adirik/gaussiandreamer 🔢✓📝 → 🖼️

▶️ 264 runs 📅 Mar 2024 ⚙️ Cog 0.8.3 🔗 GitHub 📄 Paper
game-asset-generation gaussian-splatting text-to-3d

About

Fast text-to-3D Gaussian generation by bridging 2D and 3D diffusion models

Example Output

Prompt:

"A pineapple"

Output

Example output

Performance Metrics

227.97s Prediction Time
404.43s Total Time
All Input Parameters 
{
  "avatar": false,
  "prompt": "A pineapple",
  "max_steps": 200,
  "guidance_scale": 100,
  "negative_prompt": "ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions"
}
Input Parameters
seed Type: integer
The seed to use for the generation. If not specified, a random value will be used.
avatar Type: booleanDefault: false
Whether to generate an avatar or not.
prompt (required) Type: string
Prompt to generate a 3D object.
max_steps Type: integerDefault: 1200
Number of iterations to run the model for.
guidance_scale Type: numberDefault: 100
The scale of the guidance loss. Higher values will result in more accurate meshes but may also result in artifacts.
negative_prompt Type: stringDefault: ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions
Prompt for the negative class. If not specified, default value will be used.
Output Schema

Output

Type: string • Format: uri

Example Execution Logs 
Seed set to 2631897297
Using 16bit Automatic Mixed Precision (AMP)
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
You are using a CUDA device ('NVIDIA A40') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
prompt A pineapple
  0%|          | 0/64 [00:00<?, ?it/s]
  2%|▏         | 1/64 [00:00<00:08,  7.75it/s]
  3%|▎         | 2/64 [00:00<00:07,  7.96it/s]
  5%|▍         | 3/64 [00:00<00:07,  8.04it/s]
  6%|▋         | 4/64 [00:00<00:07,  8.08it/s]
  8%|▊         | 5/64 [00:00<00:07,  8.10it/s]
  9%|▉         | 6/64 [00:00<00:07,  8.11it/s]
 11%|█         | 7/64 [00:00<00:07,  8.09it/s]
 12%|█▎        | 8/64 [00:00<00:06,  8.08it/s]
 14%|█▍        | 9/64 [00:01<00:06,  8.09it/s]
 16%|█▌        | 10/64 [00:01<00:06,  8.10it/s]
 17%|█▋        | 11/64 [00:01<00:06,  8.10it/s]
 19%|█▉        | 12/64 [00:01<00:06,  8.11it/s]
 20%|██        | 13/64 [00:01<00:06,  8.11it/s]
 22%|██▏       | 14/64 [00:01<00:06,  8.12it/s]
 23%|██▎       | 15/64 [00:01<00:06,  8.12it/s]
 25%|██▌       | 16/64 [00:01<00:05,  8.11it/s]
 27%|██▋       | 17/64 [00:02<00:05,  8.10it/s]
 28%|██▊       | 18/64 [00:02<00:05,  8.12it/s]
 30%|██▉       | 19/64 [00:02<00:05,  8.13it/s]
 31%|███▏      | 20/64 [00:02<00:05,  8.12it/s]
 33%|███▎      | 21/64 [00:02<00:05,  8.13it/s]
 34%|███▍      | 22/64 [00:02<00:05,  8.14it/s]
 36%|███▌      | 23/64 [00:02<00:05,  8.14it/s]
 38%|███▊      | 24/64 [00:02<00:04,  8.15it/s]
 39%|███▉      | 25/64 [00:03<00:04,  8.15it/s]
 41%|████      | 26/64 [00:03<00:04,  8.15it/s]
 42%|████▏     | 27/64 [00:03<00:04,  8.14it/s]
 44%|████▍     | 28/64 [00:03<00:04,  8.11it/s]
 45%|████▌     | 29/64 [00:03<00:04,  8.11it/s]
 47%|████▋     | 30/64 [00:03<00:04,  8.12it/s]
 48%|████▊     | 31/64 [00:03<00:04,  8.13it/s]
 50%|█████     | 32/64 [00:03<00:03,  8.13it/s]
 52%|█████▏    | 33/64 [00:04<00:03,  8.13it/s]
 53%|█████▎    | 34/64 [00:04<00:03,  8.13it/s]
 55%|█████▍    | 35/64 [00:04<00:03,  8.13it/s]
 56%|█████▋    | 36/64 [00:04<00:03,  8.14it/s]
 58%|█████▊    | 37/64 [00:04<00:03,  8.14it/s]
 59%|█████▉    | 38/64 [00:04<00:03,  8.14it/s]
 61%|██████    | 39/64 [00:04<00:03,  8.13it/s]
 62%|██████▎   | 40/64 [00:04<00:02,  8.13it/s]
 64%|██████▍   | 41/64 [00:05<00:02,  8.13it/s]
 66%|██████▌   | 42/64 [00:05<00:02,  8.13it/s]
 67%|██████▋   | 43/64 [00:05<00:02,  8.13it/s]
 69%|██████▉   | 44/64 [00:05<00:02,  8.14it/s]
 70%|███████   | 45/64 [00:05<00:02,  8.14it/s]
 72%|███████▏  | 46/64 [00:05<00:02,  8.14it/s]
 73%|███████▎  | 47/64 [00:05<00:02,  8.15it/s]
 75%|███████▌  | 48/64 [00:05<00:01,  8.15it/s]
 77%|███████▋  | 49/64 [00:06<00:01,  8.15it/s]
 78%|███████▊  | 50/64 [00:06<00:01,  8.15it/s]
 80%|███████▉  | 51/64 [00:06<00:01,  8.15it/s]
 81%|████████▏ | 52/64 [00:06<00:01,  8.16it/s]
 83%|████████▎ | 53/64 [00:06<00:01,  8.16it/s]
 84%|████████▍ | 54/64 [00:06<00:01,  8.16it/s]
 86%|████████▌ | 55/64 [00:06<00:01,  8.16it/s]
 88%|████████▊ | 56/64 [00:06<00:00,  8.16it/s]
 89%|████████▉ | 57/64 [00:07<00:00,  8.16it/s]
 91%|█████████ | 58/64 [00:07<00:00,  8.15it/s]
 92%|█████████▏| 59/64 [00:07<00:00,  8.15it/s]
 94%|█████████▍| 60/64 [00:07<00:00,  8.15it/s]
 95%|█████████▌| 61/64 [00:07<00:00,  8.16it/s]
 97%|█████████▋| 62/64 [00:07<00:00,  8.16it/s]
 98%|█████████▊| 63/64 [00:07<00:00,  8.16it/s]
100%|██████████| 64/64 [00:07<00:00,  8.19it/s]
/shap-e/shap_e/models/stf/renderer.py:286: UserWarning: exception rendering with PyTorch3D: No module named 'pytorch3d'
warnings.warn(f"exception rendering with PyTorch3D: {exc}")
/shap-e/shap_e/models/stf/renderer.py:287: UserWarning: falling back on native PyTorch renderer, which does not support full gradients
warnings.warn(
Number of points at initialisation :  113504
| Name | Type | Params
------------------------------
------------------------------
0         Trainable params
0         Non-trainable params
0         Total params
0.000     Total estimated model params size (MB)
Validation results will be saved to outputs/gaussiandreamer-sd/A_pineapple@20240304-151448/save
Using prompt [A pineapple] and negative prompt [ugly, bad anatomy, blurry, pixelated obscure, unnatural colors, poor lighting, dull, and unclear, cropped, lowres, low quality, artifacts, duplicate, morbid, mutilated, poorly drawn face, deformed, dehydrated, bad proportions]
Using view-dependent prompts [side]:[A pineapple, side view] [front]:[A pineapple, front view] [back]:[A pineapple, back view] [overhead]:[A pineapple, overhead view]
Loading Stable Diffusion ...
Loading pipeline components...:   0%|          | 0/4 [00:00<?, ?it/s]
Loading pipeline components...:  25%|██▌       | 1/4 [00:00<00:00,  8.42it/s]
Loading pipeline components...:  50%|█████     | 2/4 [00:00<00:00,  2.53it/s]
Loading pipeline components...: 100%|██████████| 4/4 [00:01<00:00,  2.68it/s]
Loading pipeline components...: 100%|██████████| 4/4 [00:01<00:00,  2.80it/s]
Loaded Stable Diffusion!
/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.
/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.
Training: |          | 0/? [00:00<?, ?it/s]
Training: |          | 0/? [00:00<?, ?it/s]
  0%|          | 0/9 [00:00<?, ?it/s]
 11%|█         | 1/9 [00:00<00:01,  5.97it/s]
 56%|█████▌    | 5/9 [00:00<00:00, 18.48it/s]
 89%|████████▉ | 8/9 [00:00<00:00, 21.70it/s]
                                             
  0%|          | 0/35 [00:00<?, ?it/s]
 11%|█▏        | 4/35 [00:00<00:01, 29.11it/s]
 20%|██        | 7/35 [00:00<00:01, 27.71it/s]
 29%|██▊       | 10/35 [00:00<00:00, 27.21it/s]
 37%|███▋      | 13/35 [00:00<00:00, 27.01it/s]
 46%|████▌     | 16/35 [00:00<00:00, 26.85it/s]
 54%|█████▍    | 19/35 [00:00<00:00, 26.79it/s]
 63%|██████▎   | 22/35 [00:00<00:00, 26.70it/s]
 71%|███████▏  | 25/35 [00:00<00:00, 26.61it/s]
 80%|████████  | 28/35 [00:01<00:00, 26.61it/s]
 89%|████████▊ | 31/35 [00:01<00:00, 26.60it/s]
 97%|█████████▋| 34/35 [00:01<00:00, 26.53it/s]
                                               
  0%|          | 0/7 [00:00<?, ?it/s]
 57%|█████▋    | 4/7 [00:00<00:00, 29.14it/s]
100%|██████████| 7/7 [00:00<00:00, 27.74it/s]
                                             
  0%|          | 0/42 [00:00<?, ?it/s]
 10%|▉         | 4/42 [00:00<00:01, 29.04it/s]
 17%|█▋        | 7/42 [00:00<00:01, 27.67it/s]
 24%|██▍       | 10/42 [00:00<00:01, 27.18it/s]
 31%|███       | 13/42 [00:00<00:01, 26.91it/s]
 38%|███▊      | 16/42 [00:00<00:00, 26.81it/s]
 45%|████▌     | 19/42 [00:00<00:00, 26.73it/s]
 52%|█████▏    | 22/42 [00:00<00:00, 26.67it/s]
 60%|█████▉    | 25/42 [00:00<00:00, 26.62it/s]
 67%|██████▋   | 28/42 [00:01<00:00, 26.50it/s]
 74%|███████▍  | 31/42 [00:01<00:00, 26.51it/s]
 81%|████████  | 34/42 [00:01<00:00, 26.51it/s]
 88%|████████▊ | 37/42 [00:01<00:00, 26.49it/s]
 95%|█████████▌| 40/42 [00:01<00:00, 26.52it/s]
Epoch 0: |          | 0/? [00:00<?, ?it/s] 
Epoch 0: |          | 1/? [00:06<00:00,  0.15it/s]
Epoch 0: |          | 1/? [00:06<00:00,  0.15it/s]
Epoch 0: |          | 2/? [00:06<00:00,  0.29it/s]
Epoch 0: |          | 2/? [00:06<00:00,  0.29it/s]
Epoch 0: |          | 3/? [00:07<00:00,  0.41it/s]
Epoch 0: |          | 3/? [00:07<00:00,  0.41it/s]
Epoch 0: |          | 4/? [00:07<00:00,  0.51it/s]
Epoch 0: |          | 4/? [00:07<00:00,  0.51it/s]
Epoch 0: |          | 5/? [00:08<00:00,  0.61it/s]
Epoch 0: |          | 5/? [00:08<00:00,  0.61it/s]
Epoch 0: |          | 6/? [00:08<00:00,  0.69it/s]
Epoch 0: |          | 6/? [00:08<00:00,  0.69it/s]
Epoch 0: |          | 7/? [00:09<00:00,  0.76it/s]
Epoch 0: |          | 7/? [00:09<00:00,  0.76it/s]
Epoch 0: |          | 8/? [00:09<00:00,  0.83it/s]
Epoch 0: |          | 8/? [00:09<00:00,  0.83it/s]
Epoch 0: |          | 9/? [00:10<00:00,  0.90it/s]
Epoch 0: |          | 9/? [00:10<00:00,  0.90it/s]
Epoch 0: |          | 10/? [00:10<00:00,  0.95it/s]
Epoch 0: |          | 10/? [00:10<00:00,  0.95it/s]
Epoch 0: |          | 11/? [00:10<00:00,  1.00it/s]
Epoch 0: |          | 11/? [00:10<00:00,  1.00it/s]
Epoch 0: |          | 12/? [00:11<00:00,  1.05it/s]
Epoch 0: |          | 12/? [00:11<00:00,  1.05it/s]
Epoch 0: |          | 13/? [00:11<00:00,  1.09it/s]
Epoch 0: |          | 13/? [00:11<00:00,  1.09it/s]
Epoch 0: |          | 14/? [00:12<00:00,  1.14it/s]
Epoch 0: |          | 14/? [00:12<00:00,  1.14it/s]
Epoch 0: |          | 15/? [00:12<00:00,  1.17it/s]
Epoch 0: |          | 15/? [00:12<00:00,  1.17it/s]
Epoch 0: |          | 16/? [00:13<00:00,  1.21it/s]
Epoch 0: |          | 16/? [00:13<00:00,  1.21it/s]
Epoch 0: |          | 17/? [00:13<00:00,  1.24it/s]
Epoch 0: |          | 17/? [00:13<00:00,  1.24it/s]
Epoch 0: |          | 18/? [00:14<00:00,  1.27it/s]
Epoch 0: |          | 18/? [00:14<00:00,  1.27it/s]
Epoch 0: |          | 19/? [00:14<00:00,  1.30it/s]
Epoch 0: |          | 19/? [00:14<00:00,  1.30it/s]
Epoch 0: |          | 20/? [00:15<00:00,  1.33it/s]
Epoch 0: |          | 20/? [00:15<00:00,  1.33it/s]
Epoch 0: |          | 21/? [00:15<00:00,  1.36it/s]
Epoch 0: |          | 21/? [00:15<00:00,  1.36it/s]
Epoch 0: |          | 22/? [00:15<00:00,  1.38it/s]
Epoch 0: |          | 22/? [00:15<00:00,  1.38it/s]
Epoch 0: |          | 23/? [00:16<00:00,  1.41it/s]
Epoch 0: |          | 23/? [00:16<00:00,  1.41it/s]
Epoch 0: |          | 24/? [00:16<00:00,  1.43it/s]
Epoch 0: |          | 24/? [00:16<00:00,  1.43it/s]
Epoch 0: |          | 25/? [00:17<00:00,  1.45it/s]
Epoch 0: |          | 25/? [00:17<00:00,  1.45it/s]
Epoch 0: |          | 26/? [00:17<00:00,  1.47it/s]
Epoch 0: |          | 26/? [00:17<00:00,  1.47it/s]
Epoch 0: |          | 27/? [00:18<00:00,  1.49it/s]
Epoch 0: |          | 27/? [00:18<00:00,  1.49it/s]
Epoch 0: |          | 28/? [00:18<00:00,  1.50it/s]
Epoch 0: |          | 28/? [00:18<00:00,  1.50it/s]
Epoch 0: |          | 29/? [00:19<00:00,  1.52it/s]
Epoch 0: |          | 29/? [00:19<00:00,  1.52it/s]
Epoch 0: |          | 30/? [00:19<00:00,  1.53it/s]
Epoch 0: |          | 30/? [00:19<00:00,  1.53it/s]
Epoch 0: |          | 31/? [00:19<00:00,  1.55it/s]
Epoch 0: |          | 31/? [00:19<00:00,  1.55it/s]
Epoch 0: |          | 32/? [00:20<00:00,  1.56it/s]
Epoch 0: |          | 32/? [00:20<00:00,  1.56it/s]
Epoch 0: |          | 33/? [00:20<00:00,  1.58it/s]
Epoch 0: |          | 33/? [00:20<00:00,  1.58it/s]
Epoch 0: |          | 34/? [00:21<00:00,  1.59it/s]
Epoch 0: |          | 34/? [00:21<00:00,  1.59it/s]
Epoch 0: |          | 35/? [00:21<00:00,  1.61it/s]
Epoch 0: |          | 35/? [00:21<00:00,  1.61it/s]
Epoch 0: |          | 36/? [00:22<00:00,  1.62it/s]
Epoch 0: |          | 36/? [00:22<00:00,  1.62it/s]
Epoch 0: |          | 37/? [00:22<00:00,  1.63it/s]
Epoch 0: |          | 37/? [00:22<00:00,  1.63it/s]
Epoch 0: |          | 38/? [00:23<00:00,  1.64it/s]
Epoch 0: |          | 38/? [00:23<00:00,  1.64it/s]
Epoch 0: |          | 39/? [00:23<00:00,  1.66it/s]
Epoch 0: |          | 39/? [00:23<00:00,  1.66it/s]
Epoch 0: |          | 40/? [00:24<00:00,  1.67it/s]
Epoch 0: |          | 40/? [00:24<00:00,  1.67it/s]
Epoch 0: |          | 41/? [00:24<00:00,  1.68it/s]
Epoch 0: |          | 41/? [00:24<00:00,  1.68it/s]
Epoch 0: |          | 42/? [00:24<00:00,  1.69it/s]
Epoch 0: |          | 42/? [00:24<00:00,  1.69it/s]
Epoch 0: |          | 43/? [00:25<00:00,  1.70it/s]
Epoch 0: |          | 43/? [00:25<00:00,  1.70it/s]
Epoch 0: |          | 44/? [00:25<00:00,  1.71it/s]
Epoch 0: |          | 44/? [00:25<00:00,  1.71it/s]
Epoch 0: |          | 45/? [00:26<00:00,  1.72it/s]
Epoch 0: |          | 45/? [00:26<00:00,  1.72it/s]
Epoch 0: |          | 46/? [00:26<00:00,  1.72it/s]
Epoch 0: |          | 46/? [00:26<00:00,  1.72it/s]
Epoch 0: |          | 47/? [00:27<00:00,  1.73it/s]
Epoch 0: |          | 47/? [00:27<00:00,  1.73it/s]
Epoch 0: |          | 48/? [00:27<00:00,  1.74it/s]
Epoch 0: |          | 48/? [00:27<00:00,  1.74it/s]
Epoch 0: |          | 49/? [00:28<00:00,  1.75it/s]
Epoch 0: |          | 49/? [00:28<00:00,  1.75it/s]
Epoch 0: |          | 50/? [00:28<00:00,  1.76it/s]
Epoch 0: |          | 50/? [00:28<00:00,  1.76it/s]
Epoch 0: |          | 51/? [00:28<00:00,  1.76it/s]
Epoch 0: |          | 51/? [00:28<00:00,  1.76it/s]
Epoch 0: |          | 52/? [00:29<00:00,  1.77it/s]
Epoch 0: |          | 52/? [00:29<00:00,  1.77it/s]
Epoch 0: |          | 53/? [00:29<00:00,  1.78it/s]
Epoch 0: |          | 53/? [00:29<00:00,  1.78it/s]
Epoch 0: |          | 54/? [00:30<00:00,  1.78it/s]
Epoch 0: |          | 54/? [00:30<00:00,  1.78it/s]
Epoch 0: |          | 55/? [00:30<00:00,  1.79it/s]
Epoch 0: |          | 55/? [00:30<00:00,  1.79it/s]
Epoch 0: |          | 56/? [00:31<00:00,  1.80it/s]
Epoch 0: |          | 56/? [00:31<00:00,  1.80it/s]
Epoch 0: |          | 57/? [00:31<00:00,  1.80it/s]
Epoch 0: |          | 57/? [00:31<00:00,  1.80it/s]
Epoch 0: |          | 58/? [00:32<00:00,  1.81it/s]
Epoch 0: |          | 58/? [00:32<00:00,  1.81it/s]
Epoch 0: |          | 59/? [00:32<00:00,  1.81it/s]
Epoch 0: |          | 59/? [00:32<00:00,  1.81it/s]
Epoch 0: |          | 60/? [00:32<00:00,  1.82it/s]
Epoch 0: |          | 60/? [00:32<00:00,  1.82it/s]
Epoch 0: |          | 61/? [00:33<00:00,  1.83it/s]
Epoch 0: |          | 61/? [00:33<00:00,  1.83it/s]
Epoch 0: |          | 62/? [00:33<00:00,  1.83it/s]
Epoch 0: |          | 62/? [00:33<00:00,  1.83it/s]
Epoch 0: |          | 63/? [00:34<00:00,  1.84it/s]
Epoch 0: |          | 63/? [00:34<00:00,  1.84it/s]
Epoch 0: |          | 64/? [00:34<00:00,  1.84it/s]
Epoch 0: |          | 64/? [00:34<00:00,  1.84it/s]
Epoch 0: |          | 65/? [00:35<00:00,  1.85it/s]
Epoch 0: |          | 65/? [00:35<00:00,  1.85it/s]
Epoch 0: |          | 66/? [00:35<00:00,  1.85it/s]
Epoch 0: |          | 66/? [00:35<00:00,  1.85it/s]
Epoch 0: |          | 67/? [00:36<00:00,  1.86it/s]
Epoch 0: |          | 67/? [00:36<00:00,  1.86it/s]
Epoch 0: |          | 68/? [00:36<00:00,  1.86it/s]
Epoch 0: |          | 68/? [00:36<00:00,  1.86it/s]
Epoch 0: |          | 69/? [00:36<00:00,  1.87it/s]
Epoch 0: |          | 69/? [00:36<00:00,  1.87it/s]
Epoch 0: |          | 70/? [00:37<00:00,  1.87it/s]
Epoch 0: |          | 70/? [00:37<00:00,  1.87it/s]
Epoch 0: |          | 71/? [00:37<00:00,  1.87it/s]
Epoch 0: |          | 71/? [00:37<00:00,  1.87it/s]
Epoch 0: |          | 72/? [00:38<00:00,  1.88it/s]
Epoch 0: |          | 72/? [00:38<00:00,  1.88it/s]
Epoch 0: |          | 73/? [00:38<00:00,  1.88it/s]
Epoch 0: |          | 73/? [00:38<00:00,  1.88it/s]
Epoch 0: |          | 74/? [00:39<00:00,  1.88it/s]
Epoch 0: |          | 74/? [00:39<00:00,  1.88it/s]
Epoch 0: |          | 75/? [00:39<00:00,  1.89it/s]
Epoch 0: |          | 75/? [00:39<00:00,  1.89it/s]
Epoch 0: |          | 76/? [00:40<00:00,  1.89it/s]
Epoch 0: |          | 76/? [00:40<00:00,  1.89it/s]
Epoch 0: |          | 77/? [00:40<00:00,  1.90it/s]
Epoch 0: |          | 77/? [00:40<00:00,  1.90it/s]
Epoch 0: |          | 78/? [00:41<00:00,  1.90it/s]
Epoch 0: |          | 78/? [00:41<00:00,  1.90it/s]
Epoch 0: |          | 79/? [00:41<00:00,  1.90it/s]
Epoch 0: |          | 79/? [00:41<00:00,  1.90it/s]
Epoch 0: |          | 80/? [00:41<00:00,  1.91it/s]
Epoch 0: |          | 80/? [00:41<00:00,  1.91it/s]
Epoch 0: |          | 81/? [00:42<00:00,  1.91it/s]
Epoch 0: |          | 81/? [00:42<00:00,  1.91it/s]
Epoch 0: |          | 82/? [00:42<00:00,  1.91it/s]
Epoch 0: |          | 82/? [00:42<00:00,  1.91it/s]
Epoch 0: |          | 83/? [00:43<00:00,  1.92it/s]
Epoch 0: |          | 83/? [00:43<00:00,  1.92it/s]
Epoch 0: |          | 84/? [00:43<00:00,  1.92it/s]
Epoch 0: |          | 84/? [00:43<00:00,  1.92it/s]
Epoch 0: |          | 85/? [00:44<00:00,  1.92it/s]
Epoch 0: |          | 85/? [00:44<00:00,  1.92it/s]
Epoch 0: |          | 86/? [00:44<00:00,  1.93it/s]
Epoch 0: |          | 86/? [00:44<00:00,  1.93it/s]
Epoch 0: |          | 87/? [00:45<00:00,  1.93it/s]
Epoch 0: |          | 87/? [00:45<00:00,  1.93it/s]
Epoch 0: |          | 88/? [00:45<00:00,  1.93it/s]
Epoch 0: |          | 88/? [00:45<00:00,  1.93it/s]
Epoch 0: |          | 89/? [00:46<00:00,  1.93it/s]
Epoch 0: |          | 89/? [00:46<00:00,  1.93it/s]
Epoch 0: |          | 90/? [00:46<00:00,  1.94it/s]
Epoch 0: |          | 90/? [00:46<00:00,  1.94it/s]
Epoch 0: |          | 91/? [00:46<00:00,  1.94it/s]
Epoch 0: |          | 91/? [00:46<00:00,  1.94it/s]
Epoch 0: |          | 92/? [00:47<00:00,  1.94it/s]
Epoch 0: |          | 92/? [00:47<00:00,  1.94it/s]
Epoch 0: |          | 93/? [00:47<00:00,  1.95it/s]
Epoch 0: |          | 93/? [00:47<00:00,  1.95it/s]
Epoch 0: |          | 94/? [00:48<00:00,  1.95it/s]
Epoch 0: |          | 94/? [00:48<00:00,  1.95it/s]
Epoch 0: |          | 95/? [00:48<00:00,  1.95it/s]
Epoch 0: |          | 95/? [00:48<00:00,  1.95it/s]
Epoch 0: |          | 96/? [00:49<00:00,  1.95it/s]
Epoch 0: |          | 96/? [00:49<00:00,  1.95it/s]
Epoch 0: |          | 97/? [00:49<00:00,  1.95it/s]
Epoch 0: |          | 97/? [00:49<00:00,  1.95it/s]
Epoch 0: |          | 98/? [00:50<00:00,  1.95it/s]
Epoch 0: |          | 98/? [00:50<00:00,  1.95it/s]
Epoch 0: |          | 99/? [00:50<00:00,  1.95it/s]
Epoch 0: |          | 99/? [00:50<00:00,  1.95it/s]
Epoch 0: |          | 100/? [00:51<00:00,  1.95it/s]
Epoch 0: |          | 100/? [00:51<00:00,  1.95it/s]
Validation: |          | 0/? [00:00<?, ?it/s]
Validation:   0%|          | 0/1 [00:00<?, ?it/s]
Validation DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]
Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 36.17it/s]

Epoch 0: |          | 100/? [00:51<00:00,  1.95it/s]
Epoch 0: |          | 101/? [00:51<00:00,  1.95it/s]
Epoch 0: |          | 101/? [00:51<00:00,  1.95it/s]
Epoch 0: |          | 102/? [00:52<00:00,  1.96it/s]
Epoch 0: |          | 102/? [00:52<00:00,  1.96it/s]
Epoch 0: |          | 103/? [00:52<00:00,  1.96it/s]
Epoch 0: |          | 103/? [00:52<00:00,  1.96it/s]
Epoch 0: |          | 104/? [00:53<00:00,  1.96it/s]
Epoch 0: |          | 104/? [00:53<00:00,  1.96it/s]
Epoch 0: |          | 105/? [00:53<00:00,  1.96it/s]
Epoch 0: |          | 105/? [00:53<00:00,  1.96it/s]
Epoch 0: |          | 106/? [00:53<00:00,  1.96it/s]
Epoch 0: |          | 106/? [00:53<00:00,  1.96it/s]
Epoch 0: |          | 107/? [00:54<00:00,  1.97it/s]
Epoch 0: |          | 107/? [00:54<00:00,  1.97it/s]
Epoch 0: |          | 108/? [00:54<00:00,  1.97it/s]
Epoch 0: |          | 108/? [00:54<00:00,  1.97it/s]
Epoch 0: |          | 109/? [00:55<00:00,  1.97it/s]
Epoch 0: |          | 109/? [00:55<00:00,  1.97it/s]
Epoch 0: |          | 110/? [00:55<00:00,  1.97it/s]
Epoch 0: |          | 110/? [00:55<00:00,  1.97it/s]
Epoch 0: |          | 111/? [00:56<00:00,  1.97it/s]
Epoch 0: |          | 111/? [00:56<00:00,  1.97it/s]
Epoch 0: |          | 112/? [00:56<00:00,  1.97it/s]
Epoch 0: |          | 112/? [00:56<00:00,  1.97it/s]
Epoch 0: |          | 113/? [00:57<00:00,  1.98it/s]
Epoch 0: |          | 113/? [00:57<00:00,  1.98it/s]
Epoch 0: |          | 114/? [00:57<00:00,  1.98it/s]
Epoch 0: |          | 114/? [00:57<00:00,  1.98it/s]
Epoch 0: |          | 115/? [00:58<00:00,  1.98it/s]
Epoch 0: |          | 115/? [00:58<00:00,  1.98it/s]
Epoch 0: |          | 116/? [00:58<00:00,  1.98it/s]
Epoch 0: |          | 116/? [00:58<00:00,  1.98it/s]
Epoch 0: |          | 117/? [00:59<00:00,  1.98it/s]
Epoch 0: |          | 117/? [00:59<00:00,  1.98it/s]
Epoch 0: |          | 118/? [00:59<00:00,  1.98it/s]
Epoch 0: |          | 118/? [00:59<00:00,  1.98it/s]
Epoch 0: |          | 119/? [00:59<00:00,  1.99it/s]
Epoch 0: |          | 119/? [00:59<00:00,  1.99it/s]
Epoch 0: |          | 120/? [01:00<00:00,  1.99it/s]
Epoch 0: |          | 120/? [01:00<00:00,  1.99it/s]
Epoch 0: |          | 121/? [01:00<00:00,  1.99it/s]
Epoch 0: |          | 121/? [01:00<00:00,  1.99it/s]
Epoch 0: |          | 122/? [01:01<00:00,  1.99it/s]
Epoch 0: |          | 122/? [01:01<00:00,  1.99it/s]
Epoch 0: |          | 123/? [01:01<00:00,  1.99it/s]
Epoch 0: |          | 123/? [01:01<00:00,  1.99it/s]
Epoch 0: |          | 124/? [01:02<00:00,  2.00it/s]
Epoch 0: |          | 124/? [01:02<00:00,  2.00it/s]
Epoch 0: |          | 125/? [01:02<00:00,  2.00it/s]
Epoch 0: |          | 125/? [01:02<00:00,  2.00it/s]
Epoch 0: |          | 126/? [01:03<00:00,  2.00it/s]
Epoch 0: |          | 126/? [01:03<00:00,  2.00it/s]
Epoch 0: |          | 127/? [01:03<00:00,  2.00it/s]
Epoch 0: |          | 127/? [01:03<00:00,  2.00it/s]
Epoch 0: |          | 128/? [01:03<00:00,  2.00it/s]
Epoch 0: |          | 128/? [01:03<00:00,  2.00it/s]
Epoch 0: |          | 129/? [01:04<00:00,  2.00it/s]
Epoch 0: |          | 129/? [01:04<00:00,  2.00it/s]
Epoch 0: |          | 130/? [01:04<00:00,  2.00it/s]
Epoch 0: |          | 130/? [01:04<00:00,  2.00it/s]
Epoch 0: |          | 131/? [01:05<00:00,  2.00it/s]
Epoch 0: |          | 131/? [01:05<00:00,  2.00it/s]
Epoch 0: |          | 132/? [01:05<00:00,  2.01it/s]
Epoch 0: |          | 132/? [01:05<00:00,  2.01it/s]
Epoch 0: |          | 133/? [01:06<00:00,  2.01it/s]
Epoch 0: |          | 133/? [01:06<00:00,  2.01it/s]
Epoch 0: |          | 134/? [01:06<00:00,  2.01it/s]
Epoch 0: |          | 134/? [01:06<00:00,  2.01it/s]
Epoch 0: |          | 135/? [01:07<00:00,  2.01it/s]
Epoch 0: |          | 135/? [01:07<00:00,  2.01it/s]
Epoch 0: |          | 136/? [01:07<00:00,  2.01it/s]
Epoch 0: |          | 136/? [01:07<00:00,  2.01it/s]
Epoch 0: |          | 137/? [01:08<00:00,  2.01it/s]
Epoch 0: |          | 137/? [01:08<00:00,  2.01it/s]
Epoch 0: |          | 138/? [01:08<00:00,  2.01it/s]
Epoch 0: |          | 138/? [01:08<00:00,  2.01it/s]
Epoch 0: |          | 139/? [01:08<00:00,  2.02it/s]
Epoch 0: |          | 139/? [01:08<00:00,  2.02it/s]
Epoch 0: |          | 140/? [01:09<00:00,  2.02it/s]
Epoch 0: |          | 140/? [01:09<00:00,  2.02it/s]
Epoch 0: |          | 141/? [01:09<00:00,  2.02it/s]
Epoch 0: |          | 141/? [01:09<00:00,  2.02it/s]
Epoch 0: |          | 142/? [01:10<00:00,  2.02it/s]
Epoch 0: |          | 142/? [01:10<00:00,  2.02it/s]
Epoch 0: |          | 143/? [01:10<00:00,  2.02it/s]
Epoch 0: |          | 143/? [01:10<00:00,  2.02it/s]
Epoch 0: |          | 144/? [01:11<00:00,  2.02it/s]
Epoch 0: |          | 144/? [01:11<00:00,  2.02it/s]
Epoch 0: |          | 145/? [01:11<00:00,  2.02it/s]
Epoch 0: |          | 145/? [01:11<00:00,  2.02it/s]
Epoch 0: |          | 146/? [01:12<00:00,  2.02it/s]
Epoch 0: |          | 146/? [01:12<00:00,  2.02it/s]
Epoch 0: |          | 147/? [01:12<00:00,  2.03it/s]
Epoch 0: |          | 147/? [01:12<00:00,  2.03it/s]
Epoch 0: |          | 148/? [01:13<00:00,  2.03it/s]
Epoch 0: |          | 148/? [01:13<00:00,  2.03it/s]
Epoch 0: |          | 149/? [01:13<00:00,  2.03it/s]
Epoch 0: |          | 149/? [01:13<00:00,  2.03it/s]
Epoch 0: |          | 150/? [01:13<00:00,  2.03it/s]
Epoch 0: |          | 150/? [01:13<00:00,  2.03it/s]
Epoch 0: |          | 151/? [01:14<00:00,  2.03it/s]
Epoch 0: |          | 151/? [01:14<00:00,  2.03it/s]
Epoch 0: |          | 152/? [01:14<00:00,  2.03it/s]
Epoch 0: |          | 152/? [01:14<00:00,  2.03it/s]
Epoch 0: |          | 153/? [01:15<00:00,  2.03it/s]
Epoch 0: |          | 153/? [01:15<00:00,  2.03it/s]
Epoch 0: |          | 154/? [01:15<00:00,  2.03it/s]
Epoch 0: |          | 154/? [01:15<00:00,  2.03it/s]
Epoch 0: |          | 155/? [01:16<00:00,  2.04it/s]
Epoch 0: |          | 155/? [01:16<00:00,  2.04it/s]
Epoch 0: |          | 156/? [01:16<00:00,  2.04it/s]
Epoch 0: |          | 156/? [01:16<00:00,  2.04it/s]
Epoch 0: |          | 157/? [01:17<00:00,  2.04it/s]
Epoch 0: |          | 157/? [01:17<00:00,  2.04it/s]
Epoch 0: |          | 158/? [01:17<00:00,  2.04it/s]
Epoch 0: |          | 158/? [01:17<00:00,  2.04it/s]
Epoch 0: |          | 159/? [01:17<00:00,  2.04it/s]
Epoch 0: |          | 159/? [01:17<00:00,  2.04it/s]
Epoch 0: |          | 160/? [01:18<00:00,  2.04it/s]
Epoch 0: |          | 160/? [01:18<00:00,  2.04it/s]
Epoch 0: |          | 161/? [01:18<00:00,  2.04it/s]
Epoch 0: |          | 161/? [01:18<00:00,  2.04it/s]
Epoch 0: |          | 162/? [01:19<00:00,  2.04it/s]
Epoch 0: |          | 162/? [01:19<00:00,  2.04it/s]
Epoch 0: |          | 163/? [01:19<00:00,  2.04it/s]
Epoch 0: |          | 163/? [01:19<00:00,  2.04it/s]
Epoch 0: |          | 164/? [01:20<00:00,  2.04it/s]
Epoch 0: |          | 164/? [01:20<00:00,  2.04it/s]
Epoch 0: |          | 165/? [01:20<00:00,  2.05it/s]
Epoch 0: |          | 165/? [01:20<00:00,  2.05it/s]
Epoch 0: |          | 166/? [01:21<00:00,  2.05it/s]
Epoch 0: |          | 166/? [01:21<00:00,  2.05it/s]
Epoch 0: |          | 167/? [01:21<00:00,  2.05it/s]
Epoch 0: |          | 167/? [01:21<00:00,  2.05it/s]
Epoch 0: |          | 168/? [01:22<00:00,  2.05it/s]
Epoch 0: |          | 168/? [01:22<00:00,  2.05it/s]
Epoch 0: |          | 169/? [01:22<00:00,  2.05it/s]
Epoch 0: |          | 169/? [01:22<00:00,  2.05it/s]
Epoch 0: |          | 170/? [01:22<00:00,  2.05it/s]
Epoch 0: |          | 170/? [01:22<00:00,  2.05it/s]
Epoch 0: |          | 171/? [01:23<00:00,  2.05it/s]
Epoch 0: |          | 171/? [01:23<00:00,  2.05it/s]
Epoch 0: |          | 172/? [01:23<00:00,  2.05it/s]
Epoch 0: |          | 172/? [01:23<00:00,  2.05it/s]
Epoch 0: |          | 173/? [01:24<00:00,  2.05it/s]
Epoch 0: |          | 173/? [01:24<00:00,  2.05it/s]
Epoch 0: |          | 174/? [01:24<00:00,  2.05it/s]
Epoch 0: |          | 174/? [01:24<00:00,  2.05it/s]
Epoch 0: |          | 175/? [01:25<00:00,  2.05it/s]
Epoch 0: |          | 175/? [01:25<00:00,  2.05it/s]
Epoch 0: |          | 176/? [01:25<00:00,  2.05it/s]
Epoch 0: |          | 176/? [01:25<00:00,  2.05it/s]
Epoch 0: |          | 177/? [01:26<00:00,  2.05it/s]
Epoch 0: |          | 177/? [01:26<00:00,  2.05it/s]
Epoch 0: |          | 178/? [01:26<00:00,  2.05it/s]
Epoch 0: |          | 178/? [01:26<00:00,  2.05it/s]
Epoch 0: |          | 179/? [01:27<00:00,  2.05it/s]
Epoch 0: |          | 179/? [01:27<00:00,  2.05it/s]
Epoch 0: |          | 180/? [01:27<00:00,  2.05it/s]
Epoch 0: |          | 180/? [01:27<00:00,  2.05it/s]
Epoch 0: |          | 181/? [01:28<00:00,  2.06it/s]
Epoch 0: |          | 181/? [01:28<00:00,  2.06it/s]
Epoch 0: |          | 182/? [01:28<00:00,  2.06it/s]
Epoch 0: |          | 182/? [01:28<00:00,  2.06it/s]
Epoch 0: |          | 183/? [01:28<00:00,  2.06it/s]
Epoch 0: |          | 183/? [01:28<00:00,  2.06it/s]
Epoch 0: |          | 184/? [01:29<00:00,  2.06it/s]
Epoch 0: |          | 184/? [01:29<00:00,  2.06it/s]
Epoch 0: |          | 185/? [01:29<00:00,  2.06it/s]
Epoch 0: |          | 185/? [01:29<00:00,  2.06it/s]
Epoch 0: |          | 186/? [01:30<00:00,  2.06it/s]
Epoch 0: |          | 186/? [01:30<00:00,  2.06it/s]
Epoch 0: |          | 187/? [01:30<00:00,  2.06it/s]
Epoch 0: |          | 187/? [01:30<00:00,  2.06it/s]
Epoch 0: |          | 188/? [01:31<00:00,  2.06it/s]
Epoch 0: |          | 188/? [01:31<00:00,  2.06it/s]
Epoch 0: |          | 189/? [01:31<00:00,  2.06it/s]
Epoch 0: |          | 189/? [01:31<00:00,  2.06it/s]
Epoch 0: |          | 190/? [01:32<00:00,  2.06it/s]
Epoch 0: |          | 190/? [01:32<00:00,  2.06it/s]
Epoch 0: |          | 191/? [01:32<00:00,  2.06it/s]
Epoch 0: |          | 191/? [01:32<00:00,  2.06it/s]
Epoch 0: |          | 192/? [01:33<00:00,  2.06it/s]
Epoch 0: |          | 192/? [01:33<00:00,  2.06it/s]
Epoch 0: |          | 193/? [01:33<00:00,  2.07it/s]
Epoch 0: |          | 193/? [01:33<00:00,  2.07it/s]
Epoch 0: |          | 194/? [01:33<00:00,  2.07it/s]
Epoch 0: |          | 194/? [01:33<00:00,  2.07it/s]
Epoch 0: |          | 195/? [01:34<00:00,  2.07it/s]
Epoch 0: |          | 195/? [01:34<00:00,  2.07it/s]
Epoch 0: |          | 196/? [01:34<00:00,  2.07it/s]
Epoch 0: |          | 196/? [01:34<00:00,  2.07it/s]
Epoch 0: |          | 197/? [01:35<00:00,  2.07it/s]
Epoch 0: |          | 197/? [01:35<00:00,  2.07it/s]
Epoch 0: |          | 198/? [01:35<00:00,  2.07it/s]
Epoch 0: |          | 198/? [01:35<00:00,  2.07it/s]
Epoch 0: |          | 199/? [01:36<00:00,  2.07it/s]
Epoch 0: |          | 199/? [01:36<00:00,  2.07it/s]
Epoch 0: |          | 200/? [01:36<00:00,  2.07it/s]
Epoch 0: |          | 200/? [01:36<00:00,  2.07it/s]
Validation: |          | 0/? [00:00<?, ?it/s]
Validation:   0%|          | 0/1 [00:00<?, ?it/s]
Validation DataLoader 0:   0%|          | 0/1 [00:00<?, ?it/s]
Validation DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 40.40it/s]

Epoch 0: |          | 200/? [01:36<00:00,  2.07it/s]
                                               `Trainer.fit` stopped: `max_steps=200` reached.
Epoch 0: |          | 200/? [01:36<00:00,  2.07it/s]
Epoch 0: |          | 200/? [01:36<00:00,  2.07it/s]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'test_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=9` in the `DataLoader` to improve performance.
Testing: |          | 0/? [00:00<?, ?it/s]
Testing:   0%|          | 0/120 [00:00<?, ?it/s]
Testing DataLoader 0:   0%|          | 0/120 [00:00<?, ?it/s]
Testing DataLoader 0:   1%|          | 1/120 [00:00<00:02, 44.87it/s]
Testing DataLoader 0:   2%|▏         | 2/120 [00:00<00:02, 44.37it/s]
Testing DataLoader 0:   2%|▎         | 3/120 [00:00<00:02, 43.91it/s]
Testing DataLoader 0:   3%|▎         | 4/120 [00:00<00:02, 44.00it/s]
Testing DataLoader 0:   4%|▍         | 5/120 [00:00<00:02, 43.95it/s]
Testing DataLoader 0:   5%|▌         | 6/120 [00:00<00:02, 44.07it/s]
Testing DataLoader 0:   6%|▌         | 7/120 [00:00<00:02, 44.00it/s]
Testing DataLoader 0:   7%|▋         | 8/120 [00:00<00:02, 44.11it/s]
Testing DataLoader 0:   8%|▊         | 9/120 [00:00<00:02, 44.05it/s]
Testing DataLoader 0:   8%|▊         | 10/120 [00:00<00:02, 44.10it/s]
Testing DataLoader 0:   9%|▉         | 11/120 [00:00<00:02, 44.05it/s]
Testing DataLoader 0:  10%|█         | 12/120 [00:00<00:02, 44.07it/s]
Testing DataLoader 0:  11%|█         | 13/120 [00:00<00:02, 44.03it/s]
Testing DataLoader 0:  12%|█▏        | 14/120 [00:00<00:02, 44.05it/s]
Testing DataLoader 0:  12%|█▎        | 15/120 [00:00<00:02, 44.07it/s]
Testing DataLoader 0:  13%|█▎        | 16/120 [00:00<00:02, 44.14it/s]
Testing DataLoader 0:  14%|█▍        | 17/120 [00:00<00:02, 44.16it/s]
Testing DataLoader 0:  15%|█▌        | 18/120 [00:00<00:02, 44.19it/s]
Testing DataLoader 0:  16%|█▌        | 19/120 [00:00<00:02, 44.08it/s]
Testing DataLoader 0:  17%|█▋        | 20/120 [00:00<00:02, 44.03it/s]
Testing DataLoader 0:  18%|█▊        | 21/120 [00:00<00:02, 43.95it/s]
Testing DataLoader 0:  18%|█▊        | 22/120 [00:00<00:02, 43.95it/s]
Testing DataLoader 0:  19%|█▉        | 23/120 [00:00<00:02, 43.97it/s]
Testing DataLoader 0:  20%|██        | 24/120 [00:00<00:02, 44.01it/s]
Testing DataLoader 0:  21%|██        | 25/120 [00:00<00:02, 44.04it/s]
Testing DataLoader 0:  22%|██▏       | 26/120 [00:00<00:02, 44.04it/s]
Testing DataLoader 0:  22%|██▎       | 27/120 [00:00<00:02, 44.02it/s]
Testing DataLoader 0:  23%|██▎       | 28/120 [00:00<00:02, 44.03it/s]
Testing DataLoader 0:  24%|██▍       | 29/120 [00:00<00:02, 44.07it/s]
Testing DataLoader 0:  25%|██▌       | 30/120 [00:00<00:02, 44.12it/s]
Testing DataLoader 0:  26%|██▌       | 31/120 [00:00<00:02, 44.14it/s]
Testing DataLoader 0:  27%|██▋       | 32/120 [00:00<00:01, 44.15it/s]
Testing DataLoader 0:  28%|██▊       | 33/120 [00:00<00:01, 44.13it/s]
Testing DataLoader 0:  28%|██▊       | 34/120 [00:00<00:01, 44.13it/s]
Testing DataLoader 0:  29%|██▉       | 35/120 [00:00<00:01, 44.14it/s]
Testing DataLoader 0:  30%|███       | 36/120 [00:00<00:01, 44.17it/s]
Testing DataLoader 0:  31%|███       | 37/120 [00:00<00:01, 44.17it/s]
Testing DataLoader 0:  32%|███▏      | 38/120 [00:00<00:01, 44.17it/s]
Testing DataLoader 0:  32%|███▎      | 39/120 [00:00<00:01, 44.14it/s]
Testing DataLoader 0:  33%|███▎      | 40/120 [00:00<00:01, 44.15it/s]
Testing DataLoader 0:  34%|███▍      | 41/120 [00:00<00:01, 44.15it/s]
Testing DataLoader 0:  35%|███▌      | 42/120 [00:00<00:01, 44.16it/s]
Testing DataLoader 0:  36%|███▌      | 43/120 [00:00<00:01, 44.15it/s]
Testing DataLoader 0:  37%|███▋      | 44/120 [00:00<00:01, 44.16it/s]
Testing DataLoader 0:  38%|███▊      | 45/120 [00:01<00:01, 44.15it/s]
Testing DataLoader 0:  38%|███▊      | 46/120 [00:01<00:01, 44.14it/s]
Testing DataLoader 0:  39%|███▉      | 47/120 [00:01<00:01, 44.14it/s]
Testing DataLoader 0:  40%|████      | 48/120 [00:01<00:01, 44.15it/s]
Testing DataLoader 0:  41%|████      | 49/120 [00:01<00:01, 44.16it/s]
Testing DataLoader 0:  42%|████▏     | 50/120 [00:01<00:01, 44.16it/s]
Testing DataLoader 0:  42%|████▎     | 51/120 [00:01<00:01, 44.17it/s]
Testing DataLoader 0:  43%|████▎     | 52/120 [00:01<00:01, 44.18it/s]
Testing DataLoader 0:  44%|████▍     | 53/120 [00:01<00:01, 44.17it/s]
Testing DataLoader 0:  45%|████▌     | 54/120 [00:01<00:01, 44.19it/s]
Testing DataLoader 0:  46%|████▌     | 55/120 [00:01<00:01, 44.13it/s]
Testing DataLoader 0:  47%|████▋     | 56/120 [00:01<00:01, 44.14it/s]
Testing DataLoader 0:  48%|████▊     | 57/120 [00:01<00:01, 44.12it/s]
Testing DataLoader 0:  48%|████▊     | 58/120 [00:01<00:01, 44.14it/s]
Testing DataLoader 0:  49%|████▉     | 59/120 [00:01<00:01, 44.13it/s]
Testing DataLoader 0:  50%|█████     | 60/120 [00:01<00:01, 44.12it/s]
Testing DataLoader 0:  51%|█████     | 61/120 [00:01<00:01, 44.12it/s]
Testing DataLoader 0:  52%|█████▏    | 62/120 [00:01<00:01, 44.13it/s]
Testing DataLoader 0:  52%|█████▎    | 63/120 [00:01<00:01, 44.12it/s]
Testing DataLoader 0:  53%|█████▎    | 64/120 [00:01<00:01, 44.13it/s]
Testing DataLoader 0:  54%|█████▍    | 65/120 [00:01<00:01, 44.13it/s]
Testing DataLoader 0:  55%|█████▌    | 66/120 [00:01<00:01, 44.11it/s]
Testing DataLoader 0:  56%|█████▌    | 67/120 [00:01<00:01, 44.11it/s]
Testing DataLoader 0:  57%|█████▋    | 68/120 [00:01<00:01, 44.06it/s]
Testing DataLoader 0:  57%|█████▊    | 69/120 [00:01<00:01, 44.06it/s]
Testing DataLoader 0:  58%|█████▊    | 70/120 [00:01<00:01, 44.04it/s]
Testing DataLoader 0:  59%|█████▉    | 71/120 [00:01<00:01, 44.04it/s]
Testing DataLoader 0:  60%|██████    | 72/120 [00:01<00:01, 44.03it/s]
Testing DataLoader 0:  61%|██████    | 73/120 [00:01<00:01, 44.05it/s]
Testing DataLoader 0:  62%|██████▏   | 74/120 [00:01<00:01, 44.06it/s]
Testing DataLoader 0:  62%|██████▎   | 75/120 [00:01<00:01, 44.07it/s]
Testing DataLoader 0:  63%|██████▎   | 76/120 [00:01<00:00, 44.09it/s]
Testing DataLoader 0:  64%|██████▍   | 77/120 [00:01<00:00, 44.11it/s]
Testing DataLoader 0:  65%|██████▌   | 78/120 [00:01<00:00, 44.10it/s]
Testing DataLoader 0:  66%|██████▌   | 79/120 [00:01<00:00, 44.12it/s]
Testing DataLoader 0:  67%|██████▋   | 80/120 [00:01<00:00, 44.12it/s]
Testing DataLoader 0:  68%|██████▊   | 81/120 [00:01<00:00, 44.13it/s]
Testing DataLoader 0:  68%|██████▊   | 82/120 [00:01<00:00, 44.14it/s]
Testing DataLoader 0:  69%|██████▉   | 83/120 [00:01<00:00, 44.16it/s]
Testing DataLoader 0:  70%|███████   | 84/120 [00:01<00:00, 44.16it/s]
Testing DataLoader 0:  71%|███████   | 85/120 [00:01<00:00, 44.16it/s]
Testing DataLoader 0:  72%|███████▏  | 86/120 [00:01<00:00, 44.16it/s]
Testing DataLoader 0:  72%|███████▎  | 87/120 [00:01<00:00, 44.17it/s]
Testing DataLoader 0:  73%|███████▎  | 88/120 [00:01<00:00, 44.15it/s]
Testing DataLoader 0:  74%|███████▍  | 89/120 [00:02<00:00, 44.14it/s]
Testing DataLoader 0:  75%|███████▌  | 90/120 [00:02<00:00, 44.12it/s]
Testing DataLoader 0:  76%|███████▌  | 91/120 [00:02<00:00, 44.11it/s]
Testing DataLoader 0:  77%|███████▋  | 92/120 [00:02<00:00, 44.11it/s]
Testing DataLoader 0:  78%|███████▊  | 93/120 [00:02<00:00, 44.11it/s]
Testing DataLoader 0:  78%|███████▊  | 94/120 [00:02<00:00, 44.09it/s]
Testing DataLoader 0:  79%|███████▉  | 95/120 [00:02<00:00, 44.07it/s]
Testing DataLoader 0:  80%|████████  | 96/120 [00:02<00:00, 44.09it/s]
Testing DataLoader 0:  81%|████████  | 97/120 [00:02<00:00, 44.10it/s]
Testing DataLoader 0:  82%|████████▏ | 98/120 [00:02<00:00, 44.10it/s]
Testing DataLoader 0:  82%|████████▎ | 99/120 [00:02<00:00, 44.08it/s]
Testing DataLoader 0:  83%|████████▎ | 100/120 [00:02<00:00, 44.07it/s]
Testing DataLoader 0:  84%|████████▍ | 101/120 [00:02<00:00, 44.06it/s]
Testing DataLoader 0:  85%|████████▌ | 102/120 [00:02<00:00, 44.04it/s]
Testing DataLoader 0:  86%|████████▌ | 103/120 [00:02<00:00, 44.03it/s]
Testing DataLoader 0:  87%|████████▋ | 104/120 [00:02<00:00, 44.03it/s]
Testing DataLoader 0:  88%|████████▊ | 105/120 [00:02<00:00, 44.03it/s]
Testing DataLoader 0:  88%|████████▊ | 106/120 [00:02<00:00, 44.03it/s]
Testing DataLoader 0:  89%|████████▉ | 107/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  90%|█████████ | 108/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  91%|█████████ | 109/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  92%|█████████▏| 110/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  92%|█████████▎| 111/120 [00:02<00:00, 44.01it/s]
Testing DataLoader 0:  93%|█████████▎| 112/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  94%|█████████▍| 113/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  95%|█████████▌| 114/120 [00:02<00:00, 44.02it/s]
Testing DataLoader 0:  96%|█████████▌| 115/120 [00:02<00:00, 44.01it/s]
Testing DataLoader 0:  97%|█████████▋| 116/120 [00:02<00:00, 44.01it/s]
Testing DataLoader 0:  98%|█████████▊| 117/120 [00:02<00:00, 43.95it/s]
Testing DataLoader 0:  98%|█████████▊| 118/120 [00:02<00:00, 43.96it/s]
Testing DataLoader 0:  99%|█████████▉| 119/120 [00:02<00:00, 43.96it/s]
Testing DataLoader 0: 100%|██████████| 120/120 [00:02<00:00, 43.97it/s][Open3D WARNING] Write Ply clamped color value to valid range
Testing DataLoader 0: 100%|██████████| 120/120 [00:05<00:00, 20.40it/s]
Test results saved to outputs/gaussiandreamer-sd/A_pineapple@20240304-151448/save
Version Details
Version ID
138abc0aed076d5a1d3c17c5f157e9092e6279c8c1d7d92f1618dc7f707290a4
Version Created
March 4, 2024
Run on Replicate →