/pretrained_model/model.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/pretrained_model/train_config.json +204 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/optimizer_param_groups.json +331 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/optimizer_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/rng_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/scheduler_state.json +15 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/training_step.json +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/pretrained_model/config.json +94 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/pretrained_model/model.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/pretrained_model/train_config.json +204 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/training_state/optimizer_param_groups.json +331 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/training_state/optimizer_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/training_state/rng_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/training_state/scheduler_state.json +15 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/training_state/training_step.json +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/pretrained_model/config.json +94 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/pretrained_model/model.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/pretrained_model/train_config.json +204 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/optimizer_param_groups.json +331 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/optimizer_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/rng_state.safetensors +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/scheduler_state.json +15 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/training_step.json +3 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/debug-internal.log +1 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/run-20250502_093126-icmt0scg/files/output.log +652 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/run-20250502_093126-icmt0scg/logs/debug-internal.log +1 -0
- DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/run-20250502_093126-icmt0scg/run-icmt0scg.wandb +2 -2
DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/pretrained_model/config.json
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"optimizer_eps": 1e-08,
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/pretrained_model/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0f3e39677d4abdcdeed365e59118060903363c356ce76e29abe8574d87379fd
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size 369243880
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/pretrained_model/train_config.json
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@@ -0,0 +1,204 @@
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{
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|
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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| 190 |
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|
| 191 |
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|
| 192 |
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|
| 194 |
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| 196 |
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|
| 197 |
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| 198 |
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|
| 203 |
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|
| 204 |
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}
|
DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,331 @@
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/180000/training_state/training_step.json
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|
DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/240000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,331 @@
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/pretrained_model/model.safetensors
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|
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|
|
|
|
|
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|
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|
|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
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|
|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"dataset": {
|
| 3 |
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|
| 4 |
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|
| 5 |
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| 7 |
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|
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 17 |
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|
| 18 |
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| 19 |
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|
| 20 |
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| 21 |
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|
| 22 |
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| 23 |
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| 24 |
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| 25 |
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|
| 26 |
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| 27 |
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|
| 28 |
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| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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"VISUAL": "MEAN_STD",
|
| 73 |
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"STATE": "MIN_MAX",
|
| 74 |
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"ACTION": "MIN_MAX"
|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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"action": {
|
| 110 |
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"type": "ACTION",
|
| 111 |
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|
| 112 |
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6
|
| 113 |
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|
| 114 |
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|
| 115 |
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},
|
| 116 |
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"device": "cuda",
|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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"optimizer_betas": [
|
| 152 |
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0.95,
|
| 153 |
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0.999
|
| 154 |
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],
|
| 155 |
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"optimizer_eps": 1e-08,
|
| 156 |
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"optimizer_weight_decay": 1e-06,
|
| 157 |
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"scheduler_name": "cosine",
|
| 158 |
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"scheduler_warmup_steps": 500,
|
| 159 |
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"pre_resize_shape": null,
|
| 160 |
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"freeze_vision_backbone": false
|
| 161 |
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},
|
| 162 |
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"output_dir": "/SSD/LSY/lerobot_model/DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4",
|
| 163 |
+
"job_name": "DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4",
|
| 164 |
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"resume": true,
|
| 165 |
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"seed": 1000,
|
| 166 |
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|
| 167 |
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"batch_size": 8,
|
| 168 |
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"steps": 480000,
|
| 169 |
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"eval_freq": 20000,
|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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"use_policy_training_preset": true,
|
| 174 |
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"optimizer": {
|
| 175 |
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"type": "adam",
|
| 176 |
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"lr": 0.0001,
|
| 177 |
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"weight_decay": 1e-06,
|
| 178 |
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"grad_clip_norm": 10.0,
|
| 179 |
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"betas": [
|
| 180 |
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|
| 181 |
+
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|
| 182 |
+
],
|
| 183 |
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"eps": 1e-08
|
| 184 |
+
},
|
| 185 |
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|
| 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 |
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"notes": null,
|
| 201 |
+
"run_id": "icmt0scg",
|
| 202 |
+
"mode": null
|
| 203 |
+
}
|
| 204 |
+
}
|
DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/optimizer_param_groups.json
ADDED
|
@@ -0,0 +1,331 @@
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
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|
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|
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|
|
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|
| 1 |
+
[
|
| 2 |
+
{
|
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/checkpoints/300000/training_state/training_step.json
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/debug-internal.log
CHANGED
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/run-20250502_093126-icmt0scg/files/output.log
CHANGED
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@@ -249,3 +249,655 @@ INFO 2025-05-02 15:02:55 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.30 loss
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|
| 249 |
INFO 2025-05-02 15:04:19 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.32 loss:0.008 grdn:0.194 lr:7.0e-05 updt_s:0.422 data_s:0.000
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| 249 |
INFO 2025-05-02 15:04:19 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.32 loss:0.008 grdn:0.194 lr:7.0e-05 updt_s:0.422 data_s:0.000
|
| 250 |
INFO 2025-05-02 15:05:44 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.35 loss:0.008 grdn:0.196 lr:7.0e-05 updt_s:0.422 data_s:0.000
|
| 251 |
INFO 2025-05-02 15:07:08 ts/train.py:232 step:177K smpl:1M ep:5K epch:24.38 loss:0.008 grdn:0.191 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 252 |
+
INFO 2025-05-02 15:08:33 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.41 loss:0.008 grdn:0.180 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 253 |
+
INFO 2025-05-02 15:09:57 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.43 loss:0.008 grdn:0.190 lr:7.0e-05 updt_s:0.422 data_s:0.000
|
| 254 |
+
INFO 2025-05-02 15:11:22 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.46 loss:0.007 grdn:0.182 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 255 |
+
INFO 2025-05-02 15:12:47 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.49 loss:0.007 grdn:0.171 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 256 |
+
INFO 2025-05-02 15:14:11 ts/train.py:232 step:178K smpl:1M ep:5K epch:24.52 loss:0.008 grdn:0.179 lr:7.0e-05 updt_s:0.421 data_s:0.001
|
| 257 |
+
INFO 2025-05-02 15:15:36 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.54 loss:0.007 grdn:0.178 lr:7.0e-05 updt_s:0.422 data_s:0.001
|
| 258 |
+
INFO 2025-05-02 15:17:00 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.57 loss:0.008 grdn:0.187 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 259 |
+
INFO 2025-05-02 15:18:25 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.60 loss:0.008 grdn:0.181 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 260 |
+
INFO 2025-05-02 15:19:49 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.63 loss:0.008 grdn:0.184 lr:7.0e-05 updt_s:0.421 data_s:0.000
|
| 261 |
+
INFO 2025-05-02 15:21:14 ts/train.py:232 step:179K smpl:1M ep:5K epch:24.65 loss:0.009 grdn:0.191 lr:6.9e-05 updt_s:0.422 data_s:0.000
|
| 262 |
+
INFO 2025-05-02 15:22:38 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.68 loss:0.008 grdn:0.188 lr:6.9e-05 updt_s:0.421 data_s:0.001
|
| 263 |
+
INFO 2025-05-02 15:24:04 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.71 loss:0.008 grdn:0.185 lr:6.9e-05 updt_s:0.421 data_s:0.009
|
| 264 |
+
INFO 2025-05-02 15:25:29 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.74 loss:0.008 grdn:0.194 lr:6.9e-05 updt_s:0.421 data_s:0.000
|
| 265 |
+
INFO 2025-05-02 15:25:29 ts/train.py:241 Checkpoint policy after step 180000
|
| 266 |
+
INFO 2025-05-02 15:26:56 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.76 loss:0.008 grdn:0.188 lr:6.9e-05 updt_s:0.421 data_s:0.000
|
| 267 |
+
INFO 2025-05-02 15:28:21 ts/train.py:232 step:180K smpl:1M ep:5K epch:24.79 loss:0.008 grdn:0.186 lr:6.9e-05 updt_s:0.422 data_s:0.001
|
| 268 |
+
INFO 2025-05-02 15:29:46 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.82 loss:0.009 grdn:0.205 lr:6.9e-05 updt_s:0.422 data_s:0.000
|
| 269 |
+
INFO 2025-05-02 15:31:10 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.85 loss:0.007 grdn:0.173 lr:6.9e-05 updt_s:0.422 data_s:0.001
|
| 270 |
+
INFO 2025-05-02 15:32:35 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.87 loss:0.008 grdn:0.193 lr:6.9e-05 updt_s:0.421 data_s:0.001
|
| 271 |
+
INFO 2025-05-02 15:33:59 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.90 loss:0.008 grdn:0.183 lr:6.9e-05 updt_s:0.421 data_s:0.000
|
| 272 |
+
INFO 2025-05-02 15:35:24 ts/train.py:232 step:181K smpl:1M ep:5K epch:24.93 loss:0.008 grdn:0.188 lr:6.9e-05 updt_s:0.422 data_s:0.000
|
| 273 |
+
INFO 2025-05-02 15:36:48 ts/train.py:232 step:182K smpl:1M ep:5K epch:24.96 loss:0.007 grdn:0.181 lr:6.9e-05 updt_s:0.421 data_s:0.000
|
| 274 |
+
INFO 2025-05-02 15:38:13 ts/train.py:232 step:182K smpl:1M ep:5K epch:24.98 loss:0.008 grdn:0.183 lr:6.9e-05 updt_s:0.421 data_s:0.001
|
| 275 |
+
INFO 2025-05-02 15:39:37 ts/train.py:232 step:182K smpl:1M ep:5K epch:25.01 loss:0.007 grdn:0.172 lr:6.9e-05 updt_s:0.422 data_s:0.001
|
| 276 |
+
INFO 2025-05-02 15:41:02 ts/train.py:232 step:182K smpl:1M ep:5K epch:25.04 loss:0.008 grdn:0.196 lr:6.9e-05 updt_s:0.422 data_s:0.000
|
| 277 |
+
INFO 2025-05-02 15:42:27 ts/train.py:232 step:182K smpl:1M ep:5K epch:25.07 loss:0.007 grdn:0.184 lr:6.9e-05 updt_s:0.421 data_s:0.000
|
| 278 |
+
INFO 2025-05-02 15:43:51 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.09 loss:0.008 grdn:0.189 lr:6.8e-05 updt_s:0.421 data_s:0.000
|
| 279 |
+
INFO 2025-05-02 15:45:16 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.12 loss:0.007 grdn:0.173 lr:6.8e-05 updt_s:0.422 data_s:0.001
|
| 280 |
+
INFO 2025-05-02 15:46:40 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.15 loss:0.008 grdn:0.186 lr:6.8e-05 updt_s:0.422 data_s:0.000
|
| 281 |
+
INFO 2025-05-02 15:48:05 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.18 loss:0.008 grdn:0.192 lr:6.8e-05 updt_s:0.421 data_s:0.000
|
| 282 |
+
INFO 2025-05-02 15:49:29 ts/train.py:232 step:183K smpl:1M ep:5K epch:25.20 loss:0.008 grdn:0.187 lr:6.8e-05 updt_s:0.421 data_s:0.000
|
| 283 |
+
INFO 2025-05-02 15:50:54 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.23 loss:0.008 grdn:0.196 lr:6.8e-05 updt_s:0.421 data_s:0.001
|
| 284 |
+
INFO 2025-05-02 15:52:18 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.26 loss:0.008 grdn:0.188 lr:6.8e-05 updt_s:0.421 data_s:0.000
|
| 285 |
+
INFO 2025-05-02 15:53:43 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.29 loss:0.008 grdn:0.182 lr:6.8e-05 updt_s:0.421 data_s:0.001
|
| 286 |
+
INFO 2025-05-02 15:55:07 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.31 loss:0.008 grdn:0.188 lr:6.8e-05 updt_s:0.421 data_s:0.000
|
| 287 |
+
INFO 2025-05-02 15:56:32 ts/train.py:232 step:184K smpl:1M ep:5K epch:25.34 loss:0.008 grdn:0.183 lr:6.8e-05 updt_s:0.422 data_s:0.000
|
| 288 |
+
INFO 2025-05-02 15:57:56 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.37 loss:0.008 grdn:0.184 lr:6.8e-05 updt_s:0.422 data_s:0.000
|
| 289 |
+
INFO 2025-05-02 15:59:21 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.40 loss:0.007 grdn:0.172 lr:6.8e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:00:45 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.42 loss:0.008 grdn:0.190 lr:6.8e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:02:10 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.45 loss:0.007 grdn:0.181 lr:6.8e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:03:34 ts/train.py:232 step:185K smpl:1M ep:5K epch:25.48 loss:0.007 grdn:0.182 lr:6.8e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:04:59 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.51 loss:0.008 grdn:0.188 lr:6.8e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:06:23 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.53 loss:0.008 grdn:0.184 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:07:48 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.56 loss:0.008 grdn:0.186 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:09:12 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.59 loss:0.007 grdn:0.184 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:10:37 ts/train.py:232 step:186K smpl:1M ep:5K epch:25.62 loss:0.007 grdn:0.189 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:12:01 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.64 loss:0.007 grdn:0.182 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:13:26 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.67 loss:0.007 grdn:0.174 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:14:52 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.70 loss:0.007 grdn:0.184 lr:6.7e-05 updt_s:0.421 data_s:0.008
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INFO 2025-05-02 16:16:16 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.73 loss:0.007 grdn:0.184 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:17:41 ts/train.py:232 step:187K smpl:1M ep:5K epch:25.75 loss:0.007 grdn:0.193 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:19:05 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.78 loss:0.007 grdn:0.188 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:20:30 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.81 loss:0.007 grdn:0.180 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:21:54 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.84 loss:0.008 grdn:0.177 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:23:19 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.86 loss:0.008 grdn:0.185 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:24:43 ts/train.py:232 step:188K smpl:2M ep:5K epch:25.89 loss:0.008 grdn:0.180 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:26:08 ts/train.py:232 step:189K smpl:2M ep:5K epch:25.92 loss:0.006 grdn:0.172 lr:6.7e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:27:32 ts/train.py:232 step:189K smpl:2M ep:5K epch:25.95 loss:0.008 grdn:0.196 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:28:57 ts/train.py:232 step:189K smpl:2M ep:5K epch:25.97 loss:0.008 grdn:0.188 lr:6.7e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:30:21 ts/train.py:232 step:189K smpl:2M ep:5K epch:26.00 loss:0.007 grdn:0.189 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:31:46 ts/train.py:232 step:189K smpl:2M ep:5K epch:26.03 loss:0.008 grdn:0.188 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:33:10 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.06 loss:0.006 grdn:0.169 lr:6.6e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:34:35 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.08 loss:0.008 grdn:0.203 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:36:00 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.11 loss:0.007 grdn:0.177 lr:6.6e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:37:24 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.14 loss:0.008 grdn:0.187 lr:6.6e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:38:49 ts/train.py:232 step:190K smpl:2M ep:5K epch:26.17 loss:0.009 grdn:0.198 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:40:13 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.19 loss:0.007 grdn:0.179 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:41:38 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.22 loss:0.007 grdn:0.175 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:43:02 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.25 loss:0.007 grdn:0.174 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:44:27 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.28 loss:0.007 grdn:0.182 lr:6.6e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:45:51 ts/train.py:232 step:191K smpl:2M ep:5K epch:26.30 loss:0.009 grdn:0.190 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:47:16 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.33 loss:0.007 grdn:0.180 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:48:40 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.36 loss:0.007 grdn:0.183 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:50:05 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.39 loss:0.006 grdn:0.174 lr:6.6e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:51:29 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.41 loss:0.008 grdn:0.194 lr:6.6e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:52:54 ts/train.py:232 step:192K smpl:2M ep:5K epch:26.44 loss:0.008 grdn:0.202 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:54:18 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.47 loss:0.007 grdn:0.181 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:55:43 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.50 loss:0.008 grdn:0.189 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:57:07 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.52 loss:0.008 grdn:0.183 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 16:58:32 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.55 loss:0.007 grdn:0.170 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 16:59:56 ts/train.py:232 step:193K smpl:2M ep:5K epch:26.58 loss:0.008 grdn:0.184 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 17:01:21 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.61 loss:0.008 grdn:0.183 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:02:45 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.63 loss:0.007 grdn:0.183 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 17:04:11 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.66 loss:0.008 grdn:0.185 lr:6.5e-05 updt_s:0.421 data_s:0.008
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INFO 2025-05-02 17:05:36 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.69 loss:0.008 grdn:0.189 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 17:07:00 ts/train.py:232 step:194K smpl:2M ep:5K epch:26.72 loss:0.008 grdn:0.184 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:08:25 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.74 loss:0.008 grdn:0.189 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:09:49 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.77 loss:0.007 grdn:0.178 lr:6.5e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 17:11:14 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.80 loss:0.007 grdn:0.181 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:12:38 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.83 loss:0.007 grdn:0.178 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:14:03 ts/train.py:232 step:195K smpl:2M ep:5K epch:26.85 loss:0.007 grdn:0.182 lr:6.5e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:15:27 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.88 loss:0.007 grdn:0.184 lr:6.4e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 17:16:52 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.91 loss:0.007 grdn:0.179 lr:6.4e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:18:16 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.94 loss:0.009 grdn:0.206 lr:6.4e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:19:41 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.96 loss:0.007 grdn:0.179 lr:6.4e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:21:05 ts/train.py:232 step:196K smpl:2M ep:5K epch:26.99 loss:0.008 grdn:0.188 lr:6.4e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:22:30 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.02 loss:0.007 grdn:0.177 lr:6.4e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:23:54 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.05 loss:0.008 grdn:0.195 lr:6.4e-05 updt_s:0.421 data_s:0.000
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INFO 2025-05-02 17:25:19 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.07 loss:0.008 grdn:0.191 lr:6.4e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 17:26:44 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.10 loss:0.007 grdn:0.187 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:28:08 ts/train.py:232 step:197K smpl:2M ep:5K epch:27.13 loss:0.008 grdn:0.186 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:29:33 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.16 loss:0.007 grdn:0.190 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:30:57 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.18 loss:0.007 grdn:0.179 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:32:22 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.21 loss:0.008 grdn:0.193 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:33:47 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.24 loss:0.007 grdn:0.182 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:35:11 ts/train.py:232 step:198K smpl:2M ep:5K epch:27.27 loss:0.007 grdn:0.181 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:36:36 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.29 loss:0.007 grdn:0.185 lr:6.4e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:38:01 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.32 loss:0.007 grdn:0.187 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:39:25 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.35 loss:0.007 grdn:0.188 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:40:50 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.38 loss:0.007 grdn:0.186 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:42:15 ts/train.py:232 step:199K smpl:2M ep:5K epch:27.40 loss:0.007 grdn:0.180 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:43:39 ts/train.py:232 step:200K smpl:2M ep:5K epch:27.43 loss:0.007 grdn:0.189 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:45:04 ts/train.py:232 step:200K smpl:2M ep:5K epch:27.46 loss:0.007 grdn:0.172 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:46:29 ts/train.py:232 step:200K smpl:2M ep:5K epch:27.49 loss:0.008 grdn:0.197 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:47:53 ts/train.py:232 step:200K smpl:2M ep:6K epch:27.51 loss:0.007 grdn:0.176 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:49:18 ts/train.py:232 step:200K smpl:2M ep:6K epch:27.54 loss:0.007 grdn:0.182 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:50:43 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.57 loss:0.007 grdn:0.179 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:52:07 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.60 loss:0.008 grdn:0.195 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:53:32 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.62 loss:0.007 grdn:0.185 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:54:58 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.65 loss:0.007 grdn:0.181 lr:6.3e-05 updt_s:0.421 data_s:0.008
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INFO 2025-05-02 17:56:23 ts/train.py:232 step:201K smpl:2M ep:6K epch:27.68 loss:0.007 grdn:0.193 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:57:47 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.71 loss:0.007 grdn:0.180 lr:6.3e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 17:59:12 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.73 loss:0.007 grdn:0.185 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:00:37 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.76 loss:0.007 grdn:0.183 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:02:01 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.79 loss:0.007 grdn:0.179 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:03:26 ts/train.py:232 step:202K smpl:2M ep:6K epch:27.82 loss:0.008 grdn:0.188 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:04:51 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.84 loss:0.007 grdn:0.186 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:06:15 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.87 loss:0.007 grdn:0.180 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:07:40 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.90 loss:0.007 grdn:0.189 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:09:05 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.93 loss:0.007 grdn:0.191 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:10:29 ts/train.py:232 step:203K smpl:2M ep:6K epch:27.95 loss:0.008 grdn:0.189 lr:6.2e-05 updt_s:0.421 data_s:0.001
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INFO 2025-05-02 18:11:54 ts/train.py:232 step:204K smpl:2M ep:6K epch:27.98 loss:0.007 grdn:0.176 lr:6.2e-05 updt_s:0.422 data_s:0.001
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INFO 2025-05-02 18:13:18 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.01 loss:0.007 grdn:0.195 lr:6.2e-05 updt_s:0.421 data_s:0.000
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| 385 |
+
INFO 2025-05-02 18:14:43 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.03 loss:0.007 grdn:0.180 lr:6.2e-05 updt_s:0.421 data_s:0.001
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| 386 |
+
INFO 2025-05-02 18:16:07 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.06 loss:0.007 grdn:0.183 lr:6.2e-05 updt_s:0.421 data_s:0.001
|
| 387 |
+
INFO 2025-05-02 18:17:32 ts/train.py:232 step:204K smpl:2M ep:6K epch:28.09 loss:0.007 grdn:0.183 lr:6.2e-05 updt_s:0.421 data_s:0.001
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| 388 |
+
INFO 2025-05-02 18:18:57 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.12 loss:0.008 grdn:0.204 lr:6.2e-05 updt_s:0.421 data_s:0.001
|
| 389 |
+
INFO 2025-05-02 18:20:21 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.14 loss:0.007 grdn:0.191 lr:6.2e-05 updt_s:0.421 data_s:0.001
|
| 390 |
+
INFO 2025-05-02 18:21:46 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.17 loss:0.008 grdn:0.195 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 391 |
+
INFO 2025-05-02 18:23:10 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.20 loss:0.007 grdn:0.183 lr:6.1e-05 updt_s:0.421 data_s:0.001
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| 392 |
+
INFO 2025-05-02 18:24:35 ts/train.py:232 step:205K smpl:2M ep:6K epch:28.23 loss:0.007 grdn:0.179 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 393 |
+
INFO 2025-05-02 18:25:59 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.25 loss:0.008 grdn:0.192 lr:6.1e-05 updt_s:0.421 data_s:0.001
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| 394 |
+
INFO 2025-05-02 18:27:24 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.28 loss:0.008 grdn:0.192 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 395 |
+
INFO 2025-05-02 18:28:49 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.31 loss:0.007 grdn:0.175 lr:6.1e-05 updt_s:0.422 data_s:0.001
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| 396 |
+
INFO 2025-05-02 18:30:13 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.34 loss:0.006 grdn:0.182 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 397 |
+
INFO 2025-05-02 18:31:38 ts/train.py:232 step:206K smpl:2M ep:6K epch:28.36 loss:0.007 grdn:0.189 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 398 |
+
INFO 2025-05-02 18:33:02 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.39 loss:0.007 grdn:0.176 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 399 |
+
INFO 2025-05-02 18:34:27 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.42 loss:0.007 grdn:0.186 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 400 |
+
INFO 2025-05-02 18:35:52 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.45 loss:0.007 grdn:0.183 lr:6.1e-05 updt_s:0.422 data_s:0.001
|
| 401 |
+
INFO 2025-05-02 18:37:16 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.47 loss:0.007 grdn:0.194 lr:6.1e-05 updt_s:0.422 data_s:0.001
|
| 402 |
+
INFO 2025-05-02 18:38:41 ts/train.py:232 step:207K smpl:2M ep:6K epch:28.50 loss:0.007 grdn:0.177 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 403 |
+
INFO 2025-05-02 18:40:05 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.53 loss:0.007 grdn:0.180 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 404 |
+
INFO 2025-05-02 18:41:30 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.56 loss:0.007 grdn:0.191 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 405 |
+
INFO 2025-05-02 18:42:55 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.58 loss:0.007 grdn:0.177 lr:6.1e-05 updt_s:0.421 data_s:0.001
|
| 406 |
+
INFO 2025-05-02 18:44:21 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.61 loss:0.006 grdn:0.168 lr:6.0e-05 updt_s:0.421 data_s:0.010
|
| 407 |
+
INFO 2025-05-02 18:45:46 ts/train.py:232 step:208K smpl:2M ep:6K epch:28.64 loss:0.006 grdn:0.174 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 408 |
+
INFO 2025-05-02 18:47:10 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.67 loss:0.007 grdn:0.197 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 409 |
+
INFO 2025-05-02 18:48:35 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.69 loss:0.007 grdn:0.181 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 410 |
+
INFO 2025-05-02 18:50:00 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.72 loss:0.006 grdn:0.170 lr:6.0e-05 updt_s:0.423 data_s:0.001
|
| 411 |
+
INFO 2025-05-02 18:51:25 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.75 loss:0.006 grdn:0.180 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 412 |
+
INFO 2025-05-02 18:52:50 ts/train.py:232 step:209K smpl:2M ep:6K epch:28.78 loss:0.008 grdn:0.200 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 413 |
+
INFO 2025-05-02 18:54:14 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.80 loss:0.007 grdn:0.184 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 414 |
+
INFO 2025-05-02 18:55:39 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.83 loss:0.007 grdn:0.185 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 415 |
+
INFO 2025-05-02 18:57:04 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.86 loss:0.008 grdn:0.199 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 416 |
+
INFO 2025-05-02 18:58:28 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.89 loss:0.007 grdn:0.182 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 417 |
+
INFO 2025-05-02 18:59:53 ts/train.py:232 step:210K smpl:2M ep:6K epch:28.91 loss:0.007 grdn:0.186 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 418 |
+
INFO 2025-05-02 19:01:18 ts/train.py:232 step:211K smpl:2M ep:6K epch:28.94 loss:0.007 grdn:0.192 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 419 |
+
INFO 2025-05-02 19:02:43 ts/train.py:232 step:211K smpl:2M ep:6K epch:28.97 loss:0.007 grdn:0.186 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 420 |
+
INFO 2025-05-02 19:04:07 ts/train.py:232 step:211K smpl:2M ep:6K epch:29.00 loss:0.007 grdn:0.183 lr:6.0e-05 updt_s:0.422 data_s:0.001
|
| 421 |
+
INFO 2025-05-02 19:05:32 ts/train.py:232 step:211K smpl:2M ep:6K epch:29.02 loss:0.006 grdn:0.176 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 422 |
+
INFO 2025-05-02 19:06:57 ts/train.py:232 step:211K smpl:2M ep:6K epch:29.05 loss:0.008 grdn:0.201 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 423 |
+
INFO 2025-05-02 19:08:22 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.08 loss:0.007 grdn:0.179 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 424 |
+
INFO 2025-05-02 19:09:47 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.11 loss:0.006 grdn:0.177 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 425 |
+
INFO 2025-05-02 19:11:11 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.13 loss:0.007 grdn:0.190 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 426 |
+
INFO 2025-05-02 19:12:36 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.16 loss:0.007 grdn:0.197 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 427 |
+
INFO 2025-05-02 19:14:01 ts/train.py:232 step:212K smpl:2M ep:6K epch:29.19 loss:0.007 grdn:0.185 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 428 |
+
INFO 2025-05-02 19:15:26 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.22 loss:0.007 grdn:0.189 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 429 |
+
INFO 2025-05-02 19:16:50 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.24 loss:0.008 grdn:0.191 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 430 |
+
INFO 2025-05-02 19:18:15 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.27 loss:0.007 grdn:0.183 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 431 |
+
INFO 2025-05-02 19:19:40 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.30 loss:0.007 grdn:0.198 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 432 |
+
INFO 2025-05-02 19:21:05 ts/train.py:232 step:213K smpl:2M ep:6K epch:29.33 loss:0.007 grdn:0.198 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 433 |
+
INFO 2025-05-02 19:22:30 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.35 loss:0.007 grdn:0.186 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 434 |
+
INFO 2025-05-02 19:23:54 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.38 loss:0.007 grdn:0.178 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 435 |
+
INFO 2025-05-02 19:25:19 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.41 loss:0.007 grdn:0.197 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 436 |
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INFO 2025-05-02 19:26:44 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.44 loss:0.007 grdn:0.189 lr:5.9e-05 updt_s:0.422 data_s:0.001
|
| 437 |
+
INFO 2025-05-02 19:28:09 ts/train.py:232 step:214K smpl:2M ep:6K epch:29.46 loss:0.008 grdn:0.199 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 438 |
+
INFO 2025-05-02 19:29:34 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.49 loss:0.007 grdn:0.193 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 439 |
+
INFO 2025-05-02 19:30:58 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.52 loss:0.007 grdn:0.182 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 440 |
+
INFO 2025-05-02 19:32:23 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.55 loss:0.006 grdn:0.178 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 441 |
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INFO 2025-05-02 19:33:48 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.57 loss:0.007 grdn:0.175 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 442 |
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INFO 2025-05-02 19:35:14 ts/train.py:232 step:215K smpl:2M ep:6K epch:29.60 loss:0.006 grdn:0.180 lr:5.8e-05 updt_s:0.421 data_s:0.010
|
| 443 |
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INFO 2025-05-02 19:36:39 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.63 loss:0.006 grdn:0.176 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 444 |
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INFO 2025-05-02 19:38:04 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.66 loss:0.007 grdn:0.179 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 445 |
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INFO 2025-05-02 19:39:28 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.68 loss:0.007 grdn:0.172 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 446 |
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INFO 2025-05-02 19:40:53 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.71 loss:0.007 grdn:0.188 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 447 |
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INFO 2025-05-02 19:42:18 ts/train.py:232 step:216K smpl:2M ep:6K epch:29.74 loss:0.008 grdn:0.199 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 448 |
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INFO 2025-05-02 19:43:43 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.77 loss:0.006 grdn:0.165 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 449 |
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INFO 2025-05-02 19:45:07 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.79 loss:0.007 grdn:0.183 lr:5.8e-05 updt_s:0.422 data_s:0.001
|
| 450 |
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INFO 2025-05-02 19:46:32 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.82 loss:0.007 grdn:0.180 lr:5.8e-05 updt_s:0.421 data_s:0.001
|
| 451 |
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INFO 2025-05-02 19:47:56 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.85 loss:0.007 grdn:0.182 lr:5.8e-05 updt_s:0.421 data_s:0.001
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| 452 |
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INFO 2025-05-02 19:49:21 ts/train.py:232 step:217K smpl:2M ep:6K epch:29.88 loss:0.008 grdn:0.196 lr:5.7e-05 updt_s:0.421 data_s:0.001
|
| 453 |
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INFO 2025-05-02 19:50:45 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.90 loss:0.006 grdn:0.168 lr:5.7e-05 updt_s:0.421 data_s:0.001
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| 454 |
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INFO 2025-05-02 19:52:09 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.93 loss:0.007 grdn:0.190 lr:5.7e-05 updt_s:0.420 data_s:0.000
|
| 455 |
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INFO 2025-05-02 19:53:34 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.96 loss:0.007 grdn:0.185 lr:5.7e-05 updt_s:0.421 data_s:0.001
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| 456 |
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INFO 2025-05-02 19:54:58 ts/train.py:232 step:218K smpl:2M ep:6K epch:29.99 loss:0.006 grdn:0.182 lr:5.7e-05 updt_s:0.421 data_s:0.001
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| 457 |
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INFO 2025-05-02 19:56:23 ts/train.py:232 step:218K smpl:2M ep:6K epch:30.01 loss:0.006 grdn:0.159 lr:5.7e-05 updt_s:0.421 data_s:0.001
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| 458 |
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INFO 2025-05-02 19:57:47 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.04 loss:0.007 grdn:0.182 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 459 |
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INFO 2025-05-02 19:59:11 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.07 loss:0.007 grdn:0.190 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 460 |
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INFO 2025-05-02 20:00:35 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.10 loss:0.007 grdn:0.184 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 461 |
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INFO 2025-05-02 20:02:00 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.12 loss:0.007 grdn:0.185 lr:5.7e-05 updt_s:0.421 data_s:0.000
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| 462 |
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INFO 2025-05-02 20:03:24 ts/train.py:232 step:219K smpl:2M ep:6K epch:30.15 loss:0.007 grdn:0.182 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 463 |
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INFO 2025-05-02 20:04:48 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.18 loss:0.008 grdn:0.195 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 464 |
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INFO 2025-05-02 20:06:12 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.21 loss:0.007 grdn:0.184 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 465 |
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INFO 2025-05-02 20:07:37 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.23 loss:0.006 grdn:0.181 lr:5.7e-05 updt_s:0.421 data_s:0.000
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| 466 |
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INFO 2025-05-02 20:09:01 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.26 loss:0.006 grdn:0.183 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 467 |
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INFO 2025-05-02 20:10:25 ts/train.py:232 step:220K smpl:2M ep:6K epch:30.29 loss:0.007 grdn:0.182 lr:5.7e-05 updt_s:0.420 data_s:0.000
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| 468 |
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INFO 2025-05-02 20:11:50 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.32 loss:0.007 grdn:0.190 lr:5.6e-05 updt_s:0.421 data_s:0.000
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| 469 |
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INFO 2025-05-02 20:13:14 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.34 loss:0.007 grdn:0.184 lr:5.6e-05 updt_s:0.421 data_s:0.000
|
| 470 |
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INFO 2025-05-02 20:14:38 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.37 loss:0.006 grdn:0.180 lr:5.6e-05 updt_s:0.420 data_s:0.000
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| 471 |
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INFO 2025-05-02 20:16:02 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.40 loss:0.007 grdn:0.193 lr:5.6e-05 updt_s:0.420 data_s:0.000
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| 472 |
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INFO 2025-05-02 20:17:27 ts/train.py:232 step:221K smpl:2M ep:6K epch:30.43 loss:0.006 grdn:0.174 lr:5.6e-05 updt_s:0.420 data_s:0.000
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| 473 |
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INFO 2025-05-02 20:18:51 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.45 loss:0.007 grdn:0.178 lr:5.6e-05 updt_s:0.420 data_s:0.000
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| 474 |
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INFO 2025-05-02 20:20:15 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.48 loss:0.007 grdn:0.190 lr:5.6e-05 updt_s:0.420 data_s:0.000
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| 475 |
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INFO 2025-05-02 20:21:39 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.51 loss:0.006 grdn:0.182 lr:5.6e-05 updt_s:0.420 data_s:0.001
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| 476 |
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INFO 2025-05-02 20:23:04 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.54 loss:0.007 grdn:0.187 lr:5.6e-05 updt_s:0.420 data_s:0.000
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| 477 |
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INFO 2025-05-02 20:24:29 ts/train.py:232 step:222K smpl:2M ep:6K epch:30.56 loss:0.007 grdn:0.185 lr:5.6e-05 updt_s:0.420 data_s:0.008
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| 478 |
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INFO 2025-05-02 20:25:53 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.59 loss:0.007 grdn:0.188 lr:5.6e-05 updt_s:0.420 data_s:0.000
|
| 479 |
+
INFO 2025-05-02 20:27:18 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.62 loss:0.006 grdn:0.166 lr:5.6e-05 updt_s:0.420 data_s:0.000
|
| 480 |
+
INFO 2025-05-02 20:28:42 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.65 loss:0.007 grdn:0.192 lr:5.6e-05 updt_s:0.421 data_s:0.000
|
| 481 |
+
INFO 2025-05-02 20:30:06 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.67 loss:0.007 grdn:0.183 lr:5.6e-05 updt_s:0.420 data_s:0.000
|
| 482 |
+
INFO 2025-05-02 20:31:31 ts/train.py:232 step:223K smpl:2M ep:6K epch:30.70 loss:0.007 grdn:0.194 lr:5.6e-05 updt_s:0.420 data_s:0.000
|
| 483 |
+
INFO 2025-05-02 20:32:55 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.73 loss:0.006 grdn:0.175 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 484 |
+
INFO 2025-05-02 20:34:19 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.76 loss:0.007 grdn:0.193 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 485 |
+
INFO 2025-05-02 20:35:43 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.78 loss:0.006 grdn:0.179 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 486 |
+
INFO 2025-05-02 20:37:07 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.81 loss:0.007 grdn:0.192 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 487 |
+
INFO 2025-05-02 20:38:32 ts/train.py:232 step:224K smpl:2M ep:6K epch:30.84 loss:0.006 grdn:0.192 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 488 |
+
INFO 2025-05-02 20:39:56 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.87 loss:0.006 grdn:0.174 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 489 |
+
INFO 2025-05-02 20:41:20 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.89 loss:0.006 grdn:0.178 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 490 |
+
INFO 2025-05-02 20:42:44 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.92 loss:0.007 grdn:0.184 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 491 |
+
INFO 2025-05-02 20:44:08 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.95 loss:0.007 grdn:0.201 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 492 |
+
INFO 2025-05-02 20:45:33 ts/train.py:232 step:225K smpl:2M ep:6K epch:30.98 loss:0.006 grdn:0.172 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 493 |
+
INFO 2025-05-02 20:46:57 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.00 loss:0.007 grdn:0.184 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 494 |
+
INFO 2025-05-02 20:48:21 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.03 loss:0.006 grdn:0.176 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 495 |
+
INFO 2025-05-02 20:49:45 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.06 loss:0.007 grdn:0.192 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 496 |
+
INFO 2025-05-02 20:51:09 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.09 loss:0.006 grdn:0.175 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 497 |
+
INFO 2025-05-02 20:52:34 ts/train.py:232 step:226K smpl:2M ep:6K epch:31.11 loss:0.006 grdn:0.176 lr:5.5e-05 updt_s:0.420 data_s:0.000
|
| 498 |
+
INFO 2025-05-02 20:53:58 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.14 loss:0.007 grdn:0.190 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 499 |
+
INFO 2025-05-02 20:55:22 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.17 loss:0.006 grdn:0.181 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 500 |
+
INFO 2025-05-02 20:56:46 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.20 loss:0.007 grdn:0.190 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 501 |
+
INFO 2025-05-02 20:58:10 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.22 loss:0.006 grdn:0.173 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 502 |
+
INFO 2025-05-02 20:59:34 ts/train.py:232 step:227K smpl:2M ep:6K epch:31.25 loss:0.008 grdn:0.199 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 503 |
+
INFO 2025-05-02 21:00:58 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.28 loss:0.007 grdn:0.183 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 504 |
+
INFO 2025-05-02 21:02:22 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.31 loss:0.007 grdn:0.184 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 505 |
+
INFO 2025-05-02 21:03:47 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.33 loss:0.007 grdn:0.188 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 506 |
+
INFO 2025-05-02 21:05:11 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.36 loss:0.008 grdn:0.198 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 507 |
+
INFO 2025-05-02 21:06:35 ts/train.py:232 step:228K smpl:2M ep:6K epch:31.39 loss:0.006 grdn:0.180 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 508 |
+
INFO 2025-05-02 21:07:59 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.42 loss:0.007 grdn:0.191 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 509 |
+
INFO 2025-05-02 21:09:23 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.44 loss:0.007 grdn:0.195 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 510 |
+
INFO 2025-05-02 21:10:47 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.47 loss:0.006 grdn:0.177 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 511 |
+
INFO 2025-05-02 21:12:12 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.50 loss:0.007 grdn:0.182 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 512 |
+
INFO 2025-05-02 21:13:36 ts/train.py:232 step:229K smpl:2M ep:6K epch:31.53 loss:0.006 grdn:0.189 lr:5.4e-05 updt_s:0.420 data_s:0.000
|
| 513 |
+
INFO 2025-05-02 21:15:01 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.55 loss:0.007 grdn:0.203 lr:5.4e-05 updt_s:0.419 data_s:0.007
|
| 514 |
+
INFO 2025-05-02 21:16:25 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.58 loss:0.007 grdn:0.192 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 515 |
+
INFO 2025-05-02 21:17:49 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.61 loss:0.006 grdn:0.179 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 516 |
+
INFO 2025-05-02 21:19:13 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.64 loss:0.006 grdn:0.180 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 517 |
+
INFO 2025-05-02 21:20:38 ts/train.py:232 step:230K smpl:2M ep:6K epch:31.66 loss:0.007 grdn:0.187 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 518 |
+
INFO 2025-05-02 21:22:02 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.69 loss:0.006 grdn:0.183 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 519 |
+
INFO 2025-05-02 21:23:26 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.72 loss:0.007 grdn:0.191 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 520 |
+
INFO 2025-05-02 21:24:50 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.75 loss:0.006 grdn:0.181 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 521 |
+
INFO 2025-05-02 21:26:14 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.77 loss:0.007 grdn:0.196 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 522 |
+
INFO 2025-05-02 21:27:38 ts/train.py:232 step:231K smpl:2M ep:6K epch:31.80 loss:0.006 grdn:0.179 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 523 |
+
INFO 2025-05-02 21:29:02 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.83 loss:0.006 grdn:0.179 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 524 |
+
INFO 2025-05-02 21:30:27 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.86 loss:0.006 grdn:0.183 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 525 |
+
INFO 2025-05-02 21:31:51 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.88 loss:0.007 grdn:0.195 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 526 |
+
INFO 2025-05-02 21:33:15 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.91 loss:0.006 grdn:0.185 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 527 |
+
INFO 2025-05-02 21:34:39 ts/train.py:232 step:232K smpl:2M ep:6K epch:31.94 loss:0.006 grdn:0.174 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 528 |
+
INFO 2025-05-02 21:36:03 ts/train.py:232 step:233K smpl:2M ep:6K epch:31.97 loss:0.006 grdn:0.188 lr:5.3e-05 updt_s:0.420 data_s:0.000
|
| 529 |
+
INFO 2025-05-02 21:37:28 ts/train.py:232 step:233K smpl:2M ep:6K epch:31.99 loss:0.007 grdn:0.197 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 530 |
+
INFO 2025-05-02 21:38:52 ts/train.py:232 step:233K smpl:2M ep:6K epch:32.02 loss:0.006 grdn:0.187 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 531 |
+
INFO 2025-05-02 21:40:16 ts/train.py:232 step:233K smpl:2M ep:6K epch:32.05 loss:0.007 grdn:0.187 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 532 |
+
INFO 2025-05-02 21:41:40 ts/train.py:232 step:233K smpl:2M ep:6K epch:32.08 loss:0.007 grdn:0.186 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 533 |
+
INFO 2025-05-02 21:43:05 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.10 loss:0.006 grdn:0.180 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 534 |
+
INFO 2025-05-02 21:44:29 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.13 loss:0.006 grdn:0.177 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 535 |
+
INFO 2025-05-02 21:45:53 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.16 loss:0.006 grdn:0.172 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 536 |
+
INFO 2025-05-02 21:47:17 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.19 loss:0.007 grdn:0.197 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 537 |
+
INFO 2025-05-02 21:48:41 ts/train.py:232 step:234K smpl:2M ep:6K epch:32.21 loss:0.006 grdn:0.174 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 538 |
+
INFO 2025-05-02 21:50:05 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.24 loss:0.007 grdn:0.183 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 539 |
+
INFO 2025-05-02 21:51:29 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.27 loss:0.007 grdn:0.188 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 540 |
+
INFO 2025-05-02 21:52:54 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.30 loss:0.007 grdn:0.192 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 541 |
+
INFO 2025-05-02 21:54:18 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.32 loss:0.007 grdn:0.192 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 542 |
+
INFO 2025-05-02 21:55:42 ts/train.py:232 step:235K smpl:2M ep:6K epch:32.35 loss:0.007 grdn:0.185 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 543 |
+
INFO 2025-05-02 21:57:06 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.38 loss:0.006 grdn:0.166 lr:5.2e-05 updt_s:0.420 data_s:0.000
|
| 544 |
+
INFO 2025-05-02 21:58:30 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.41 loss:0.006 grdn:0.186 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 545 |
+
INFO 2025-05-02 21:59:54 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.43 loss:0.006 grdn:0.179 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 546 |
+
INFO 2025-05-02 22:01:19 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.46 loss:0.007 grdn:0.179 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 547 |
+
INFO 2025-05-02 22:02:43 ts/train.py:232 step:236K smpl:2M ep:6K epch:32.49 loss:0.006 grdn:0.188 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 548 |
+
INFO 2025-05-02 22:04:09 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.52 loss:0.007 grdn:0.184 lr:5.1e-05 updt_s:0.419 data_s:0.009
|
| 549 |
+
INFO 2025-05-02 22:05:33 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.54 loss:0.006 grdn:0.174 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 550 |
+
INFO 2025-05-02 22:06:57 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.57 loss:0.006 grdn:0.185 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 551 |
+
INFO 2025-05-02 22:08:21 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.60 loss:0.007 grdn:0.192 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 552 |
+
INFO 2025-05-02 22:09:45 ts/train.py:232 step:237K smpl:2M ep:7K epch:32.63 loss:0.006 grdn:0.183 lr:5.1e-05 updt_s:0.419 data_s:0.000
|
| 553 |
+
INFO 2025-05-02 22:11:09 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.65 loss:0.007 grdn:0.191 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 554 |
+
INFO 2025-05-02 22:12:33 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.68 loss:0.007 grdn:0.201 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 555 |
+
INFO 2025-05-02 22:13:57 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.71 loss:0.007 grdn:0.187 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 556 |
+
INFO 2025-05-02 22:15:21 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.73 loss:0.006 grdn:0.186 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 557 |
+
INFO 2025-05-02 22:16:46 ts/train.py:232 step:238K smpl:2M ep:7K epch:32.76 loss:0.007 grdn:0.190 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 558 |
+
INFO 2025-05-02 22:18:10 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.79 loss:0.006 grdn:0.183 lr:5.1e-05 updt_s:0.419 data_s:0.000
|
| 559 |
+
INFO 2025-05-02 22:19:34 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.82 loss:0.007 grdn:0.194 lr:5.1e-05 updt_s:0.420 data_s:0.000
|
| 560 |
+
INFO 2025-05-02 22:20:58 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.84 loss:0.006 grdn:0.187 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 561 |
+
INFO 2025-05-02 22:22:22 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.87 loss:0.005 grdn:0.166 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 562 |
+
INFO 2025-05-02 22:23:46 ts/train.py:232 step:239K smpl:2M ep:7K epch:32.90 loss:0.006 grdn:0.175 lr:5.0e-05 updt_s:0.419 data_s:0.000
|
| 563 |
+
INFO 2025-05-02 22:25:10 ts/train.py:232 step:240K smpl:2M ep:7K epch:32.93 loss:0.006 grdn:0.184 lr:5.0e-05 updt_s:0.419 data_s:0.000
|
| 564 |
+
INFO 2025-05-02 22:26:34 ts/train.py:232 step:240K smpl:2M ep:7K epch:32.95 loss:0.006 grdn:0.172 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 565 |
+
INFO 2025-05-02 22:27:58 ts/train.py:232 step:240K smpl:2M ep:7K epch:32.98 loss:0.007 grdn:0.181 lr:5.0e-05 updt_s:0.419 data_s:0.000
|
| 566 |
+
INFO 2025-05-02 22:27:58 ts/train.py:241 Checkpoint policy after step 240000
|
| 567 |
+
INFO 2025-05-02 22:29:25 ts/train.py:232 step:240K smpl:2M ep:7K epch:33.01 loss:0.007 grdn:0.194 lr:5.0e-05 updt_s:0.419 data_s:0.000
|
| 568 |
+
INFO 2025-05-02 22:30:49 ts/train.py:232 step:240K smpl:2M ep:7K epch:33.04 loss:0.007 grdn:0.184 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 569 |
+
INFO 2025-05-02 22:32:14 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.06 loss:0.006 grdn:0.177 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 570 |
+
INFO 2025-05-02 22:33:38 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.09 loss:0.007 grdn:0.189 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 571 |
+
INFO 2025-05-02 22:35:02 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.12 loss:0.007 grdn:0.189 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 572 |
+
INFO 2025-05-02 22:36:26 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.15 loss:0.006 grdn:0.179 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 573 |
+
INFO 2025-05-02 22:37:50 ts/train.py:232 step:241K smpl:2M ep:7K epch:33.17 loss:0.006 grdn:0.181 lr:5.0e-05 updt_s:0.419 data_s:0.000
|
| 574 |
+
INFO 2025-05-02 22:39:14 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.20 loss:0.006 grdn:0.184 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 575 |
+
INFO 2025-05-02 22:40:38 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.23 loss:0.006 grdn:0.169 lr:5.0e-05 updt_s:0.420 data_s:0.000
|
| 576 |
+
INFO 2025-05-02 22:42:02 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.26 loss:0.006 grdn:0.182 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 577 |
+
INFO 2025-05-02 22:43:26 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.28 loss:0.006 grdn:0.190 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 578 |
+
INFO 2025-05-02 22:44:50 ts/train.py:232 step:242K smpl:2M ep:7K epch:33.31 loss:0.006 grdn:0.191 lr:4.9e-05 updt_s:0.419 data_s:0.000
|
| 579 |
+
INFO 2025-05-02 22:46:14 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.34 loss:0.007 grdn:0.196 lr:4.9e-05 updt_s:0.419 data_s:0.000
|
| 580 |
+
INFO 2025-05-02 22:47:38 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.37 loss:0.006 grdn:0.183 lr:4.9e-05 updt_s:0.419 data_s:0.000
|
| 581 |
+
INFO 2025-05-02 22:49:02 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.39 loss:0.006 grdn:0.190 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 582 |
+
INFO 2025-05-02 22:50:27 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.42 loss:0.007 grdn:0.197 lr:4.9e-05 updt_s:0.419 data_s:0.000
|
| 583 |
+
INFO 2025-05-02 22:51:51 ts/train.py:232 step:243K smpl:2M ep:7K epch:33.45 loss:0.006 grdn:0.176 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 584 |
+
INFO 2025-05-02 22:53:15 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.48 loss:0.006 grdn:0.181 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 585 |
+
INFO 2025-05-02 22:54:40 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.50 loss:0.006 grdn:0.173 lr:4.9e-05 updt_s:0.419 data_s:0.007
|
| 586 |
+
INFO 2025-05-02 22:56:04 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.53 loss:0.006 grdn:0.173 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 587 |
+
INFO 2025-05-02 22:57:28 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.56 loss:0.006 grdn:0.193 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 588 |
+
INFO 2025-05-02 22:58:52 ts/train.py:232 step:244K smpl:2M ep:7K epch:33.59 loss:0.006 grdn:0.186 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 589 |
+
INFO 2025-05-02 23:00:17 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.61 loss:0.006 grdn:0.187 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 590 |
+
INFO 2025-05-02 23:01:41 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.64 loss:0.007 grdn:0.191 lr:4.9e-05 updt_s:0.420 data_s:0.000
|
| 591 |
+
INFO 2025-05-02 23:03:05 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.67 loss:0.006 grdn:0.179 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 592 |
+
INFO 2025-05-02 23:04:29 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.70 loss:0.006 grdn:0.187 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 593 |
+
INFO 2025-05-02 23:05:54 ts/train.py:232 step:245K smpl:2M ep:7K epch:33.72 loss:0.006 grdn:0.176 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 594 |
+
INFO 2025-05-02 23:07:18 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.75 loss:0.006 grdn:0.189 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 595 |
+
INFO 2025-05-02 23:08:42 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.78 loss:0.006 grdn:0.185 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 596 |
+
INFO 2025-05-02 23:10:06 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.81 loss:0.006 grdn:0.175 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 597 |
+
INFO 2025-05-02 23:11:30 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.83 loss:0.006 grdn:0.183 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 598 |
+
INFO 2025-05-02 23:12:54 ts/train.py:232 step:246K smpl:2M ep:7K epch:33.86 loss:0.006 grdn:0.194 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 599 |
+
INFO 2025-05-02 23:14:19 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.89 loss:0.006 grdn:0.192 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 600 |
+
INFO 2025-05-02 23:15:43 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.92 loss:0.006 grdn:0.183 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 601 |
+
INFO 2025-05-02 23:17:07 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.94 loss:0.006 grdn:0.184 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 602 |
+
INFO 2025-05-02 23:18:31 ts/train.py:232 step:247K smpl:2M ep:7K epch:33.97 loss:0.005 grdn:0.168 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 603 |
+
INFO 2025-05-02 23:19:55 ts/train.py:232 step:247K smpl:2M ep:7K epch:34.00 loss:0.006 grdn:0.182 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 604 |
+
INFO 2025-05-02 23:21:19 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.03 loss:0.006 grdn:0.185 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 605 |
+
INFO 2025-05-02 23:22:43 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.05 loss:0.007 grdn:0.198 lr:4.8e-05 updt_s:0.420 data_s:0.000
|
| 606 |
+
INFO 2025-05-02 23:24:08 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.08 loss:0.006 grdn:0.176 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 607 |
+
INFO 2025-05-02 23:25:32 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.11 loss:0.006 grdn:0.178 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 608 |
+
INFO 2025-05-02 23:26:56 ts/train.py:232 step:248K smpl:2M ep:7K epch:34.14 loss:0.005 grdn:0.170 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 609 |
+
INFO 2025-05-02 23:28:20 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.16 loss:0.006 grdn:0.195 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 610 |
+
INFO 2025-05-02 23:29:44 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.19 loss:0.006 grdn:0.181 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 611 |
+
INFO 2025-05-02 23:31:08 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.22 loss:0.006 grdn:0.184 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 612 |
+
INFO 2025-05-02 23:32:32 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.25 loss:0.006 grdn:0.182 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 613 |
+
INFO 2025-05-02 23:33:56 ts/train.py:232 step:249K smpl:2M ep:7K epch:34.27 loss:0.006 grdn:0.189 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 614 |
+
INFO 2025-05-02 23:35:20 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.30 loss:0.006 grdn:0.179 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 615 |
+
INFO 2025-05-02 23:36:45 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.33 loss:0.006 grdn:0.180 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 616 |
+
INFO 2025-05-02 23:38:09 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.36 loss:0.006 grdn:0.198 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 617 |
+
INFO 2025-05-02 23:39:33 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.38 loss:0.006 grdn:0.199 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 618 |
+
INFO 2025-05-02 23:40:57 ts/train.py:232 step:250K smpl:2M ep:7K epch:34.41 loss:0.005 grdn:0.166 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 619 |
+
INFO 2025-05-02 23:42:21 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.44 loss:0.006 grdn:0.175 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 620 |
+
INFO 2025-05-02 23:43:47 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.47 loss:0.005 grdn:0.175 lr:4.7e-05 updt_s:0.419 data_s:0.007
|
| 621 |
+
INFO 2025-05-02 23:45:11 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.49 loss:0.006 grdn:0.179 lr:4.7e-05 updt_s:0.420 data_s:0.000
|
| 622 |
+
INFO 2025-05-02 23:46:35 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.52 loss:0.006 grdn:0.178 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 623 |
+
INFO 2025-05-02 23:47:59 ts/train.py:232 step:251K smpl:2M ep:7K epch:34.55 loss:0.007 grdn:0.190 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 624 |
+
INFO 2025-05-02 23:49:23 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.58 loss:0.006 grdn:0.187 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 625 |
+
INFO 2025-05-02 23:50:48 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.60 loss:0.006 grdn:0.186 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 626 |
+
INFO 2025-05-02 23:52:12 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.63 loss:0.005 grdn:0.175 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 627 |
+
INFO 2025-05-02 23:53:36 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.66 loss:0.006 grdn:0.193 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 628 |
+
INFO 2025-05-02 23:55:00 ts/train.py:232 step:252K smpl:2M ep:7K epch:34.69 loss:0.006 grdn:0.176 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 629 |
+
INFO 2025-05-02 23:56:24 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.71 loss:0.006 grdn:0.185 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 630 |
+
INFO 2025-05-02 23:57:48 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.74 loss:0.006 grdn:0.191 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 631 |
+
INFO 2025-05-02 23:59:12 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.77 loss:0.006 grdn:0.193 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 632 |
+
INFO 2025-05-03 00:00:36 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.80 loss:0.007 grdn:0.203 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 633 |
+
INFO 2025-05-03 00:02:01 ts/train.py:232 step:253K smpl:2M ep:7K epch:34.82 loss:0.006 grdn:0.190 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 634 |
+
INFO 2025-05-03 00:03:25 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.85 loss:0.005 grdn:0.185 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 635 |
+
INFO 2025-05-03 00:04:49 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.88 loss:0.005 grdn:0.171 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 636 |
+
INFO 2025-05-03 00:06:13 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.91 loss:0.005 grdn:0.173 lr:4.6e-05 updt_s:0.420 data_s:0.000
|
| 637 |
+
INFO 2025-05-03 00:07:37 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.93 loss:0.005 grdn:0.174 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 638 |
+
INFO 2025-05-03 00:09:02 ts/train.py:232 step:254K smpl:2M ep:7K epch:34.96 loss:0.006 grdn:0.190 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 639 |
+
INFO 2025-05-03 00:10:26 ts/train.py:232 step:255K smpl:2M ep:7K epch:34.99 loss:0.006 grdn:0.186 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 640 |
+
INFO 2025-05-03 00:11:50 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.02 loss:0.006 grdn:0.184 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 641 |
+
INFO 2025-05-03 00:13:14 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.04 loss:0.006 grdn:0.184 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 642 |
+
INFO 2025-05-03 00:14:38 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.07 loss:0.006 grdn:0.179 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 643 |
+
INFO 2025-05-03 00:16:02 ts/train.py:232 step:255K smpl:2M ep:7K epch:35.10 loss:0.005 grdn:0.176 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 644 |
+
INFO 2025-05-03 00:17:26 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.13 loss:0.006 grdn:0.180 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 645 |
+
INFO 2025-05-03 00:18:50 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.15 loss:0.006 grdn:0.178 lr:4.5e-05 updt_s:0.419 data_s:0.000
|
| 646 |
+
INFO 2025-05-03 00:20:15 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.18 loss:0.005 grdn:0.179 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 647 |
+
INFO 2025-05-03 00:21:39 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.21 loss:0.007 grdn:0.207 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 648 |
+
INFO 2025-05-03 00:23:03 ts/train.py:232 step:256K smpl:2M ep:7K epch:35.24 loss:0.006 grdn:0.192 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 649 |
+
INFO 2025-05-03 00:24:27 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.26 loss:0.006 grdn:0.190 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 650 |
+
INFO 2025-05-03 00:25:51 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.29 loss:0.006 grdn:0.178 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 651 |
+
INFO 2025-05-03 00:27:15 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.32 loss:0.005 grdn:0.173 lr:4.5e-05 updt_s:0.420 data_s:0.000
|
| 652 |
+
INFO 2025-05-03 00:28:40 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.35 loss:0.006 grdn:0.175 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 653 |
+
INFO 2025-05-03 00:30:04 ts/train.py:232 step:257K smpl:2M ep:7K epch:35.37 loss:0.006 grdn:0.184 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 654 |
+
INFO 2025-05-03 00:31:28 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.40 loss:0.005 grdn:0.170 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 655 |
+
INFO 2025-05-03 00:32:52 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.43 loss:0.005 grdn:0.167 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 656 |
+
INFO 2025-05-03 00:34:18 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.46 loss:0.005 grdn:0.174 lr:4.4e-05 updt_s:0.419 data_s:0.009
|
| 657 |
+
INFO 2025-05-03 00:35:42 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.48 loss:0.006 grdn:0.185 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 658 |
+
INFO 2025-05-03 00:37:06 ts/train.py:232 step:258K smpl:2M ep:7K epch:35.51 loss:0.006 grdn:0.181 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 659 |
+
INFO 2025-05-03 00:38:30 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.54 loss:0.006 grdn:0.180 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 660 |
+
INFO 2025-05-03 00:39:55 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.57 loss:0.006 grdn:0.187 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 661 |
+
INFO 2025-05-03 00:41:19 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.59 loss:0.006 grdn:0.177 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 662 |
+
INFO 2025-05-03 00:42:43 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.62 loss:0.006 grdn:0.181 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 663 |
+
INFO 2025-05-03 00:44:07 ts/train.py:232 step:259K smpl:2M ep:7K epch:35.65 loss:0.006 grdn:0.190 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 664 |
+
INFO 2025-05-03 00:45:31 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.68 loss:0.006 grdn:0.187 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 665 |
+
INFO 2025-05-03 00:46:56 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.70 loss:0.006 grdn:0.196 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 666 |
+
INFO 2025-05-03 00:48:20 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.73 loss:0.006 grdn:0.183 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 667 |
+
INFO 2025-05-03 00:49:44 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.76 loss:0.006 grdn:0.184 lr:4.4e-05 updt_s:0.420 data_s:0.000
|
| 668 |
+
INFO 2025-05-03 00:51:08 ts/train.py:232 step:260K smpl:2M ep:7K epch:35.79 loss:0.006 grdn:0.188 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 669 |
+
INFO 2025-05-03 00:52:32 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.81 loss:0.006 grdn:0.182 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 670 |
+
INFO 2025-05-03 00:53:57 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.84 loss:0.006 grdn:0.189 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 671 |
+
INFO 2025-05-03 00:55:21 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.87 loss:0.006 grdn:0.188 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 672 |
+
INFO 2025-05-03 00:56:45 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.90 loss:0.006 grdn:0.185 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 673 |
+
INFO 2025-05-03 00:58:09 ts/train.py:232 step:261K smpl:2M ep:7K epch:35.92 loss:0.006 grdn:0.200 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 674 |
+
INFO 2025-05-03 00:59:33 ts/train.py:232 step:262K smpl:2M ep:7K epch:35.95 loss:0.005 grdn:0.183 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 675 |
+
INFO 2025-05-03 01:00:57 ts/train.py:232 step:262K smpl:2M ep:7K epch:35.98 loss:0.006 grdn:0.194 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 676 |
+
INFO 2025-05-03 01:02:21 ts/train.py:232 step:262K smpl:2M ep:7K epch:36.01 loss:0.006 grdn:0.175 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 677 |
+
INFO 2025-05-03 01:03:45 ts/train.py:232 step:262K smpl:2M ep:7K epch:36.03 loss:0.005 grdn:0.175 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 678 |
+
INFO 2025-05-03 01:05:10 ts/train.py:232 step:262K smpl:2M ep:7K epch:36.06 loss:0.006 grdn:0.183 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 679 |
+
INFO 2025-05-03 01:06:34 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.09 loss:0.006 grdn:0.188 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 680 |
+
INFO 2025-05-03 01:07:58 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.12 loss:0.006 grdn:0.186 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 681 |
+
INFO 2025-05-03 01:09:22 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.14 loss:0.006 grdn:0.180 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 682 |
+
INFO 2025-05-03 01:10:46 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.17 loss:0.006 grdn:0.187 lr:4.3e-05 updt_s:0.420 data_s:0.000
|
| 683 |
+
INFO 2025-05-03 01:12:10 ts/train.py:232 step:263K smpl:2M ep:7K epch:36.20 loss:0.006 grdn:0.187 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 684 |
+
INFO 2025-05-03 01:13:34 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.23 loss:0.007 grdn:0.202 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 685 |
+
INFO 2025-05-03 01:14:58 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.25 loss:0.005 grdn:0.169 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 686 |
+
INFO 2025-05-03 01:16:22 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.28 loss:0.006 grdn:0.180 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 687 |
+
INFO 2025-05-03 01:17:47 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.31 loss:0.005 grdn:0.187 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 688 |
+
INFO 2025-05-03 01:19:11 ts/train.py:232 step:264K smpl:2M ep:7K epch:36.34 loss:0.006 grdn:0.186 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 689 |
+
INFO 2025-05-03 01:20:35 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.36 loss:0.005 grdn:0.162 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 690 |
+
INFO 2025-05-03 01:21:59 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.39 loss:0.006 grdn:0.180 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 691 |
+
INFO 2025-05-03 01:23:25 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.42 loss:0.006 grdn:0.188 lr:4.2e-05 updt_s:0.419 data_s:0.008
|
| 692 |
+
INFO 2025-05-03 01:24:49 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.45 loss:0.006 grdn:0.194 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 693 |
+
INFO 2025-05-03 01:26:13 ts/train.py:232 step:265K smpl:2M ep:7K epch:36.47 loss:0.005 grdn:0.171 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 694 |
+
INFO 2025-05-03 01:27:37 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.50 loss:0.006 grdn:0.191 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 695 |
+
INFO 2025-05-03 01:29:01 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.53 loss:0.005 grdn:0.177 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 696 |
+
INFO 2025-05-03 01:30:25 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.56 loss:0.006 grdn:0.185 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 697 |
+
INFO 2025-05-03 01:31:49 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.58 loss:0.006 grdn:0.175 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 698 |
+
INFO 2025-05-03 01:33:13 ts/train.py:232 step:266K smpl:2M ep:7K epch:36.61 loss:0.005 grdn:0.184 lr:4.2e-05 updt_s:0.420 data_s:0.000
|
| 699 |
+
INFO 2025-05-03 01:34:37 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.64 loss:0.006 grdn:0.187 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 700 |
+
INFO 2025-05-03 01:36:02 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.67 loss:0.005 grdn:0.180 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 701 |
+
INFO 2025-05-03 01:37:26 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.69 loss:0.006 grdn:0.191 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 702 |
+
INFO 2025-05-03 01:38:50 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.72 loss:0.006 grdn:0.200 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 703 |
+
INFO 2025-05-03 01:40:14 ts/train.py:232 step:267K smpl:2M ep:7K epch:36.75 loss:0.005 grdn:0.170 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 704 |
+
INFO 2025-05-03 01:41:38 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.78 loss:0.005 grdn:0.187 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 705 |
+
INFO 2025-05-03 01:43:02 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.80 loss:0.005 grdn:0.183 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 706 |
+
INFO 2025-05-03 01:44:27 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.83 loss:0.006 grdn:0.190 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 707 |
+
INFO 2025-05-03 01:45:51 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.86 loss:0.006 grdn:0.188 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 708 |
+
INFO 2025-05-03 01:47:15 ts/train.py:232 step:268K smpl:2M ep:7K epch:36.89 loss:0.006 grdn:0.192 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 709 |
+
INFO 2025-05-03 01:48:39 ts/train.py:232 step:269K smpl:2M ep:7K epch:36.91 loss:0.005 grdn:0.163 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 710 |
+
INFO 2025-05-03 01:50:03 ts/train.py:232 step:269K smpl:2M ep:7K epch:36.94 loss:0.006 grdn:0.178 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 711 |
+
INFO 2025-05-03 01:51:27 ts/train.py:232 step:269K smpl:2M ep:7K epch:36.97 loss:0.006 grdn:0.187 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 712 |
+
INFO 2025-05-03 01:52:52 ts/train.py:232 step:269K smpl:2M ep:7K epch:37.00 loss:0.006 grdn:0.176 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 713 |
+
INFO 2025-05-03 01:54:16 ts/train.py:232 step:269K smpl:2M ep:7K epch:37.02 loss:0.005 grdn:0.163 lr:4.1e-05 updt_s:0.420 data_s:0.000
|
| 714 |
+
INFO 2025-05-03 01:55:40 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.05 loss:0.006 grdn:0.184 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 715 |
+
INFO 2025-05-03 01:57:04 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.08 loss:0.005 grdn:0.185 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 716 |
+
INFO 2025-05-03 01:58:28 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.11 loss:0.006 grdn:0.191 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 717 |
+
INFO 2025-05-03 01:59:52 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.13 loss:0.006 grdn:0.190 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 718 |
+
INFO 2025-05-03 02:01:17 ts/train.py:232 step:270K smpl:2M ep:7K epch:37.16 loss:0.006 grdn:0.199 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 719 |
+
INFO 2025-05-03 02:02:41 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.19 loss:0.005 grdn:0.174 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 720 |
+
INFO 2025-05-03 02:04:05 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.22 loss:0.006 grdn:0.185 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 721 |
+
INFO 2025-05-03 02:05:29 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.24 loss:0.007 grdn:0.203 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 722 |
+
INFO 2025-05-03 02:06:53 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.27 loss:0.006 grdn:0.194 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 723 |
+
INFO 2025-05-03 02:08:17 ts/train.py:232 step:271K smpl:2M ep:7K epch:37.30 loss:0.005 grdn:0.172 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 724 |
+
INFO 2025-05-03 02:09:42 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.32 loss:0.005 grdn:0.171 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 725 |
+
INFO 2025-05-03 02:11:06 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.35 loss:0.005 grdn:0.184 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 726 |
+
INFO 2025-05-03 02:12:30 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.38 loss:0.006 grdn:0.193 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 727 |
+
INFO 2025-05-03 02:13:56 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.41 loss:0.006 grdn:0.183 lr:4.0e-05 updt_s:0.419 data_s:0.009
|
| 728 |
+
INFO 2025-05-03 02:15:20 ts/train.py:232 step:272K smpl:2M ep:7K epch:37.43 loss:0.005 grdn:0.178 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 729 |
+
INFO 2025-05-03 02:16:44 ts/train.py:232 step:273K smpl:2M ep:7K epch:37.46 loss:0.006 grdn:0.189 lr:4.0e-05 updt_s:0.420 data_s:0.000
|
| 730 |
+
INFO 2025-05-03 02:18:08 ts/train.py:232 step:273K smpl:2M ep:7K epch:37.49 loss:0.006 grdn:0.187 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 731 |
+
INFO 2025-05-03 02:19:32 ts/train.py:232 step:273K smpl:2M ep:8K epch:37.52 loss:0.006 grdn:0.191 lr:3.9e-05 updt_s:0.419 data_s:0.000
|
| 732 |
+
INFO 2025-05-03 02:20:56 ts/train.py:232 step:273K smpl:2M ep:8K epch:37.54 loss:0.005 grdn:0.172 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 733 |
+
INFO 2025-05-03 02:22:20 ts/train.py:232 step:273K smpl:2M ep:8K epch:37.57 loss:0.005 grdn:0.171 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 734 |
+
INFO 2025-05-03 02:23:44 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.60 loss:0.005 grdn:0.178 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 735 |
+
INFO 2025-05-03 02:25:08 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.63 loss:0.006 grdn:0.205 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 736 |
+
INFO 2025-05-03 02:26:33 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.65 loss:0.006 grdn:0.188 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 737 |
+
INFO 2025-05-03 02:27:57 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.68 loss:0.005 grdn:0.180 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 738 |
+
INFO 2025-05-03 02:29:21 ts/train.py:232 step:274K smpl:2M ep:8K epch:37.71 loss:0.006 grdn:0.182 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 739 |
+
INFO 2025-05-03 02:30:45 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.74 loss:0.006 grdn:0.204 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 740 |
+
INFO 2025-05-03 02:32:09 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.76 loss:0.006 grdn:0.196 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 741 |
+
INFO 2025-05-03 02:33:34 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.79 loss:0.006 grdn:0.183 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 742 |
+
INFO 2025-05-03 02:34:58 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.82 loss:0.005 grdn:0.174 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 743 |
+
INFO 2025-05-03 02:36:22 ts/train.py:232 step:275K smpl:2M ep:8K epch:37.85 loss:0.006 grdn:0.186 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 744 |
+
INFO 2025-05-03 02:37:46 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.87 loss:0.005 grdn:0.185 lr:3.9e-05 updt_s:0.420 data_s:0.000
|
| 745 |
+
INFO 2025-05-03 02:39:10 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.90 loss:0.006 grdn:0.187 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 746 |
+
INFO 2025-05-03 02:40:34 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.93 loss:0.006 grdn:0.184 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 747 |
+
INFO 2025-05-03 02:41:59 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.96 loss:0.005 grdn:0.178 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 748 |
+
INFO 2025-05-03 02:43:23 ts/train.py:232 step:276K smpl:2M ep:8K epch:37.98 loss:0.006 grdn:0.188 lr:3.8e-05 updt_s:0.419 data_s:0.000
|
| 749 |
+
INFO 2025-05-03 02:44:47 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.01 loss:0.006 grdn:0.189 lr:3.8e-05 updt_s:0.419 data_s:0.000
|
| 750 |
+
INFO 2025-05-03 02:46:11 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.04 loss:0.006 grdn:0.187 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 751 |
+
INFO 2025-05-03 02:47:35 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.07 loss:0.006 grdn:0.188 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 752 |
+
INFO 2025-05-03 02:48:59 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.09 loss:0.006 grdn:0.193 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 753 |
+
INFO 2025-05-03 02:50:23 ts/train.py:232 step:277K smpl:2M ep:8K epch:38.12 loss:0.005 grdn:0.180 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 754 |
+
INFO 2025-05-03 02:51:47 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.15 loss:0.006 grdn:0.194 lr:3.8e-05 updt_s:0.419 data_s:0.000
|
| 755 |
+
INFO 2025-05-03 02:53:11 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.18 loss:0.006 grdn:0.192 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 756 |
+
INFO 2025-05-03 02:54:35 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.20 loss:0.006 grdn:0.197 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 757 |
+
INFO 2025-05-03 02:55:59 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.23 loss:0.005 grdn:0.179 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 758 |
+
INFO 2025-05-03 02:57:24 ts/train.py:232 step:278K smpl:2M ep:8K epch:38.26 loss:0.005 grdn:0.189 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 759 |
+
INFO 2025-05-03 02:58:48 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.29 loss:0.005 grdn:0.183 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 760 |
+
INFO 2025-05-03 03:00:12 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.31 loss:0.005 grdn:0.176 lr:3.8e-05 updt_s:0.420 data_s:0.000
|
| 761 |
+
INFO 2025-05-03 03:01:36 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.34 loss:0.005 grdn:0.177 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 762 |
+
INFO 2025-05-03 03:03:02 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.37 loss:0.005 grdn:0.181 lr:3.7e-05 updt_s:0.419 data_s:0.009
|
| 763 |
+
INFO 2025-05-03 03:04:26 ts/train.py:232 step:279K smpl:2M ep:8K epch:38.40 loss:0.005 grdn:0.185 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 764 |
+
INFO 2025-05-03 03:05:50 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.42 loss:0.006 grdn:0.192 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 765 |
+
INFO 2025-05-03 03:07:14 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.45 loss:0.005 grdn:0.165 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 766 |
+
INFO 2025-05-03 03:08:38 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.48 loss:0.005 grdn:0.183 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 767 |
+
INFO 2025-05-03 03:10:02 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.51 loss:0.005 grdn:0.188 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 768 |
+
INFO 2025-05-03 03:11:27 ts/train.py:232 step:280K smpl:2M ep:8K epch:38.53 loss:0.005 grdn:0.181 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 769 |
+
INFO 2025-05-03 03:12:51 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.56 loss:0.005 grdn:0.174 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 770 |
+
INFO 2025-05-03 03:14:15 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.59 loss:0.005 grdn:0.174 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 771 |
+
INFO 2025-05-03 03:15:39 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.62 loss:0.006 grdn:0.186 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 772 |
+
INFO 2025-05-03 03:17:03 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.64 loss:0.006 grdn:0.195 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 773 |
+
INFO 2025-05-03 03:18:27 ts/train.py:232 step:281K smpl:2M ep:8K epch:38.67 loss:0.005 grdn:0.188 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 774 |
+
INFO 2025-05-03 03:19:51 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.70 loss:0.004 grdn:0.164 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 775 |
+
INFO 2025-05-03 03:21:15 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.73 loss:0.005 grdn:0.176 lr:3.7e-05 updt_s:0.419 data_s:0.000
|
| 776 |
+
INFO 2025-05-03 03:22:39 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.75 loss:0.006 grdn:0.194 lr:3.7e-05 updt_s:0.420 data_s:0.000
|
| 777 |
+
INFO 2025-05-03 03:24:04 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.78 loss:0.006 grdn:0.193 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 778 |
+
INFO 2025-05-03 03:25:28 ts/train.py:232 step:282K smpl:2M ep:8K epch:38.81 loss:0.006 grdn:0.191 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 779 |
+
INFO 2025-05-03 03:26:52 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.84 loss:0.005 grdn:0.185 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 780 |
+
INFO 2025-05-03 03:28:16 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.86 loss:0.005 grdn:0.189 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 781 |
+
INFO 2025-05-03 03:29:40 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.89 loss:0.004 grdn:0.167 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 782 |
+
INFO 2025-05-03 03:31:04 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.92 loss:0.005 grdn:0.174 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 783 |
+
INFO 2025-05-03 03:32:28 ts/train.py:232 step:283K smpl:2M ep:8K epch:38.95 loss:0.005 grdn:0.179 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 784 |
+
INFO 2025-05-03 03:33:52 ts/train.py:232 step:284K smpl:2M ep:8K epch:38.97 loss:0.005 grdn:0.176 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 785 |
+
INFO 2025-05-03 03:35:16 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.00 loss:0.005 grdn:0.188 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 786 |
+
INFO 2025-05-03 03:36:40 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.03 loss:0.005 grdn:0.186 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 787 |
+
INFO 2025-05-03 03:38:04 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.06 loss:0.005 grdn:0.183 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 788 |
+
INFO 2025-05-03 03:39:28 ts/train.py:232 step:284K smpl:2M ep:8K epch:39.08 loss:0.006 grdn:0.197 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 789 |
+
INFO 2025-05-03 03:40:52 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.11 loss:0.005 grdn:0.187 lr:3.6e-05 updt_s:0.419 data_s:0.000
|
| 790 |
+
INFO 2025-05-03 03:42:17 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.14 loss:0.005 grdn:0.178 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 791 |
+
INFO 2025-05-03 03:43:41 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.17 loss:0.005 grdn:0.175 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 792 |
+
INFO 2025-05-03 03:45:05 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.19 loss:0.006 grdn:0.183 lr:3.6e-05 updt_s:0.420 data_s:0.000
|
| 793 |
+
INFO 2025-05-03 03:46:29 ts/train.py:232 step:285K smpl:2M ep:8K epch:39.22 loss:0.006 grdn:0.191 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 794 |
+
INFO 2025-05-03 03:47:53 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.25 loss:0.005 grdn:0.192 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 795 |
+
INFO 2025-05-03 03:49:17 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.28 loss:0.005 grdn:0.173 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 796 |
+
INFO 2025-05-03 03:50:41 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.30 loss:0.005 grdn:0.184 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 797 |
+
INFO 2025-05-03 03:52:05 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.33 loss:0.005 grdn:0.171 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 798 |
+
INFO 2025-05-03 03:53:31 ts/train.py:232 step:286K smpl:2M ep:8K epch:39.36 loss:0.005 grdn:0.180 lr:3.5e-05 updt_s:0.419 data_s:0.008
|
| 799 |
+
INFO 2025-05-03 03:54:55 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.39 loss:0.005 grdn:0.177 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 800 |
+
INFO 2025-05-03 03:56:19 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.41 loss:0.005 grdn:0.190 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 801 |
+
INFO 2025-05-03 03:57:43 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.44 loss:0.006 grdn:0.189 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 802 |
+
INFO 2025-05-03 03:59:07 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.47 loss:0.005 grdn:0.172 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 803 |
+
INFO 2025-05-03 04:00:31 ts/train.py:232 step:287K smpl:2M ep:8K epch:39.50 loss:0.005 grdn:0.171 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 804 |
+
INFO 2025-05-03 04:01:55 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.52 loss:0.006 grdn:0.195 lr:3.5e-05 updt_s:0.419 data_s:0.000
|
| 805 |
+
INFO 2025-05-03 04:03:19 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.55 loss:0.005 grdn:0.180 lr:3.5e-05 updt_s:0.419 data_s:0.000
|
| 806 |
+
INFO 2025-05-03 04:04:44 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.58 loss:0.005 grdn:0.184 lr:3.5e-05 updt_s:0.419 data_s:0.000
|
| 807 |
+
INFO 2025-05-03 04:06:08 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.61 loss:0.005 grdn:0.178 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 808 |
+
INFO 2025-05-03 04:07:32 ts/train.py:232 step:288K smpl:2M ep:8K epch:39.63 loss:0.005 grdn:0.177 lr:3.5e-05 updt_s:0.420 data_s:0.000
|
| 809 |
+
INFO 2025-05-03 04:08:56 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.66 loss:0.005 grdn:0.185 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 810 |
+
INFO 2025-05-03 04:10:20 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.69 loss:0.005 grdn:0.186 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 811 |
+
INFO 2025-05-03 04:11:44 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.72 loss:0.006 grdn:0.199 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 812 |
+
INFO 2025-05-03 04:13:08 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.74 loss:0.006 grdn:0.198 lr:3.4e-05 updt_s:0.419 data_s:0.000
|
| 813 |
+
INFO 2025-05-03 04:14:32 ts/train.py:232 step:289K smpl:2M ep:8K epch:39.77 loss:0.005 grdn:0.180 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 814 |
+
INFO 2025-05-03 04:15:56 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.80 loss:0.005 grdn:0.186 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 815 |
+
INFO 2025-05-03 04:17:20 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.83 loss:0.005 grdn:0.191 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 816 |
+
INFO 2025-05-03 04:18:44 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.85 loss:0.005 grdn:0.174 lr:3.4e-05 updt_s:0.419 data_s:0.000
|
| 817 |
+
INFO 2025-05-03 04:20:08 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.88 loss:0.005 grdn:0.187 lr:3.4e-05 updt_s:0.419 data_s:0.000
|
| 818 |
+
INFO 2025-05-03 04:21:32 ts/train.py:232 step:290K smpl:2M ep:8K epch:39.91 loss:0.004 grdn:0.175 lr:3.4e-05 updt_s:0.419 data_s:0.000
|
| 819 |
+
INFO 2025-05-03 04:22:57 ts/train.py:232 step:291K smpl:2M ep:8K epch:39.94 loss:0.005 grdn:0.184 lr:3.4e-05 updt_s:0.419 data_s:0.000
|
| 820 |
+
INFO 2025-05-03 04:24:21 ts/train.py:232 step:291K smpl:2M ep:8K epch:39.96 loss:0.006 grdn:0.185 lr:3.4e-05 updt_s:0.419 data_s:0.000
|
| 821 |
+
INFO 2025-05-03 04:25:45 ts/train.py:232 step:291K smpl:2M ep:8K epch:39.99 loss:0.005 grdn:0.181 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 822 |
+
INFO 2025-05-03 04:27:09 ts/train.py:232 step:291K smpl:2M ep:8K epch:40.02 loss:0.005 grdn:0.178 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 823 |
+
INFO 2025-05-03 04:28:33 ts/train.py:232 step:291K smpl:2M ep:8K epch:40.05 loss:0.005 grdn:0.182 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 824 |
+
INFO 2025-05-03 04:29:57 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.07 loss:0.005 grdn:0.175 lr:3.4e-05 updt_s:0.420 data_s:0.000
|
| 825 |
+
INFO 2025-05-03 04:31:21 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.10 loss:0.005 grdn:0.184 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 826 |
+
INFO 2025-05-03 04:32:45 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.13 loss:0.005 grdn:0.187 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 827 |
+
INFO 2025-05-03 04:34:10 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.16 loss:0.005 grdn:0.179 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 828 |
+
INFO 2025-05-03 04:35:34 ts/train.py:232 step:292K smpl:2M ep:8K epch:40.18 loss:0.005 grdn:0.190 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 829 |
+
INFO 2025-05-03 04:36:58 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.21 loss:0.005 grdn:0.185 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 830 |
+
INFO 2025-05-03 04:38:22 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.24 loss:0.005 grdn:0.202 lr:3.3e-05 updt_s:0.419 data_s:0.000
|
| 831 |
+
INFO 2025-05-03 04:39:46 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.27 loss:0.004 grdn:0.168 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 832 |
+
INFO 2025-05-03 04:41:10 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.29 loss:0.005 grdn:0.177 lr:3.3e-05 updt_s:0.419 data_s:0.000
|
| 833 |
+
INFO 2025-05-03 04:42:36 ts/train.py:232 step:293K smpl:2M ep:8K epch:40.32 loss:0.006 grdn:0.197 lr:3.3e-05 updt_s:0.419 data_s:0.009
|
| 834 |
+
INFO 2025-05-03 04:44:00 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.35 loss:0.005 grdn:0.184 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 835 |
+
INFO 2025-05-03 04:45:24 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.38 loss:0.005 grdn:0.180 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 836 |
+
INFO 2025-05-03 04:46:48 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.40 loss:0.005 grdn:0.184 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 837 |
+
INFO 2025-05-03 04:48:12 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.43 loss:0.004 grdn:0.166 lr:3.3e-05 updt_s:0.419 data_s:0.000
|
| 838 |
+
INFO 2025-05-03 04:49:36 ts/train.py:232 step:294K smpl:2M ep:8K epch:40.46 loss:0.005 grdn:0.180 lr:3.3e-05 updt_s:0.420 data_s:0.000
|
| 839 |
+
INFO 2025-05-03 04:51:00 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.49 loss:0.005 grdn:0.171 lr:3.3e-05 updt_s:0.419 data_s:0.000
|
| 840 |
+
INFO 2025-05-03 04:52:24 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.51 loss:0.004 grdn:0.176 lr:3.3e-05 updt_s:0.419 data_s:0.000
|
| 841 |
+
INFO 2025-05-03 04:53:48 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.54 loss:0.005 grdn:0.183 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 842 |
+
INFO 2025-05-03 04:55:12 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.57 loss:0.004 grdn:0.156 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 843 |
+
INFO 2025-05-03 04:56:37 ts/train.py:232 step:295K smpl:2M ep:8K epch:40.60 loss:0.005 grdn:0.180 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 844 |
+
INFO 2025-05-03 04:58:01 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.62 loss:0.005 grdn:0.188 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 845 |
+
INFO 2025-05-03 04:59:25 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.65 loss:0.004 grdn:0.171 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 846 |
+
INFO 2025-05-03 05:00:49 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.68 loss:0.005 grdn:0.193 lr:3.2e-05 updt_s:0.420 data_s:0.000
|
| 847 |
+
INFO 2025-05-03 05:02:13 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.71 loss:0.005 grdn:0.183 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 848 |
+
INFO 2025-05-03 05:03:37 ts/train.py:232 step:296K smpl:2M ep:8K epch:40.73 loss:0.006 grdn:0.201 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 849 |
+
INFO 2025-05-03 05:05:01 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.76 loss:0.005 grdn:0.188 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 850 |
+
INFO 2025-05-03 05:06:25 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.79 loss:0.005 grdn:0.185 lr:3.2e-05 updt_s:0.420 data_s:0.000
|
| 851 |
+
INFO 2025-05-03 05:07:49 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.82 loss:0.005 grdn:0.189 lr:3.2e-05 updt_s:0.420 data_s:0.000
|
| 852 |
+
INFO 2025-05-03 05:09:13 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.84 loss:0.006 grdn:0.196 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 853 |
+
INFO 2025-05-03 05:10:37 ts/train.py:232 step:297K smpl:2M ep:8K epch:40.87 loss:0.005 grdn:0.180 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 854 |
+
INFO 2025-05-03 05:12:01 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.90 loss:0.005 grdn:0.184 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 855 |
+
INFO 2025-05-03 05:13:25 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.93 loss:0.005 grdn:0.182 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 856 |
+
INFO 2025-05-03 05:14:49 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.95 loss:0.004 grdn:0.166 lr:3.2e-05 updt_s:0.419 data_s:0.000
|
| 857 |
+
INFO 2025-05-03 05:16:13 ts/train.py:232 step:298K smpl:2M ep:8K epch:40.98 loss:0.005 grdn:0.183 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 858 |
+
INFO 2025-05-03 05:17:37 ts/train.py:232 step:298K smpl:2M ep:8K epch:41.01 loss:0.005 grdn:0.189 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 859 |
+
INFO 2025-05-03 05:19:01 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.04 loss:0.005 grdn:0.182 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 860 |
+
INFO 2025-05-03 05:20:25 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.06 loss:0.005 grdn:0.187 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 861 |
+
INFO 2025-05-03 05:21:49 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.09 loss:0.005 grdn:0.181 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 862 |
+
INFO 2025-05-03 05:23:14 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.12 loss:0.006 grdn:0.192 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 863 |
+
INFO 2025-05-03 05:24:38 ts/train.py:232 step:299K smpl:2M ep:8K epch:41.15 loss:0.004 grdn:0.170 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 864 |
+
INFO 2025-05-03 05:26:02 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.17 loss:0.005 grdn:0.183 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 865 |
+
INFO 2025-05-03 05:27:26 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.20 loss:0.005 grdn:0.186 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 866 |
+
INFO 2025-05-03 05:28:50 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.23 loss:0.005 grdn:0.171 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 867 |
+
INFO 2025-05-03 05:28:50 ts/train.py:241 Checkpoint policy after step 300000
|
| 868 |
+
INFO 2025-05-03 05:30:17 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.26 loss:0.004 grdn:0.175 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 869 |
+
INFO 2025-05-03 05:31:41 ts/train.py:232 step:300K smpl:2M ep:8K epch:41.28 loss:0.005 grdn:0.185 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 870 |
+
INFO 2025-05-03 05:33:07 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.31 loss:0.005 grdn:0.191 lr:3.1e-05 updt_s:0.419 data_s:0.008
|
| 871 |
+
INFO 2025-05-03 05:34:31 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.34 loss:0.005 grdn:0.187 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 872 |
+
INFO 2025-05-03 05:35:55 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.37 loss:0.005 grdn:0.182 lr:3.1e-05 updt_s:0.420 data_s:0.000
|
| 873 |
+
INFO 2025-05-03 05:37:19 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.39 loss:0.005 grdn:0.183 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 874 |
+
INFO 2025-05-03 05:38:43 ts/train.py:232 step:301K smpl:2M ep:8K epch:41.42 loss:0.005 grdn:0.178 lr:3.1e-05 updt_s:0.419 data_s:0.000
|
| 875 |
+
INFO 2025-05-03 05:40:07 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.45 loss:0.005 grdn:0.194 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 876 |
+
INFO 2025-05-03 05:41:31 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.48 loss:0.005 grdn:0.189 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 877 |
+
INFO 2025-05-03 05:42:55 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.50 loss:0.005 grdn:0.181 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 878 |
+
INFO 2025-05-03 05:44:20 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.53 loss:0.005 grdn:0.188 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 879 |
+
INFO 2025-05-03 05:45:44 ts/train.py:232 step:302K smpl:2M ep:8K epch:41.56 loss:0.004 grdn:0.176 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 880 |
+
INFO 2025-05-03 05:47:08 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.59 loss:0.005 grdn:0.190 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 881 |
+
INFO 2025-05-03 05:48:32 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.61 loss:0.005 grdn:0.189 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 882 |
+
INFO 2025-05-03 05:49:56 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.64 loss:0.005 grdn:0.181 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 883 |
+
INFO 2025-05-03 05:51:20 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.67 loss:0.005 grdn:0.175 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 884 |
+
INFO 2025-05-03 05:52:44 ts/train.py:232 step:303K smpl:2M ep:8K epch:41.70 loss:0.005 grdn:0.198 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 885 |
+
INFO 2025-05-03 05:54:08 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.72 loss:0.005 grdn:0.181 lr:3.0e-05 updt_s:0.420 data_s:0.000
|
| 886 |
+
INFO 2025-05-03 05:55:32 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.75 loss:0.006 grdn:0.199 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 887 |
+
INFO 2025-05-03 05:56:56 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.78 loss:0.005 grdn:0.189 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 888 |
+
INFO 2025-05-03 05:58:20 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.81 loss:0.005 grdn:0.186 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 889 |
+
INFO 2025-05-03 05:59:45 ts/train.py:232 step:304K smpl:2M ep:8K epch:41.83 loss:0.005 grdn:0.179 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 890 |
+
INFO 2025-05-03 06:01:09 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.86 loss:0.004 grdn:0.178 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 891 |
+
INFO 2025-05-03 06:02:33 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.89 loss:0.004 grdn:0.164 lr:3.0e-05 updt_s:0.419 data_s:0.000
|
| 892 |
+
INFO 2025-05-03 06:03:57 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.92 loss:0.005 grdn:0.183 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 893 |
+
INFO 2025-05-03 06:05:21 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.94 loss:0.005 grdn:0.176 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 894 |
+
INFO 2025-05-03 06:06:45 ts/train.py:232 step:305K smpl:2M ep:8K epch:41.97 loss:0.005 grdn:0.194 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 895 |
+
INFO 2025-05-03 06:08:09 ts/train.py:232 step:306K smpl:2M ep:8K epch:42.00 loss:0.005 grdn:0.184 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 896 |
+
INFO 2025-05-03 06:09:33 ts/train.py:232 step:306K smpl:2M ep:8K epch:42.02 loss:0.005 grdn:0.183 lr:2.9e-05 updt_s:0.420 data_s:0.000
|
| 897 |
+
INFO 2025-05-03 06:10:57 ts/train.py:232 step:306K smpl:2M ep:8K epch:42.05 loss:0.005 grdn:0.178 lr:2.9e-05 updt_s:0.420 data_s:0.000
|
| 898 |
+
INFO 2025-05-03 06:12:21 ts/train.py:232 step:306K smpl:2M ep:8K epch:42.08 loss:0.004 grdn:0.172 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 899 |
+
INFO 2025-05-03 06:13:45 ts/train.py:232 step:306K smpl:2M ep:8K epch:42.11 loss:0.005 grdn:0.177 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 900 |
+
INFO 2025-05-03 06:15:09 ts/train.py:232 step:307K smpl:2M ep:8K epch:42.13 loss:0.005 grdn:0.178 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 901 |
+
INFO 2025-05-03 06:16:33 ts/train.py:232 step:307K smpl:2M ep:8K epch:42.16 loss:0.005 grdn:0.185 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 902 |
+
INFO 2025-05-03 06:17:57 ts/train.py:232 step:307K smpl:2M ep:8K epch:42.19 loss:0.004 grdn:0.178 lr:2.9e-05 updt_s:0.419 data_s:0.000
|
| 903 |
+
INFO 2025-05-03 06:19:21 ts/train.py:232 step:307K smpl:2M ep:8K epch:42.22 loss:0.005 grdn:0.180 lr:2.9e-05 updt_s:0.420 data_s:0.000
|
DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/run-20250502_093126-icmt0scg/logs/debug-internal.log
CHANGED
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DP_cube_downDims1_cropNo_freeze0_32_32_ema0_1e-4/wandb/run-20250502_093126-icmt0scg/run-icmt0scg.wandb
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