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- 0303_ACID_FULL_2v/main.log +92 -0
- 0303_ACID_FULL_2v/wandb/run-20260302_173247-8vipx6wd/run-8vipx6wd.wandb +0 -0
- 0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/output.log +97 -0
- 0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/wandb-summary.json +1 -0
- 0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/logs/debug-core.log +15 -0
- acid/0303_ACID_FULL_2v/.hydra/config.yaml +188 -0
- acid/0303_ACID_FULL_2v/train_ddp_process_4.log +324 -0
- acid/0303_ACID_FULL_2v/train_ddp_process_5.log +324 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/output.log +135 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-metadata.json +92 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-summary.json +1 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/logs/debug-core.log +107 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/config.yaml +309 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/requirements.txt +159 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/wandb-summary.json +1 -0
- acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/logs/debug-internal.log +12 -0
- re10k/0303_RE10K_FULL_2v/.hydra/config.yaml +188 -0
- re10k/0303_RE10K_FULL_2v/.hydra/hydra.yaml +164 -0
- re10k/0303_RE10K_FULL_2v/.hydra/overrides.yaml +3 -0
- re10k/0303_RE10K_FULL_2v/main.log +76 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_1.log +21 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_2.log +21 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_3.log +21 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_4.log +21 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_5.log +21 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_6.log +21 -0
- re10k/0303_RE10K_FULL_2v/train_ddp_process_7.log +21 -0
- re10k/0303_RE10K_FULL_2v/wandb/debug-internal.log +6 -0
- re10k/0303_RE10K_FULL_2v/wandb/debug.log +19 -0
- re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-metadata.json +92 -0
- re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-summary.json +1 -0
- re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/logs/debug-internal.log +50 -0
- re10k/0303_RE10k_FULL_24v/.hydra/config.yaml +188 -0
- re10k/0303_RE10k_FULL_24v/.hydra/hydra.yaml +164 -0
- re10k/0303_RE10k_FULL_24v/.hydra/overrides.yaml +3 -0
- re10k/0303_RE10k_FULL_24v/wandb/debug-internal.log +50 -0
- re10k/0303_RE10k_FULL_24v/wandb/debug.log +0 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/config.yaml +309 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/output.log +110 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/requirements.txt +159 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/wandb-metadata.json +92 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/wandb-summary.json +1 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug-core.log +15 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug-internal.log +11 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug.log +21 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/files/output.log +92 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/files/requirements.txt +159 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/files/wandb-metadata.json +92 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/logs/debug-core.log +8 -0
- re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/logs/debug-internal.log +6 -0
0303_ACID_FULL_2v/main.log
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[2026-03-02 17:30:18,235][dinov2][INFO] - using MLP layer as FFN
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[2026-03-02 17:30:23,492][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:30:23,492][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:30:27,395][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
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[2026-03-02 17:30:29,620][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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[2026-03-02 17:30:29,630][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
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[2026-03-02 17:30:29,631][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:30:29,631][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:30:31,020][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
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return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
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[2026-03-02 17:32:39,801][dinov2][INFO] - using MLP layer as FFN
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[2026-03-02 17:32:45,152][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:32:45,154][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:32:49,423][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
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[2026-03-02 17:32:51,298][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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[2026-03-02 17:32:51,308][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
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[2026-03-02 17:32:51,308][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:32:51,309][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:32:52,684][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
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return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
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[2026-03-02 17:33:41,599][dinov2][INFO] - using MLP layer as FFN
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[2026-03-02 17:33:46,994][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:33:46,994][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:33:51,128][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
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[2026-03-02 17:33:53,066][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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[2026-03-02 17:33:53,077][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
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[2026-03-02 17:33:53,077][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:33:53,077][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:33:54,465][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
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return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
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[2026-03-02 17:37:59,587][dinov2][INFO] - using MLP layer as FFN
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[2026-03-02 17:38:05,026][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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warnings.warn(
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[2026-03-02 17:38:05,027][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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[2026-03-02 17:38:09,087][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
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[2026-03-02 17:38:11,320][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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[2026-03-02 17:38:11,330][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
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[2026-03-02 17:38:11,331][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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| 85 |
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warnings.warn(
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[2026-03-02 17:38:11,331][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
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warnings.warn(msg)
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| 89 |
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[2026-03-02 17:38:12,740][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
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return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
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0303_ACID_FULL_2v/wandb/run-20260302_173247-8vipx6wd/run-8vipx6wd.wandb
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Binary file (27.6 kB). View file
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0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/output.log
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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 |
+
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
|
| 2 |
+
|
| 3 |
+
| Name | Type | Params | Mode
|
| 4 |
+
------------------------------------------------------------------------
|
| 5 |
+
0 | encoder | OurSplat | 888 M | train
|
| 6 |
+
1 | density_control_module | DensityControlModule | 514 | train
|
| 7 |
+
2 | decoder | DecoderSplattingCUDA | 0 | train
|
| 8 |
+
3 | render_losses | ModuleList | 0 | train
|
| 9 |
+
4 | density_control_losses | ModuleList | 0 | train
|
| 10 |
+
5 | direct_losses | ModuleList | 0 | train
|
| 11 |
+
------------------------------------------------------------------------
|
| 12 |
+
888 M Trainable params
|
| 13 |
+
0 Non-trainable params
|
| 14 |
+
888 M Total params
|
| 15 |
+
3,553.936 Total estimated model params size (MB)
|
| 16 |
+
1207 Modules in train mode
|
| 17 |
+
522 Modules in eval mode
|
| 18 |
+
Sanity Checking: | | 0/? [00:00<?, ?it/s][2026-03-02 17:38:09,087][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
|
| 19 |
+
|
| 20 |
+
Validation epoch start on rank 0
|
| 21 |
+
Sanity Checking DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s]validation step 0; scene = ['fcbd42c6ad4b2529'];
|
| 22 |
+
target intrinsic: tensor(0.9452, device='cuda:0') tensor(0.9454, device='cuda:0')
|
| 23 |
+
pred intrinsic: tensor(1.5447, device='cuda:0') tensor(1.5103, device='cuda:0')
|
| 24 |
+
W0302 17:38:11.266000 6180 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
|
| 25 |
+
W0302 17:38:11.266000 6180 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
|
| 26 |
+
[2026-03-02 17:38:11,320][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 27 |
+
result[selector] = overlay
|
| 28 |
+
|
| 29 |
+
[2026-03-02 17:38:11,330][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
|
| 30 |
+
|
| 31 |
+
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
|
| 32 |
+
[2026-03-02 17:38:11,331][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 33 |
+
warnings.warn(
|
| 34 |
+
|
| 35 |
+
[2026-03-02 17:38:11,331][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 36 |
+
warnings.warn(msg)
|
| 37 |
+
|
| 38 |
+
Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth
|
| 39 |
+
[2026-03-02 17:38:12,740][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
|
| 40 |
+
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
|
| 41 |
+
|
| 42 |
+
Epoch 0: | | 0/? [00:00<?, ?it/s]context = [[20, 45], [46, 71], [55, 80], [104, 129], [0, 25], [13, 38], [17, 42], [57, 82], [543, 568], [28, 53], [53, 78], [2, 27], [146, 171], [3, 28], [71, 96], [8, 33]]target = [[23, 25, 31, 27], [50, 65, 55, 70], [79, 68, 60, 69], [108, 119, 110, 124], [23, 3, 22, 12], [33, 36, 22, 28], [30, 20, 32, 36], [69, 66, 71, 64], [563, 567, 565, 550], [50, 31, 42, 37], [74, 63, 58, 72], [10, 24, 6, 23], [162, 149, 155, 168], [4, 19, 12, 25], [78, 73, 74, 75], [27, 16, 26, 14]]
|
| 43 |
+
Error executing job with overrides: ['+experiment=acid', 'wandb.mode=online', 'wandb.name=0303_ACID_FULL_2v']
|
| 44 |
+
Traceback (most recent call last):
|
| 45 |
+
File "/workspace/code/CVPR2026/src/main.py", line 226, in train
|
| 46 |
+
trainer.fit(model_wrapper, datamodule=data_module)#, ckpt_path=checkpoint_path)
|
| 47 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 48 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 561, in fit
|
| 49 |
+
call._call_and_handle_interrupt(
|
| 50 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/call.py", line 48, in _call_and_handle_interrupt
|
| 51 |
+
return trainer_fn(*args, **kwargs)
|
| 52 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 53 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 599, in _fit_impl
|
| 54 |
+
self._run(model, ckpt_path=ckpt_path)
|
| 55 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 1012, in _run
|
| 56 |
+
results = self._run_stage()
|
| 57 |
+
^^^^^^^^^^^^^^^^^
|
| 58 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 1056, in _run_stage
|
| 59 |
+
self.fit_loop.run()
|
| 60 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py", line 216, in run
|
| 61 |
+
self.advance()
|
| 62 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py", line 455, in advance
|
| 63 |
+
self.epoch_loop.run(self._data_fetcher)
|
| 64 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 150, in run
|
| 65 |
+
self.advance(data_fetcher)
|
| 66 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 322, in advance
|
| 67 |
+
batch_output = self.manual_optimization.run(kwargs)
|
| 68 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 69 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/optimization/manual.py", line 94, in run
|
| 70 |
+
self.advance(kwargs)
|
| 71 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/optimization/manual.py", line 114, in advance
|
| 72 |
+
training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
|
| 73 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 74 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/call.py", line 328, in _call_strategy_hook
|
| 75 |
+
output = fn(*args, **kwargs)
|
| 76 |
+
^^^^^^^^^^^^^^^^^^^
|
| 77 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/strategies/strategy.py", line 391, in training_step
|
| 78 |
+
return self.lightning_module.training_step(*args, **kwargs)
|
| 79 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 80 |
+
File "/venv/main/lib/python3.12/site-packages/jaxtyping/_decorator.py", line 562, in wrapped_fn
|
| 81 |
+
return wrapped_fn_impl(args, kwargs, bound, memos)
|
| 82 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 83 |
+
File "/venv/main/lib/python3.12/site-packages/jaxtyping/_decorator.py", line 486, in wrapped_fn_impl
|
| 84 |
+
out = fn(*args, **kwargs)
|
| 85 |
+
^^^^^^^^^^^^^^^^^^^
|
| 86 |
+
File "/workspace/code/CVPR2026/src/model/model_wrapper.py", line 481, in training_step
|
| 87 |
+
all_gs_params_grad = torch.autograd.grad(render_loss, all_gs_params, retain_graph=True)
|
| 88 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 89 |
+
File "/venv/main/lib/python3.12/site-packages/torch/autograd/__init__.py", line 503, in grad
|
| 90 |
+
result = _engine_run_backward(
|
| 91 |
+
^^^^^^^^^^^^^^^^^^^^^
|
| 92 |
+
File "/venv/main/lib/python3.12/site-packages/torch/autograd/graph.py", line 829, in _engine_run_backward
|
| 93 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 94 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 95 |
+
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [4, 3]], which is output 0 of AsStridedBackward0, is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
|
| 96 |
+
|
| 97 |
+
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
|
0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/wandb-summary.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"_wandb":{"runtime":16},"_runtime":16,"comparison":{"height":1098,"format":"png","count":1,"filenames":["media/images/comparison_0_12d8ce60e732230809db.png"],"captions":["fcbd42c6ad4b2529"],"_type":"images/separated","width":1064},"trainer/global_step":0,"_timestamp":1.772473093014709e+09,"_step":2,"active_mask_imgs":{"captions":["fcbd42c6ad4b2529"],"_type":"images/separated","width":536,"height":800,"format":"png","count":1,"filenames":["media/images/active_mask_imgs_1_652ab96fa4327fc5f617.png"]},"error_scores":{"captions":["fcbd42c6ad4b2529"],"_type":"images/separated","width":800,"height":536,"format":"png","count":1,"filenames":["media/images/error_scores_2_36deb93328b08091d126.png"]}}
|
0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/logs/debug-core.log
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2026-03-02T17:38:07.068192494Z","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpyg2rvfzx/port-6180.txt","pid":6180,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
|
| 2 |
+
{"time":"2026-03-02T17:38:07.069046292Z","level":"INFO","msg":"server: will exit if parent process dies","ppid":6180}
|
| 3 |
+
{"time":"2026-03-02T17:38:07.069032242Z","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-6180-6460-3956421381/socket","Net":"unix"}}
|
| 4 |
+
{"time":"2026-03-02T17:38:07.239682516Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
|
| 5 |
+
{"time":"2026-03-02T17:38:07.25209719Z","level":"INFO","msg":"handleInformInit: received","streamId":"q9zn619i","id":"1(@)"}
|
| 6 |
+
{"time":"2026-03-02T17:38:07.742281159Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"q9zn619i","id":"1(@)"}
|
| 7 |
+
{"time":"2026-03-02T17:38:13.226363322Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"5pngaa1y82j3"}
|
| 8 |
+
{"time":"2026-03-02T17:38:24.944595103Z","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
|
| 9 |
+
{"time":"2026-03-02T17:38:24.944673915Z","level":"INFO","msg":"connection: closing","id":"1(@)"}
|
| 10 |
+
{"time":"2026-03-02T17:38:24.944766956Z","level":"INFO","msg":"server is shutting down"}
|
| 11 |
+
{"time":"2026-03-02T17:38:24.944787932Z","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
|
| 12 |
+
{"time":"2026-03-02T17:38:24.944902719Z","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-6180-6460-3956421381/socket","Net":"unix"}}
|
| 13 |
+
{"time":"2026-03-02T17:38:25.464657282Z","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"}
|
| 14 |
+
{"time":"2026-03-02T17:38:25.46468763Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"}
|
| 15 |
+
{"time":"2026-03-02T17:38:25.46469648Z","level":"INFO","msg":"server is closed"}
|
acid/0303_ACID_FULL_2v/.hydra/config.yaml
ADDED
|
@@ -0,0 +1,188 @@
|
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|
|
|
| 1 |
+
model:
|
| 2 |
+
encoder:
|
| 3 |
+
name: dcsplat
|
| 4 |
+
input_image_shape:
|
| 5 |
+
- 518
|
| 6 |
+
- 518
|
| 7 |
+
head_mode: pcd
|
| 8 |
+
num_level: 3
|
| 9 |
+
gs_param_dim: 256
|
| 10 |
+
align_corners: false
|
| 11 |
+
use_voxelize: true
|
| 12 |
+
decoder:
|
| 13 |
+
name: splatting_cuda
|
| 14 |
+
background_color:
|
| 15 |
+
- 0.0
|
| 16 |
+
- 0.0
|
| 17 |
+
- 0.0
|
| 18 |
+
make_scale_invariant: false
|
| 19 |
+
density_control:
|
| 20 |
+
name: density_control_module
|
| 21 |
+
mean_dim: 32
|
| 22 |
+
gs_param_dim: 256
|
| 23 |
+
refinement_layer_num: 1
|
| 24 |
+
num_level: 3
|
| 25 |
+
grad_mode: absgrad
|
| 26 |
+
use_mean_features: true
|
| 27 |
+
refinement_type: voxelize
|
| 28 |
+
refinement_hidden_dim: 32
|
| 29 |
+
aggregation_mode: mean
|
| 30 |
+
num_heads: 1
|
| 31 |
+
score_mode: absgrad
|
| 32 |
+
latent_dim: 128
|
| 33 |
+
num_latents: 64
|
| 34 |
+
num_self_attn_per_block: 2
|
| 35 |
+
voxel_size: 0.001
|
| 36 |
+
aux_refine: false
|
| 37 |
+
refine_error: false
|
| 38 |
+
use_refine_module: false
|
| 39 |
+
voxelize_activate: false
|
| 40 |
+
use_depth: false
|
| 41 |
+
render_loss:
|
| 42 |
+
mse:
|
| 43 |
+
weight: 1.0
|
| 44 |
+
lpips:
|
| 45 |
+
weight: 0.05
|
| 46 |
+
apply_after_step: 0
|
| 47 |
+
density_control_loss:
|
| 48 |
+
error_score:
|
| 49 |
+
weight: 0.0001
|
| 50 |
+
log_scale: false
|
| 51 |
+
grad_scale: 10000.0
|
| 52 |
+
mode: original
|
| 53 |
+
direct_loss:
|
| 54 |
+
l1:
|
| 55 |
+
weight: 0.8
|
| 56 |
+
ssim:
|
| 57 |
+
weight: 0.2
|
| 58 |
+
wandb:
|
| 59 |
+
project: DCSplat
|
| 60 |
+
entity: scene-representation-group
|
| 61 |
+
name: 0303_ACID_FULL_2v
|
| 62 |
+
mode: online
|
| 63 |
+
tags:
|
| 64 |
+
- acid
|
| 65 |
+
- 256x256
|
| 66 |
+
mode: train
|
| 67 |
+
data_loader:
|
| 68 |
+
train:
|
| 69 |
+
num_workers: 16
|
| 70 |
+
persistent_workers: true
|
| 71 |
+
batch_size: 16
|
| 72 |
+
seed: 1234
|
| 73 |
+
test:
|
| 74 |
+
num_workers: 4
|
| 75 |
+
persistent_workers: false
|
| 76 |
+
batch_size: 1
|
| 77 |
+
seed: 2345
|
| 78 |
+
val:
|
| 79 |
+
num_workers: 1
|
| 80 |
+
persistent_workers: true
|
| 81 |
+
batch_size: 1
|
| 82 |
+
seed: 3456
|
| 83 |
+
optimizer:
|
| 84 |
+
lr: 0.0002
|
| 85 |
+
warm_up_steps: 125
|
| 86 |
+
backbone_lr_multiplier: 0.1
|
| 87 |
+
backbone_trainable: T+H
|
| 88 |
+
accumulate: 1
|
| 89 |
+
checkpointing:
|
| 90 |
+
load: null
|
| 91 |
+
every_n_train_steps: 1875
|
| 92 |
+
save_top_k: 2
|
| 93 |
+
save_weights_only: false
|
| 94 |
+
train:
|
| 95 |
+
extended_visualization: false
|
| 96 |
+
print_log_every_n_steps: 10
|
| 97 |
+
camera_loss: 10.0
|
| 98 |
+
one_sample_validation: null
|
| 99 |
+
align_corners: false
|
| 100 |
+
intrinsic_scaling: false
|
| 101 |
+
verbose: false
|
| 102 |
+
beta_dist_param:
|
| 103 |
+
- 0.5
|
| 104 |
+
- 4.0
|
| 105 |
+
use_refine_aux: false
|
| 106 |
+
train_target_set: true
|
| 107 |
+
train_gs_num: 1
|
| 108 |
+
ext_scale_detach: false
|
| 109 |
+
cam_scale_mode: sum
|
| 110 |
+
scene_scale_reg_loss: 0.01
|
| 111 |
+
train_aux: true
|
| 112 |
+
vggt_cam_loss: true
|
| 113 |
+
vggt_distil: false
|
| 114 |
+
context_view_train: false
|
| 115 |
+
test:
|
| 116 |
+
output_path: test/full/acid
|
| 117 |
+
align_pose: false
|
| 118 |
+
pose_align_steps: 100
|
| 119 |
+
rot_opt_lr: 0.005
|
| 120 |
+
trans_opt_lr: 0.005
|
| 121 |
+
compute_scores: true
|
| 122 |
+
save_image: false
|
| 123 |
+
save_video: false
|
| 124 |
+
save_active_mask_image: false
|
| 125 |
+
save_error_score_image: false
|
| 126 |
+
save_compare: false
|
| 127 |
+
save_gs: false
|
| 128 |
+
save_sample_wise_metrics: true
|
| 129 |
+
pred_intrinsic: false
|
| 130 |
+
error_threshold: 0.4
|
| 131 |
+
error_threshold_list:
|
| 132 |
+
- 0.2
|
| 133 |
+
- 0.4
|
| 134 |
+
- 0.6
|
| 135 |
+
- 0.8
|
| 136 |
+
- 1.0
|
| 137 |
+
threshold_mode: ratio
|
| 138 |
+
nvs_view_N_list:
|
| 139 |
+
- 3
|
| 140 |
+
- 6
|
| 141 |
+
- 16
|
| 142 |
+
- 32
|
| 143 |
+
- 64
|
| 144 |
+
seed: 111123
|
| 145 |
+
trainer:
|
| 146 |
+
max_steps: 18751
|
| 147 |
+
val_check_interval: 500
|
| 148 |
+
gradient_clip_val: 0.5
|
| 149 |
+
num_nodes: 1
|
| 150 |
+
dataset:
|
| 151 |
+
re10k:
|
| 152 |
+
make_baseline_1: true
|
| 153 |
+
relative_pose: true
|
| 154 |
+
augment: true
|
| 155 |
+
background_color:
|
| 156 |
+
- 0.0
|
| 157 |
+
- 0.0
|
| 158 |
+
- 0.0
|
| 159 |
+
overfit_to_scene: null
|
| 160 |
+
skip_bad_shape: true
|
| 161 |
+
view_sampler:
|
| 162 |
+
name: bounded
|
| 163 |
+
num_target_views: 4
|
| 164 |
+
num_context_views: 2
|
| 165 |
+
min_distance_between_context_views: 45
|
| 166 |
+
max_distance_between_context_views: 90
|
| 167 |
+
min_distance_to_context_views: 0
|
| 168 |
+
warm_up_steps: 9375
|
| 169 |
+
initial_min_distance_between_context_views: 25
|
| 170 |
+
initial_max_distance_between_context_views: 25
|
| 171 |
+
same_target_gap: false
|
| 172 |
+
num_target_set: 3
|
| 173 |
+
target_align: true
|
| 174 |
+
name: re10k
|
| 175 |
+
roots:
|
| 176 |
+
- datasets/acid
|
| 177 |
+
input_image_shape:
|
| 178 |
+
- 256
|
| 179 |
+
- 256
|
| 180 |
+
original_image_shape:
|
| 181 |
+
- 360
|
| 182 |
+
- 640
|
| 183 |
+
cameras_are_circular: false
|
| 184 |
+
baseline_min: 0.001
|
| 185 |
+
baseline_max: 10000000000.0
|
| 186 |
+
max_fov: 100.0
|
| 187 |
+
dynamic_context_views: false
|
| 188 |
+
max_context_views_per_gpu: 16
|
acid/0303_ACID_FULL_2v/train_ddp_process_4.log
ADDED
|
@@ -0,0 +1,324 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
[2026-03-02 18:03:28,922][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-02 18:03:53,507][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-02 18:03:53,507][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-02 18:04:02,007][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-02 18:04:26,095][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-02 18:04:26,233][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-02 18:10:26,419][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
| 22 |
+
[2026-03-02 18:14:05,298][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 23 |
+
result[selector] = overlay
|
| 24 |
+
|
| 25 |
+
[2026-03-02 18:23:45,840][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 26 |
+
result[selector] = overlay
|
| 27 |
+
|
| 28 |
+
[2026-03-02 18:33:27,674][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 29 |
+
result[selector] = overlay
|
| 30 |
+
|
| 31 |
+
[2026-03-02 18:43:07,398][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 32 |
+
result[selector] = overlay
|
| 33 |
+
|
| 34 |
+
[2026-03-02 18:52:49,564][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 35 |
+
result[selector] = overlay
|
| 36 |
+
|
| 37 |
+
[2026-03-02 19:02:31,137][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 38 |
+
result[selector] = overlay
|
| 39 |
+
|
| 40 |
+
[2026-03-02 19:12:09,590][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 41 |
+
result[selector] = overlay
|
| 42 |
+
|
| 43 |
+
[2026-03-02 19:21:48,974][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 44 |
+
result[selector] = overlay
|
| 45 |
+
|
| 46 |
+
[2026-03-02 19:31:29,018][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 47 |
+
result[selector] = overlay
|
| 48 |
+
|
| 49 |
+
[2026-03-02 19:41:26,976][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 50 |
+
result[selector] = overlay
|
| 51 |
+
|
| 52 |
+
[2026-03-02 19:51:09,982][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 53 |
+
result[selector] = overlay
|
| 54 |
+
|
| 55 |
+
[2026-03-02 20:00:51,879][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 56 |
+
result[selector] = overlay
|
| 57 |
+
|
| 58 |
+
[2026-03-02 20:10:33,966][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 59 |
+
result[selector] = overlay
|
| 60 |
+
|
| 61 |
+
[2026-03-02 20:20:14,682][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 62 |
+
result[selector] = overlay
|
| 63 |
+
|
| 64 |
+
[2026-03-02 20:29:56,588][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 65 |
+
result[selector] = overlay
|
| 66 |
+
|
| 67 |
+
[2026-03-02 20:39:36,952][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 68 |
+
result[selector] = overlay
|
| 69 |
+
|
| 70 |
+
[2026-03-02 20:49:14,512][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 71 |
+
result[selector] = overlay
|
| 72 |
+
|
| 73 |
+
[2026-03-02 20:58:52,049][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 74 |
+
result[selector] = overlay
|
| 75 |
+
|
| 76 |
+
[2026-03-02 21:08:43,478][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 77 |
+
result[selector] = overlay
|
| 78 |
+
|
| 79 |
+
[2026-03-02 21:18:23,412][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 80 |
+
result[selector] = overlay
|
| 81 |
+
|
| 82 |
+
[2026-03-02 21:28:00,804][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 83 |
+
result[selector] = overlay
|
| 84 |
+
|
| 85 |
+
[2026-03-02 21:37:37,884][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 86 |
+
result[selector] = overlay
|
| 87 |
+
|
| 88 |
+
[2026-03-02 21:47:17,545][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 89 |
+
result[selector] = overlay
|
| 90 |
+
|
| 91 |
+
[2026-03-02 21:56:55,800][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 92 |
+
result[selector] = overlay
|
| 93 |
+
|
| 94 |
+
[2026-03-02 22:06:34,360][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 95 |
+
result[selector] = overlay
|
| 96 |
+
|
| 97 |
+
[2026-03-02 22:16:12,090][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 98 |
+
result[selector] = overlay
|
| 99 |
+
|
| 100 |
+
[2026-03-02 22:25:50,621][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 101 |
+
result[selector] = overlay
|
| 102 |
+
|
| 103 |
+
[2026-03-02 22:35:28,288][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 104 |
+
result[selector] = overlay
|
| 105 |
+
|
| 106 |
+
[2026-03-02 22:36:37,992][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 107 |
+
warnings.warn( # warn only once
|
| 108 |
+
|
| 109 |
+
[2026-03-02 22:45:20,471][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 110 |
+
result[selector] = overlay
|
| 111 |
+
|
| 112 |
+
[2026-03-02 22:55:01,124][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 113 |
+
result[selector] = overlay
|
| 114 |
+
|
| 115 |
+
[2026-03-02 23:04:39,455][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 116 |
+
result[selector] = overlay
|
| 117 |
+
|
| 118 |
+
[2026-03-02 23:14:17,249][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 119 |
+
result[selector] = overlay
|
| 120 |
+
|
| 121 |
+
[2026-03-02 23:23:55,698][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 122 |
+
result[selector] = overlay
|
| 123 |
+
|
| 124 |
+
[2026-03-02 23:33:33,247][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 125 |
+
result[selector] = overlay
|
| 126 |
+
|
| 127 |
+
[2026-03-02 23:43:12,955][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 128 |
+
result[selector] = overlay
|
| 129 |
+
|
| 130 |
+
[2026-03-02 23:52:51,817][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 131 |
+
result[selector] = overlay
|
| 132 |
+
|
| 133 |
+
[2026-03-03 00:02:31,189][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 134 |
+
result[selector] = overlay
|
| 135 |
+
|
| 136 |
+
[2026-03-03 00:07:18,516][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 137 |
+
warnings.warn( # warn only once
|
| 138 |
+
|
| 139 |
+
[2026-03-03 00:12:26,594][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 140 |
+
result[selector] = overlay
|
| 141 |
+
|
| 142 |
+
[2026-03-03 00:22:39,880][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 143 |
+
result[selector] = overlay
|
| 144 |
+
|
| 145 |
+
[2026-03-03 00:32:28,511][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 146 |
+
result[selector] = overlay
|
| 147 |
+
|
| 148 |
+
[2026-03-03 00:42:14,871][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 149 |
+
result[selector] = overlay
|
| 150 |
+
|
| 151 |
+
[2026-03-03 00:52:00,866][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 152 |
+
result[selector] = overlay
|
| 153 |
+
|
| 154 |
+
[2026-03-03 01:01:44,799][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 155 |
+
result[selector] = overlay
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+
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+
[2026-03-03 01:11:26,708][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
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+
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+
[2026-03-03 01:21:12,176][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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result[selector] = overlay
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+
[2026-03-03 01:30:57,373][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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+
[2026-03-03 01:39:24,812][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
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+
warnings.warn( # warn only once
|
| 168 |
+
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+
[2026-03-03 01:40:55,359][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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| 171 |
+
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+
[2026-03-03 01:50:43,795][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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+
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+
[2026-03-03 02:00:27,654][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
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+
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+
[2026-03-03 02:10:14,621][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 180 |
+
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+
[2026-03-03 02:20:01,278][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 183 |
+
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| 184 |
+
[2026-03-03 02:29:45,775][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 186 |
+
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+
[2026-03-03 02:39:31,377][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 189 |
+
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+
[2026-03-03 02:49:12,645][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
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+
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+
[2026-03-03 02:58:54,457][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 195 |
+
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| 196 |
+
[2026-03-03 03:08:36,615][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 197 |
+
result[selector] = overlay
|
| 198 |
+
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| 199 |
+
[2026-03-03 03:10:59,582][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 200 |
+
warnings.warn( # warn only once
|
| 201 |
+
|
| 202 |
+
[2026-03-03 03:18:31,625][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 203 |
+
result[selector] = overlay
|
| 204 |
+
|
| 205 |
+
[2026-03-03 03:28:13,183][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 206 |
+
result[selector] = overlay
|
| 207 |
+
|
| 208 |
+
[2026-03-03 03:37:54,561][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 209 |
+
result[selector] = overlay
|
| 210 |
+
|
| 211 |
+
[2026-03-03 03:47:35,897][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 212 |
+
result[selector] = overlay
|
| 213 |
+
|
| 214 |
+
[2026-03-03 03:57:15,205][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 215 |
+
result[selector] = overlay
|
| 216 |
+
|
| 217 |
+
[2026-03-03 04:06:56,179][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 218 |
+
result[selector] = overlay
|
| 219 |
+
|
| 220 |
+
[2026-03-03 04:16:35,089][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 221 |
+
result[selector] = overlay
|
| 222 |
+
|
| 223 |
+
[2026-03-03 04:26:11,512][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 224 |
+
result[selector] = overlay
|
| 225 |
+
|
| 226 |
+
[2026-03-03 04:35:48,377][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 227 |
+
result[selector] = overlay
|
| 228 |
+
|
| 229 |
+
[2026-03-03 04:41:44,956][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 230 |
+
warnings.warn( # warn only once
|
| 231 |
+
|
| 232 |
+
[2026-03-03 04:45:38,870][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 233 |
+
result[selector] = overlay
|
| 234 |
+
|
| 235 |
+
[2026-03-03 04:55:15,252][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 236 |
+
result[selector] = overlay
|
| 237 |
+
|
| 238 |
+
[2026-03-03 05:04:53,345][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 239 |
+
result[selector] = overlay
|
| 240 |
+
|
| 241 |
+
[2026-03-03 05:14:30,270][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 242 |
+
result[selector] = overlay
|
| 243 |
+
|
| 244 |
+
[2026-03-03 05:24:09,062][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 245 |
+
result[selector] = overlay
|
| 246 |
+
|
| 247 |
+
[2026-03-03 05:33:45,835][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 248 |
+
result[selector] = overlay
|
| 249 |
+
|
| 250 |
+
[2026-03-03 05:43:21,358][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 251 |
+
result[selector] = overlay
|
| 252 |
+
|
| 253 |
+
[2026-03-03 05:53:26,952][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 254 |
+
result[selector] = overlay
|
| 255 |
+
|
| 256 |
+
[2026-03-03 06:03:14,500][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 257 |
+
result[selector] = overlay
|
| 258 |
+
|
| 259 |
+
[2026-03-03 06:12:56,661][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 260 |
+
warnings.warn( # warn only once
|
| 261 |
+
|
| 262 |
+
[2026-03-03 06:13:16,462][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 263 |
+
result[selector] = overlay
|
| 264 |
+
|
| 265 |
+
[2026-03-03 06:23:01,938][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 266 |
+
result[selector] = overlay
|
| 267 |
+
|
| 268 |
+
[2026-03-03 06:32:45,011][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 269 |
+
result[selector] = overlay
|
| 270 |
+
|
| 271 |
+
[2026-03-03 06:42:29,839][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 272 |
+
result[selector] = overlay
|
| 273 |
+
|
| 274 |
+
[2026-03-03 06:52:13,025][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 275 |
+
result[selector] = overlay
|
| 276 |
+
|
| 277 |
+
[2026-03-03 07:01:59,425][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 278 |
+
result[selector] = overlay
|
| 279 |
+
|
| 280 |
+
[2026-03-03 07:11:44,187][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 281 |
+
result[selector] = overlay
|
| 282 |
+
|
| 283 |
+
[2026-03-03 07:21:26,733][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 284 |
+
result[selector] = overlay
|
| 285 |
+
|
| 286 |
+
[2026-03-03 07:31:11,044][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 287 |
+
result[selector] = overlay
|
| 288 |
+
|
| 289 |
+
[2026-03-03 07:40:58,343][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 290 |
+
result[selector] = overlay
|
| 291 |
+
|
| 292 |
+
[2026-03-03 07:44:34,873][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 293 |
+
warnings.warn( # warn only once
|
| 294 |
+
|
| 295 |
+
[2026-03-03 07:50:59,534][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 296 |
+
result[selector] = overlay
|
| 297 |
+
|
| 298 |
+
[2026-03-03 08:00:43,157][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 299 |
+
result[selector] = overlay
|
| 300 |
+
|
| 301 |
+
[2026-03-03 08:10:28,955][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 302 |
+
result[selector] = overlay
|
| 303 |
+
|
| 304 |
+
[2026-03-03 08:20:12,214][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 305 |
+
result[selector] = overlay
|
| 306 |
+
|
| 307 |
+
[2026-03-03 08:29:54,373][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 308 |
+
result[selector] = overlay
|
| 309 |
+
|
| 310 |
+
[2026-03-03 08:39:39,424][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 311 |
+
result[selector] = overlay
|
| 312 |
+
|
| 313 |
+
[2026-03-03 08:49:24,628][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 314 |
+
result[selector] = overlay
|
| 315 |
+
|
| 316 |
+
[2026-03-03 08:59:10,185][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 317 |
+
result[selector] = overlay
|
| 318 |
+
|
| 319 |
+
[2026-03-03 09:08:56,248][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 320 |
+
result[selector] = overlay
|
| 321 |
+
|
| 322 |
+
[2026-03-03 09:16:10,701][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 323 |
+
warnings.warn( # warn only once
|
| 324 |
+
|
acid/0303_ACID_FULL_2v/train_ddp_process_5.log
ADDED
|
@@ -0,0 +1,324 @@
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|
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|
|
|
|
| 1 |
+
[2026-03-02 18:03:29,368][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-02 18:03:46,174][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-02 18:03:46,175][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-02 18:04:02,007][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-02 18:04:26,094][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-02 18:04:26,214][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-02 18:10:26,140][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
| 22 |
+
[2026-03-02 18:14:05,298][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 23 |
+
result[selector] = overlay
|
| 24 |
+
|
| 25 |
+
[2026-03-02 18:23:45,841][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 26 |
+
result[selector] = overlay
|
| 27 |
+
|
| 28 |
+
[2026-03-02 18:33:27,677][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 29 |
+
result[selector] = overlay
|
| 30 |
+
|
| 31 |
+
[2026-03-02 18:43:07,397][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 32 |
+
result[selector] = overlay
|
| 33 |
+
|
| 34 |
+
[2026-03-02 18:52:49,564][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 35 |
+
result[selector] = overlay
|
| 36 |
+
|
| 37 |
+
[2026-03-02 19:02:31,137][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 38 |
+
result[selector] = overlay
|
| 39 |
+
|
| 40 |
+
[2026-03-02 19:12:09,590][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 41 |
+
result[selector] = overlay
|
| 42 |
+
|
| 43 |
+
[2026-03-02 19:21:48,975][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 44 |
+
result[selector] = overlay
|
| 45 |
+
|
| 46 |
+
[2026-03-02 19:31:29,019][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 47 |
+
result[selector] = overlay
|
| 48 |
+
|
| 49 |
+
[2026-03-02 19:41:26,976][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 50 |
+
result[selector] = overlay
|
| 51 |
+
|
| 52 |
+
[2026-03-02 19:51:09,982][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 53 |
+
result[selector] = overlay
|
| 54 |
+
|
| 55 |
+
[2026-03-02 20:00:51,879][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 56 |
+
result[selector] = overlay
|
| 57 |
+
|
| 58 |
+
[2026-03-02 20:10:33,966][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 59 |
+
result[selector] = overlay
|
| 60 |
+
|
| 61 |
+
[2026-03-02 20:20:14,684][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 62 |
+
result[selector] = overlay
|
| 63 |
+
|
| 64 |
+
[2026-03-02 20:29:56,588][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 65 |
+
result[selector] = overlay
|
| 66 |
+
|
| 67 |
+
[2026-03-02 20:39:36,952][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 68 |
+
result[selector] = overlay
|
| 69 |
+
|
| 70 |
+
[2026-03-02 20:49:14,514][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 71 |
+
result[selector] = overlay
|
| 72 |
+
|
| 73 |
+
[2026-03-02 20:58:52,050][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 74 |
+
result[selector] = overlay
|
| 75 |
+
|
| 76 |
+
[2026-03-02 21:08:43,478][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 77 |
+
result[selector] = overlay
|
| 78 |
+
|
| 79 |
+
[2026-03-02 21:18:23,413][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 80 |
+
result[selector] = overlay
|
| 81 |
+
|
| 82 |
+
[2026-03-02 21:28:00,804][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 83 |
+
result[selector] = overlay
|
| 84 |
+
|
| 85 |
+
[2026-03-02 21:37:37,884][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 86 |
+
result[selector] = overlay
|
| 87 |
+
|
| 88 |
+
[2026-03-02 21:47:17,550][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 89 |
+
result[selector] = overlay
|
| 90 |
+
|
| 91 |
+
[2026-03-02 21:56:55,800][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 92 |
+
result[selector] = overlay
|
| 93 |
+
|
| 94 |
+
[2026-03-02 22:06:34,360][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 95 |
+
result[selector] = overlay
|
| 96 |
+
|
| 97 |
+
[2026-03-02 22:16:12,086][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 98 |
+
result[selector] = overlay
|
| 99 |
+
|
| 100 |
+
[2026-03-02 22:25:50,622][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 101 |
+
result[selector] = overlay
|
| 102 |
+
|
| 103 |
+
[2026-03-02 22:35:28,288][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 104 |
+
result[selector] = overlay
|
| 105 |
+
|
| 106 |
+
[2026-03-02 22:36:37,993][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 107 |
+
warnings.warn( # warn only once
|
| 108 |
+
|
| 109 |
+
[2026-03-02 22:45:20,472][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 110 |
+
result[selector] = overlay
|
| 111 |
+
|
| 112 |
+
[2026-03-02 22:55:01,125][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 113 |
+
result[selector] = overlay
|
| 114 |
+
|
| 115 |
+
[2026-03-02 23:04:39,455][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 116 |
+
result[selector] = overlay
|
| 117 |
+
|
| 118 |
+
[2026-03-02 23:14:17,249][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 119 |
+
result[selector] = overlay
|
| 120 |
+
|
| 121 |
+
[2026-03-02 23:23:55,697][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 122 |
+
result[selector] = overlay
|
| 123 |
+
|
| 124 |
+
[2026-03-02 23:33:33,251][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 125 |
+
result[selector] = overlay
|
| 126 |
+
|
| 127 |
+
[2026-03-02 23:43:12,955][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 128 |
+
result[selector] = overlay
|
| 129 |
+
|
| 130 |
+
[2026-03-02 23:52:51,817][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 131 |
+
result[selector] = overlay
|
| 132 |
+
|
| 133 |
+
[2026-03-03 00:02:31,189][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 134 |
+
result[selector] = overlay
|
| 135 |
+
|
| 136 |
+
[2026-03-03 00:07:18,516][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 137 |
+
warnings.warn( # warn only once
|
| 138 |
+
|
| 139 |
+
[2026-03-03 00:12:26,594][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 140 |
+
result[selector] = overlay
|
| 141 |
+
|
| 142 |
+
[2026-03-03 00:22:39,880][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 143 |
+
result[selector] = overlay
|
| 144 |
+
|
| 145 |
+
[2026-03-03 00:32:28,511][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 146 |
+
result[selector] = overlay
|
| 147 |
+
|
| 148 |
+
[2026-03-03 00:42:14,874][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 149 |
+
result[selector] = overlay
|
| 150 |
+
|
| 151 |
+
[2026-03-03 00:52:00,866][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 152 |
+
result[selector] = overlay
|
| 153 |
+
|
| 154 |
+
[2026-03-03 01:01:45,053][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 155 |
+
result[selector] = overlay
|
| 156 |
+
|
| 157 |
+
[2026-03-03 01:11:26,708][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 158 |
+
result[selector] = overlay
|
| 159 |
+
|
| 160 |
+
[2026-03-03 01:21:12,176][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 161 |
+
result[selector] = overlay
|
| 162 |
+
|
| 163 |
+
[2026-03-03 01:30:57,373][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 164 |
+
result[selector] = overlay
|
| 165 |
+
|
| 166 |
+
[2026-03-03 01:39:24,814][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 167 |
+
warnings.warn( # warn only once
|
| 168 |
+
|
| 169 |
+
[2026-03-03 01:40:55,359][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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result[selector] = overlay
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| 171 |
+
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+
[2026-03-03 01:50:43,795][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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result[selector] = overlay
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+
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+
[2026-03-03 02:00:27,654][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
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+
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+
[2026-03-03 02:10:14,622][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
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| 180 |
+
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+
[2026-03-03 02:20:01,279][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 183 |
+
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| 184 |
+
[2026-03-03 02:29:45,775][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 186 |
+
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| 187 |
+
[2026-03-03 02:39:31,377][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 189 |
+
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| 190 |
+
[2026-03-03 02:49:12,646][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 192 |
+
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+
[2026-03-03 02:58:54,457][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 195 |
+
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+
[2026-03-03 03:08:36,615][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 198 |
+
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+
[2026-03-03 03:10:59,582][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 200 |
+
warnings.warn( # warn only once
|
| 201 |
+
|
| 202 |
+
[2026-03-03 03:18:31,625][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
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+
result[selector] = overlay
|
| 204 |
+
|
| 205 |
+
[2026-03-03 03:28:13,183][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 206 |
+
result[selector] = overlay
|
| 207 |
+
|
| 208 |
+
[2026-03-03 03:37:54,561][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 209 |
+
result[selector] = overlay
|
| 210 |
+
|
| 211 |
+
[2026-03-03 03:47:35,897][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 212 |
+
result[selector] = overlay
|
| 213 |
+
|
| 214 |
+
[2026-03-03 03:57:15,205][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 215 |
+
result[selector] = overlay
|
| 216 |
+
|
| 217 |
+
[2026-03-03 04:06:56,179][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 218 |
+
result[selector] = overlay
|
| 219 |
+
|
| 220 |
+
[2026-03-03 04:16:35,094][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 221 |
+
result[selector] = overlay
|
| 222 |
+
|
| 223 |
+
[2026-03-03 04:26:11,512][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 224 |
+
result[selector] = overlay
|
| 225 |
+
|
| 226 |
+
[2026-03-03 04:35:48,377][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 227 |
+
result[selector] = overlay
|
| 228 |
+
|
| 229 |
+
[2026-03-03 04:41:44,956][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 230 |
+
warnings.warn( # warn only once
|
| 231 |
+
|
| 232 |
+
[2026-03-03 04:45:38,871][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 233 |
+
result[selector] = overlay
|
| 234 |
+
|
| 235 |
+
[2026-03-03 04:55:15,252][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 236 |
+
result[selector] = overlay
|
| 237 |
+
|
| 238 |
+
[2026-03-03 05:04:53,345][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 239 |
+
result[selector] = overlay
|
| 240 |
+
|
| 241 |
+
[2026-03-03 05:14:30,270][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 242 |
+
result[selector] = overlay
|
| 243 |
+
|
| 244 |
+
[2026-03-03 05:24:09,062][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 245 |
+
result[selector] = overlay
|
| 246 |
+
|
| 247 |
+
[2026-03-03 05:33:45,835][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 248 |
+
result[selector] = overlay
|
| 249 |
+
|
| 250 |
+
[2026-03-03 05:43:21,359][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 251 |
+
result[selector] = overlay
|
| 252 |
+
|
| 253 |
+
[2026-03-03 05:53:26,952][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 254 |
+
result[selector] = overlay
|
| 255 |
+
|
| 256 |
+
[2026-03-03 06:03:14,500][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 257 |
+
result[selector] = overlay
|
| 258 |
+
|
| 259 |
+
[2026-03-03 06:12:56,662][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 260 |
+
warnings.warn( # warn only once
|
| 261 |
+
|
| 262 |
+
[2026-03-03 06:13:16,463][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 263 |
+
result[selector] = overlay
|
| 264 |
+
|
| 265 |
+
[2026-03-03 06:23:01,938][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 266 |
+
result[selector] = overlay
|
| 267 |
+
|
| 268 |
+
[2026-03-03 06:32:45,011][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 269 |
+
result[selector] = overlay
|
| 270 |
+
|
| 271 |
+
[2026-03-03 06:42:29,839][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 272 |
+
result[selector] = overlay
|
| 273 |
+
|
| 274 |
+
[2026-03-03 06:52:13,025][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 275 |
+
result[selector] = overlay
|
| 276 |
+
|
| 277 |
+
[2026-03-03 07:01:59,426][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 278 |
+
result[selector] = overlay
|
| 279 |
+
|
| 280 |
+
[2026-03-03 07:11:44,191][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 281 |
+
result[selector] = overlay
|
| 282 |
+
|
| 283 |
+
[2026-03-03 07:21:26,733][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 284 |
+
result[selector] = overlay
|
| 285 |
+
|
| 286 |
+
[2026-03-03 07:31:11,044][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 287 |
+
result[selector] = overlay
|
| 288 |
+
|
| 289 |
+
[2026-03-03 07:40:58,343][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 290 |
+
result[selector] = overlay
|
| 291 |
+
|
| 292 |
+
[2026-03-03 07:44:34,872][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 293 |
+
warnings.warn( # warn only once
|
| 294 |
+
|
| 295 |
+
[2026-03-03 07:50:59,534][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 296 |
+
result[selector] = overlay
|
| 297 |
+
|
| 298 |
+
[2026-03-03 08:00:43,157][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 299 |
+
result[selector] = overlay
|
| 300 |
+
|
| 301 |
+
[2026-03-03 08:10:28,955][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 302 |
+
result[selector] = overlay
|
| 303 |
+
|
| 304 |
+
[2026-03-03 08:20:12,398][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 305 |
+
result[selector] = overlay
|
| 306 |
+
|
| 307 |
+
[2026-03-03 08:29:54,373][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 308 |
+
result[selector] = overlay
|
| 309 |
+
|
| 310 |
+
[2026-03-03 08:39:39,424][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 311 |
+
result[selector] = overlay
|
| 312 |
+
|
| 313 |
+
[2026-03-03 08:49:24,628][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 314 |
+
result[selector] = overlay
|
| 315 |
+
|
| 316 |
+
[2026-03-03 08:59:10,185][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 317 |
+
result[selector] = overlay
|
| 318 |
+
|
| 319 |
+
[2026-03-03 09:08:56,247][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 320 |
+
result[selector] = overlay
|
| 321 |
+
|
| 322 |
+
[2026-03-03 09:16:10,703][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 323 |
+
warnings.warn( # warn only once
|
| 324 |
+
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/output.log
ADDED
|
@@ -0,0 +1,135 @@
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| 1 |
+
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
|
| 2 |
+
|
| 3 |
+
| Name | Type | Params | Mode
|
| 4 |
+
------------------------------------------------------------------------
|
| 5 |
+
0 | encoder | OurSplat | 888 M | train
|
| 6 |
+
1 | density_control_module | DensityControlModule | 514 | train
|
| 7 |
+
2 | decoder | DecoderSplattingCUDA | 0 | train
|
| 8 |
+
3 | render_losses | ModuleList | 0 | train
|
| 9 |
+
4 | density_control_losses | ModuleList | 0 | train
|
| 10 |
+
5 | direct_losses | ModuleList | 0 | train
|
| 11 |
+
------------------------------------------------------------------------
|
| 12 |
+
888 M Trainable params
|
| 13 |
+
0 Non-trainable params
|
| 14 |
+
888 M Total params
|
| 15 |
+
3,553.936 Total estimated model params size (MB)
|
| 16 |
+
1207 Modules in train mode
|
| 17 |
+
522 Modules in eval mode
|
| 18 |
+
Sanity Checking: | | 0/? [00:00<?, ?it/s][2026-03-02 17:41:26,346][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
|
| 19 |
+
|
| 20 |
+
Validation epoch start on rank 0
|
| 21 |
+
Sanity Checking DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s]validation step 0; scene = ['fcbd42c6ad4b2529'];
|
| 22 |
+
target intrinsic: tensor(0.9452, device='cuda:0') tensor(0.9454, device='cuda:0')
|
| 23 |
+
pred intrinsic: tensor(1.5447, device='cuda:0') tensor(1.5103, device='cuda:0')
|
| 24 |
+
W0302 17:41:28.386000 7180 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
|
| 25 |
+
W0302 17:41:28.386000 7180 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
|
| 26 |
+
[2026-03-02 17:41:28,446][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 27 |
+
result[selector] = overlay
|
| 28 |
+
|
| 29 |
+
[2026-03-02 17:41:28,456][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
|
| 30 |
+
|
| 31 |
+
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
|
| 32 |
+
[2026-03-02 17:41:28,457][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 33 |
+
warnings.warn(
|
| 34 |
+
|
| 35 |
+
[2026-03-02 17:41:28,457][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 36 |
+
warnings.warn(msg)
|
| 37 |
+
|
| 38 |
+
Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth
|
| 39 |
+
[2026-03-02 17:41:29,838][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
|
| 40 |
+
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
|
| 41 |
+
|
| 42 |
+
Epoch 0: | | 0/? [00:00<?, ?it/s]context = [[20, 45], [46, 71], [55, 80], [104, 129], [0, 25], [13, 38], [17, 42], [57, 82], [543, 568], [28, 53], [53, 78], [2, 27], [146, 171], [3, 28], [71, 96], [8, 33]]target = [[23, 25, 31, 27], [50, 65, 55, 70], [79, 68, 60, 69], [108, 119, 110, 124], [23, 3, 22, 12], [33, 36, 22, 28], [30, 20, 32, 36], [69, 66, 71, 64], [563, 567, 565, 550], [50, 31, 42, 37], [74, 63, 58, 72], [10, 24, 6, 23], [162, 149, 155, 168], [4, 19, 12, 25], [78, 73, 74, 75], [27, 16, 26, 14]]
|
| 43 |
+
[2026-03-02 17:41:37,803][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 44 |
+
result[selector] = overlay
|
| 45 |
+
|
| 46 |
+
Epoch 0: | | 9/? [00:26<00:00, 0.34it/s, v_num=ntcx]train step 10; scene = ['8b82391ffe0fdc40', 'b132b56c2f71f2bb', 'a0e91a6d89006676', '263383a6650bd133', '8115c82e12facb94', '7f83bf7a0ea82e76', 'fc950fc75c178d71', '702f59af91e8a5ef', 'edd8601700a51f90', 'c164c9ee6d9c6db0', '773214529722555f', '20d29832adadc3c6', '06e65d6d431c5af0', '257c29fd7ac74539', '1ce3057d9d5e580e', '8101e87ca48d5aac']; loss = 0.685463
|
| 47 |
+
Epoch 0: | | 10/? [00:28<00:00, 0.35it/s, v_num=ntcx]context = [[8, 33], [105, 130], [620, 645], [5, 30], [84, 109], [54, 79], [55, 80], [118, 143], [49, 74], [222, 247], [61, 86], [94, 119], [44, 69], [146, 171], [76, 101], [2, 27]]target = [[18, 23, 21, 11], [116, 112, 118, 126], [627, 630, 622, 629], [28, 6, 26, 9], [91, 103, 100, 96], [78, 55, 59, 74], [62, 78, 69, 60], [130, 140, 131, 124], [71, 63, 67, 66], [241, 232, 234, 229], [65, 78, 84, 63], [113, 118, 97, 100], [62, 53, 68, 64], [166, 169, 159, 152], [85, 89, 93, 90], [17, 26, 23, 9]]
|
| 48 |
+
Epoch 0: | | 19/? [00:53<00:00, 0.36it/s, v_num=ntcx]train step 20; scene = ['bb004f3b156d4fa0', '08d730bf5171ae1f', '310ed36d08330941', '2274990dd54b2301', '80f22f63bcbfa585', 'f415ed7c59e6c897', '6f140b5415168f1a', 'b48e491afc33826b', '4ee96526ace7d664', 'f0a68fbd8ea3ce7d', '58e29f76fdd72bfe', '6ccb67e1de2352ae', 'bfd7042dd3800ec3', '4d657499de6173d6', 'ccbcebf1b97fb0d0', '5254670ba2280cb5']; loss = 0.533170
|
| 49 |
+
Epoch 0: | | 20/? [00:56<00:00, 0.36it/s, v_num=ntcx]context = [[263, 288], [71, 96], [94, 119], [21, 46], [11, 36], [48, 73], [2, 27], [38, 63], [27, 52], [39, 64], [422, 447], [14, 39], [142, 167], [12, 37], [83, 108], [15, 40]]target = [[287, 277, 274, 284], [87, 86, 90, 76], [102, 110, 95, 106], [33, 45, 38, 36], [19, 13, 22, 15], [52, 59, 65, 63], [9, 17, 18, 24], [51, 46, 41, 52], [28, 44, 48, 43], [55, 50, 52, 56], [427, 430, 442, 423], [33, 16, 17, 27], [153, 166, 165, 163], [35, 36, 28, 25], [104, 98, 86, 92], [24, 28, 25, 19]]
|
| 50 |
+
Epoch 0: | | 29/? [01:21<00:00, 0.36it/s, v_num=ntcx]train step 30; scene = ['61320598b5b0c144', 'e9c34ef46b2961ae', '81675960876b8950', 'e1f25474118e488c', '2eb9843b2929be3b', 'afab3c06ae87e76d', 'dda3e0c9dbf6dfa8', '7290a9836b58c2cd', '850d687cdfc47997', 'bf7d82dcd9121446', '23a2dd7a563aa92c', 'dc654ad716469827', '7b11643f1e7b14f9', 'bc14847314b63040', '7c2cd4905919647e', 'b3c77b811cf2a0db']; loss = 0.484556
|
| 51 |
+
Epoch 0: | | 30/? [01:23<00:00, 0.36it/s, v_num=ntcx]context = [[10, 35], [31, 56], [10, 35], [128, 153], [24, 49], [42, 67], [0, 25], [9, 34], [111, 136], [14, 39], [0, 25], [115, 140], [113, 138], [1, 26], [0, 25], [6, 31]]target = [[27, 24, 20, 14], [34, 41, 36, 47], [30, 20, 31, 33], [129, 138, 135, 143], [41, 38, 36, 46], [54, 66, 62, 49], [6, 1, 9, 19], [11, 18, 16, 12], [128, 130, 117, 113], [27, 23, 24, 32], [11, 13, 1, 12], [136, 118, 133, 137], [118, 125, 121, 115], [18, 5, 17, 9], [11, 14, 6, 3], [28, 13, 16, 17]]
|
| 52 |
+
Epoch 0: | | 39/? [01:48<00:00, 0.36it/s, v_num=ntcx]train step 40; scene = ['f958dc278635a817', '77975a8feed2286e', 'a5f51009c68abb89', '288c33c9016e214c', 'f314a831b66516c0', 'a4c76fcfba517d08', 'd9afecd9f6310a2a', '4a44e96319f6469b', 'e05dfa20182ad158', '4d1818b063dd8223', '21995cf8520f62a3', '95c0517501bcad07', '5066f7467175d7b9', '28c12bff04b8065d', 'adfde31148a7ac3d', '959b6c33da27e48b']; loss = 0.242372
|
| 53 |
+
Epoch 0: | | 40/? [01:51<00:00, 0.36it/s, v_num=ntcx]context = [[1, 26], [101, 126], [108, 133], [14, 39], [199, 224], [32, 57], [159, 184], [38, 63], [47, 72], [4, 29], [33, 58], [0, 25], [2, 27], [35, 60], [433, 458], [5, 30]]target = [[18, 7, 15, 4], [109, 112, 103, 124], [120, 111, 118, 114], [37, 22, 15, 26], [218, 203, 215, 201], [43, 50, 51, 46], [165, 183, 169, 162], [50, 40, 56, 59], [49, 54, 48, 59], [9, 8, 12, 18], [43, 39, 56, 51], [15, 5, 3, 22], [24, 7, 3, 6], [54, 56, 42, 45], [451, 456, 436, 442], [24, 15, 19, 29]]
|
| 54 |
+
Epoch 0: | | 49/? [02:15<00:00, 0.36it/s, v_num=ntcx]train step 50; scene = ['0bc74fbe010a34a9', 'ec79e7c6e475c3b4', '9106825d455282e4', '27baca6d132a2548', '80e2cd727dbbab4d', '9c5fad8f6c2c1d30', '7e045ded29651933', '606e7ff3de54704e', 'ef5c8c3991478315', '61cf7149ee31b7fe', 'abe1a7d84be623b7', '1d609ce53fc03ddd', 'c64d102d65db2bce', '19c65186ea9e89d8', 'e6b7a6545acbb4a3', '766ee07fa34b27e9']; loss = 0.212864
|
| 55 |
+
Epoch 0: | | 50/? [02:18<00:00, 0.36it/s, v_num=ntcx]context = [[1, 26], [32, 57], [9, 34], [62, 87], [25, 50], [29, 54], [29, 54], [53, 78], [18, 43], [104, 129], [185, 210], [5, 30], [3, 28], [169, 194], [240, 265], [176, 201]]target = [[9, 17, 12, 22], [35, 45, 38, 42], [33, 21, 23, 20], [67, 72, 79, 68], [46, 30, 41, 26], [36, 35, 34, 41], [32, 50, 46, 41], [69, 59, 58, 74], [28, 29, 38, 21], [112, 115, 107, 113], [188, 200, 195, 207], [24, 15, 21, 7], [5, 4, 7, 14], [175, 190, 182, 191], [259, 257, 256, 251], [178, 186, 183, 188]]
|
| 56 |
+
Epoch 0: | | 59/? [02:42<00:00, 0.36it/s, v_num=ntcx]train step 60; scene = ['a79b756c3662021b', '0486901d34599e55', '1f076dcb69c714fc', 'a7eacd79d440cbc4', 'a4a25b75943ff6f3', 'b90c013a8e74c448', '3e9710a0bec08679', '6845dc795d55b73d', '23fc189cbe853a87', 'd0cd86c7f23be1e8', '1222bdc580ab86e8', '1a2c1d71a0859814', '10b63bc8563a5e36', 'f9064505005b244e', '8f7068319d98730a', '07f666f2c676fa0a']; loss = 0.202259
|
| 57 |
+
Epoch 0: | | 60/? [02:45<00:00, 0.36it/s, v_num=ntcx]context = [[433, 458], [73, 98], [10, 35], [0, 25], [0, 25], [19, 44], [23, 48], [8, 33], [46, 71], [3, 28], [42, 67], [9, 34], [224, 249], [10, 35], [183, 208], [0, 25]]target = [[447, 441, 434, 455], [83, 78, 79, 81], [28, 14, 20, 23], [3, 21, 4, 9], [12, 20, 14, 4], [38, 20, 32, 35], [36, 32, 41, 31], [25, 12, 9, 15], [47, 49, 53, 68], [15, 23, 21, 5], [59, 52, 44, 57], [20, 25, 15, 13], [240, 231, 244, 248], [32, 31, 27, 18], [203, 187, 199, 185], [12, 20, 19, 15]]
|
| 58 |
+
Epoch 0: | | 69/? [03:10<00:00, 0.36it/s, v_num=ntcx]train step 70; scene = ['a7eacd79d440cbc4', '4da3ab97ce833b31', 'a79b756c3662021b', 'b282c05ee6fbfeec', '8eed172bfca006f6', 'b90c013a8e74c448', 'c0460f7eb5ef0d64', '3e9710a0bec08679', '0486901d34599e55', '1f076dcb69c714fc', '4707f93b69487726', '66b53eb50f69046f', '02fa36729f05052b', 'b232aaf8b823ccc0', 'd7a974c2fce2e20c', '342b7c4207f94818']; loss = 0.147199
|
| 59 |
+
Epoch 0: | | 70/? [03:12<00:00, 0.36it/s, v_num=ntcx]context = [[7, 32], [14, 39], [53, 78], [56, 81], [198, 223], [107, 132], [1, 26], [18, 43], [77, 102], [5, 30], [2, 27], [8, 33], [0, 25], [10, 35], [356, 381], [41, 66]]target = [[8, 29, 17, 12], [36, 16, 29, 23], [61, 67, 62, 75], [76, 60, 64, 69], [207, 222, 217, 214], [127, 112, 126, 124], [19, 25, 16, 8], [42, 34, 20, 37], [98, 84, 88, 81], [10, 23, 18, 25], [25, 17, 19, 15], [20, 15, 26, 29], [15, 10, 19, 13], [11, 13, 29, 12], [370, 376, 371, 363], [56, 58, 60, 43]]
|
| 60 |
+
Epoch 0: | | 79/? [03:37<00:00, 0.36it/s, v_num=ntcx]train step 80; scene = ['8f7068319d98730a', '07f666f2c676fa0a', 'd0cd86c7f23be1e8', 'f9064505005b244e', '10b63bc8563a5e36', '1a2c1d71a0859814', 'd9847bf9d7bbc5c8', '1222bdc580ab86e8', 'badf0a423a91e052', '6845dc795d55b73d', '23fc189cbe853a87', '32110a3f67181f96', 'c4df0719e16569ec', '555ffc07e719329c', 'eedb484ea2213850', '38173f49bc2aa9f2']; loss = 0.139647
|
| 61 |
+
Epoch 0: | | 80/? [03:39<00:00, 0.36it/s, v_num=ntcx]context = [[3, 28], [17, 42], [7, 32], [11, 36], [146, 171], [33, 58], [2, 27], [2, 27], [17, 42], [10, 35], [49, 74], [2, 27], [294, 319], [0, 25], [40, 65], [48, 73]]target = [[15, 7, 17, 9], [31, 41, 26, 40], [12, 10, 19, 25], [24, 18, 25, 35], [160, 170, 165, 152], [52, 50, 38, 36], [3, 18, 7, 10], [26, 20, 11, 24], [22, 18, 26, 37], [33, 13, 17, 31], [55, 53, 65, 69], [10, 16, 4, 17], [309, 307, 295, 302], [6, 5, 1, 15], [51, 59, 52, 46], [59, 61, 64, 60]]
|
| 62 |
+
Epoch 0: | | 89/? [04:04<00:00, 0.36it/s, v_num=ntcx]train step 90; scene = ['eca07a5e77d5a138', '18f8f0c5a4b16520', '5254670ba2280cb5', 'ac725872c1b038d4', '479ebf60319a865d', '10a5794eaf4dc065', '4d657499de6173d6', 'ccbcebf1b97fb0d0', 'bfd7042dd3800ec3', '25c98f7dfba55d39', '59d27cecb348afd6', '1cd1da8e148c84b6', '91c1c13bbe4b0113', '4b83fa4f29deaac8', 'd3248841e4815cc8', 'cc4287659b0eb5a0']; loss = 0.115869
|
| 63 |
+
Epoch 0: | | 90/? [04:07<00:00, 0.36it/s, v_num=ntcx]context = [[825, 850], [2, 27], [8, 33], [64, 89], [30, 55], [27, 52], [26, 51], [409, 434], [10, 35], [202, 227], [25, 50], [458, 483], [17, 42], [2, 27], [2, 27], [21, 46]]target = [[828, 839, 832, 831], [10, 23, 6, 13], [19, 25, 12, 31], [65, 81, 75, 83], [50, 42, 53, 41], [49, 32, 37, 28], [44, 38, 45, 36], [421, 423, 414, 418], [20, 32, 11, 34], [203, 221, 205, 210], [37, 38, 32, 48], [464, 460, 461, 481], [34, 37, 35, 21], [24, 25, 5, 14], [23, 17, 10, 18], [32, 25, 45, 31]]
|
| 64 |
+
Epoch 0: | | 99/? [04:31<00:00, 0.36it/s, v_num=ntcx]train step 100; scene = ['bcada108ffdadc07', 'e21cbd9c7546cb1e', '7f4627eac7a97e71', 'dcbf449171deae24', '6285632e5f08a0fd', '12167d7d9cb2a489', '18fcc1f2a035be8a', 'ff7c82d129cf7b49', '0bfa5ff6e69542cd', '2479e98b51f7d179', '72b15c11f285db11', '081fbad41243399b', 'e1e30cd983d3f335', '953bbcb632cf3adb', '61d4ffc3c9b46e71', '2add4958d42abcbe']; loss = 0.138332
|
| 65 |
+
Epoch 0: | | 100/? [04:33<00:00, 0.36it/s, v_num=ntcx]context = [[39, 64], [21, 46], [15, 40], [24, 49], [3, 28], [135, 160], [18, 43], [50, 75], [71, 96], [144, 169], [4, 29], [3, 28], [38, 63], [56, 81], [69, 94], [36, 61]]target = [[62, 47, 42, 51], [39, 30, 44, 35], [30, 32, 33, 18], [48, 27, 29, 37], [9, 26, 4, 19], [157, 143, 150, 141], [22, 40, 37, 24], [53, 57, 59, 70], [75, 73, 93, 79], [158, 168, 159, 152], [7, 28, 27, 12], [6, 8, 24, 5], [59, 60, 51, 61], [63, 68, 67, 70], [76, 71, 86, 90], [38, 46, 57, 53]]
|
| 66 |
+
Epoch 0: | | 109/? [04:58<00:00, 0.37it/s, v_num=ntcx]train step 110; scene = ['309c9d290263107a', '99f460ebba9d29e7', '83aec9281d9c6d31', '0403012f54afdb33', 'e0a50668672827fd', 'a785f9636c36bfea', '167b352e1c48ae54', '2358ed8352c77bd1', '1bcbe5e8d4f9e631', '67c00485431a8d84', '88292ddb6f1444f0', 'a189e56d7997c823', '4a444e958163e96b', 'c350ae2c6fde2e82', '4c5bca38fed0154d', '3c6533cc00794e9e']; loss = 0.144588
|
| 67 |
+
Epoch 0: | | 110/? [05:01<00:00, 0.37it/s, v_num=ntcx]context = [[190, 215], [18, 43], [1, 26], [297, 322], [41, 66], [41, 66], [31, 56], [9, 34], [3, 28], [2, 27], [34, 59], [191, 216], [21, 46], [8, 33], [3, 28], [60, 85]]target = [[198, 193, 192, 209], [37, 33, 26, 22], [2, 24, 14, 11], [307, 314, 304, 312], [62, 43, 45, 52], [42, 54, 44, 48], [46, 37, 40, 41], [18, 26, 33, 30], [26, 19, 10, 14], [11, 18, 7, 5], [37, 42, 51, 41], [204, 212, 206, 208], [38, 35, 39, 22], [10, 27, 20, 28], [24, 5, 6, 15], [80, 66, 83, 77]]
|
| 68 |
+
Epoch 0: | | 119/? [05:25<00:00, 0.37it/s, v_num=ntcx]train step 120; scene = ['b17f5d114ad79003', 'a5487eda963ef4a7', '133c9caff8e077ac', '8fcce93022cd9e30', '45074bd32cdc1515', 'edd8601700a51f90', 'c164c9ee6d9c6db0', '773214529722555f', '45209afbc3a55c93', '04bf0e2d97063b80', '63b3051b9a257224', 'e35132f4de028fce', '733e013a9173971c', '11b6c8d609e64e1c', '5be900496d702922', '5607db62beef5df7']; loss = 0.130907
|
| 69 |
+
Epoch 0: | | 120/? [05:28<00:00, 0.37it/s, v_num=ntcx]context = [[10, 35], [9, 34], [155, 180], [20, 45], [654, 679], [7, 32], [170, 195], [62, 87], [7, 32], [59, 84], [12, 37], [3, 28], [105, 130], [12, 37], [53, 78], [24, 49]]target = [[14, 11, 23, 20], [20, 31, 30, 12], [172, 171, 166, 162], [34, 21, 31, 42], [665, 666, 673, 677], [15, 27, 29, 12], [181, 183, 171, 192], [71, 72, 65, 76], [16, 23, 8, 25], [77, 79, 64, 82], [31, 36, 25, 22], [17, 24, 19, 11], [119, 116, 126, 112], [28, 32, 15, 19], [72, 62, 54, 70], [28, 27, 33, 43]]
|
| 70 |
+
Epoch 0: | | 124/? [05:39<00:00, 0.37it/s, v_num=ntcx][2026-03-02 17:47:16,369][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 71 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 72 |
+
|
| 73 |
+
Epoch 0: | | 129/? [05:52<00:00, 0.37it/s, v_num=ntcx]train step 130; scene = ['3d3fcb72a9a5de02', 'fb62839962c81095', '5e18a096f58cf335', '6ea3b8df77a8b37a', '95cc1d116282a37e', 'dffda286697396eb', '7cf4acbea22bd686', '5b14e8d3a592e7c0', 'f0480b23f65e7e3f', 'aee73595d65893a1', '1ba2739ebdb5ed83', 'dc199e54b8981fd8', '37c7c3392a02e8ea', 'dac3af60a1d0112b', '24419cfdbc518f69', '9d6b559ad8369a83']; loss = 0.128441
|
| 74 |
+
Epoch 0: | | 130/? [05:55<00:00, 0.37it/s, v_num=ntcx]context = [[16, 41], [114, 139], [0, 25], [0, 25], [74, 99], [315, 340], [72, 97], [14, 39], [0, 25], [42, 67], [207, 232], [23, 48], [51, 76], [1, 26], [38, 63], [30, 55]]target = [[33, 36, 20, 37], [135, 115, 137, 132], [15, 10, 2, 6], [4, 15, 18, 13], [81, 98, 85, 92], [334, 319, 326, 328], [95, 93, 84, 86], [22, 28, 38, 31], [23, 10, 3, 6], [58, 65, 62, 57], [219, 213, 226, 214], [29, 25, 41, 37], [62, 53, 67, 66], [11, 22, 13, 10], [49, 50, 61, 42], [35, 45, 39, 31]]
|
| 75 |
+
Epoch 0: | | 139/? [06:19<00:00, 0.37it/s, v_num=ntcx]train step 140; scene = ['dac3af60a1d0112b', '24419cfdbc518f69', 'dc199e54b8981fd8', '9d6b559ad8369a83', '37c7c3392a02e8ea', 'a0e91a6d89006676', 'b132b56c2f71f2bb', '8b82391ffe0fdc40', '7f83bf7a0ea82e76', '702f59af91e8a5ef', '8115c82e12facb94', 'fc950fc75c178d71', '263383a6650bd133', '18fcc1f2a035be8a', 'ff7c82d129cf7b49', '0bfa5ff6e69542cd']; loss = 0.125431
|
| 76 |
+
Epoch 0: | | 140/? [06:22<00:00, 0.37it/s, v_num=ntcx]context = [[38, 63], [64, 89], [1, 26], [88, 113], [3, 28], [284, 309], [530, 555], [176, 201], [2, 27], [59, 84], [157, 182], [81, 106], [14, 39], [5, 30], [165, 190], [908, 933]]target = [[58, 52, 46, 55], [66, 83, 88, 71], [17, 10, 4, 24], [99, 90, 106, 108], [21, 9, 13, 14], [305, 307, 298, 285], [531, 549, 540, 541], [185, 189, 195, 193], [5, 6, 13, 8], [74, 62, 81, 80], [172, 174, 170, 177], [95, 105, 89, 97], [21, 26, 25, 20], [10, 29, 15, 16], [178, 182, 188, 187], [913, 909, 919, 922]]
|
| 77 |
+
Epoch 0: | | 149/? [06:46<00:00, 0.37it/s, v_num=ntcx]train step 150; scene = ['513ac6672e6e2938', '1b7d734a8199ef60', '9a7988656fa7f947', '4a444e958163e96b', '88292ddb6f1444f0', '3c6533cc00794e9e', '4c5bca38fed0154d', 'c350ae2c6fde2e82', 'a189e56d7997c823', 'c10405bc7d35b35a', 'c164c9ee6d9c6db0', '773214529722555f', 'edd8601700a51f90', '27cb8d9e8ba2ee97', 'abda2acba4699696', '6f538a5947860580']; loss = 0.127402
|
| 78 |
+
Epoch 0: | | 150/? [06:49<00:00, 0.37it/s, v_num=ntcx]context = [[210, 235], [238, 263], [43, 68], [522, 547], [61, 86], [33, 58], [117, 142], [17, 42], [2, 27], [44, 69], [78, 103], [93, 118], [44, 69], [24, 49], [10, 35], [0, 25]]target = [[211, 228, 219, 225], [248, 239, 257, 259], [44, 60, 50, 45], [531, 524, 536, 541], [77, 76, 83, 66], [48, 35, 45, 46], [126, 141, 129, 138], [21, 20, 36, 35], [19, 9, 26, 21], [54, 49, 57, 46], [84, 79, 85, 80], [101, 106, 100, 117], [66, 59, 47, 51], [44, 31, 34, 36], [17, 23, 27, 20], [2, 21, 13, 9]]
|
| 79 |
+
Epoch 0: | | 159/? [07:14<00:00, 0.37it/s, v_num=ntcx]train step 160; scene = ['5bd0688af43c5416', '2c4adb33fed018e2', '5cc281d499982c01', '12f37df94449e6ca', 'eeee5ca94ad049ea', '7d912211524e6168', 'eab88855b6827725', '8828a5d34375f552', 'fbdd07103c15fb17', 'c5a52b834e16d4fb', 'ee389c7efe1bd3db', 'cf31a6d87ae1beba', '6cff401403f7370c', '48a198e24170bfe5', 'ffc7fe33acb15ea6', 'dff9a90c26925346']; loss = 0.097318
|
| 80 |
+
Epoch 0: | | 160/? [07:16<00:00, 0.37it/s, v_num=ntcx]context = [[35, 60], [8, 33], [36, 61], [510, 535], [3, 28], [67, 92], [80, 105], [73, 98], [47, 72], [4, 29], [7, 32], [6, 31], [101, 126], [24, 49], [0, 25], [57, 82]]target = [[49, 52, 50, 44], [27, 17, 32, 24], [52, 40, 51, 47], [520, 513, 534, 515], [16, 9, 7, 24], [84, 75, 83, 85], [97, 93, 101, 86], [88, 83, 89, 77], [71, 50, 66, 61], [12, 10, 24, 21], [11, 25, 20, 29], [28, 30, 21, 10], [123, 109, 118, 107], [48, 32, 44, 47], [18, 21, 6, 4], [67, 63, 58, 74]]
|
| 81 |
+
Epoch 0: | | 169/? [07:41<00:00, 0.37it/s, v_num=ntcx]train step 170; scene = ['7c991ed0cb1903bf', 'dd574bb1c0d31833', 'ecdef74c6b5c81b4', '133c9caff8e077ac', 'cde4b89bff2028c2', 'a5487eda963ef4a7', '8fcce93022cd9e30', '35a45dc316d11109', 'b17f5d114ad79003', '17927ab189bb7220', '45074bd32cdc1515', 'b0715140be239559', '98f6fc5c38fb5f32', '8b400b055c2d198e', 'b534ad12242ba0a5', '20176aec38591e61']; loss = 0.113552
|
| 82 |
+
Epoch 0: | | 170/? [07:43<00:00, 0.37it/s, v_num=ntcx]context = [[87, 112], [10, 35], [29, 54], [284, 309], [9, 34], [3, 28], [18, 43], [70, 95], [147, 172], [8, 33], [51, 76], [7, 32], [11, 36], [3, 28], [145, 170], [11, 36]]target = [[108, 107, 98, 110], [21, 18, 27, 11], [40, 34, 47, 39], [303, 299, 292, 307], [15, 33, 19, 16], [10, 26, 11, 22], [37, 31, 34, 29], [73, 77, 75, 89], [151, 153, 168, 148], [16, 17, 14, 21], [65, 58, 69, 60], [31, 25, 8, 30], [33, 13, 16, 26], [24, 8, 9, 26], [161, 152, 168, 166], [20, 25, 23, 16]]
|
| 83 |
+
Epoch 0: | | 179/? [08:08<00:00, 0.37it/s, v_num=ntcx]train step 180; scene = ['f3e2a5f45a5034bd', '4c32de568e68a9ff', 'f5481a6a0260e12c', '1a2c1d71a0859814', '1222bdc580ab86e8', '23fc189cbe853a87', 'badf0a423a91e052', 'd0cd86c7f23be1e8', '10b63bc8563a5e36', 'f9064505005b244e', '07f666f2c676fa0a', '6845dc795d55b73d', '8f7068319d98730a', 'd9847bf9d7bbc5c8', '498231a6a203decb', '84da42600a770208']; loss = 0.106532
|
| 84 |
+
Epoch 0: | | 180/? [08:10<00:00, 0.37it/s, v_num=ntcx]context = [[72, 97], [541, 566], [52, 78], [112, 137], [45, 70], [4, 30], [23, 49], [7, 33], [142, 168], [54, 80], [32, 58], [1, 26], [19, 44], [8, 33], [165, 190], [0, 25]]target = [[73, 85, 80, 81], [562, 559, 542, 554], [65, 60, 64, 74], [116, 128, 118, 115], [51, 66, 67, 64], [26, 8, 18, 7], [43, 34, 33, 45], [24, 14, 30, 16], [165, 156, 150, 158], [75, 68, 63, 69], [44, 43, 36, 41], [19, 15, 16, 8], [35, 43, 39, 23], [17, 29, 26, 30], [176, 186, 180, 171], [7, 6, 21, 19]]
|
| 85 |
+
Epoch 0: | | 189/? [08:35<00:00, 0.37it/s, v_num=ntcx]train step 190; scene = ['2274990dd54b2301', '80f22f63bcbfa585', '4ee96526ace7d664', '08d730bf5171ae1f', '58e29f76fdd72bfe', 'f415ed7c59e6c897', 'f0a68fbd8ea3ce7d', '310ed36d08330941', '6ccb67e1de2352ae', '6f140b5415168f1a', 'b48e491afc33826b', 'e3e29755afa06cfb', '77809cb8299e7199', '47e57a81ee491d27', 'f33f27cd323123f7', 'df017fa4b4a83840']; loss = 0.116100
|
| 86 |
+
Epoch 0: | | 190/? [08:37<00:00, 0.37it/s, v_num=ntcx]context = [[41, 67], [23, 48], [45, 71], [364, 389], [46, 71], [49, 75], [70, 95], [5, 31], [347, 373], [5, 30], [0, 25], [0, 26], [82, 107], [90, 115], [5, 31], [314, 340]]target = [[45, 51, 57, 47], [24, 43, 45, 46], [58, 66, 47, 46], [374, 365, 371, 368], [48, 50, 56, 65], [73, 70, 56, 66], [76, 81, 82, 93], [26, 17, 6, 20], [365, 349, 361, 350], [24, 7, 22, 18], [1, 11, 22, 20], [12, 22, 17, 18], [104, 103, 91, 102], [92, 111, 108, 107], [7, 18, 9, 28], [335, 323, 315, 336]]
|
| 87 |
+
Epoch 0: | | 199/? [09:02<00:00, 0.37it/s, v_num=ntcx]train step 200; scene = ['d833ea7b23a7b8e8', 'eb0a2d526fab8463', '4adcc636ebb3aa3d', '3dfba607dfcda21a', 'a194dd6211fcf79e', 'e894ac86dd2f472a', '97cbc518499bf6fc', 'de24356d4da85965', '91c4f34ac7068795', 'd9152de256b0b020', 'e516af5ae9b05ffe', 'f3802efb68444590', '194aa09a28bea0ce', 'dee2b3e8e2917054', '7471a6751a16048f', 'b20d86eb2c981941']; loss = 0.103970
|
| 88 |
+
Epoch 0: | | 200/? [09:05<00:00, 0.37it/s, v_num=ntcx]context = [[4, 30], [106, 132], [1, 26], [14, 40], [179, 204], [26, 51], [75, 100], [141, 166], [0, 25], [0, 25], [155, 180], [93, 119], [29, 55], [7, 33], [2, 27], [17, 42]]target = [[25, 14, 23, 13], [110, 126, 112, 119], [16, 7, 17, 19], [33, 31, 37, 35], [180, 197, 201, 196], [44, 46, 30, 41], [76, 81, 97, 93], [152, 145, 153, 156], [16, 19, 9, 18], [15, 18, 23, 3], [159, 164, 162, 167], [105, 100, 106, 96], [54, 45, 46, 33], [31, 29, 8, 25], [13, 26, 21, 8], [28, 22, 19, 33]]
|
| 89 |
+
[2026-03-02 17:50:42,280][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 90 |
+
result[selector] = overlay
|
| 91 |
+
|
| 92 |
+
Epoch 0: | | 209/? [09:30<00:00, 0.37it/s, v_num=ntcx]train step 210; scene = ['dee2b3e8e2917054', 'e516af5ae9b05ffe', 'f3802efb68444590', 'de24356d4da85965', '67ccda8041c7a135', 'd46b6805d4ac6a01', '0be92b9c1aeada53', '31d61fceb166c9af', 'f104f34b2485e22a', '89482391bdb0bcdb', 'a459940b42a66c49', 'f33f27cd323123f7', 'e3e29755afa06cfb', '47e57a81ee491d27', 'df017fa4b4a83840', '77809cb8299e7199']; loss = 0.124059
|
| 93 |
+
Epoch 0: | | 210/? [09:33<00:00, 0.37it/s, v_num=ntcx]context = [[7, 32], [13, 39], [397, 423], [35, 61], [46, 72], [19, 44], [559, 584], [30, 55], [4, 30], [18, 44], [64, 89], [9, 34], [17, 42], [15, 41], [9, 35], [25, 50]]target = [[9, 18, 29, 8], [31, 20, 21, 22], [415, 404, 408, 410], [37, 43, 39, 49], [60, 65, 51, 64], [32, 20, 41, 43], [570, 573, 567, 568], [31, 46, 54, 37], [7, 17, 25, 22], [31, 24, 28, 37], [80, 79, 85, 83], [21, 11, 14, 24], [28, 39, 22, 19], [16, 23, 37, 32], [14, 12, 22, 20], [49, 44, 37, 36]]
|
| 94 |
+
Epoch 0: | | 219/? [09:57<00:00, 0.37it/s, v_num=ntcx]train step 220; scene = ['91d068c68484c62c', '77987f932139e523', 'f8f033fb194bc69c', '60a948f0fc825987', '074a96d452567eb5', '71ca20c53c1689fa', '5a7b821a5a6e852b', 'f936a235c3ea1958', 'c6759f41d7f4030d', '7802e528acacbdcf', 'be7a3b8bafd86333', '3806a08f5388de05', 'dcbf449171deae24', 'ac5521f34f97afb7', 'bcada108ffdadc07', '6285632e5f08a0fd']; loss = 0.098712
|
| 95 |
+
Epoch 0: | | 220/? [09:59<00:00, 0.37it/s, v_num=ntcx]context = [[141, 166], [10, 35], [58, 83], [153, 178], [9, 34], [81, 107], [91, 117], [2, 28], [88, 114], [17, 43], [3, 28], [60, 85], [32, 57], [2, 27], [3, 29], [16, 41]]target = [[164, 162, 152, 142], [25, 15, 11, 34], [62, 69, 80, 64], [156, 155, 164, 171], [19, 27, 33, 22], [91, 101, 105, 104], [105, 114, 98, 93], [14, 7, 15, 21], [99, 101, 103, 93], [31, 32, 36, 26], [23, 21, 5, 11], [71, 77, 81, 70], [42, 44, 45, 43], [11, 17, 19, 22], [11, 5, 21, 8], [38, 30, 27, 39]]
|
| 96 |
+
Epoch 0: | | 229/? [10:24<00:00, 0.37it/s, v_num=ntcx]train step 230; scene = ['14bb9367783f205b', '90285a311e77d664', 'f9f2ca53a40c5d46', '7c06176ed41d7318', '9f1d2dbf65cf8785', 'bf6d40d6a768bc6d', 'eb05878893ec6f93', '02ef92461ca528f0', '88f57ce2e119cb36', 'f2afc924419e4860', '3f81bfbfc5902c40', 'f0284c608a3ca461', 'd6434bd25ca8b08a', '4d87441f9bed4283', 'b3df82c215c962dc', 'a6f095987b57e785']; loss = 0.098986
|
| 97 |
+
Epoch 0: | | 230/? [10:27<00:00, 0.37it/s, v_num=ntcx]context = [[76, 102], [173, 198], [412, 437], [123, 148], [43, 68], [3, 28], [3, 29], [33, 59], [59, 85], [298, 323], [0, 26], [56, 81], [95, 121], [147, 173], [7, 32], [159, 184]]target = [[94, 98, 87, 84], [179, 183, 181, 174], [418, 425, 428, 433], [140, 133, 131, 136], [49, 47, 46, 54], [19, 26, 16, 8], [13, 17, 23, 8], [49, 45, 36, 56], [68, 81, 76, 63], [313, 307, 316, 318], [3, 13, 9, 11], [77, 62, 80, 65], [99, 120, 97, 119], [153, 169, 172, 152], [31, 15, 27, 26], [168, 166, 177, 183]]
|
| 98 |
+
Epoch 0: | | 239/? [10:51<00:00, 0.37it/s, v_num=ntcx]train step 240; scene = ['31058a6534eae4c1', 'ebe9623c566e4147', '80471736d57448a8', 'f90d44c8297a9899', '9550cb9a6330c860', '4ae7ab6894406c30', 'e35efb1c7af0ff6e', '1f6eae9380e3493e', '21b8a4520f418cc6', 'a91f80dd5cd064f4', '030049419ed1881b', '44a26a53457d010c', 'a1c14b86d373ebbd', 'bf563c080a37a077', 'f63194b4cd52b182', 'd8b6e28454064ee2']; loss = 0.155843
|
| 99 |
+
Epoch 0: | | 240/? [10:54<00:00, 0.37it/s, v_num=ntcx]context = [[17, 43], [6, 32], [3, 28], [1, 26], [5, 31], [90, 116], [31, 56], [3, 29], [109, 135], [154, 179], [121, 146], [40, 66], [72, 98], [33, 58], [457, 482], [36, 61]]target = [[36, 30, 37, 21], [28, 21, 20, 24], [21, 11, 6, 19], [22, 20, 19, 16], [20, 14, 27, 11], [94, 97, 96, 105], [52, 38, 41, 51], [14, 28, 27, 10], [115, 114, 125, 122], [173, 169, 155, 175], [129, 133, 140, 134], [42, 50, 62, 61], [85, 94, 84, 73], [43, 44, 34, 42], [468, 463, 459, 464], [59, 55, 53, 58]]
|
| 100 |
+
Epoch 0: | | 249/? [11:18<00:00, 0.37it/s, v_num=ntcx]train step 250; scene = ['71ca20c53c1689fa', 'f936a235c3ea1958', '5a7b821a5a6e852b', '7802e528acacbdcf', 'c6759f41d7f4030d', '3806a08f5388de05', '074a96d452567eb5', '60a948f0fc825987', 'be7a3b8bafd86333', '2cddac5152a75ea0', '6b1031f6d4f21fec', '4900533e0672975d', 'e69ceb9b3ff8ed33', '8e1ec7a9dd985fe9', '95cc1d116282a37e', '5e18a096f58cf335']; loss = 0.083334
|
| 101 |
+
Epoch 0: | | 250/? [11:21<00:00, 0.37it/s, v_num=ntcx]context = [[114, 139], [213, 239], [5, 31], [6, 31], [48, 74], [14, 39], [5, 30], [6, 32], [0, 26], [79, 104], [40, 66], [28, 53], [21, 46], [0, 26], [22, 47], [16, 42]]target = [[136, 119, 122, 128], [226, 236, 234, 238], [10, 23, 27, 6], [10, 25, 19, 24], [52, 69, 60, 59], [36, 31, 22, 37], [6, 14, 22, 12], [20, 9, 18, 15], [24, 13, 14, 17], [94, 85, 93, 103], [49, 54, 47, 56], [45, 42, 32, 37], [31, 27, 23, 28], [8, 16, 25, 15], [36, 38, 42, 28], [24, 31, 34, 19]]
|
| 102 |
+
Epoch 0: | | 259/? [11:45<00:00, 0.37it/s, v_num=ntcx]train step 260; scene = ['5d2a8789fdb9df6f', '7d4977c78bfc8244', 'f9b15f3e0d5c8c88', 'b782d15e4999fc50', 'ddaa57d2d69e265f', '5165ffef8849c4c9', 'da346299952a3625', 'd0c1bb83a47adb82', 'b4bbcd3ee7d37a28', '02d242b89d2ec767', 'c4dbb1ec409bebef', 'fb62839962c81095', '3d3fcb72a9a5de02', '28f26a9d31bcec94', '371dbb3bf62424ab', '5fdba2f580306477']; loss = 0.098538
|
| 103 |
+
Epoch 0: | | 260/? [11:48<00:00, 0.37it/s, v_num=ntcx]context = [[30, 55], [45, 71], [91, 117], [0, 26], [1, 27], [32, 57], [289, 314], [138, 163], [51, 77], [60, 86], [98, 123], [9, 35], [6, 32], [27, 53], [46, 71], [0, 26]]target = [[38, 32, 39, 51], [61, 59, 63, 49], [116, 114, 115, 99], [6, 12, 18, 1], [22, 25, 17, 24], [49, 54, 39, 40], [313, 294, 296, 297], [155, 162, 147, 150], [71, 58, 76, 66], [85, 76, 67, 80], [107, 112, 118, 119], [18, 15, 32, 33], [26, 23, 30, 10], [52, 45, 49, 40], [58, 66, 55, 67], [4, 18, 6, 2]]
|
| 104 |
+
Epoch 0: | | 269/? [12:12<00:00, 0.37it/s, v_num=ntcx]train step 270; scene = ['a194dd6211fcf79e', '982f56585e9dab78', 'e894ac86dd2f472a', 'd833ea7b23a7b8e8', '97cbc518499bf6fc', '032d805a46ba1c08', '3dfba607dfcda21a', 'eb0a2d526fab8463', '91c1c13bbe4b0113', 'd3248841e4815cc8', '30ac9a59ec0f43b2', '1cd1da8e148c84b6', 'cba271e11f70cd58', '8a7effe646752c79', 'e647c140b6b27936', '0fd589c9a05b4432']; loss = 0.097746
|
| 105 |
+
Epoch 0: | | 270/? [12:15<00:00, 0.37it/s, v_num=ntcx]context = [[94, 119], [1, 27], [60, 86], [369, 394], [17, 42], [411, 437], [67, 93], [5, 31], [65, 90], [92, 117], [44, 70], [17, 42], [19, 45], [40, 65], [19, 45], [0, 25]]target = [[104, 114, 95, 105], [23, 7, 25, 3], [65, 68, 71, 63], [370, 380, 384, 371], [28, 18, 38, 26], [436, 435, 418, 425], [76, 82, 87, 81], [28, 15, 21, 22], [69, 67, 78, 81], [111, 115, 107, 93], [69, 47, 56, 66], [21, 40, 31, 34], [35, 33, 23, 39], [43, 51, 46, 59], [44, 39, 23, 27], [4, 3, 14, 22]]
|
| 106 |
+
Epoch 0: | | 279/? [12:40<00:00, 0.37it/s, v_num=ntcx]train step 280; scene = ['bc14847314b63040', 'b3c77b811cf2a0db', 'bd912d994d4f26d2', '23a2dd7a563aa92c', 'dc654ad716469827', 'dda3e0c9dbf6dfa8', '7c2cd4905919647e', '7b11643f1e7b14f9', '7290a9836b58c2cd', 'bf7d82dcd9121446', '7f4627eac7a97e71', 'e21cbd9c7546cb1e', '93205806902442cb', 'ac5521f34f97afb7', '12167d7d9cb2a489', 'dcbf449171deae24']; loss = 0.093302
|
| 107 |
+
Epoch 0: | | 280/? [12:42<00:00, 0.37it/s, v_num=ntcx]context = [[4, 30], [8, 34], [4, 29], [8, 33], [437, 462], [81, 107], [154, 179], [418, 443], [6, 32], [11, 36], [10, 36], [29, 55], [40, 66], [114, 140], [61, 86], [662, 688]]target = [[6, 15, 13, 17], [19, 25, 26, 11], [26, 13, 22, 15], [25, 9, 23, 32], [457, 448, 461, 452], [105, 97, 87, 98], [176, 155, 157, 171], [429, 426, 420, 436], [18, 24, 12, 21], [24, 14, 35, 29], [21, 18, 22, 34], [51, 40, 38, 47], [42, 56, 59, 55], [120, 133, 115, 135], [84, 66, 68, 85], [669, 663, 675, 681]]
|
| 108 |
+
Epoch 0: | | 289/? [13:07<00:00, 0.37it/s, v_num=ntcx]train step 290; scene = ['e60f797c64ddce80', '865e4fcf1b77a301', '20fa52543dc38d2d', '747c1e94340ab4d8', '929e798504704a7e', 'ae6f07808985c452', 'ebf965e268e6c574', 'fd1a641def9e3c8c', '850d687cdfc47997', '7290a9836b58c2cd', '7b11643f1e7b14f9', 'dda3e0c9dbf6dfa8', '23a2dd7a563aa92c', 'dc654ad716469827', 'b3c77b811cf2a0db', '827b10975fd868d5']; loss = 0.091030
|
| 109 |
+
Epoch 0: | | 290/? [13:10<00:00, 0.37it/s, v_num=ntcx]context = [[47, 73], [11, 36], [6, 32], [159, 184], [123, 149], [87, 112], [22, 48], [51, 76], [10, 36], [9, 34], [18, 44], [11, 37], [143, 168], [76, 102], [80, 106], [9, 34]]target = [[62, 69, 50, 52], [35, 19, 18, 31], [28, 23, 31, 15], [163, 181, 172, 178], [140, 127, 143, 147], [104, 100, 94, 109], [24, 41, 39, 27], [58, 52, 65, 64], [34, 22, 31, 25], [21, 33, 14, 18], [22, 41, 20, 35], [13, 21, 30, 24], [156, 145, 147, 155], [82, 99, 88, 95], [93, 82, 90, 89], [10, 31, 26, 32]]
|
| 110 |
+
Epoch 0: | | 299/? [13:34<00:00, 0.37it/s, v_num=ntcx]train step 300; scene = ['91fddb0dc70b7394', '38e01e5b91d482d3', '1bba41dba10b2bde', 'fe4a25ecd113086d', 'f69b566385e1a6f9', '784010bceb5991e4', 'e264776ab6f09e5c', 'bbf894b9a4d0d4e5', 'f0336104588d3481', '1787e49e21e2c8e2', 'd8b6e28454064ee2', '40ef44633e622f26', '8ee5d6783a835e42', 'f63194b4cd52b182', 'bf563c080a37a077', 'a1c14b86d373ebbd']; loss = 0.101163
|
| 111 |
+
Epoch 0: | | 300/? [13:37<00:00, 0.37it/s, v_num=ntcx]context = [[1, 26], [45, 70], [37, 62], [36, 62], [9, 35], [187, 212], [7, 33], [5, 30], [193, 218], [122, 147], [100, 126], [4, 29], [9, 35], [155, 180], [11, 37], [588, 613]]target = [[2, 20, 17, 24], [62, 69, 49, 60], [59, 49, 55, 61], [54, 60, 52, 42], [18, 34, 25, 32], [193, 192, 204, 198], [13, 10, 31, 30], [28, 11, 20, 8], [208, 217, 215, 214], [129, 131, 140, 130], [101, 117, 123, 109], [22, 14, 24, 8], [22, 29, 14, 13], [168, 167, 158, 178], [24, 13, 16, 12], [598, 592, 611, 591]]
|
| 112 |
+
Epoch 0: | | 309/? [14:02<00:00, 0.37it/s, v_num=ntcx]train step 310; scene = ['bc14847314b63040', '7c2cd4905919647e', '23a2dd7a563aa92c', 'bd912d994d4f26d2', 'dc654ad716469827', 'dda3e0c9dbf6dfa8', 'bf7d82dcd9121446', 'a194dd6211fcf79e', 'eb0a2d526fab8463', '081159162a6321dd', 'd833ea7b23a7b8e8', 'e894ac86dd2f472a', 'fbde918ca5cec4b7', '982f56585e9dab78', '3dfba607dfcda21a', '4adcc636ebb3aa3d']; loss = 0.090640
|
| 113 |
+
Epoch 0: | | 310/? [14:05<00:00, 0.37it/s, v_num=ntcx]context = [[63, 88], [11, 36], [63, 89], [106, 131], [62, 88], [27, 53], [3, 29], [13, 39], [1, 27], [138, 163], [22, 48], [2, 28], [446, 472], [146, 171], [3, 29], [0, 26]]target = [[82, 83, 64, 87], [25, 33, 15, 24], [87, 67, 73, 82], [121, 111, 116, 120], [72, 87, 76, 86], [52, 35, 28, 49], [5, 26, 7, 11], [30, 21, 27, 16], [12, 11, 21, 15], [142, 152, 146, 153], [41, 44, 40, 46], [16, 7, 24, 12], [449, 453, 468, 457], [162, 154, 161, 168], [21, 12, 27, 26], [3, 25, 20, 1]]
|
| 114 |
+
Epoch 0: | | 319/? [14:29<00:00, 0.37it/s, v_num=ntcx]train step 320; scene = ['113c59785e6c4c98', '0c0d3883fea88059', '6f21035d66869fd2', '6d30b3cf0bb78378', '3c4a490a92fb950b', '3086d78b28f1d53d', '0ba349220dedb974', '2fd374c8f5372593', 'f2b93e86804f9015', 'd4fd4dfe65acff7a', 'bfcb4fd58755467a', 'e7bbf34f45d380cb', '570969c40acd2f94', 'b74570355631bd23', '569ab6faf147ff20', '77433b73c5ec6e97']; loss = 0.082708
|
| 115 |
+
Epoch 0: | | 320/? [14:32<00:00, 0.37it/s, v_num=ntcx]context = [[8, 33], [65, 90], [99, 125], [83, 109], [16, 41], [20, 45], [1, 27], [24, 50], [72, 97], [4, 30], [188, 213], [127, 152], [10, 35], [139, 164], [0, 25], [326, 352]]target = [[25, 26, 16, 21], [71, 80, 85, 70], [119, 101, 108, 107], [86, 98, 93, 101], [36, 27, 24, 37], [36, 31, 35, 33], [2, 23, 16, 8], [33, 31, 27, 29], [76, 91, 84, 92], [7, 18, 29, 8], [206, 190, 192, 191], [139, 132, 146, 147], [18, 17, 13, 20], [154, 151, 163, 152], [20, 10, 1, 24], [338, 332, 343, 333]]
|
| 116 |
+
Epoch 0: | | 329/? [14:57<00:00, 0.37it/s, v_num=ntcx]train step 330; scene = ['abe1a7d84be623b7', '9106825d455282e4', '9c5fad8f6c2c1d30', '9faf2198cbfc50ab', '0bc74fbe010a34a9', '80e2cd727dbbab4d', '2395aa81cd2237cd', '8daf78247478d130', 'ec79e7c6e475c3b4', '61cf7149ee31b7fe', 'ef5c8c3991478315', '7e045ded29651933', 'c0b0c9ecc4d13d7c', '4381e9ba82476aab', 'f69b566385e1a6f9', 'e264776ab6f09e5c']; loss = 0.082658
|
| 117 |
+
Epoch 0: | | 330/? [14:59<00:00, 0.37it/s, v_num=ntcx]context = [[7, 34], [46, 71], [30, 57], [19, 46], [2, 27], [51, 76], [85, 111], [31, 58], [2, 28], [10, 36], [85, 111], [734, 760], [1, 26], [0, 26], [115, 141], [37, 63]]target = [[29, 32, 9, 22], [69, 60, 53, 64], [54, 48, 46, 31], [33, 26, 37, 27], [26, 5, 13, 11], [52, 53, 60, 73], [90, 93, 107, 86], [44, 48, 51, 50], [10, 14, 17, 7], [22, 33, 26, 13], [89, 100, 93, 87], [735, 736, 737, 746], [24, 3, 10, 23], [15, 17, 5, 12], [137, 138, 127, 118], [43, 51, 49, 42]]
|
| 118 |
+
Epoch 0: | | 339/? [15:24<00:00, 0.37it/s, v_num=ntcx]train step 340; scene = ['5bf78bfdbba1c8ea', '5bb1c85935d35d6e', '61320598b5b0c144', '81675960876b8950', '65af0b15a2781a82', 'e9c34ef46b2961ae', '7d14bc1f04fa7a13', 'b229221257c7a162', '2eb9843b2929be3b', 'afab3c06ae87e76d', 'e1f25474118e488c', '192af8a40ca6ec2a', '766d9f0c04d48c46', 'e0e1c4ed260f5c1f', '5aa8589865f89ab9', '098bf652a47892d0']; loss = 0.081222
|
| 119 |
+
Epoch 0: | | 340/? [15:27<00:00, 0.37it/s, v_num=ntcx]context = [[128, 155], [62, 88], [13, 40], [6, 33], [39, 64], [42, 67], [47, 74], [66, 91], [28, 53], [183, 209], [30, 56], [45, 71], [46, 72], [53, 78], [453, 480], [86, 111]]target = [[147, 135, 132, 136], [74, 82, 75, 63], [37, 28, 21, 24], [20, 22, 30, 13], [51, 42, 47, 57], [46, 48, 43, 44], [73, 71, 66, 57], [88, 89, 83, 67], [31, 49, 45, 47], [203, 198, 204, 208], [53, 55, 42, 36], [53, 54, 56, 65], [66, 56, 49, 65], [55, 66, 73, 57], [470, 459, 467, 477], [93, 100, 92, 102]]
|
| 120 |
+
Epoch 0: | | 349/? [15:52<00:00, 0.37it/s, v_num=ntcx]train step 350; scene = ['8e21b392cc92060a', '9fe1911897e2ca13', '645b715000164959', '5b6ae58437dd2284', '246d21c803fe254d', '8444370c2b752c76', 'b5bfcbb3f78c6ecf', '3b21e626f046c5fa', '3ce67dbfc44960b0', 'ec64173395ae4123', '91d068c68484c62c', '0c88d0d5b3df129d', 'f8f033fb194bc69c', '6bf19e5f9004f829', 'd55a8f30c89d0bdd', '71023f6bd518c752']; loss = 0.092283
|
| 121 |
+
Epoch 0: | | 350/? [15:55<00:00, 0.37it/s, v_num=ntcx]context = [[10, 36], [1, 26], [9, 35], [26, 53], [2, 27], [6, 31], [235, 262], [271, 298], [9, 35], [116, 142], [5, 31], [0, 27], [146, 173], [3, 29], [28, 53], [10, 35]]target = [[33, 20, 14, 32], [21, 17, 7, 9], [22, 18, 33, 25], [33, 39, 48, 47], [12, 25, 6, 9], [23, 25, 28, 16], [256, 246, 247, 239], [285, 287, 280, 296], [31, 32, 20, 14], [129, 140, 119, 117], [27, 17, 13, 9], [14, 8, 11, 15], [168, 156, 149, 162], [28, 8, 18, 17], [36, 30, 32, 35], [16, 28, 11, 14]]
|
| 122 |
+
Epoch 0: | | 359/? [16:19<00:00, 0.37it/s, v_num=ntcx]train step 360; scene = ['6b1031f6d4f21fec', '4900533e0672975d', '1b7d734a8199ef60', '9a7988656fa7f947', '6a92edc0fb39bfbc', '513ac6672e6e2938', '4a986ed9d60492b7', '865e4fcf1b77a301', 'ebf965e268e6c574', 'e60f797c64ddce80', '8ff73226308caf17', 'fd1a641def9e3c8c', '929e798504704a7e', '7a21c49ef32dddf8', 'ae6f07808985c452', '20fa52543dc38d2d']; loss = 0.093247
|
| 123 |
+
Epoch 0: | | 360/? [16:22<00:00, 0.37it/s, v_num=ntcx]context = [[5, 30], [4, 29], [9, 35], [4, 31], [22, 47], [13, 38], [97, 123], [76, 103], [9, 34], [466, 492], [30, 56], [327, 353], [67, 94], [19, 44], [2, 27], [95, 122]]target = [[25, 28, 20, 19], [17, 27, 14, 16], [11, 33, 26, 20], [29, 18, 15, 8], [28, 30, 31, 24], [29, 15, 27, 24], [99, 120, 121, 102], [82, 91, 95, 99], [26, 18, 27, 30], [481, 477, 489, 469], [49, 52, 46, 34], [352, 340, 332, 333], [78, 84, 68, 86], [34, 39, 27, 37], [4, 20, 24, 16], [100, 98, 120, 99]]
|
| 124 |
+
Epoch 0: | | 369/? [16:47<00:00, 0.37it/s, v_num=ntcx]train step 370; scene = ['66b53eb50f69046f', '7be26ba4d09d4658', 'de4f4714a3ca731b', 'd6612645490185a2', 'f1b8c9039e119248', 'a3beac5349332cf3', '1766f7bc16882b41', '2c6edab05649ee23', '1a96339a9a4957eb', 'f9064505005b244e', '1222bdc580ab86e8', '07f666f2c676fa0a', 'd9847bf9d7bbc5c8', '6845dc795d55b73d', '10b63bc8563a5e36', '1a2c1d71a0859814']; loss = 0.091472
|
| 125 |
+
Epoch 0: | | 370/? [16:49<00:00, 0.37it/s, v_num=ntcx]context = [[6, 32], [125, 150], [18, 44], [5, 30], [5, 31], [8, 35], [50, 77], [7, 34], [539, 564], [66, 91], [0, 25], [23, 49], [446, 471], [23, 49], [159, 184], [11, 37]]target = [[17, 10, 26, 14], [127, 145, 144, 131], [42, 39, 43, 27], [14, 13, 29, 8], [20, 9, 25, 19], [28, 33, 18, 32], [64, 59, 51, 52], [17, 12, 32, 22], [556, 543, 558, 547], [78, 88, 82, 69], [5, 21, 1, 11], [28, 36, 40, 48], [450, 458, 468, 459], [36, 35, 42, 34], [165, 176, 182, 170], [23, 25, 28, 22]]
|
| 126 |
+
Epoch 0: | | 379/? [17:14<00:00, 0.37it/s, v_num=ntcx]train step 380; scene = ['45074bd32cdc1515', 'ecdef74c6b5c81b4', '3f75d3dfd9a7e213', '0cd3ea1a0281ba61', '9e4d00de2080bede', 'e67c3e43b11a2143', 'a71bf73a52ecf614', '61d4ffc3c9b46e71', '081fbad41243399b', 'afa10ca354cbe46e', 'a39a007092365b0e', '2add4958d42abcbe', '2479e98b51f7d179', 'a2c79e13058048db', '953bbcb632cf3adb', '72b15c11f285db11']; loss = 0.081081
|
| 127 |
+
Epoch 0: | | 380/? [17:17<00:00, 0.37it/s, v_num=ntcx]context = [[50, 76], [0, 25], [259, 285], [334, 359], [1, 26], [0, 25], [0, 25], [28, 53], [16, 43], [28, 54], [51, 77], [89, 114], [4, 31], [23, 50], [44, 69], [54, 81]]target = [[58, 62, 61, 65], [2, 1, 13, 11], [273, 271, 280, 279], [339, 351, 335, 345], [14, 10, 13, 20], [6, 16, 4, 12], [22, 2, 20, 21], [41, 37, 29, 47], [23, 34, 29, 42], [44, 30, 47, 32], [73, 60, 68, 75], [92, 99, 108, 103], [16, 30, 28, 12], [27, 45, 46, 36], [64, 51, 67, 57], [65, 73, 59, 76]]
|
| 128 |
+
Epoch 0: | | 389/? [17:41<00:00, 0.37it/s, v_num=ntcx]train step 390; scene = ['50750b8d4cbcf3aa', 'efbb1d48ca3cc4ed', 'de4f4714a3ca731b', '7be26ba4d09d4658', '702f59af91e8a5ef', 'a0e91a6d89006676', '8b82391ffe0fdc40', 'b132b56c2f71f2bb', '8115c82e12facb94', '263383a6650bd133', 'fc950fc75c178d71', '7f83bf7a0ea82e76', 'f936a235c3ea1958', '60a948f0fc825987', '074a96d452567eb5', 'c6759f41d7f4030d']; loss = 0.078728
|
| 129 |
+
Epoch 0: | | 390/? [17:44<00:00, 0.37it/s, v_num=ntcx]context = [[21, 48], [70, 95], [54, 81], [0, 27], [59, 86], [0, 26], [10, 37], [136, 162], [20, 45], [102, 127], [4, 29], [76, 101], [3, 30], [3, 30], [20, 46], [12, 37]]target = [[42, 23, 26, 38], [73, 76, 84, 86], [57, 72, 60, 68], [25, 17, 5, 26], [71, 72, 78, 83], [17, 10, 9, 4], [12, 21, 14, 17], [159, 139, 156, 146], [28, 41, 26, 42], [125, 124, 120, 115], [10, 25, 28, 15], [80, 85, 84, 82], [6, 24, 16, 8], [8, 13, 11, 16], [40, 22, 25, 31], [36, 23, 28, 19]]
|
| 130 |
+
Epoch 0: | | 399/? [18:09<00:00, 0.37it/s, v_num=ntcx]train step 400; scene = ['9cbe1ed1a6e235a6', '8e6fe647b47526d9', '637dc40073af6833', 'ca27d5e15b921b92', '423a1a1b9e3667d3', '3adceb8217c23205', 'f3e2a5f45a5034bd', 'b1989145be25808b', '52bd3a28632e9e01', '4c32de568e68a9ff', '4cacb2db50dee5cc', '96e2c2970833b655', '8777083db5332123', 'f5481a6a0260e12c', 'c6759f41d7f4030d', '3806a08f5388de05']; loss = 0.084916
|
| 131 |
+
Epoch 0: | | 400/? [18:11<00:00, 0.37it/s, v_num=ntcx]context = [[0, 26], [2, 28], [1, 27], [5, 31], [47, 73], [58, 83], [7, 34], [152, 177], [74, 99], [6, 33], [6, 31], [9, 34], [4, 30], [20, 45], [87, 114], [30, 56]]target = [[4, 1, 22, 12], [15, 18, 3, 8], [6, 17, 16, 23], [27, 22, 28, 20], [68, 60, 69, 62], [76, 71, 80, 81], [18, 13, 8, 25], [161, 171, 162, 156], [96, 92, 84, 81], [29, 31, 25, 8], [27, 14, 17, 18], [27, 13, 32, 14], [9, 24, 18, 10], [27, 23, 39, 35], [113, 88, 97, 112], [35, 52, 43, 47]]
|
| 132 |
+
[2026-03-02 17:59:48,909][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 133 |
+
result[selector] = overlay
|
| 134 |
+
|
| 135 |
+
Epoch 0: | | 404/? [18:23<00:00, 0.37it/s, v_num=ntcx]
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-metadata.json
ADDED
|
@@ -0,0 +1,92 @@
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| 1 |
+
{
|
| 2 |
+
"os": "Linux-5.15.0-157-generic-x86_64-with-glibc2.39",
|
| 3 |
+
"python": "CPython 3.12.12",
|
| 4 |
+
"startedAt": "2026-03-02T17:41:24.267732Z",
|
| 5 |
+
"args": [
|
| 6 |
+
"+experiment=acid",
|
| 7 |
+
"wandb.mode=online",
|
| 8 |
+
"wandb.name=0303_ACID_FULL_2v"
|
| 9 |
+
],
|
| 10 |
+
"program": "-m src.main",
|
| 11 |
+
"git": {
|
| 12 |
+
"remote": "git@github.com:K-nowing/CVPR2026.git",
|
| 13 |
+
"commit": "9dfce172a0f8c7ce85e763899f7ef741ecffc454"
|
| 14 |
+
},
|
| 15 |
+
"email": "dna9041@korea.ac.kr",
|
| 16 |
+
"root": "/workspace/code/CVPR2026/outputs/full/acid/0303_ACID_FULL_2v",
|
| 17 |
+
"host": "0258ae8f3852",
|
| 18 |
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"executable": "/venv/main/bin/python",
|
| 19 |
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"cpu_count": 112,
|
| 20 |
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|
| 21 |
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"gpu": "NVIDIA H200",
|
| 22 |
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|
| 23 |
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|
| 24 |
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| 29 |
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"memory": {
|
| 30 |
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"total": "2164193775616"
|
| 31 |
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|
| 32 |
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"gpu_nvidia": [
|
| 33 |
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{
|
| 34 |
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"name": "NVIDIA H200",
|
| 35 |
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"memoryTotal": "150754820096",
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| 36 |
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"cudaCores": 16896,
|
| 37 |
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"architecture": "Hopper",
|
| 38 |
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"uuid": "GPU-79687643-93f8-7b36-349a-8f05b89e6678"
|
| 39 |
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|
| 40 |
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| 41 |
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"name": "NVIDIA H200",
|
| 42 |
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|
| 43 |
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| 44 |
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"architecture": "Hopper",
|
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"uuid": "GPU-317bba70-b882-ca12-2b8b-173e2db3be03"
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| 46 |
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},
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| 47 |
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{
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| 48 |
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"name": "NVIDIA H200",
|
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"memoryTotal": "150754820096",
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| 50 |
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| 51 |
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|
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"uuid": "GPU-cc84663f-d6cd-d900-0d4c-118462dced2e"
|
| 53 |
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|
| 54 |
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| 55 |
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| 58 |
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"architecture": "Hopper",
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| 59 |
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"uuid": "GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210"
|
| 60 |
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|
| 61 |
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{
|
| 62 |
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"name": "NVIDIA H200",
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| 63 |
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|
| 64 |
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|
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|
| 66 |
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|
| 67 |
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|
| 68 |
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{
|
| 69 |
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"name": "NVIDIA H200",
|
| 70 |
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"memoryTotal": "150754820096",
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| 71 |
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"cudaCores": 16896,
|
| 72 |
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"architecture": "Hopper",
|
| 73 |
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"uuid": "GPU-522b1630-b9aa-5aa3-9985-ced479a7780e"
|
| 74 |
+
},
|
| 75 |
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{
|
| 76 |
+
"name": "NVIDIA H200",
|
| 77 |
+
"memoryTotal": "150754820096",
|
| 78 |
+
"cudaCores": 16896,
|
| 79 |
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"architecture": "Hopper",
|
| 80 |
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"uuid": "GPU-4c86a636-acfc-e976-3b9e-78425c9c44df"
|
| 81 |
+
},
|
| 82 |
+
{
|
| 83 |
+
"name": "NVIDIA H200",
|
| 84 |
+
"memoryTotal": "150754820096",
|
| 85 |
+
"cudaCores": 16896,
|
| 86 |
+
"architecture": "Hopper",
|
| 87 |
+
"uuid": "GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b"
|
| 88 |
+
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|
| 89 |
+
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|
| 90 |
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"cudaVersion": "12.8",
|
| 91 |
+
"writerId": "vtb6mv5kfj6ggthszg138jy7ckopxu32"
|
| 92 |
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}
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-summary.json
ADDED
|
@@ -0,0 +1 @@
|
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|
| 1 |
+
{"loss/camera":0.0006606340757571161,"loss/final_3dgs/lpips":0.013662220910191536,"loss/aux_1/error_score":0.7482300400733948,"_timestamp":1.7724743894347792e+09,"active_mask_imgs":{"_type":"images/separated","width":536,"height":800,"format":"png","count":1,"filenames":["media/images/active_mask_imgs_1_652ab96fa4327fc5f617.png"],"captions":["fcbd42c6ad4b2529"]},"trainer/global_step":401,"train/scene_scale":1.003018856048584,"loss/aux_0/mse":0.005677284672856331,"loss/aux_1/lpips":0.013727227225899696,"lr-AdamW/pg1-momentum":0.9,"info/global_step":400,"train/psnr_probabilistic":23.489381790161133,"_step":24,"loss/aux_1/mse":0.004746518563479185,"loss/scene_scale_reg":8.063319546636194e-05,"loss/aux_0/error_score":1.0000075101852417,"_wandb":{"runtime":1113},"epoch":0,"error_scores":{"filenames":["media/images/error_scores_2_36deb93328b08091d126.png"],"captions":["fcbd42c6ad4b2529"],"_type":"images/separated","width":800,"height":536,"format":"png","count":1},"train/error_scores":{"filenames":["media/images/train/error_scores_23_29c8c689566f90e97011.png"],"captions":["23a2dd7a563aa92c"],"_type":"images/separated","width":1328,"height":536,"format":"png","count":1},"loss/aux_0/lpips":0.019155198708176613,"loss/aux_2/lpips":0.010820882394909859,"comparison":{"format":"png","count":1,"filenames":["media/images/comparison_0_757326aadb7b85544ee2.png"],"captions":["fcbd42c6ad4b2529"],"_type":"images/separated","width":1064,"height":1098},"loss/aux_2/mse":0.00525673758238554,"lr-AdamW/pg2":2e-05,"_runtime":1113,"lr-AdamW/pg2-momentum":0.9,"loss/total":0.08491560071706772,"loss/final_3dgs/mse":0.0050077298656105995,"train/comparison":{"format":"png","count":1,"filenames":["media/images/train/comparison_24_7975af092b57eba76635.png"],"captions":["23a2dd7a563aa92c"],"_type":"images/separated","width":1328,"height":1098},"lr-AdamW/pg1":0.00019990518255561578}
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/logs/debug-core.log
ADDED
|
@@ -0,0 +1,107 @@
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| 1 |
+
{"time":"2026-03-02T17:41:24.333948956Z","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpfp4arfq_/port-7180.txt","pid":7180,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
|
| 2 |
+
{"time":"2026-03-02T17:41:24.334867292Z","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-7180-7459-20721055/socket","Net":"unix"}}
|
| 3 |
+
{"time":"2026-03-02T17:41:24.335037756Z","level":"INFO","msg":"server: will exit if parent process dies","ppid":7180}
|
| 4 |
+
{"time":"2026-03-02T17:41:24.508655225Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
|
| 5 |
+
{"time":"2026-03-02T17:41:24.519239733Z","level":"INFO","msg":"handleInformInit: received","streamId":"qfkhntcx","id":"1(@)"}
|
| 6 |
+
{"time":"2026-03-02T17:41:24.996686178Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"qfkhntcx","id":"1(@)"}
|
| 7 |
+
{"time":"2026-03-02T17:41:30.469455269Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"tmp0o1sledbf"}
|
| 8 |
+
{"time":"2026-03-02T17:59:59.284332485Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"2(@)"}
|
| 9 |
+
{"time":"2026-03-02T17:59:59.284569991Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"3(@)"}
|
| 10 |
+
{"time":"2026-03-02T17:59:59.285551435Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"4(@)"}
|
| 11 |
+
{"time":"2026-03-02T17:59:59.287836681Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"5(@)"}
|
| 12 |
+
{"time":"2026-03-02T17:59:59.288310149Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"6(@)"}
|
| 13 |
+
{"time":"2026-03-02T17:59:59.288719529Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"7(@)"}
|
| 14 |
+
{"time":"2026-03-02T17:59:59.288762617Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"8(@)"}
|
| 15 |
+
{"time":"2026-03-02T17:59:59.288814311Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"9(@)"}
|
| 16 |
+
{"time":"2026-03-02T17:59:59.290460205Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"11(@)"}
|
| 17 |
+
{"time":"2026-03-02T17:59:59.290588718Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"12(@)"}
|
| 18 |
+
{"time":"2026-03-02T17:59:59.290681054Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"15(@)"}
|
| 19 |
+
{"time":"2026-03-02T17:59:59.290673411Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"14(@)"}
|
| 20 |
+
{"time":"2026-03-02T17:59:59.290777052Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"17(@)"}
|
| 21 |
+
{"time":"2026-03-02T17:59:59.290791175Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"13(@)"}
|
| 22 |
+
{"time":"2026-03-02T17:59:59.290862451Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"18(@)"}
|
| 23 |
+
{"time":"2026-03-02T17:59:59.29082318Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"16(@)"}
|
| 24 |
+
{"time":"2026-03-02T17:59:59.290903055Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"10(@)"}
|
| 25 |
+
{"time":"2026-03-02T18:00:00.052160168Z","level":"INFO","msg":"handleInformFinish: finish message received","streamId":"qfkhntcx","id":"4(@)"}
|
| 26 |
+
{"time":"2026-03-02T18:00:00.056643517Z","level":"INFO","msg":"handleInformFinish: stream closed","streamId":"qfkhntcx","id":"4(@)"}
|
| 27 |
+
{"time":"2026-03-02T18:00:00.265091444Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"}
|
| 28 |
+
{"time":"2026-03-02T18:00:00.265205545Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"}
|
| 29 |
+
{"time":"2026-03-02T18:00:00.265668779Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"}
|
| 30 |
+
{"time":"2026-03-02T18:00:00.26741403Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"tmp0o1sledbf"}
|
| 31 |
+
{"time":"2026-03-02T18:00:00.268154833Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"}
|
| 32 |
+
{"time":"2026-03-02T18:00:00.270091219Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"}
|
| 33 |
+
{"time":"2026-03-02T18:00:00.295715166Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"3(@)"}
|
| 34 |
+
{"time":"2026-03-02T18:00:00.296632465Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"2(@)"}
|
| 35 |
+
{"time":"2026-03-02T18:00:00.298626077Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"7(@)"}
|
| 36 |
+
{"time":"2026-03-02T18:00:00.299943654Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"9(@)"}
|
| 37 |
+
{"time":"2026-03-02T18:00:00.29998061Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"12(@)"}
|
| 38 |
+
{"time":"2026-03-02T18:00:00.300000162Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"10(@)"}
|
| 39 |
+
{"time":"2026-03-02T18:00:00.300755533Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"6(@)"}
|
| 40 |
+
{"time":"2026-03-02T18:00:00.300792804Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"14(@)"}
|
| 41 |
+
{"time":"2026-03-02T18:00:00.300979482Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"18(@)"}
|
| 42 |
+
{"time":"2026-03-02T18:00:00.301443952Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"11(@)"}
|
| 43 |
+
{"time":"2026-03-02T18:00:00.301528179Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"17(@)"}
|
| 44 |
+
{"time":"2026-03-02T18:00:00.301785579Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"5(@)"}
|
| 45 |
+
{"time":"2026-03-02T18:00:00.3018205Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"8(@)"}
|
| 46 |
+
{"time":"2026-03-02T18:00:00.302118915Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"16(@)"}
|
| 47 |
+
{"time":"2026-03-02T18:00:00.302816248Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"13(@)"}
|
| 48 |
+
{"time":"2026-03-02T18:00:00.30294025Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"15(@)"}
|
| 49 |
+
{"time":"2026-03-02T18:00:02.48915676Z","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
|
| 50 |
+
{"time":"2026-03-02T18:00:02.489234129Z","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"}
|
| 51 |
+
{"time":"2026-03-02T18:00:02.489249192Z","level":"INFO","msg":"server is shutting down"}
|
| 52 |
+
{"time":"2026-03-02T18:00:02.489306958Z","level":"INFO","msg":"connection: closing","id":"11(@)"}
|
| 53 |
+
{"time":"2026-03-02T18:00:02.489369792Z","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-7180-7459-20721055/socket","Net":"unix"}}
|
| 54 |
+
{"time":"2026-03-02T18:00:02.489431132Z","level":"INFO","msg":"connection: closing","id":"18(@)"}
|
| 55 |
+
{"time":"2026-03-02T18:00:02.489441756Z","level":"INFO","msg":"connection: closing","id":"9(@)"}
|
| 56 |
+
{"time":"2026-03-02T18:00:02.489493598Z","level":"INFO","msg":"connection: closing","id":"16(@)"}
|
| 57 |
+
{"time":"2026-03-02T18:00:02.489468369Z","level":"INFO","msg":"connection: closing","id":"5(@)"}
|
| 58 |
+
{"time":"2026-03-02T18:00:02.489565063Z","level":"INFO","msg":"connection: closing","id":"14(@)"}
|
| 59 |
+
{"time":"2026-03-02T18:00:02.489573177Z","level":"INFO","msg":"connection: closing","id":"3(@)"}
|
| 60 |
+
{"time":"2026-03-02T18:00:02.489610279Z","level":"INFO","msg":"connection: closing","id":"17(@)"}
|
| 61 |
+
{"time":"2026-03-02T18:00:02.48966333Z","level":"INFO","msg":"connection: closing","id":"15(@)"}
|
| 62 |
+
{"time":"2026-03-02T18:00:02.489544798Z","level":"INFO","msg":"connection: closing","id":"10(@)"}
|
| 63 |
+
{"time":"2026-03-02T18:00:02.489681142Z","level":"INFO","msg":"connection: closing","id":"1(@)"}
|
| 64 |
+
{"time":"2026-03-02T18:00:02.489705507Z","level":"INFO","msg":"connection: closing","id":"12(@)"}
|
| 65 |
+
{"time":"2026-03-02T18:00:02.489671059Z","level":"INFO","msg":"connection: closing","id":"6(@)"}
|
| 66 |
+
{"time":"2026-03-02T18:00:02.489747298Z","level":"INFO","msg":"connection: closed successfully","id":"11(@)"}
|
| 67 |
+
{"time":"2026-03-02T18:00:02.489762807Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"11(@)"}
|
| 68 |
+
{"time":"2026-03-02T18:00:02.489774395Z","level":"INFO","msg":"connection: closed successfully","id":"18(@)"}
|
| 69 |
+
{"time":"2026-03-02T18:00:02.489782085Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"18(@)"}
|
| 70 |
+
{"time":"2026-03-02T18:00:02.489790255Z","level":"INFO","msg":"connection: closed successfully","id":"16(@)"}
|
| 71 |
+
{"time":"2026-03-02T18:00:02.489797193Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"16(@)"}
|
| 72 |
+
{"time":"2026-03-02T18:00:02.489806218Z","level":"INFO","msg":"connection: closed successfully","id":"14(@)"}
|
| 73 |
+
{"time":"2026-03-02T18:00:02.489731563Z","level":"INFO","msg":"connection: closed successfully","id":"10(@)"}
|
| 74 |
+
{"time":"2026-03-02T18:00:02.489813273Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"14(@)"}
|
| 75 |
+
{"time":"2026-03-02T18:00:02.489824831Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"10(@)"}
|
| 76 |
+
{"time":"2026-03-02T18:00:02.489624247Z","level":"INFO","msg":"connection: closed successfully","id":"3(@)"}
|
| 77 |
+
{"time":"2026-03-02T18:00:02.48983519Z","level":"INFO","msg":"connection: closed successfully","id":"15(@)"}
|
| 78 |
+
{"time":"2026-03-02T18:00:02.489815437Z","level":"INFO","msg":"connection: closing","id":"7(@)"}
|
| 79 |
+
{"time":"2026-03-02T18:00:02.48984933Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"15(@)"}
|
| 80 |
+
{"time":"2026-03-02T18:00:02.489646811Z","level":"INFO","msg":"connection: closing","id":"8(@)"}
|
| 81 |
+
{"time":"2026-03-02T18:00:02.489859949Z","level":"INFO","msg":"connection: closed successfully","id":"12(@)"}
|
| 82 |
+
{"time":"2026-03-02T18:00:02.489857585Z","level":"INFO","msg":"connection: closed successfully","id":"17(@)"}
|
| 83 |
+
{"time":"2026-03-02T18:00:02.489872803Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"12(@)"}
|
| 84 |
+
{"time":"2026-03-02T18:00:02.489877543Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"17(@)"}
|
| 85 |
+
{"time":"2026-03-02T18:00:02.489552094Z","level":"INFO","msg":"connection: closing","id":"4(@)"}
|
| 86 |
+
{"time":"2026-03-02T18:00:02.489885256Z","level":"INFO","msg":"connection: closed successfully","id":"6(@)"}
|
| 87 |
+
{"time":"2026-03-02T18:00:02.489897775Z","level":"INFO","msg":"connection: closed successfully","id":"8(@)"}
|
| 88 |
+
{"time":"2026-03-02T18:00:02.48990122Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"6(@)"}
|
| 89 |
+
{"time":"2026-03-02T18:00:02.489907766Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"8(@)"}
|
| 90 |
+
{"time":"2026-03-02T18:00:02.489723035Z","level":"INFO","msg":"connection: closed successfully","id":"5(@)"}
|
| 91 |
+
{"time":"2026-03-02T18:00:02.489922198Z","level":"INFO","msg":"connection: closed successfully","id":"4(@)"}
|
| 92 |
+
{"time":"2026-03-02T18:00:02.489927529Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"5(@)"}
|
| 93 |
+
{"time":"2026-03-02T18:00:02.489916435Z","level":"INFO","msg":"connection: closed successfully","id":"7(@)"}
|
| 94 |
+
{"time":"2026-03-02T18:00:02.48997986Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"7(@)"}
|
| 95 |
+
{"time":"2026-03-02T18:00:02.489552128Z","level":"INFO","msg":"connection: closing","id":"13(@)"}
|
| 96 |
+
{"time":"2026-03-02T18:00:02.489729345Z","level":"INFO","msg":"connection: closed successfully","id":"9(@)"}
|
| 97 |
+
{"time":"2026-03-02T18:00:02.49002383Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"9(@)"}
|
| 98 |
+
{"time":"2026-03-02T18:00:02.489735804Z","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
|
| 99 |
+
{"time":"2026-03-02T18:00:02.490038185Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"}
|
| 100 |
+
{"time":"2026-03-02T18:00:02.48984709Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"3(@)"}
|
| 101 |
+
{"time":"2026-03-02T18:00:02.489637712Z","level":"INFO","msg":"connection: closing","id":"2(@)"}
|
| 102 |
+
{"time":"2026-03-02T18:00:02.490058988Z","level":"INFO","msg":"connection: closed successfully","id":"13(@)"}
|
| 103 |
+
{"time":"2026-03-02T18:00:02.490069508Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"13(@)"}
|
| 104 |
+
{"time":"2026-03-02T18:00:02.489934173Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"4(@)"}
|
| 105 |
+
{"time":"2026-03-02T18:00:02.490087101Z","level":"INFO","msg":"connection: closed successfully","id":"2(@)"}
|
| 106 |
+
{"time":"2026-03-02T18:00:02.490095905Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"2(@)"}
|
| 107 |
+
{"time":"2026-03-02T18:00:02.490108029Z","level":"INFO","msg":"server is closed"}
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/config.yaml
ADDED
|
@@ -0,0 +1,309 @@
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|
|
| 1 |
+
_wandb:
|
| 2 |
+
value:
|
| 3 |
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cli_version: 0.25.0
|
| 4 |
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e:
|
| 5 |
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8jvz7m9157bluwjfwu52vsf17ozbsnyw:
|
| 6 |
+
args:
|
| 7 |
+
- +experiment=acid
|
| 8 |
+
- wandb.mode=online
|
| 9 |
+
- wandb.name=0303_ACID_FULL_2v
|
| 10 |
+
cpu_count: 112
|
| 11 |
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cpu_count_logical: 224
|
| 12 |
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cudaVersion: "12.8"
|
| 13 |
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disk:
|
| 14 |
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/:
|
| 15 |
+
total: "1170378588160"
|
| 16 |
+
used: "759094677504"
|
| 17 |
+
email: dna9041@korea.ac.kr
|
| 18 |
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executable: /venv/main/bin/python
|
| 19 |
+
git:
|
| 20 |
+
commit: 9dfce172a0f8c7ce85e763899f7ef741ecffc454
|
| 21 |
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remote: git@github.com:K-nowing/CVPR2026.git
|
| 22 |
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gpu: NVIDIA H200
|
| 23 |
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gpu_count: 8
|
| 24 |
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gpu_nvidia:
|
| 25 |
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- architecture: Hopper
|
| 26 |
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cudaCores: 16896
|
| 27 |
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memoryTotal: "150754820096"
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| 28 |
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name: NVIDIA H200
|
| 29 |
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uuid: GPU-79687643-93f8-7b36-349a-8f05b89e6678
|
| 30 |
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- architecture: Hopper
|
| 31 |
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cudaCores: 16896
|
| 32 |
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memoryTotal: "150754820096"
|
| 33 |
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name: NVIDIA H200
|
| 34 |
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uuid: GPU-317bba70-b882-ca12-2b8b-173e2db3be03
|
| 35 |
+
- architecture: Hopper
|
| 36 |
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cudaCores: 16896
|
| 37 |
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memoryTotal: "150754820096"
|
| 38 |
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name: NVIDIA H200
|
| 39 |
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uuid: GPU-cc84663f-d6cd-d900-0d4c-118462dced2e
|
| 40 |
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- architecture: Hopper
|
| 41 |
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cudaCores: 16896
|
| 42 |
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memoryTotal: "150754820096"
|
| 43 |
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name: NVIDIA H200
|
| 44 |
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uuid: GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210
|
| 45 |
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- architecture: Hopper
|
| 46 |
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cudaCores: 16896
|
| 47 |
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memoryTotal: "150754820096"
|
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name: NVIDIA H200
|
| 49 |
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uuid: GPU-331b6fb4-1872-8ae5-e5de-e34efc869d56
|
| 50 |
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- architecture: Hopper
|
| 51 |
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cudaCores: 16896
|
| 52 |
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memoryTotal: "150754820096"
|
| 53 |
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name: NVIDIA H200
|
| 54 |
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uuid: GPU-522b1630-b9aa-5aa3-9985-ced479a7780e
|
| 55 |
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- architecture: Hopper
|
| 56 |
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cudaCores: 16896
|
| 57 |
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memoryTotal: "150754820096"
|
| 58 |
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name: NVIDIA H200
|
| 59 |
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uuid: GPU-4c86a636-acfc-e976-3b9e-78425c9c44df
|
| 60 |
+
- architecture: Hopper
|
| 61 |
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cudaCores: 16896
|
| 62 |
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memoryTotal: "150754820096"
|
| 63 |
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name: NVIDIA H200
|
| 64 |
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uuid: GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b
|
| 65 |
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host: 0258ae8f3852
|
| 66 |
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memory:
|
| 67 |
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total: "2164193775616"
|
| 68 |
+
os: Linux-5.15.0-157-generic-x86_64-with-glibc2.39
|
| 69 |
+
program: -m src.main
|
| 70 |
+
python: CPython 3.12.12
|
| 71 |
+
root: /workspace/code/CVPR2026/outputs/full/acid/0303_ACID_FULL_2v
|
| 72 |
+
startedAt: "2026-03-02T18:03:58.051099Z"
|
| 73 |
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writerId: 8jvz7m9157bluwjfwu52vsf17ozbsnyw
|
| 74 |
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m:
|
| 75 |
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- "1": trainer/global_step
|
| 76 |
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"6":
|
| 77 |
+
- 3
|
| 78 |
+
"7": []
|
| 79 |
+
- "2": '*'
|
| 80 |
+
"5": 1
|
| 81 |
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"6":
|
| 82 |
+
- 1
|
| 83 |
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"7": []
|
| 84 |
+
python_version: 3.12.12
|
| 85 |
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t:
|
| 86 |
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"1":
|
| 87 |
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- 1
|
| 88 |
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|
| 89 |
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- 49
|
| 90 |
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|
| 91 |
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- 106
|
| 92 |
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"2":
|
| 93 |
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- 1
|
| 94 |
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- 41
|
| 95 |
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- 49
|
| 96 |
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- 50
|
| 97 |
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- 106
|
| 98 |
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"3":
|
| 99 |
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- 7
|
| 100 |
+
- 13
|
| 101 |
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- 15
|
| 102 |
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- 16
|
| 103 |
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- 66
|
| 104 |
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"4": 3.12.12
|
| 105 |
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"5": 0.25.0
|
| 106 |
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"12": 0.25.0
|
| 107 |
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"13": linux-x86_64
|
| 108 |
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checkpointing:
|
| 109 |
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value:
|
| 110 |
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every_n_train_steps: 1875
|
| 111 |
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load: null
|
| 112 |
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save_top_k: 2
|
| 113 |
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save_weights_only: false
|
| 114 |
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data_loader:
|
| 115 |
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value:
|
| 116 |
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test:
|
| 117 |
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batch_size: 1
|
| 118 |
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num_workers: 4
|
| 119 |
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persistent_workers: false
|
| 120 |
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seed: 2345
|
| 121 |
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train:
|
| 122 |
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batch_size: 16
|
| 123 |
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num_workers: 16
|
| 124 |
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persistent_workers: true
|
| 125 |
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seed: 1234
|
| 126 |
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val:
|
| 127 |
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batch_size: 1
|
| 128 |
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num_workers: 1
|
| 129 |
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persistent_workers: true
|
| 130 |
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seed: 3456
|
| 131 |
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dataset:
|
| 132 |
+
value:
|
| 133 |
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re10k:
|
| 134 |
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augment: true
|
| 135 |
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background_color:
|
| 136 |
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- 0
|
| 137 |
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- 0
|
| 138 |
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- 0
|
| 139 |
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baseline_max: 1e+10
|
| 140 |
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baseline_min: 0.001
|
| 141 |
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cameras_are_circular: false
|
| 142 |
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dynamic_context_views: false
|
| 143 |
+
input_image_shape:
|
| 144 |
+
- 256
|
| 145 |
+
- 256
|
| 146 |
+
make_baseline_1: true
|
| 147 |
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max_context_views_per_gpu: 16
|
| 148 |
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max_fov: 100
|
| 149 |
+
name: re10k
|
| 150 |
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original_image_shape:
|
| 151 |
+
- 360
|
| 152 |
+
- 640
|
| 153 |
+
overfit_to_scene: null
|
| 154 |
+
relative_pose: true
|
| 155 |
+
roots:
|
| 156 |
+
- datasets/acid
|
| 157 |
+
skip_bad_shape: true
|
| 158 |
+
view_sampler:
|
| 159 |
+
initial_max_distance_between_context_views: 25
|
| 160 |
+
initial_min_distance_between_context_views: 25
|
| 161 |
+
max_distance_between_context_views: 90
|
| 162 |
+
min_distance_between_context_views: 45
|
| 163 |
+
min_distance_to_context_views: 0
|
| 164 |
+
name: bounded
|
| 165 |
+
num_context_views: 2
|
| 166 |
+
num_target_set: 3
|
| 167 |
+
num_target_views: 4
|
| 168 |
+
same_target_gap: false
|
| 169 |
+
target_align: true
|
| 170 |
+
warm_up_steps: 9375
|
| 171 |
+
density_control_loss:
|
| 172 |
+
value:
|
| 173 |
+
error_score:
|
| 174 |
+
grad_scale: 10000
|
| 175 |
+
log_scale: false
|
| 176 |
+
mode: original
|
| 177 |
+
weight: 0.0001
|
| 178 |
+
direct_loss:
|
| 179 |
+
value:
|
| 180 |
+
l1:
|
| 181 |
+
weight: 0.8
|
| 182 |
+
ssim:
|
| 183 |
+
weight: 0.2
|
| 184 |
+
mode:
|
| 185 |
+
value: train
|
| 186 |
+
model:
|
| 187 |
+
value:
|
| 188 |
+
decoder:
|
| 189 |
+
background_color:
|
| 190 |
+
- 0
|
| 191 |
+
- 0
|
| 192 |
+
- 0
|
| 193 |
+
make_scale_invariant: false
|
| 194 |
+
name: splatting_cuda
|
| 195 |
+
density_control:
|
| 196 |
+
aggregation_mode: mean
|
| 197 |
+
aux_refine: false
|
| 198 |
+
grad_mode: absgrad
|
| 199 |
+
gs_param_dim: 256
|
| 200 |
+
latent_dim: 128
|
| 201 |
+
mean_dim: 32
|
| 202 |
+
name: density_control_module
|
| 203 |
+
num_heads: 1
|
| 204 |
+
num_latents: 64
|
| 205 |
+
num_level: 3
|
| 206 |
+
num_self_attn_per_block: 2
|
| 207 |
+
refine_error: false
|
| 208 |
+
refinement_hidden_dim: 32
|
| 209 |
+
refinement_layer_num: 1
|
| 210 |
+
refinement_type: voxelize
|
| 211 |
+
score_mode: absgrad
|
| 212 |
+
use_depth: false
|
| 213 |
+
use_mean_features: true
|
| 214 |
+
use_refine_module: false
|
| 215 |
+
voxel_size: 0.001
|
| 216 |
+
voxelize_activate: false
|
| 217 |
+
encoder:
|
| 218 |
+
align_corners: false
|
| 219 |
+
gs_param_dim: 256
|
| 220 |
+
head_mode: pcd
|
| 221 |
+
input_image_shape:
|
| 222 |
+
- 518
|
| 223 |
+
- 518
|
| 224 |
+
name: dcsplat
|
| 225 |
+
num_level: 3
|
| 226 |
+
use_voxelize: true
|
| 227 |
+
optimizer:
|
| 228 |
+
value:
|
| 229 |
+
accumulate: 1
|
| 230 |
+
backbone_lr_multiplier: 0.1
|
| 231 |
+
backbone_trainable: T+H
|
| 232 |
+
lr: 0.0002
|
| 233 |
+
warm_up_steps: 125
|
| 234 |
+
render_loss:
|
| 235 |
+
value:
|
| 236 |
+
lpips:
|
| 237 |
+
apply_after_step: 0
|
| 238 |
+
weight: 0.05
|
| 239 |
+
mse:
|
| 240 |
+
weight: 1
|
| 241 |
+
seed:
|
| 242 |
+
value: 111123
|
| 243 |
+
test:
|
| 244 |
+
value:
|
| 245 |
+
align_pose: false
|
| 246 |
+
compute_scores: true
|
| 247 |
+
error_threshold: 0.4
|
| 248 |
+
error_threshold_list:
|
| 249 |
+
- 0.2
|
| 250 |
+
- 0.4
|
| 251 |
+
- 0.6
|
| 252 |
+
- 0.8
|
| 253 |
+
- 1
|
| 254 |
+
nvs_view_N_list:
|
| 255 |
+
- 3
|
| 256 |
+
- 6
|
| 257 |
+
- 16
|
| 258 |
+
- 32
|
| 259 |
+
- 64
|
| 260 |
+
output_path: test/full/acid
|
| 261 |
+
pose_align_steps: 100
|
| 262 |
+
pred_intrinsic: false
|
| 263 |
+
rot_opt_lr: 0.005
|
| 264 |
+
save_active_mask_image: false
|
| 265 |
+
save_compare: false
|
| 266 |
+
save_error_score_image: false
|
| 267 |
+
save_gs: false
|
| 268 |
+
save_image: false
|
| 269 |
+
save_sample_wise_metrics: true
|
| 270 |
+
save_video: false
|
| 271 |
+
threshold_mode: ratio
|
| 272 |
+
trans_opt_lr: 0.005
|
| 273 |
+
train:
|
| 274 |
+
value:
|
| 275 |
+
align_corners: false
|
| 276 |
+
beta_dist_param:
|
| 277 |
+
- 0.5
|
| 278 |
+
- 4
|
| 279 |
+
cam_scale_mode: sum
|
| 280 |
+
camera_loss: 10
|
| 281 |
+
context_view_train: false
|
| 282 |
+
ext_scale_detach: false
|
| 283 |
+
extended_visualization: false
|
| 284 |
+
intrinsic_scaling: false
|
| 285 |
+
one_sample_validation: null
|
| 286 |
+
print_log_every_n_steps: 10
|
| 287 |
+
scene_scale_reg_loss: 0.01
|
| 288 |
+
train_aux: true
|
| 289 |
+
train_gs_num: 1
|
| 290 |
+
train_target_set: true
|
| 291 |
+
use_refine_aux: false
|
| 292 |
+
verbose: false
|
| 293 |
+
vggt_cam_loss: true
|
| 294 |
+
vggt_distil: false
|
| 295 |
+
trainer:
|
| 296 |
+
value:
|
| 297 |
+
gradient_clip_val: 0.5
|
| 298 |
+
max_steps: 18751
|
| 299 |
+
num_nodes: 1
|
| 300 |
+
val_check_interval: 500
|
| 301 |
+
wandb:
|
| 302 |
+
value:
|
| 303 |
+
entity: scene-representation-group
|
| 304 |
+
mode: online
|
| 305 |
+
name: 0303_ACID_FULL_2v
|
| 306 |
+
project: DCSplat
|
| 307 |
+
tags:
|
| 308 |
+
- acid
|
| 309 |
+
- 256x256
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/requirements.txt
ADDED
|
@@ -0,0 +1,159 @@
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
wheel==0.45.1
|
| 2 |
+
triton==3.4.0
|
| 3 |
+
nvidia-nccl-cu12==2.27.3
|
| 4 |
+
pytz==2025.2
|
| 5 |
+
easydict==1.13
|
| 6 |
+
antlr4-python3-runtime==4.9.3
|
| 7 |
+
wadler_lindig==0.1.7
|
| 8 |
+
packaging==24.2
|
| 9 |
+
urllib3==2.5.0
|
| 10 |
+
tzdata==2025.2
|
| 11 |
+
typing-inspection==0.4.1
|
| 12 |
+
tabulate==0.9.0
|
| 13 |
+
smmap==5.0.2
|
| 14 |
+
opt_einsum==3.4.0
|
| 15 |
+
setuptools==78.1.1
|
| 16 |
+
safetensors==0.5.3
|
| 17 |
+
PyYAML==6.0.2
|
| 18 |
+
PySocks==1.7.1
|
| 19 |
+
pyparsing==3.2.5
|
| 20 |
+
pydantic_core==2.33.2
|
| 21 |
+
pycparser==2.23
|
| 22 |
+
protobuf==6.32.1
|
| 23 |
+
propcache==0.3.2
|
| 24 |
+
proglog==0.1.12
|
| 25 |
+
kiwisolver==1.4.9
|
| 26 |
+
platformdirs==4.4.0
|
| 27 |
+
idna==3.7
|
| 28 |
+
pip==25.2
|
| 29 |
+
pillow==10.4.0
|
| 30 |
+
numpy==1.26.4
|
| 31 |
+
torch==2.8.0+cu128
|
| 32 |
+
ninja==1.13.0
|
| 33 |
+
gmpy2==2.2.1
|
| 34 |
+
networkx==3.4.2
|
| 35 |
+
multidict==6.6.4
|
| 36 |
+
mdurl==0.1.2
|
| 37 |
+
MarkupSafe==3.0.2
|
| 38 |
+
kornia_rs==0.1.9
|
| 39 |
+
imageio-ffmpeg==0.6.0
|
| 40 |
+
hf-xet==1.1.10
|
| 41 |
+
kornia==0.8.1
|
| 42 |
+
fsspec==2024.6.1
|
| 43 |
+
frozenlist==1.7.0
|
| 44 |
+
fonttools==4.60.0
|
| 45 |
+
filelock==3.17.0
|
| 46 |
+
einops==0.8.1
|
| 47 |
+
torchmetrics==1.8.2
|
| 48 |
+
decorator==4.4.2
|
| 49 |
+
torchvision==0.23.0+cu128
|
| 50 |
+
dacite==1.9.2
|
| 51 |
+
cycler==0.12.1
|
| 52 |
+
colorama==0.4.6
|
| 53 |
+
click==8.3.0
|
| 54 |
+
charset-normalizer==3.3.2
|
| 55 |
+
certifi==2025.8.3
|
| 56 |
+
beartype==0.19.0
|
| 57 |
+
opt-einsum-fx==0.1.4
|
| 58 |
+
torchaudio==2.8.0+cu128
|
| 59 |
+
attrs==25.3.0
|
| 60 |
+
async-timeout==5.0.1
|
| 61 |
+
annotated-types==0.7.0
|
| 62 |
+
aiohappyeyeballs==2.6.1
|
| 63 |
+
yarl==1.20.1
|
| 64 |
+
tifffile==2025.5.10
|
| 65 |
+
sentry-sdk==2.39.0
|
| 66 |
+
scipy==1.15.3
|
| 67 |
+
pydantic==2.11.9
|
| 68 |
+
pandas==2.3.2
|
| 69 |
+
opencv-python==4.11.0.86
|
| 70 |
+
omegaconf==2.3.0
|
| 71 |
+
markdown-it-py==4.0.0
|
| 72 |
+
lightning-utilities==0.14.3
|
| 73 |
+
lazy_loader==0.4
|
| 74 |
+
jaxtyping==0.2.37
|
| 75 |
+
imageio==2.37.0
|
| 76 |
+
gitdb==4.0.12
|
| 77 |
+
contourpy==1.3.2
|
| 78 |
+
colorspacious==1.1.2
|
| 79 |
+
cffi==1.17.1
|
| 80 |
+
aiosignal==1.4.0
|
| 81 |
+
scikit-video==1.1.11
|
| 82 |
+
scikit-image==0.25.2
|
| 83 |
+
rich==14.1.0
|
| 84 |
+
moviepy==1.0.3
|
| 85 |
+
matplotlib==3.10.6
|
| 86 |
+
hydra-core==1.3.2
|
| 87 |
+
e3nn==0.6.0
|
| 88 |
+
huggingface-hub==0.35.1
|
| 89 |
+
GitPython==3.1.45
|
| 90 |
+
brotlicffi==1.0.9.2
|
| 91 |
+
aiohttp==3.12.15
|
| 92 |
+
pytorch-lightning==2.5.1
|
| 93 |
+
lpips==0.1.4
|
| 94 |
+
lightning==2.5.1
|
| 95 |
+
gsplat==1.5.3
|
| 96 |
+
torch_scatter==2.1.2+pt28cu128
|
| 97 |
+
plyfile==1.1.3
|
| 98 |
+
wandb==0.25.0
|
| 99 |
+
cuda-bindings==12.9.4
|
| 100 |
+
cuda-pathfinder==1.3.3
|
| 101 |
+
Jinja2==3.1.6
|
| 102 |
+
mpmath==1.3.0
|
| 103 |
+
nvidia-cublas-cu12==12.8.4.1
|
| 104 |
+
nvidia-cuda-cupti-cu12==12.8.90
|
| 105 |
+
nvidia-cuda-nvrtc-cu12==12.8.93
|
| 106 |
+
nvidia-cuda-runtime-cu12==12.8.90
|
| 107 |
+
nvidia-cudnn-cu12==9.10.2.21
|
| 108 |
+
nvidia-cufft-cu12==11.3.3.83
|
| 109 |
+
nvidia-cufile-cu12==1.13.1.3
|
| 110 |
+
nvidia-curand-cu12==10.3.9.90
|
| 111 |
+
nvidia-cusolver-cu12==11.7.3.90
|
| 112 |
+
nvidia-cusparse-cu12==12.5.8.93
|
| 113 |
+
nvidia-cusparselt-cu12==0.7.1
|
| 114 |
+
nvidia-nvjitlink-cu12==12.8.93
|
| 115 |
+
nvidia-nvshmem-cu12==3.4.5
|
| 116 |
+
nvidia-nvtx-cu12==12.8.90
|
| 117 |
+
requests==2.32.5
|
| 118 |
+
sentencepiece==0.2.1
|
| 119 |
+
sympy==1.14.0
|
| 120 |
+
torchcodec==0.10.0
|
| 121 |
+
torchdata==0.10.0
|
| 122 |
+
torchtext==0.6.0
|
| 123 |
+
anyio==4.12.0
|
| 124 |
+
asttokens==3.0.1
|
| 125 |
+
comm==0.2.3
|
| 126 |
+
debugpy==1.8.19
|
| 127 |
+
executing==2.2.1
|
| 128 |
+
h11==0.16.0
|
| 129 |
+
httpcore==1.0.9
|
| 130 |
+
httpx==0.28.1
|
| 131 |
+
ipykernel==7.1.0
|
| 132 |
+
ipython==9.8.0
|
| 133 |
+
ipython_pygments_lexers==1.1.1
|
| 134 |
+
ipywidgets==8.1.8
|
| 135 |
+
jedi==0.19.2
|
| 136 |
+
jupyter_client==8.7.0
|
| 137 |
+
jupyter_core==5.9.1
|
| 138 |
+
jupyterlab_widgets==3.0.16
|
| 139 |
+
matplotlib-inline==0.2.1
|
| 140 |
+
nest-asyncio==1.6.0
|
| 141 |
+
parso==0.8.5
|
| 142 |
+
pexpect==4.9.0
|
| 143 |
+
prompt_toolkit==3.0.52
|
| 144 |
+
psutil==7.2.1
|
| 145 |
+
ptyprocess==0.7.0
|
| 146 |
+
pure_eval==0.2.3
|
| 147 |
+
Pygments==2.19.2
|
| 148 |
+
python-dateutil==2.9.0.post0
|
| 149 |
+
pyzmq==27.1.0
|
| 150 |
+
shellingham==1.5.4
|
| 151 |
+
six==1.17.0
|
| 152 |
+
stack-data==0.6.3
|
| 153 |
+
tornado==6.5.4
|
| 154 |
+
tqdm==4.67.1
|
| 155 |
+
traitlets==5.14.3
|
| 156 |
+
typer-slim==0.21.0
|
| 157 |
+
typing_extensions==4.15.0
|
| 158 |
+
wcwidth==0.2.14
|
| 159 |
+
widgetsnbextension==4.0.15
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/wandb-summary.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"info/global_step":18750,"_wandb":{"runtime":54758},"loss/aux_2/lpips":0.006058057304471731,"loss/aux_2/mse":0.0032417154870927334,"loss/final_3dgs/mse":0.003249172819778323,"train/error_scores":{"filenames":["media/images/train/error_scores_1081_3a8ae34a5a7d913aa086.png"],"captions":["807e4a18469f0cf9"],"_type":"images/separated","width":1328,"height":536,"format":"png","count":1},"val/gaussian_num_ratio":0.40003204345703125,"loss/aux_1/lpips":0.006861213129013777,"lr-AdamW/pg1":2.0020372974791542e-05,"loss/scene_scale_reg":4.5954137021908537e-05,"loss/camera":0.00027877415413968265,"val/lpips":0.09945698082447052,"loss/aux_1/mse":0.0033982242457568645,"lr-AdamW/pg2-momentum":0.9,"active_mask_imgs":{"width":536,"height":800,"format":"png","count":1,"filenames":["media/images/active_mask_imgs_1074_e7833fd38d23d7123dfb.png"],"captions":["805fbbaebf73743d"],"_type":"images/separated"},"_step":1088,"loss/aux_1/error_score":0.32635700702667236,"loss/final_3dgs/lpips":0.006482797209173441,"loss/aux_0/lpips":0.009234142489731312,"val/ssim":0.9420804381370544,"train/comparison":{"width":1328,"height":1098,"format":"png","count":1,"filenames":["media/images/train/comparison_1082_854fb1a07e2d91d59cd2.png"],"captions":["807e4a18469f0cf9"],"_type":"images/separated"},"error_scores":{"count":1,"filenames":["media/images/error_scores_1075_742317ffab37bb217df2.png"],"captions":["805fbbaebf73743d"],"_type":"images/separated","width":800,"height":536,"format":"png"},"loss/aux_0/error_score":0.3482131361961365,"_timestamp":1.7725293853548381e+09,"lr-AdamW/pg2":2e-05,"loss/aux_0/mse":0.004146920517086983,"trainer/global_step":18749,"val/psnr":33.688316345214844,"loss/total":0.0455733947455883,"_runtime":54758,"comparison":{"filenames":["media/images/comparison_1073_7bd8ace4f542773ab2f1.png"],"captions":["805fbbaebf73743d"],"_type":"images/separated","width":1064,"height":1098,"format":"png","count":1},"train/psnr_probabilistic":26.834943771362305,"epoch":2,"lr-AdamW/pg1-momentum":0.9,"train/scene_scale":0.9985688924789429}
|
acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/logs/debug-internal.log
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2026-03-02T18:03:58.30488479Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
|
| 2 |
+
{"time":"2026-03-02T18:03:58.663005908Z","level":"INFO","msg":"stream: created new stream","id":"h16yffc1"}
|
| 3 |
+
{"time":"2026-03-02T18:03:58.663134658Z","level":"INFO","msg":"handler: started","stream_id":"h16yffc1"}
|
| 4 |
+
{"time":"2026-03-02T18:03:58.663747044Z","level":"INFO","msg":"stream: started","id":"h16yffc1"}
|
| 5 |
+
{"time":"2026-03-02T18:03:58.663771823Z","level":"INFO","msg":"writer: started","stream_id":"h16yffc1"}
|
| 6 |
+
{"time":"2026-03-02T18:03:58.663827643Z","level":"INFO","msg":"sender: started","stream_id":"h16yffc1"}
|
| 7 |
+
{"time":"2026-03-02T22:14:59.226144733Z","level":"INFO","msg":"api: retrying HTTP error","status":502,"url":"https://api.wandb.ai/files/know/DCSplat/h16yffc1/file_stream","body":"\n<html><head>\n<meta http-equiv=\"content-type\" content=\"text/html;charset=utf-8\">\n<title>502 Server Error</title>\n</head>\n<body text=#000000 bgcolor=#ffffff>\n<h1>Error: Server Error</h1>\n<h2>The server encountered a temporary error and could not complete your request.<p>Please try again in 30 seconds.</h2>\n<h2></h2>\n</body></html>\n"}
|
| 8 |
+
{"time":"2026-03-03T09:16:37.305354887Z","level":"INFO","msg":"stream: closing","id":"h16yffc1"}
|
| 9 |
+
{"time":"2026-03-03T09:16:37.749837263Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
|
| 10 |
+
{"time":"2026-03-03T09:16:37.880324485Z","level":"INFO","msg":"handler: closed","stream_id":"h16yffc1"}
|
| 11 |
+
{"time":"2026-03-03T09:16:37.880469696Z","level":"INFO","msg":"sender: closed","stream_id":"h16yffc1"}
|
| 12 |
+
{"time":"2026-03-03T09:16:37.880482709Z","level":"INFO","msg":"stream: closed","id":"h16yffc1"}
|
re10k/0303_RE10K_FULL_2v/.hydra/config.yaml
ADDED
|
@@ -0,0 +1,188 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model:
|
| 2 |
+
encoder:
|
| 3 |
+
name: dcsplat
|
| 4 |
+
input_image_shape:
|
| 5 |
+
- 518
|
| 6 |
+
- 518
|
| 7 |
+
head_mode: pcd
|
| 8 |
+
num_level: 3
|
| 9 |
+
gs_param_dim: 256
|
| 10 |
+
align_corners: false
|
| 11 |
+
use_voxelize: true
|
| 12 |
+
decoder:
|
| 13 |
+
name: splatting_cuda
|
| 14 |
+
background_color:
|
| 15 |
+
- 0.0
|
| 16 |
+
- 0.0
|
| 17 |
+
- 0.0
|
| 18 |
+
make_scale_invariant: false
|
| 19 |
+
density_control:
|
| 20 |
+
name: density_control_module
|
| 21 |
+
mean_dim: 32
|
| 22 |
+
gs_param_dim: 256
|
| 23 |
+
refinement_layer_num: 1
|
| 24 |
+
num_level: 3
|
| 25 |
+
grad_mode: absgrad
|
| 26 |
+
use_mean_features: true
|
| 27 |
+
refinement_type: voxelize
|
| 28 |
+
refinement_hidden_dim: 32
|
| 29 |
+
aggregation_mode: mean
|
| 30 |
+
num_heads: 1
|
| 31 |
+
score_mode: absgrad
|
| 32 |
+
latent_dim: 128
|
| 33 |
+
num_latents: 64
|
| 34 |
+
num_self_attn_per_block: 2
|
| 35 |
+
voxel_size: 0.001
|
| 36 |
+
aux_refine: false
|
| 37 |
+
refine_error: false
|
| 38 |
+
use_refine_module: false
|
| 39 |
+
voxelize_activate: false
|
| 40 |
+
use_depth: false
|
| 41 |
+
render_loss:
|
| 42 |
+
mse:
|
| 43 |
+
weight: 1.0
|
| 44 |
+
lpips:
|
| 45 |
+
weight: 0.05
|
| 46 |
+
apply_after_step: 0
|
| 47 |
+
density_control_loss:
|
| 48 |
+
error_score:
|
| 49 |
+
weight: 0.0001
|
| 50 |
+
log_scale: false
|
| 51 |
+
grad_scale: 10000.0
|
| 52 |
+
mode: original
|
| 53 |
+
direct_loss:
|
| 54 |
+
l1:
|
| 55 |
+
weight: 0.8
|
| 56 |
+
ssim:
|
| 57 |
+
weight: 0.2
|
| 58 |
+
wandb:
|
| 59 |
+
project: DCSplat
|
| 60 |
+
entity: scene-representation-group
|
| 61 |
+
name: 0303_RE10K_FULL_2v
|
| 62 |
+
mode: online
|
| 63 |
+
tags:
|
| 64 |
+
- re10k
|
| 65 |
+
- 256x256
|
| 66 |
+
mode: train
|
| 67 |
+
data_loader:
|
| 68 |
+
train:
|
| 69 |
+
num_workers: 16
|
| 70 |
+
persistent_workers: true
|
| 71 |
+
batch_size: 16
|
| 72 |
+
seed: 1234
|
| 73 |
+
test:
|
| 74 |
+
num_workers: 4
|
| 75 |
+
persistent_workers: false
|
| 76 |
+
batch_size: 1
|
| 77 |
+
seed: 2345
|
| 78 |
+
val:
|
| 79 |
+
num_workers: 1
|
| 80 |
+
persistent_workers: true
|
| 81 |
+
batch_size: 1
|
| 82 |
+
seed: 3456
|
| 83 |
+
optimizer:
|
| 84 |
+
lr: 0.0002
|
| 85 |
+
warm_up_steps: 125
|
| 86 |
+
backbone_lr_multiplier: 0.1
|
| 87 |
+
backbone_trainable: T+H
|
| 88 |
+
accumulate: 1
|
| 89 |
+
checkpointing:
|
| 90 |
+
load: null
|
| 91 |
+
every_n_train_steps: 1875
|
| 92 |
+
save_top_k: 2
|
| 93 |
+
save_weights_only: false
|
| 94 |
+
train:
|
| 95 |
+
extended_visualization: false
|
| 96 |
+
print_log_every_n_steps: 10
|
| 97 |
+
camera_loss: 10.0
|
| 98 |
+
one_sample_validation: null
|
| 99 |
+
align_corners: false
|
| 100 |
+
intrinsic_scaling: false
|
| 101 |
+
verbose: false
|
| 102 |
+
beta_dist_param:
|
| 103 |
+
- 0.5
|
| 104 |
+
- 4.0
|
| 105 |
+
use_refine_aux: false
|
| 106 |
+
train_target_set: true
|
| 107 |
+
train_gs_num: 1
|
| 108 |
+
ext_scale_detach: false
|
| 109 |
+
cam_scale_mode: sum
|
| 110 |
+
scene_scale_reg_loss: 0.01
|
| 111 |
+
train_aux: true
|
| 112 |
+
vggt_cam_loss: true
|
| 113 |
+
vggt_distil: false
|
| 114 |
+
context_view_train: false
|
| 115 |
+
test:
|
| 116 |
+
output_path: test/full/re10k
|
| 117 |
+
align_pose: false
|
| 118 |
+
pose_align_steps: 100
|
| 119 |
+
rot_opt_lr: 0.005
|
| 120 |
+
trans_opt_lr: 0.005
|
| 121 |
+
compute_scores: true
|
| 122 |
+
save_image: false
|
| 123 |
+
save_video: false
|
| 124 |
+
save_active_mask_image: false
|
| 125 |
+
save_error_score_image: false
|
| 126 |
+
save_compare: false
|
| 127 |
+
save_gs: false
|
| 128 |
+
save_sample_wise_metrics: true
|
| 129 |
+
pred_intrinsic: false
|
| 130 |
+
error_threshold: 0.4
|
| 131 |
+
error_threshold_list:
|
| 132 |
+
- 0.2
|
| 133 |
+
- 0.4
|
| 134 |
+
- 0.6
|
| 135 |
+
- 0.8
|
| 136 |
+
- 1.0
|
| 137 |
+
threshold_mode: ratio
|
| 138 |
+
nvs_view_N_list:
|
| 139 |
+
- 3
|
| 140 |
+
- 6
|
| 141 |
+
- 16
|
| 142 |
+
- 32
|
| 143 |
+
- 64
|
| 144 |
+
seed: 111123
|
| 145 |
+
trainer:
|
| 146 |
+
max_steps: 18751
|
| 147 |
+
val_check_interval: 500
|
| 148 |
+
gradient_clip_val: 0.5
|
| 149 |
+
num_nodes: 1
|
| 150 |
+
dataset:
|
| 151 |
+
re10k:
|
| 152 |
+
make_baseline_1: true
|
| 153 |
+
relative_pose: true
|
| 154 |
+
augment: true
|
| 155 |
+
background_color:
|
| 156 |
+
- 0.0
|
| 157 |
+
- 0.0
|
| 158 |
+
- 0.0
|
| 159 |
+
overfit_to_scene: null
|
| 160 |
+
skip_bad_shape: true
|
| 161 |
+
view_sampler:
|
| 162 |
+
name: bounded
|
| 163 |
+
num_target_views: 4
|
| 164 |
+
num_context_views: 2
|
| 165 |
+
min_distance_between_context_views: 45
|
| 166 |
+
max_distance_between_context_views: 90
|
| 167 |
+
min_distance_to_context_views: 0
|
| 168 |
+
warm_up_steps: 9375
|
| 169 |
+
initial_min_distance_between_context_views: 25
|
| 170 |
+
initial_max_distance_between_context_views: 25
|
| 171 |
+
same_target_gap: false
|
| 172 |
+
num_target_set: 3
|
| 173 |
+
target_align: true
|
| 174 |
+
name: re10k
|
| 175 |
+
roots:
|
| 176 |
+
- datasets/re10k
|
| 177 |
+
input_image_shape:
|
| 178 |
+
- 256
|
| 179 |
+
- 256
|
| 180 |
+
original_image_shape:
|
| 181 |
+
- 360
|
| 182 |
+
- 640
|
| 183 |
+
cameras_are_circular: false
|
| 184 |
+
baseline_min: 0.001
|
| 185 |
+
baseline_max: 10000000000.0
|
| 186 |
+
max_fov: 100.0
|
| 187 |
+
dynamic_context_views: false
|
| 188 |
+
max_context_views_per_gpu: 16
|
re10k/0303_RE10K_FULL_2v/.hydra/hydra.yaml
ADDED
|
@@ -0,0 +1,164 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
hydra:
|
| 2 |
+
run:
|
| 3 |
+
dir: outputs/full/re10k/${wandb.name}
|
| 4 |
+
sweep:
|
| 5 |
+
dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
|
| 6 |
+
subdir: ${hydra.job.num}
|
| 7 |
+
launcher:
|
| 8 |
+
_target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
|
| 9 |
+
sweeper:
|
| 10 |
+
_target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
|
| 11 |
+
max_batch_size: null
|
| 12 |
+
params: null
|
| 13 |
+
help:
|
| 14 |
+
app_name: ${hydra.job.name}
|
| 15 |
+
header: '${hydra.help.app_name} is powered by Hydra.
|
| 16 |
+
|
| 17 |
+
'
|
| 18 |
+
footer: 'Powered by Hydra (https://hydra.cc)
|
| 19 |
+
|
| 20 |
+
Use --hydra-help to view Hydra specific help
|
| 21 |
+
|
| 22 |
+
'
|
| 23 |
+
template: '${hydra.help.header}
|
| 24 |
+
|
| 25 |
+
== Configuration groups ==
|
| 26 |
+
|
| 27 |
+
Compose your configuration from those groups (group=option)
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
$APP_CONFIG_GROUPS
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
== Config ==
|
| 34 |
+
|
| 35 |
+
Override anything in the config (foo.bar=value)
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
$CONFIG
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
${hydra.help.footer}
|
| 42 |
+
|
| 43 |
+
'
|
| 44 |
+
hydra_help:
|
| 45 |
+
template: 'Hydra (${hydra.runtime.version})
|
| 46 |
+
|
| 47 |
+
See https://hydra.cc for more info.
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
== Flags ==
|
| 51 |
+
|
| 52 |
+
$FLAGS_HELP
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
== Configuration groups ==
|
| 56 |
+
|
| 57 |
+
Compose your configuration from those groups (For example, append hydra/job_logging=disabled
|
| 58 |
+
to command line)
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
$HYDRA_CONFIG_GROUPS
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
Use ''--cfg hydra'' to Show the Hydra config.
|
| 65 |
+
|
| 66 |
+
'
|
| 67 |
+
hydra_help: ???
|
| 68 |
+
hydra_logging:
|
| 69 |
+
version: 1
|
| 70 |
+
formatters:
|
| 71 |
+
simple:
|
| 72 |
+
format: '[%(asctime)s][HYDRA] %(message)s'
|
| 73 |
+
handlers:
|
| 74 |
+
console:
|
| 75 |
+
class: logging.StreamHandler
|
| 76 |
+
formatter: simple
|
| 77 |
+
stream: ext://sys.stdout
|
| 78 |
+
root:
|
| 79 |
+
level: INFO
|
| 80 |
+
handlers:
|
| 81 |
+
- console
|
| 82 |
+
loggers:
|
| 83 |
+
logging_example:
|
| 84 |
+
level: DEBUG
|
| 85 |
+
disable_existing_loggers: false
|
| 86 |
+
job_logging:
|
| 87 |
+
version: 1
|
| 88 |
+
formatters:
|
| 89 |
+
simple:
|
| 90 |
+
format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
|
| 91 |
+
handlers:
|
| 92 |
+
console:
|
| 93 |
+
class: logging.StreamHandler
|
| 94 |
+
formatter: simple
|
| 95 |
+
stream: ext://sys.stdout
|
| 96 |
+
file:
|
| 97 |
+
class: logging.FileHandler
|
| 98 |
+
formatter: simple
|
| 99 |
+
filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
|
| 100 |
+
root:
|
| 101 |
+
level: INFO
|
| 102 |
+
handlers:
|
| 103 |
+
- console
|
| 104 |
+
- file
|
| 105 |
+
disable_existing_loggers: false
|
| 106 |
+
env: {}
|
| 107 |
+
mode: RUN
|
| 108 |
+
searchpath: []
|
| 109 |
+
callbacks: {}
|
| 110 |
+
output_subdir: .hydra
|
| 111 |
+
overrides:
|
| 112 |
+
hydra:
|
| 113 |
+
- hydra.mode=RUN
|
| 114 |
+
task:
|
| 115 |
+
- +experiment=re10k
|
| 116 |
+
- wandb.mode=online
|
| 117 |
+
- wandb.name=0303_RE10K_FULL_2v
|
| 118 |
+
job:
|
| 119 |
+
name: main
|
| 120 |
+
chdir: null
|
| 121 |
+
override_dirname: +experiment=re10k,wandb.mode=online,wandb.name=0303_RE10K_FULL_2v
|
| 122 |
+
id: ???
|
| 123 |
+
num: ???
|
| 124 |
+
config_name: main
|
| 125 |
+
env_set: {}
|
| 126 |
+
env_copy: []
|
| 127 |
+
config:
|
| 128 |
+
override_dirname:
|
| 129 |
+
kv_sep: '='
|
| 130 |
+
item_sep: ','
|
| 131 |
+
exclude_keys: []
|
| 132 |
+
runtime:
|
| 133 |
+
version: 1.3.2
|
| 134 |
+
version_base: '1.3'
|
| 135 |
+
cwd: /workspace/code/CVPR2026
|
| 136 |
+
config_sources:
|
| 137 |
+
- path: hydra.conf
|
| 138 |
+
schema: pkg
|
| 139 |
+
provider: hydra
|
| 140 |
+
- path: /workspace/code/CVPR2026/config
|
| 141 |
+
schema: file
|
| 142 |
+
provider: main
|
| 143 |
+
- path: ''
|
| 144 |
+
schema: structured
|
| 145 |
+
provider: schema
|
| 146 |
+
output_dir: /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10K_FULL_2v
|
| 147 |
+
choices:
|
| 148 |
+
experiment: re10k
|
| 149 |
+
dataset@dataset.re10k: re10k
|
| 150 |
+
dataset/view_sampler_dataset_specific_config@dataset.re10k.view_sampler: bounded_re10k
|
| 151 |
+
dataset/view_sampler@dataset.re10k.view_sampler: bounded
|
| 152 |
+
model/density_control: density_control_module
|
| 153 |
+
model/decoder: splatting_cuda
|
| 154 |
+
model/encoder: dcsplat
|
| 155 |
+
hydra/env: default
|
| 156 |
+
hydra/callbacks: null
|
| 157 |
+
hydra/job_logging: default
|
| 158 |
+
hydra/hydra_logging: default
|
| 159 |
+
hydra/hydra_help: default
|
| 160 |
+
hydra/help: default
|
| 161 |
+
hydra/sweeper: basic
|
| 162 |
+
hydra/launcher: basic
|
| 163 |
+
hydra/output: default
|
| 164 |
+
verbose: false
|
re10k/0303_RE10K_FULL_2v/.hydra/overrides.yaml
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
- +experiment=re10k
|
| 2 |
+
- wandb.mode=online
|
| 3 |
+
- wandb.name=0303_RE10K_FULL_2v
|
re10k/0303_RE10K_FULL_2v/main.log
ADDED
|
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2026-03-02 17:53:26,172][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-02 17:53:31,546][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-02 17:53:31,546][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-02 17:53:35,419][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
|
| 9 |
+
|
| 10 |
+
[2026-03-02 17:53:37,385][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 11 |
+
result[selector] = overlay
|
| 12 |
+
|
| 13 |
+
[2026-03-02 17:53:37,395][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
|
| 14 |
+
|
| 15 |
+
[2026-03-02 17:53:37,397][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 16 |
+
warnings.warn(
|
| 17 |
+
|
| 18 |
+
[2026-03-02 17:53:37,397][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 19 |
+
warnings.warn(msg)
|
| 20 |
+
|
| 21 |
+
[2026-03-02 17:53:38,759][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
|
| 22 |
+
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
|
| 23 |
+
|
| 24 |
+
[2026-03-02 17:53:46,980][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 25 |
+
result[selector] = overlay
|
| 26 |
+
|
| 27 |
+
[2026-03-02 17:59:25,954][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 28 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 29 |
+
|
| 30 |
+
[2026-03-03 09:16:46,710][dinov2][INFO] - using MLP layer as FFN
|
| 31 |
+
[2026-03-03 09:16:52,053][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 32 |
+
warnings.warn(
|
| 33 |
+
|
| 34 |
+
[2026-03-03 09:16:52,053][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 35 |
+
warnings.warn(msg)
|
| 36 |
+
|
| 37 |
+
[2026-03-03 09:17:44,857][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=27` in the `DataLoader` to improve performance.
|
| 38 |
+
|
| 39 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 40 |
+
warnings.warn( # warn only once
|
| 41 |
+
|
| 42 |
+
[2026-03-03 09:17:46,959][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 43 |
+
result[selector] = overlay
|
| 44 |
+
|
| 45 |
+
[2026-03-03 09:17:46,967][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
|
| 46 |
+
|
| 47 |
+
[2026-03-03 09:17:46,968][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 48 |
+
warnings.warn(
|
| 49 |
+
|
| 50 |
+
[2026-03-03 09:17:46,968][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 51 |
+
warnings.warn(msg)
|
| 52 |
+
|
| 53 |
+
[2026-03-03 09:17:48,377][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
|
| 54 |
+
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
|
| 55 |
+
|
| 56 |
+
[2026-03-03 09:17:48,644][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:434: It is recommended to use `self.log('val/psnr', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
|
| 57 |
+
|
| 58 |
+
[2026-03-03 09:17:48,645][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:434: It is recommended to use `self.log('val/lpips', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
|
| 59 |
+
|
| 60 |
+
[2026-03-03 09:17:48,645][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:434: It is recommended to use `self.log('val/ssim', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
|
| 61 |
+
|
| 62 |
+
[2026-03-03 09:17:48,646][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:434: It is recommended to use `self.log('val/gaussian_num_ratio', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
|
| 63 |
+
|
| 64 |
+
[2026-03-03 09:17:48,646][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/logger_connector/result.py:434: It is recommended to use `self.log('info/global_step', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
|
| 65 |
+
|
| 66 |
+
[2026-03-03 09:18:07,324][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 67 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 68 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 69 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 70 |
+
|
| 71 |
+
[2026-03-03 09:18:07,412][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 72 |
+
result[selector] = overlay
|
| 73 |
+
|
| 74 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 75 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 76 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_1.log
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2026-03-03 09:17:01,942][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:33,502][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:33,503][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,325][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,424][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_2.log
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2026-03-03 09:17:02,039][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:33,508][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:33,508][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,325][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,425][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_3.log
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2026-03-03 09:17:01,837][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:33,229][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:33,229][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,323][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,422][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_4.log
ADDED
|
@@ -0,0 +1,21 @@
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|
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|
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|
| 1 |
+
[2026-03-03 09:17:01,807][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:33,016][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:33,017][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,323][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,423][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_5.log
ADDED
|
@@ -0,0 +1,21 @@
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2026-03-03 09:17:01,792][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:30,362][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:30,363][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,324][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,457][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,699][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_6.log
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
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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 |
+
[2026-03-03 09:17:01,811][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:32,701][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:32,702][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,859][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,324][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,456][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/train_ddp_process_7.log
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[2026-03-03 09:17:01,869][dinov2][INFO] - using MLP layer as FFN
|
| 2 |
+
[2026-03-03 09:17:33,155][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 3 |
+
warnings.warn(
|
| 4 |
+
|
| 5 |
+
[2026-03-03 09:17:33,155][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 6 |
+
warnings.warn(msg)
|
| 7 |
+
|
| 8 |
+
[2026-03-03 09:17:44,858][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user.
|
| 9 |
+
warnings.warn( # warn only once
|
| 10 |
+
|
| 11 |
+
[2026-03-03 09:18:07,325][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/autograd/graph.py:829: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed. This is not an error, but may impair performance.
|
| 12 |
+
grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
|
| 13 |
+
bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
|
| 14 |
+
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
|
| 15 |
+
|
| 16 |
+
[2026-03-03 09:18:07,425][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 17 |
+
result[selector] = overlay
|
| 18 |
+
|
| 19 |
+
[2026-03-03 09:24:04,660][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 20 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 21 |
+
|
re10k/0303_RE10K_FULL_2v/wandb/debug-internal.log
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2026-03-03T09:17:40.960595772Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
|
| 2 |
+
{"time":"2026-03-03T09:17:41.326404324Z","level":"INFO","msg":"stream: created new stream","id":"d18sudny"}
|
| 3 |
+
{"time":"2026-03-03T09:17:41.326554992Z","level":"INFO","msg":"handler: started","stream_id":"d18sudny"}
|
| 4 |
+
{"time":"2026-03-03T09:17:41.326775806Z","level":"INFO","msg":"stream: started","id":"d18sudny"}
|
| 5 |
+
{"time":"2026-03-03T09:17:41.32697243Z","level":"INFO","msg":"writer: started","stream_id":"d18sudny"}
|
| 6 |
+
{"time":"2026-03-03T09:17:41.327074842Z","level":"INFO","msg":"sender: started","stream_id":"d18sudny"}
|
re10k/0303_RE10K_FULL_2v/wandb/debug.log
ADDED
|
@@ -0,0 +1,19 @@
|
|
|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_setup.py:_flush():81] Current SDK version is 0.25.0
|
| 2 |
+
2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_setup.py:_flush():81] Configure stats pid to 18776
|
| 3 |
+
2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_setup.py:_flush():81] Loading settings from environment variables
|
| 4 |
+
2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_init.py:setup_run_log_directory():717] Logging user logs to /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10K_FULL_2v/wandb/run-20260303_091740-d18sudny/logs/debug.log
|
| 5 |
+
2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_init.py:setup_run_log_directory():718] Logging internal logs to /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10K_FULL_2v/wandb/run-20260303_091740-d18sudny/logs/debug-internal.log
|
| 6 |
+
2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_init.py:init():844] calling init triggers
|
| 7 |
+
2026-03-03 09:17:40,709 INFO MainThread:18776 [wandb_init.py:init():849] wandb.init called with sweep_config: {}
|
| 8 |
+
config: {'model': {'encoder': {'name': 'dcsplat', 'input_image_shape': [518, 518], 'head_mode': 'pcd', 'num_level': 3, 'gs_param_dim': 256, 'align_corners': False, 'use_voxelize': True}, 'decoder': {'name': 'splatting_cuda', 'background_color': [0.0, 0.0, 0.0], 'make_scale_invariant': False}, 'density_control': {'name': 'density_control_module', 'mean_dim': 32, 'gs_param_dim': 256, 'refinement_layer_num': 1, 'num_level': 3, 'grad_mode': 'absgrad', 'use_mean_features': True, 'refinement_type': 'voxelize', 'refinement_hidden_dim': 32, 'aggregation_mode': 'mean', 'num_heads': 1, 'score_mode': 'absgrad', 'latent_dim': 128, 'num_latents': 64, 'num_self_attn_per_block': 2, 'voxel_size': 0.001, 'aux_refine': False, 'refine_error': False, 'use_refine_module': False, 'voxelize_activate': False, 'use_depth': False}}, 'render_loss': {'mse': {'weight': 1.0}, 'lpips': {'weight': 0.05, 'apply_after_step': 0}}, 'density_control_loss': {'error_score': {'weight': 0.0001, 'log_scale': False, 'grad_scale': 10000.0, 'mode': 'original'}}, 'direct_loss': {'l1': {'weight': 0.8}, 'ssim': {'weight': 0.2}}, 'wandb': {'project': 'DCSplat', 'entity': 'scene-representation-group', 'name': '0303_RE10K_FULL_2v', 'mode': 'online', 'tags': ['re10k', '256x256']}, 'mode': 'train', 'data_loader': {'train': {'num_workers': 16, 'persistent_workers': True, 'batch_size': 16, 'seed': 1234}, 'test': {'num_workers': 4, 'persistent_workers': False, 'batch_size': 1, 'seed': 2345}, 'val': {'num_workers': 1, 'persistent_workers': True, 'batch_size': 1, 'seed': 3456}}, 'optimizer': {'lr': 0.0002, 'warm_up_steps': 125, 'backbone_lr_multiplier': 0.1, 'backbone_trainable': 'T+H', 'accumulate': 1}, 'checkpointing': {'load': None, 'every_n_train_steps': 1875, 'save_top_k': 2, 'save_weights_only': False}, 'train': {'extended_visualization': False, 'print_log_every_n_steps': 10, 'camera_loss': 10.0, 'one_sample_validation': None, 'align_corners': False, 'intrinsic_scaling': False, 'verbose': False, 'beta_dist_param': [0.5, 4.0], 'use_refine_aux': False, 'train_target_set': True, 'train_gs_num': 1, 'ext_scale_detach': False, 'cam_scale_mode': 'sum', 'scene_scale_reg_loss': 0.01, 'train_aux': True, 'vggt_cam_loss': True, 'vggt_distil': False, 'context_view_train': False}, 'test': {'output_path': 'test/full/re10k', 'align_pose': False, 'pose_align_steps': 100, 'rot_opt_lr': 0.005, 'trans_opt_lr': 0.005, 'compute_scores': True, 'save_image': False, 'save_video': False, 'save_active_mask_image': False, 'save_error_score_image': False, 'save_compare': False, 'save_gs': False, 'save_sample_wise_metrics': True, 'pred_intrinsic': False, 'error_threshold': 0.4, 'error_threshold_list': [0.2, 0.4, 0.6, 0.8, 1.0], 'threshold_mode': 'ratio', 'nvs_view_N_list': [3, 6, 16, 32, 64]}, 'seed': 111123, 'trainer': {'max_steps': 18751, 'val_check_interval': 500, 'gradient_clip_val': 0.5, 'num_nodes': 1}, 'dataset': {'re10k': {'make_baseline_1': True, 'relative_pose': True, 'augment': True, 'background_color': [0.0, 0.0, 0.0], 'overfit_to_scene': None, 'skip_bad_shape': True, 'view_sampler': {'name': 'bounded', 'num_target_views': 4, 'num_context_views': 2, 'min_distance_between_context_views': 45, 'max_distance_between_context_views': 90, 'min_distance_to_context_views': 0, 'warm_up_steps': 9375, 'initial_min_distance_between_context_views': 25, 'initial_max_distance_between_context_views': 25, 'same_target_gap': False, 'num_target_set': 3, 'target_align': True}, 'name': 're10k', 'roots': ['datasets/re10k'], 'input_image_shape': [256, 256], 'original_image_shape': [360, 640], 'cameras_are_circular': False, 'baseline_min': 0.001, 'baseline_max': 10000000000.0, 'max_fov': 100.0, 'dynamic_context_views': False, 'max_context_views_per_gpu': 16}}, '_wandb': {}}
|
| 9 |
+
2026-03-03 09:17:40,709 INFO MainThread:18776 [wandb_init.py:init():892] starting backend
|
| 10 |
+
2026-03-03 09:17:40,950 INFO MainThread:18776 [wandb_init.py:init():895] sending inform_init request
|
| 11 |
+
2026-03-03 09:17:40,958 INFO MainThread:18776 [wandb_init.py:init():903] backend started and connected
|
| 12 |
+
2026-03-03 09:17:40,960 INFO MainThread:18776 [wandb_init.py:init():973] updated telemetry
|
| 13 |
+
2026-03-03 09:17:40,964 INFO MainThread:18776 [wandb_init.py:init():997] communicating run to backend with 90.0 second timeout
|
| 14 |
+
2026-03-03 09:17:42,467 INFO MainThread:18776 [wandb_init.py:init():1042] starting run threads in backend
|
| 15 |
+
2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_console_start():2524] atexit reg
|
| 16 |
+
2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_redirect():2373] redirect: wrap_raw
|
| 17 |
+
2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_redirect():2442] Wrapping output streams.
|
| 18 |
+
2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_redirect():2465] Redirects installed.
|
| 19 |
+
2026-03-03 09:17:42,548 INFO MainThread:18776 [wandb_init.py:init():1082] run started, returning control to user process
|
re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-metadata.json
ADDED
|
@@ -0,0 +1,92 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"os": "Linux-5.15.0-157-generic-x86_64-with-glibc2.39",
|
| 3 |
+
"python": "CPython 3.12.12",
|
| 4 |
+
"startedAt": "2026-03-02T17:53:33.438580Z",
|
| 5 |
+
"args": [
|
| 6 |
+
"+experiment=re10k",
|
| 7 |
+
"wandb.mode=online",
|
| 8 |
+
"wandb.name=0303_RE10K_FULL_2v"
|
| 9 |
+
],
|
| 10 |
+
"program": "-m src.main",
|
| 11 |
+
"git": {
|
| 12 |
+
"remote": "git@github.com:K-nowing/CVPR2026.git",
|
| 13 |
+
"commit": "9dfce172a0f8c7ce85e763899f7ef741ecffc454"
|
| 14 |
+
},
|
| 15 |
+
"email": "dna9041@korea.ac.kr",
|
| 16 |
+
"root": "/workspace/code/CVPR2026/outputs/full/re10k/0303_RE10K_FULL_2v",
|
| 17 |
+
"host": "0258ae8f3852",
|
| 18 |
+
"executable": "/venv/main/bin/python",
|
| 19 |
+
"cpu_count": 112,
|
| 20 |
+
"cpu_count_logical": 224,
|
| 21 |
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"gpu": "NVIDIA H200",
|
| 22 |
+
"gpu_count": 8,
|
| 23 |
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"disk": {
|
| 24 |
+
"/": {
|
| 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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"memory": {
|
| 30 |
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"total": "2164193775616"
|
| 31 |
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},
|
| 32 |
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"gpu_nvidia": [
|
| 33 |
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{
|
| 34 |
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"name": "NVIDIA H200",
|
| 35 |
+
"memoryTotal": "150754820096",
|
| 36 |
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|
| 37 |
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"architecture": "Hopper",
|
| 38 |
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"uuid": "GPU-79687643-93f8-7b36-349a-8f05b89e6678"
|
| 39 |
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|
| 40 |
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{
|
| 41 |
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"name": "NVIDIA H200",
|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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"uuid": "GPU-317bba70-b882-ca12-2b8b-173e2db3be03"
|
| 46 |
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|
| 47 |
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{
|
| 48 |
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"name": "NVIDIA H200",
|
| 49 |
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"memoryTotal": "150754820096",
|
| 50 |
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"cudaCores": 16896,
|
| 51 |
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"architecture": "Hopper",
|
| 52 |
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"uuid": "GPU-cc84663f-d6cd-d900-0d4c-118462dced2e"
|
| 53 |
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|
| 54 |
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{
|
| 55 |
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"name": "NVIDIA H200",
|
| 56 |
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"memoryTotal": "150754820096",
|
| 57 |
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|
| 58 |
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"architecture": "Hopper",
|
| 59 |
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"uuid": "GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210"
|
| 60 |
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|
| 61 |
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{
|
| 62 |
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"name": "NVIDIA H200",
|
| 63 |
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"memoryTotal": "150754820096",
|
| 64 |
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"cudaCores": 16896,
|
| 65 |
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"architecture": "Hopper",
|
| 66 |
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"uuid": "GPU-331b6fb4-1872-8ae5-e5de-e34efc869d56"
|
| 67 |
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},
|
| 68 |
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{
|
| 69 |
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"name": "NVIDIA H200",
|
| 70 |
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"memoryTotal": "150754820096",
|
| 71 |
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"cudaCores": 16896,
|
| 72 |
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"architecture": "Hopper",
|
| 73 |
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"uuid": "GPU-522b1630-b9aa-5aa3-9985-ced479a7780e"
|
| 74 |
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},
|
| 75 |
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{
|
| 76 |
+
"name": "NVIDIA H200",
|
| 77 |
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"memoryTotal": "150754820096",
|
| 78 |
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"cudaCores": 16896,
|
| 79 |
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"architecture": "Hopper",
|
| 80 |
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"uuid": "GPU-4c86a636-acfc-e976-3b9e-78425c9c44df"
|
| 81 |
+
},
|
| 82 |
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{
|
| 83 |
+
"name": "NVIDIA H200",
|
| 84 |
+
"memoryTotal": "150754820096",
|
| 85 |
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"cudaCores": 16896,
|
| 86 |
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"architecture": "Hopper",
|
| 87 |
+
"uuid": "GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b"
|
| 88 |
+
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|
| 89 |
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|
| 90 |
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"cudaVersion": "12.8",
|
| 91 |
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"writerId": "nyyynvgq5catl0amasijx0miq9sdne8u"
|
| 92 |
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}
|
re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-summary.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"train/scene_scale":0.9576742053031921,"loss/final_3dgs/lpips":0.020093852654099464,"loss/aux_0/error_score":1.2417750358581543,"loss/aux_0/mse":0.010599546134471893,"train/comparison":{"count":1,"filenames":["media/images/train/comparison_4_bfae3dd267ee585bc6c9.png"],"captions":["0f93fdb52c6933cf"],"_type":"images/separated","width":1328,"height":1098,"format":"png"},"loss/aux_2/lpips":0.01799216866493225,"info/global_step":100,"loss/scene_scale_reg":0.0015646865358576179,"train/error_scores":{"_type":"images/separated","width":1328,"height":536,"format":"png","count":1,"filenames":["media/images/train/error_scores_3_f864b07321b8483d66b7.png"],"captions":["0f93fdb52c6933cf"]},"loss/aux_1/error_score":0.824253499507904,"lr-AdamW/pg1":0.00015873279999999994,"loss/aux_2/mse":0.006986838765442371,"_step":8,"lr-AdamW/pg2":1.5873280000000005e-05,"error_scores":{"filenames":["media/images/error_scores_2_efcb488e1c0653296910.png"],"captions":["306e2b7785657539"],"_type":"images/separated","width":800,"height":536,"format":"png","count":1},"_timestamp":1.7724742978587358e+09,"loss/final_3dgs/mse":0.007130756508558989,"_runtime":382,"_wandb":{"runtime":382},"loss/camera":0.00023625143512617797,"epoch":0,"active_mask_imgs":{"_type":"images/separated","width":536,"height":800,"format":"png","count":1,"filenames":["media/images/active_mask_imgs_1_7f6e73914e5351cf9616.png"],"captions":["306e2b7785657539"]},"comparison":{"captions":["306e2b7785657539"],"_type":"images/separated","width":1064,"height":1098,"format":"png","count":1,"filenames":["media/images/comparison_0_485cfe2fe2c25c1fd269.png"]},"lr-AdamW/pg1-momentum":0.9,"loss/aux_1/mse":0.0061654276214540005,"loss/total":0.12033132463693619,"lr-AdamW/pg2-momentum":0.9,"train/psnr_probabilistic":21.70659065246582,"trainer/global_step":99,"loss/aux_0/lpips":0.028960365802049637,"loss/aux_1/lpips":0.018268560990691185}
|
re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/logs/debug-internal.log
ADDED
|
@@ -0,0 +1,50 @@
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|
| 1 |
+
{"time":"2026-03-02T17:53:33.692951019Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
|
| 2 |
+
{"time":"2026-03-02T17:53:34.065688995Z","level":"INFO","msg":"stream: created new stream","id":"24m6myoo"}
|
| 3 |
+
{"time":"2026-03-02T17:53:34.06583962Z","level":"INFO","msg":"handler: started","stream_id":"24m6myoo"}
|
| 4 |
+
{"time":"2026-03-02T17:53:34.066046233Z","level":"INFO","msg":"stream: started","id":"24m6myoo"}
|
| 5 |
+
{"time":"2026-03-02T17:53:34.06628084Z","level":"INFO","msg":"writer: started","stream_id":"24m6myoo"}
|
| 6 |
+
{"time":"2026-03-02T17:53:34.06628786Z","level":"INFO","msg":"sender: started","stream_id":"24m6myoo"}
|
| 7 |
+
{"time":"2026-03-02T17:59:57.198981838Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 8 |
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{"time":"2026-03-02T17:59:57.19952135Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 9 |
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{"time":"2026-03-02T17:59:57.202672482Z","level":"INFO","msg":"flowcontrol: backed up, offloading to disk","recordNumber":565}
|
| 10 |
+
{"time":"2026-03-02T17:59:57.483605199Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 11 |
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{"time":"2026-03-02T17:59:57.483631704Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 12 |
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{"time":"2026-03-02T17:59:57.483636767Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 13 |
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{"time":"2026-03-02T17:59:57.48364212Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 14 |
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{"time":"2026-03-02T17:59:57.483855898Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 15 |
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{"time":"2026-03-02T17:59:57.483863454Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 16 |
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{"time":"2026-03-02T17:59:57.483961274Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 17 |
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{"time":"2026-03-02T17:59:57.483967107Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 18 |
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{"time":"2026-03-02T17:59:57.484247785Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 19 |
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{"time":"2026-03-02T17:59:57.484251758Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 20 |
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{"time":"2026-03-02T17:59:57.484255764Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 21 |
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{"time":"2026-03-02T17:59:57.484259009Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 22 |
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{"time":"2026-03-02T17:59:57.484447804Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 23 |
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{"time":"2026-03-02T17:59:57.484452017Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 24 |
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{"time":"2026-03-02T17:59:57.484456635Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 25 |
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{"time":"2026-03-02T17:59:57.484460357Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 26 |
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{"time":"2026-03-02T17:59:57.486098407Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 27 |
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{"time":"2026-03-02T17:59:57.486122217Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 28 |
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{"time":"2026-03-02T17:59:57.486135222Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 29 |
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{"time":"2026-03-02T17:59:57.486140844Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 30 |
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{"time":"2026-03-02T17:59:57.48628549Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 31 |
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{"time":"2026-03-02T17:59:57.486297255Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 32 |
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{"time":"2026-03-02T17:59:57.486301679Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 33 |
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{"time":"2026-03-02T17:59:57.486313174Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 34 |
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{"time":"2026-03-02T17:59:57.486450601Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 35 |
+
{"time":"2026-03-02T17:59:57.486455864Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 36 |
+
{"time":"2026-03-02T17:59:57.486465694Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 37 |
+
{"time":"2026-03-02T17:59:57.486469967Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 38 |
+
{"time":"2026-03-02T17:59:57.486502627Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 39 |
+
{"time":"2026-03-02T17:59:57.486550491Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 40 |
+
{"time":"2026-03-02T17:59:57.486569197Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"}
|
| 41 |
+
{"time":"2026-03-02T17:59:57.487174822Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"}
|
| 42 |
+
{"time":"2026-03-02T17:59:57.487430202Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 43 |
+
{"time":"2026-03-02T17:59:57.487444811Z","level":"INFO","msg":"flowcontrol: unblocked","totalOffloaded":24}
|
| 44 |
+
{"time":"2026-03-02T17:59:57.487454897Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 45 |
+
{"time":"2026-03-02T17:59:57.877581157Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
|
| 46 |
+
{"time":"2026-03-02T17:59:58.079133486Z","level":"INFO","msg":"handler: operation stats","stats":{}}
|
| 47 |
+
{"time":"2026-03-02T17:59:58.084611241Z","level":"INFO","msg":"stream: closing","id":"24m6myoo"}
|
| 48 |
+
{"time":"2026-03-02T17:59:58.084624435Z","level":"INFO","msg":"handler: closed","stream_id":"24m6myoo"}
|
| 49 |
+
{"time":"2026-03-02T17:59:58.084724916Z","level":"INFO","msg":"sender: closed","stream_id":"24m6myoo"}
|
| 50 |
+
{"time":"2026-03-02T17:59:58.084732416Z","level":"INFO","msg":"stream: closed","id":"24m6myoo"}
|
re10k/0303_RE10k_FULL_24v/.hydra/config.yaml
ADDED
|
@@ -0,0 +1,188 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
model:
|
| 2 |
+
encoder:
|
| 3 |
+
name: dcsplat
|
| 4 |
+
input_image_shape:
|
| 5 |
+
- 518
|
| 6 |
+
- 518
|
| 7 |
+
head_mode: pcd
|
| 8 |
+
num_level: 3
|
| 9 |
+
gs_param_dim: 256
|
| 10 |
+
align_corners: false
|
| 11 |
+
use_voxelize: true
|
| 12 |
+
decoder:
|
| 13 |
+
name: splatting_cuda
|
| 14 |
+
background_color:
|
| 15 |
+
- 0.0
|
| 16 |
+
- 0.0
|
| 17 |
+
- 0.0
|
| 18 |
+
make_scale_invariant: false
|
| 19 |
+
density_control:
|
| 20 |
+
name: density_control_module
|
| 21 |
+
mean_dim: 32
|
| 22 |
+
gs_param_dim: 256
|
| 23 |
+
refinement_layer_num: 1
|
| 24 |
+
num_level: 3
|
| 25 |
+
grad_mode: absgrad
|
| 26 |
+
use_mean_features: true
|
| 27 |
+
refinement_type: voxelize
|
| 28 |
+
refinement_hidden_dim: 32
|
| 29 |
+
aggregation_mode: mean
|
| 30 |
+
num_heads: 1
|
| 31 |
+
score_mode: absgrad
|
| 32 |
+
latent_dim: 128
|
| 33 |
+
num_latents: 64
|
| 34 |
+
num_self_attn_per_block: 2
|
| 35 |
+
voxel_size: 0.001
|
| 36 |
+
aux_refine: false
|
| 37 |
+
refine_error: false
|
| 38 |
+
use_refine_module: false
|
| 39 |
+
voxelize_activate: false
|
| 40 |
+
use_depth: false
|
| 41 |
+
render_loss:
|
| 42 |
+
mse:
|
| 43 |
+
weight: 1.0
|
| 44 |
+
lpips:
|
| 45 |
+
weight: 0.05
|
| 46 |
+
apply_after_step: 0
|
| 47 |
+
density_control_loss:
|
| 48 |
+
error_score:
|
| 49 |
+
weight: 0.0001
|
| 50 |
+
log_scale: false
|
| 51 |
+
grad_scale: 10000.0
|
| 52 |
+
mode: original
|
| 53 |
+
direct_loss:
|
| 54 |
+
l1:
|
| 55 |
+
weight: 0.8
|
| 56 |
+
ssim:
|
| 57 |
+
weight: 0.2
|
| 58 |
+
wandb:
|
| 59 |
+
project: DCSplat
|
| 60 |
+
entity: scene-representation-group
|
| 61 |
+
name: 0303_RE10k_FULL_24v
|
| 62 |
+
mode: online
|
| 63 |
+
tags:
|
| 64 |
+
- re10k
|
| 65 |
+
- 256x256
|
| 66 |
+
mode: train
|
| 67 |
+
data_loader:
|
| 68 |
+
train:
|
| 69 |
+
num_workers: 16
|
| 70 |
+
persistent_workers: true
|
| 71 |
+
batch_size: 16
|
| 72 |
+
seed: 1234
|
| 73 |
+
test:
|
| 74 |
+
num_workers: 4
|
| 75 |
+
persistent_workers: false
|
| 76 |
+
batch_size: 1
|
| 77 |
+
seed: 2345
|
| 78 |
+
val:
|
| 79 |
+
num_workers: 1
|
| 80 |
+
persistent_workers: true
|
| 81 |
+
batch_size: 1
|
| 82 |
+
seed: 3456
|
| 83 |
+
optimizer:
|
| 84 |
+
lr: 0.0002
|
| 85 |
+
warm_up_steps: 125
|
| 86 |
+
backbone_lr_multiplier: 0.1
|
| 87 |
+
backbone_trainable: T+H
|
| 88 |
+
accumulate: 1
|
| 89 |
+
checkpointing:
|
| 90 |
+
load: null
|
| 91 |
+
every_n_train_steps: 1500
|
| 92 |
+
save_top_k: 2
|
| 93 |
+
save_weights_only: false
|
| 94 |
+
train:
|
| 95 |
+
extended_visualization: false
|
| 96 |
+
print_log_every_n_steps: 10
|
| 97 |
+
camera_loss: 10.0
|
| 98 |
+
one_sample_validation: null
|
| 99 |
+
align_corners: false
|
| 100 |
+
intrinsic_scaling: false
|
| 101 |
+
verbose: false
|
| 102 |
+
beta_dist_param:
|
| 103 |
+
- 0.5
|
| 104 |
+
- 4.0
|
| 105 |
+
use_refine_aux: false
|
| 106 |
+
train_target_set: true
|
| 107 |
+
train_gs_num: 1
|
| 108 |
+
ext_scale_detach: false
|
| 109 |
+
cam_scale_mode: sum
|
| 110 |
+
scene_scale_reg_loss: 0.01
|
| 111 |
+
train_aux: true
|
| 112 |
+
vggt_cam_loss: true
|
| 113 |
+
vggt_distil: false
|
| 114 |
+
context_view_train: false
|
| 115 |
+
test:
|
| 116 |
+
output_path: test/full/re10k
|
| 117 |
+
align_pose: false
|
| 118 |
+
pose_align_steps: 100
|
| 119 |
+
rot_opt_lr: 0.005
|
| 120 |
+
trans_opt_lr: 0.005
|
| 121 |
+
compute_scores: true
|
| 122 |
+
save_image: false
|
| 123 |
+
save_video: false
|
| 124 |
+
save_active_mask_image: false
|
| 125 |
+
save_error_score_image: false
|
| 126 |
+
save_compare: false
|
| 127 |
+
save_gs: false
|
| 128 |
+
save_sample_wise_metrics: true
|
| 129 |
+
pred_intrinsic: false
|
| 130 |
+
error_threshold: 0.4
|
| 131 |
+
error_threshold_list:
|
| 132 |
+
- 0.2
|
| 133 |
+
- 0.4
|
| 134 |
+
- 0.6
|
| 135 |
+
- 0.8
|
| 136 |
+
- 1.0
|
| 137 |
+
threshold_mode: ratio
|
| 138 |
+
nvs_view_N_list:
|
| 139 |
+
- 3
|
| 140 |
+
- 6
|
| 141 |
+
- 16
|
| 142 |
+
- 32
|
| 143 |
+
- 64
|
| 144 |
+
seed: 111123
|
| 145 |
+
trainer:
|
| 146 |
+
max_steps: 15001
|
| 147 |
+
val_check_interval: 500
|
| 148 |
+
gradient_clip_val: 0.5
|
| 149 |
+
num_nodes: 1
|
| 150 |
+
dataset:
|
| 151 |
+
re10k:
|
| 152 |
+
make_baseline_1: true
|
| 153 |
+
relative_pose: true
|
| 154 |
+
augment: true
|
| 155 |
+
background_color:
|
| 156 |
+
- 0.0
|
| 157 |
+
- 0.0
|
| 158 |
+
- 0.0
|
| 159 |
+
overfit_to_scene: null
|
| 160 |
+
skip_bad_shape: true
|
| 161 |
+
view_sampler:
|
| 162 |
+
name: bounded
|
| 163 |
+
num_target_views: 4
|
| 164 |
+
num_context_views: 2
|
| 165 |
+
min_distance_between_context_views: 45
|
| 166 |
+
max_distance_between_context_views: 90
|
| 167 |
+
min_distance_to_context_views: 0
|
| 168 |
+
warm_up_steps: 5000
|
| 169 |
+
initial_min_distance_between_context_views: 25
|
| 170 |
+
initial_max_distance_between_context_views: 25
|
| 171 |
+
same_target_gap: false
|
| 172 |
+
num_target_set: 3
|
| 173 |
+
target_align: true
|
| 174 |
+
name: re10k
|
| 175 |
+
roots:
|
| 176 |
+
- datasets/re10k
|
| 177 |
+
input_image_shape:
|
| 178 |
+
- 256
|
| 179 |
+
- 256
|
| 180 |
+
original_image_shape:
|
| 181 |
+
- 360
|
| 182 |
+
- 640
|
| 183 |
+
cameras_are_circular: false
|
| 184 |
+
baseline_min: 0.001
|
| 185 |
+
baseline_max: 10000000000.0
|
| 186 |
+
max_fov: 100.0
|
| 187 |
+
dynamic_context_views: true
|
| 188 |
+
max_context_views_per_gpu: 24
|
re10k/0303_RE10k_FULL_24v/.hydra/hydra.yaml
ADDED
|
@@ -0,0 +1,164 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
hydra:
|
| 2 |
+
run:
|
| 3 |
+
dir: outputs/full/re10k/${wandb.name}
|
| 4 |
+
sweep:
|
| 5 |
+
dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S}
|
| 6 |
+
subdir: ${hydra.job.num}
|
| 7 |
+
launcher:
|
| 8 |
+
_target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
|
| 9 |
+
sweeper:
|
| 10 |
+
_target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
|
| 11 |
+
max_batch_size: null
|
| 12 |
+
params: null
|
| 13 |
+
help:
|
| 14 |
+
app_name: ${hydra.job.name}
|
| 15 |
+
header: '${hydra.help.app_name} is powered by Hydra.
|
| 16 |
+
|
| 17 |
+
'
|
| 18 |
+
footer: 'Powered by Hydra (https://hydra.cc)
|
| 19 |
+
|
| 20 |
+
Use --hydra-help to view Hydra specific help
|
| 21 |
+
|
| 22 |
+
'
|
| 23 |
+
template: '${hydra.help.header}
|
| 24 |
+
|
| 25 |
+
== Configuration groups ==
|
| 26 |
+
|
| 27 |
+
Compose your configuration from those groups (group=option)
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
$APP_CONFIG_GROUPS
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
== Config ==
|
| 34 |
+
|
| 35 |
+
Override anything in the config (foo.bar=value)
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
$CONFIG
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
${hydra.help.footer}
|
| 42 |
+
|
| 43 |
+
'
|
| 44 |
+
hydra_help:
|
| 45 |
+
template: 'Hydra (${hydra.runtime.version})
|
| 46 |
+
|
| 47 |
+
See https://hydra.cc for more info.
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
== Flags ==
|
| 51 |
+
|
| 52 |
+
$FLAGS_HELP
|
| 53 |
+
|
| 54 |
+
|
| 55 |
+
== Configuration groups ==
|
| 56 |
+
|
| 57 |
+
Compose your configuration from those groups (For example, append hydra/job_logging=disabled
|
| 58 |
+
to command line)
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
$HYDRA_CONFIG_GROUPS
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
Use ''--cfg hydra'' to Show the Hydra config.
|
| 65 |
+
|
| 66 |
+
'
|
| 67 |
+
hydra_help: ???
|
| 68 |
+
hydra_logging:
|
| 69 |
+
version: 1
|
| 70 |
+
formatters:
|
| 71 |
+
simple:
|
| 72 |
+
format: '[%(asctime)s][HYDRA] %(message)s'
|
| 73 |
+
handlers:
|
| 74 |
+
console:
|
| 75 |
+
class: logging.StreamHandler
|
| 76 |
+
formatter: simple
|
| 77 |
+
stream: ext://sys.stdout
|
| 78 |
+
root:
|
| 79 |
+
level: INFO
|
| 80 |
+
handlers:
|
| 81 |
+
- console
|
| 82 |
+
loggers:
|
| 83 |
+
logging_example:
|
| 84 |
+
level: DEBUG
|
| 85 |
+
disable_existing_loggers: false
|
| 86 |
+
job_logging:
|
| 87 |
+
version: 1
|
| 88 |
+
formatters:
|
| 89 |
+
simple:
|
| 90 |
+
format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
|
| 91 |
+
handlers:
|
| 92 |
+
console:
|
| 93 |
+
class: logging.StreamHandler
|
| 94 |
+
formatter: simple
|
| 95 |
+
stream: ext://sys.stdout
|
| 96 |
+
file:
|
| 97 |
+
class: logging.FileHandler
|
| 98 |
+
formatter: simple
|
| 99 |
+
filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
|
| 100 |
+
root:
|
| 101 |
+
level: INFO
|
| 102 |
+
handlers:
|
| 103 |
+
- console
|
| 104 |
+
- file
|
| 105 |
+
disable_existing_loggers: false
|
| 106 |
+
env: {}
|
| 107 |
+
mode: RUN
|
| 108 |
+
searchpath: []
|
| 109 |
+
callbacks: {}
|
| 110 |
+
output_subdir: .hydra
|
| 111 |
+
overrides:
|
| 112 |
+
hydra:
|
| 113 |
+
- hydra.mode=RUN
|
| 114 |
+
task:
|
| 115 |
+
- +experiment=re10k_24v
|
| 116 |
+
- wandb.mode=online
|
| 117 |
+
- wandb.name=0303_RE10k_FULL_24v
|
| 118 |
+
job:
|
| 119 |
+
name: main
|
| 120 |
+
chdir: null
|
| 121 |
+
override_dirname: +experiment=re10k_24v,wandb.mode=online,wandb.name=0303_RE10k_FULL_24v
|
| 122 |
+
id: ???
|
| 123 |
+
num: ???
|
| 124 |
+
config_name: main
|
| 125 |
+
env_set: {}
|
| 126 |
+
env_copy: []
|
| 127 |
+
config:
|
| 128 |
+
override_dirname:
|
| 129 |
+
kv_sep: '='
|
| 130 |
+
item_sep: ','
|
| 131 |
+
exclude_keys: []
|
| 132 |
+
runtime:
|
| 133 |
+
version: 1.3.2
|
| 134 |
+
version_base: '1.3'
|
| 135 |
+
cwd: /workspace/code/CVPR2026
|
| 136 |
+
config_sources:
|
| 137 |
+
- path: hydra.conf
|
| 138 |
+
schema: pkg
|
| 139 |
+
provider: hydra
|
| 140 |
+
- path: /workspace/code/CVPR2026/config
|
| 141 |
+
schema: file
|
| 142 |
+
provider: main
|
| 143 |
+
- path: ''
|
| 144 |
+
schema: structured
|
| 145 |
+
provider: schema
|
| 146 |
+
output_dir: /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10k_FULL_24v
|
| 147 |
+
choices:
|
| 148 |
+
experiment: re10k_24v
|
| 149 |
+
dataset@dataset.re10k: re10k
|
| 150 |
+
dataset/view_sampler_dataset_specific_config@dataset.re10k.view_sampler: bounded_re10k
|
| 151 |
+
dataset/view_sampler@dataset.re10k.view_sampler: bounded
|
| 152 |
+
model/density_control: density_control_module
|
| 153 |
+
model/decoder: splatting_cuda
|
| 154 |
+
model/encoder: dcsplat
|
| 155 |
+
hydra/env: default
|
| 156 |
+
hydra/callbacks: null
|
| 157 |
+
hydra/job_logging: default
|
| 158 |
+
hydra/hydra_logging: default
|
| 159 |
+
hydra/hydra_help: default
|
| 160 |
+
hydra/help: default
|
| 161 |
+
hydra/sweeper: basic
|
| 162 |
+
hydra/launcher: basic
|
| 163 |
+
hydra/output: default
|
| 164 |
+
verbose: false
|
re10k/0303_RE10k_FULL_24v/.hydra/overrides.yaml
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
- +experiment=re10k_24v
|
| 2 |
+
- wandb.mode=online
|
| 3 |
+
- wandb.name=0303_RE10k_FULL_24v
|
re10k/0303_RE10k_FULL_24v/wandb/debug-internal.log
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2026-03-02T17:34:54.017734281Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
|
| 2 |
+
{"time":"2026-03-02T17:34:54.390484792Z","level":"INFO","msg":"stream: created new stream","id":"7ul1smti"}
|
| 3 |
+
{"time":"2026-03-02T17:34:54.393055945Z","level":"INFO","msg":"stream: started","id":"7ul1smti"}
|
| 4 |
+
{"time":"2026-03-02T17:34:54.393159427Z","level":"INFO","msg":"handler: started","stream_id":"7ul1smti"}
|
| 5 |
+
{"time":"2026-03-02T17:34:54.393260469Z","level":"INFO","msg":"writer: started","stream_id":"7ul1smti"}
|
| 6 |
+
{"time":"2026-03-02T17:34:54.393274583Z","level":"INFO","msg":"sender: started","stream_id":"7ul1smti"}
|
| 7 |
+
{"time":"2026-03-02T17:35:54.404488277Z","level":"INFO","msg":"flowcontrol: backed up, offloading to disk","recordNumber":194}
|
| 8 |
+
{"time":"2026-03-02T17:35:54.723380765Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 9 |
+
{"time":"2026-03-02T17:35:54.723903957Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 10 |
+
{"time":"2026-03-02T17:35:54.725035725Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 11 |
+
{"time":"2026-03-02T17:35:54.725094746Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 12 |
+
{"time":"2026-03-02T17:35:54.72600883Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 13 |
+
{"time":"2026-03-02T17:35:54.726038861Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 14 |
+
{"time":"2026-03-02T17:35:54.726906187Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 15 |
+
{"time":"2026-03-02T17:35:54.726916179Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 16 |
+
{"time":"2026-03-02T17:35:54.726929825Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 17 |
+
{"time":"2026-03-02T17:35:54.726940046Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 18 |
+
{"time":"2026-03-02T17:35:54.726946025Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 19 |
+
{"time":"2026-03-02T17:35:54.726953664Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 20 |
+
{"time":"2026-03-02T17:35:54.726960686Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 21 |
+
{"time":"2026-03-02T17:35:54.726967766Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 22 |
+
{"time":"2026-03-02T17:35:54.727281805Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 23 |
+
{"time":"2026-03-02T17:35:54.727365903Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 24 |
+
{"time":"2026-03-02T17:35:54.727581651Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 25 |
+
{"time":"2026-03-02T17:35:54.727593656Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 26 |
+
{"time":"2026-03-02T17:35:54.72766555Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 27 |
+
{"time":"2026-03-02T17:35:54.727668326Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 28 |
+
{"time":"2026-03-02T17:35:54.727735186Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 29 |
+
{"time":"2026-03-02T17:35:54.727740979Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 30 |
+
{"time":"2026-03-02T17:35:54.727855582Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 31 |
+
{"time":"2026-03-02T17:35:54.727860456Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 32 |
+
{"time":"2026-03-02T17:35:54.727862278Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 33 |
+
{"time":"2026-03-02T17:35:54.727866553Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 34 |
+
{"time":"2026-03-02T17:35:54.728069007Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 35 |
+
{"time":"2026-03-02T17:35:54.728073576Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 36 |
+
{"time":"2026-03-02T17:35:54.728075459Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 37 |
+
{"time":"2026-03-02T17:35:54.728080037Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 38 |
+
{"time":"2026-03-02T17:35:54.728132582Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 39 |
+
{"time":"2026-03-02T17:35:54.728143993Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 40 |
+
{"time":"2026-03-02T17:35:54.728149532Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"}
|
| 41 |
+
{"time":"2026-03-02T17:35:54.728419175Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"}
|
| 42 |
+
{"time":"2026-03-02T17:35:54.728485115Z","level":"ERROR","msg":"sender: sendSummary called after exit"}
|
| 43 |
+
{"time":"2026-03-02T17:35:54.728490523Z","level":"INFO","msg":"flowcontrol: unblocked","totalOffloaded":33}
|
| 44 |
+
{"time":"2026-03-02T17:35:54.72849506Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"}
|
| 45 |
+
{"time":"2026-03-02T17:35:55.062846118Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
|
| 46 |
+
{"time":"2026-03-02T17:35:55.23103931Z","level":"INFO","msg":"handler: operation stats","stats":{}}
|
| 47 |
+
{"time":"2026-03-02T17:35:55.235466397Z","level":"INFO","msg":"stream: closing","id":"7ul1smti"}
|
| 48 |
+
{"time":"2026-03-02T17:35:55.235478181Z","level":"INFO","msg":"handler: closed","stream_id":"7ul1smti"}
|
| 49 |
+
{"time":"2026-03-02T17:35:55.235510756Z","level":"INFO","msg":"sender: closed","stream_id":"7ul1smti"}
|
| 50 |
+
{"time":"2026-03-02T17:35:55.235517876Z","level":"INFO","msg":"stream: closed","id":"7ul1smti"}
|
re10k/0303_RE10k_FULL_24v/wandb/debug.log
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/config.yaml
ADDED
|
@@ -0,0 +1,309 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
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| 1 |
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_wandb:
|
| 2 |
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value:
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cli_version: 0.25.0
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e:
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| 5 |
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q93l51s2fcdx31lzgovdol8wrrqh7ma8:
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| 6 |
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args:
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| 7 |
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- +experiment=re10k_24v
|
| 8 |
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- wandb.mode=online
|
| 9 |
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- wandb.name=0303_RE10k_FULL_24v
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| 10 |
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cpu_count: 112
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| 11 |
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cpu_count_logical: 224
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cudaVersion: "12.8"
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disk:
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/:
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total: "1170378588160"
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used: "168904552448"
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email: dna9041@korea.ac.kr
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executable: /venv/main/bin/python
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git:
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| 20 |
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commit: 9dfce172a0f8c7ce85e763899f7ef741ecffc454
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remote: git@github.com:K-nowing/CVPR2026.git
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gpu: NVIDIA H200
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gpu_count: 8
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gpu_nvidia:
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- architecture: Hopper
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cudaCores: 16896
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memoryTotal: "150754820096"
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name: NVIDIA H200
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uuid: GPU-317bba70-b882-ca12-2b8b-173e2db3be03
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name: NVIDIA H200
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name: NVIDIA H200
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name: NVIDIA H200
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uuid: GPU-522b1630-b9aa-5aa3-9985-ced479a7780e
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memoryTotal: "150754820096"
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name: NVIDIA H200
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uuid: GPU-4c86a636-acfc-e976-3b9e-78425c9c44df
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cudaCores: 16896
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memoryTotal: "150754820096"
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name: NVIDIA H200
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uuid: GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b
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host: 0258ae8f3852
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total: "2164193775616"
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os: Linux-5.15.0-157-generic-x86_64-with-glibc2.39
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| 69 |
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program: -m src.main
|
| 70 |
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python: CPython 3.12.12
|
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root: /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10k_FULL_24v
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python_version: 3.12.12
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|
| 111 |
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load: null
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|
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save_weights_only: false
|
| 114 |
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|
| 115 |
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value:
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| 116 |
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|
| 117 |
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batch_size: 1
|
| 118 |
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|
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persistent_workers: false
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seed: 2345
|
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|
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|
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|
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|
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batch_size: 1
|
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|
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persistent_workers: true
|
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| 132 |
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value:
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re10k:
|
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augment: true
|
| 135 |
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background_color:
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|
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|
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|
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baseline_min: 0.001
|
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cameras_are_circular: false
|
| 142 |
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dynamic_context_views: true
|
| 143 |
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input_image_shape:
|
| 144 |
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- 256
|
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|
| 146 |
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make_baseline_1: true
|
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max_context_views_per_gpu: 24
|
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max_fov: 100
|
| 149 |
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name: re10k
|
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original_image_shape:
|
| 151 |
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- 360
|
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- 640
|
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overfit_to_scene: null
|
| 154 |
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relative_pose: true
|
| 155 |
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roots:
|
| 156 |
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- datasets/re10k
|
| 157 |
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skip_bad_shape: true
|
| 158 |
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view_sampler:
|
| 159 |
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initial_max_distance_between_context_views: 25
|
| 160 |
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initial_min_distance_between_context_views: 25
|
| 161 |
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max_distance_between_context_views: 90
|
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min_distance_between_context_views: 45
|
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min_distance_to_context_views: 0
|
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name: bounded
|
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num_context_views: 2
|
| 166 |
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num_target_set: 3
|
| 167 |
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num_target_views: 4
|
| 168 |
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same_target_gap: false
|
| 169 |
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target_align: true
|
| 170 |
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warm_up_steps: 5000
|
| 171 |
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density_control_loss:
|
| 172 |
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value:
|
| 173 |
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error_score:
|
| 174 |
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grad_scale: 10000
|
| 175 |
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log_scale: false
|
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mode: original
|
| 177 |
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weight: 0.0001
|
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direct_loss:
|
| 179 |
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value:
|
| 180 |
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l1:
|
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weight: 0.8
|
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ssim:
|
| 183 |
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weight: 0.2
|
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mode:
|
| 185 |
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value: train
|
| 186 |
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model:
|
| 187 |
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value:
|
| 188 |
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decoder:
|
| 189 |
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background_color:
|
| 190 |
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- 0
|
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|
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- 0
|
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make_scale_invariant: false
|
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name: splatting_cuda
|
| 195 |
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density_control:
|
| 196 |
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aggregation_mode: mean
|
| 197 |
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aux_refine: false
|
| 198 |
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grad_mode: absgrad
|
| 199 |
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gs_param_dim: 256
|
| 200 |
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latent_dim: 128
|
| 201 |
+
mean_dim: 32
|
| 202 |
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name: density_control_module
|
| 203 |
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num_heads: 1
|
| 204 |
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num_latents: 64
|
| 205 |
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num_level: 3
|
| 206 |
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num_self_attn_per_block: 2
|
| 207 |
+
refine_error: false
|
| 208 |
+
refinement_hidden_dim: 32
|
| 209 |
+
refinement_layer_num: 1
|
| 210 |
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refinement_type: voxelize
|
| 211 |
+
score_mode: absgrad
|
| 212 |
+
use_depth: false
|
| 213 |
+
use_mean_features: true
|
| 214 |
+
use_refine_module: false
|
| 215 |
+
voxel_size: 0.001
|
| 216 |
+
voxelize_activate: false
|
| 217 |
+
encoder:
|
| 218 |
+
align_corners: false
|
| 219 |
+
gs_param_dim: 256
|
| 220 |
+
head_mode: pcd
|
| 221 |
+
input_image_shape:
|
| 222 |
+
- 518
|
| 223 |
+
- 518
|
| 224 |
+
name: dcsplat
|
| 225 |
+
num_level: 3
|
| 226 |
+
use_voxelize: true
|
| 227 |
+
optimizer:
|
| 228 |
+
value:
|
| 229 |
+
accumulate: 1
|
| 230 |
+
backbone_lr_multiplier: 0.1
|
| 231 |
+
backbone_trainable: T+H
|
| 232 |
+
lr: 0.0002
|
| 233 |
+
warm_up_steps: 125
|
| 234 |
+
render_loss:
|
| 235 |
+
value:
|
| 236 |
+
lpips:
|
| 237 |
+
apply_after_step: 0
|
| 238 |
+
weight: 0.05
|
| 239 |
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mse:
|
| 240 |
+
weight: 1
|
| 241 |
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seed:
|
| 242 |
+
value: 111123
|
| 243 |
+
test:
|
| 244 |
+
value:
|
| 245 |
+
align_pose: false
|
| 246 |
+
compute_scores: true
|
| 247 |
+
error_threshold: 0.4
|
| 248 |
+
error_threshold_list:
|
| 249 |
+
- 0.2
|
| 250 |
+
- 0.4
|
| 251 |
+
- 0.6
|
| 252 |
+
- 0.8
|
| 253 |
+
- 1
|
| 254 |
+
nvs_view_N_list:
|
| 255 |
+
- 3
|
| 256 |
+
- 6
|
| 257 |
+
- 16
|
| 258 |
+
- 32
|
| 259 |
+
- 64
|
| 260 |
+
output_path: test/full/re10k
|
| 261 |
+
pose_align_steps: 100
|
| 262 |
+
pred_intrinsic: false
|
| 263 |
+
rot_opt_lr: 0.005
|
| 264 |
+
save_active_mask_image: false
|
| 265 |
+
save_compare: false
|
| 266 |
+
save_error_score_image: false
|
| 267 |
+
save_gs: false
|
| 268 |
+
save_image: false
|
| 269 |
+
save_sample_wise_metrics: true
|
| 270 |
+
save_video: false
|
| 271 |
+
threshold_mode: ratio
|
| 272 |
+
trans_opt_lr: 0.005
|
| 273 |
+
train:
|
| 274 |
+
value:
|
| 275 |
+
align_corners: false
|
| 276 |
+
beta_dist_param:
|
| 277 |
+
- 0.5
|
| 278 |
+
- 4
|
| 279 |
+
cam_scale_mode: sum
|
| 280 |
+
camera_loss: 10
|
| 281 |
+
context_view_train: false
|
| 282 |
+
ext_scale_detach: false
|
| 283 |
+
extended_visualization: false
|
| 284 |
+
intrinsic_scaling: false
|
| 285 |
+
one_sample_validation: null
|
| 286 |
+
print_log_every_n_steps: 10
|
| 287 |
+
scene_scale_reg_loss: 0.01
|
| 288 |
+
train_aux: true
|
| 289 |
+
train_gs_num: 1
|
| 290 |
+
train_target_set: true
|
| 291 |
+
use_refine_aux: false
|
| 292 |
+
verbose: false
|
| 293 |
+
vggt_cam_loss: true
|
| 294 |
+
vggt_distil: false
|
| 295 |
+
trainer:
|
| 296 |
+
value:
|
| 297 |
+
gradient_clip_val: 0.5
|
| 298 |
+
max_steps: 15001
|
| 299 |
+
num_nodes: 1
|
| 300 |
+
val_check_interval: 500
|
| 301 |
+
wandb:
|
| 302 |
+
value:
|
| 303 |
+
entity: scene-representation-group
|
| 304 |
+
mode: online
|
| 305 |
+
name: 0303_RE10k_FULL_24v
|
| 306 |
+
project: DCSplat
|
| 307 |
+
tags:
|
| 308 |
+
- re10k
|
| 309 |
+
- 256x256
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/output.log
ADDED
|
@@ -0,0 +1,110 @@
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
|
| 2 |
+
|
| 3 |
+
| Name | Type | Params | Mode
|
| 4 |
+
------------------------------------------------------------------------
|
| 5 |
+
0 | encoder | OurSplat | 888 M | train
|
| 6 |
+
1 | density_control_module | DensityControlModule | 514 | train
|
| 7 |
+
2 | decoder | DecoderSplattingCUDA | 0 | train
|
| 8 |
+
3 | render_losses | ModuleList | 0 | train
|
| 9 |
+
4 | density_control_losses | ModuleList | 0 | train
|
| 10 |
+
5 | direct_losses | ModuleList | 0 | train
|
| 11 |
+
------------------------------------------------------------------------
|
| 12 |
+
888 M Trainable params
|
| 13 |
+
0 Non-trainable params
|
| 14 |
+
888 M Total params
|
| 15 |
+
3,553.936 Total estimated model params size (MB)
|
| 16 |
+
1207 Modules in train mode
|
| 17 |
+
522 Modules in eval mode
|
| 18 |
+
Sanity Checking: | | 0/? [00:00<?, ?it/s][2026-03-02 17:14:48,130][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
|
| 19 |
+
|
| 20 |
+
Validation epoch start on rank 0
|
| 21 |
+
Sanity Checking DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s]validation step 0; scene = ['306e2b7785657539'];
|
| 22 |
+
target intrinsic: tensor(0.8595, device='cuda:0') tensor(0.8597, device='cuda:0')
|
| 23 |
+
pred intrinsic: tensor(0.8779, device='cuda:0') tensor(0.8773, device='cuda:0')
|
| 24 |
+
[2K[32m( ● )[0m [1;33mgsplat: Setting up CUDA with MAX_JOBS=10 (This may take a few minutes the first time)[0m0m
|
| 25 |
+
W0302 17:14:50.131000 5636 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
|
| 26 |
+
W0302 17:14:50.131000 5636 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
|
| 27 |
+
[1A[2K[32mgsplat: CUDA extension has been set up successfully in [0m[1;32m45.38[0m[32m seconds.[0m
|
| 28 |
+
[2026-03-02 17:15:35,549][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 29 |
+
result[selector] = overlay
|
| 30 |
+
|
| 31 |
+
[2026-03-02 17:15:35,564][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
|
| 32 |
+
|
| 33 |
+
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
|
| 34 |
+
[2026-03-02 17:15:35,566][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 35 |
+
warnings.warn(
|
| 36 |
+
|
| 37 |
+
[2026-03-02 17:15:35,566][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 38 |
+
warnings.warn(msg)
|
| 39 |
+
|
| 40 |
+
Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth
|
| 41 |
+
[2026-03-02 17:15:37,194][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
|
| 42 |
+
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
|
| 43 |
+
|
| 44 |
+
|
| 45 |
+
Error executing job with overrides: ['+experiment=re10k_24v', 'wandb.mode=online', 'wandb.name=0303_RE10k_FULL_24v']
|
| 46 |
+
Traceback (most recent call last):
|
| 47 |
+
File "/workspace/code/CVPR2026/src/main.py", line 226, in train
|
| 48 |
+
trainer.fit(model_wrapper, datamodule=data_module)#, ckpt_path=checkpoint_path)
|
| 49 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 50 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 561, in fit
|
| 51 |
+
call._call_and_handle_interrupt(
|
| 52 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/call.py", line 48, in _call_and_handle_interrupt
|
| 53 |
+
return trainer_fn(*args, **kwargs)
|
| 54 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 55 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 599, in _fit_impl
|
| 56 |
+
self._run(model, ckpt_path=ckpt_path)
|
| 57 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 1012, in _run
|
| 58 |
+
results = self._run_stage()
|
| 59 |
+
^^^^^^^^^^^^^^^^^
|
| 60 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 1056, in _run_stage
|
| 61 |
+
self.fit_loop.run()
|
| 62 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py", line 208, in run
|
| 63 |
+
self.setup_data()
|
| 64 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py", line 275, in setup_data
|
| 65 |
+
iter(self._data_fetcher) # creates the iterator inside the fetcher
|
| 66 |
+
^^^^^^^^^^^^^^^^^^^^^^^^
|
| 67 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fetchers.py", line 112, in __iter__
|
| 68 |
+
batch = super().__next__()
|
| 69 |
+
^^^^^^^^^^^^^^^^^^
|
| 70 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fetchers.py", line 61, in __next__
|
| 71 |
+
batch = next(self.iterator)
|
| 72 |
+
^^^^^^^^^^^^^^^^^^^
|
| 73 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/combined_loader.py", line 341, in __next__
|
| 74 |
+
out = next(self._iterator)
|
| 75 |
+
^^^^^^^^^^^^^^^^^^^^
|
| 76 |
+
File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/combined_loader.py", line 78, in __next__
|
| 77 |
+
out[i] = next(self.iterators[i])
|
| 78 |
+
^^^^^^^^^^^^^^^^^^^^^^^
|
| 79 |
+
File "/workspace/code/CVPR2026/src/dataset/data_module.py", line 77, in __iter__
|
| 80 |
+
for example in self.data_loader:
|
| 81 |
+
^^^^^^^^^^^^^^^^
|
| 82 |
+
File "/venv/main/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 734, in __next__
|
| 83 |
+
data = self._next_data()
|
| 84 |
+
^^^^^^^^^^^^^^^^^
|
| 85 |
+
File "/venv/main/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1492, in _next_data
|
| 86 |
+
idx, data = self._get_data()
|
| 87 |
+
^^^^^^^^^^^^^^^^
|
| 88 |
+
File "/venv/main/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1454, in _get_data
|
| 89 |
+
success, data = self._try_get_data()
|
| 90 |
+
^^^^^^^^^^^^^^^^^^^^
|
| 91 |
+
File "/venv/main/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 1285, in _try_get_data
|
| 92 |
+
data = self._data_queue.get(timeout=timeout)
|
| 93 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 94 |
+
File "/venv/main/lib/python3.12/multiprocessing/queues.py", line 122, in get
|
| 95 |
+
return _ForkingPickler.loads(res)
|
| 96 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 97 |
+
File "/venv/main/lib/python3.12/site-packages/torch/multiprocessing/reductions.py", line 541, in rebuild_storage_fd
|
| 98 |
+
fd = df.detach()
|
| 99 |
+
^^^^^^^^^^^
|
| 100 |
+
File "/venv/main/lib/python3.12/multiprocessing/resource_sharer.py", line 58, in detach
|
| 101 |
+
return reduction.recv_handle(conn)
|
| 102 |
+
^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
| 103 |
+
File "/venv/main/lib/python3.12/multiprocessing/reduction.py", line 189, in recv_handle
|
| 104 |
+
return recvfds(s, 1)[0]
|
| 105 |
+
^^^^^^^^^^^^^
|
| 106 |
+
File "/venv/main/lib/python3.12/multiprocessing/reduction.py", line 164, in recvfds
|
| 107 |
+
raise RuntimeError('received %d items of ancdata' %
|
| 108 |
+
RuntimeError: received 0 items of ancdata
|
| 109 |
+
|
| 110 |
+
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/requirements.txt
ADDED
|
@@ -0,0 +1,159 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
wheel==0.45.1
|
| 2 |
+
triton==3.4.0
|
| 3 |
+
nvidia-nccl-cu12==2.27.3
|
| 4 |
+
pytz==2025.2
|
| 5 |
+
easydict==1.13
|
| 6 |
+
antlr4-python3-runtime==4.9.3
|
| 7 |
+
wadler_lindig==0.1.7
|
| 8 |
+
packaging==24.2
|
| 9 |
+
urllib3==2.5.0
|
| 10 |
+
tzdata==2025.2
|
| 11 |
+
typing-inspection==0.4.1
|
| 12 |
+
tabulate==0.9.0
|
| 13 |
+
smmap==5.0.2
|
| 14 |
+
opt_einsum==3.4.0
|
| 15 |
+
setuptools==78.1.1
|
| 16 |
+
safetensors==0.5.3
|
| 17 |
+
PyYAML==6.0.2
|
| 18 |
+
PySocks==1.7.1
|
| 19 |
+
pyparsing==3.2.5
|
| 20 |
+
pydantic_core==2.33.2
|
| 21 |
+
pycparser==2.23
|
| 22 |
+
protobuf==6.32.1
|
| 23 |
+
propcache==0.3.2
|
| 24 |
+
proglog==0.1.12
|
| 25 |
+
kiwisolver==1.4.9
|
| 26 |
+
platformdirs==4.4.0
|
| 27 |
+
idna==3.7
|
| 28 |
+
pip==25.2
|
| 29 |
+
pillow==10.4.0
|
| 30 |
+
numpy==1.26.4
|
| 31 |
+
torch==2.8.0+cu128
|
| 32 |
+
ninja==1.13.0
|
| 33 |
+
gmpy2==2.2.1
|
| 34 |
+
networkx==3.4.2
|
| 35 |
+
multidict==6.6.4
|
| 36 |
+
mdurl==0.1.2
|
| 37 |
+
MarkupSafe==3.0.2
|
| 38 |
+
kornia_rs==0.1.9
|
| 39 |
+
imageio-ffmpeg==0.6.0
|
| 40 |
+
hf-xet==1.1.10
|
| 41 |
+
kornia==0.8.1
|
| 42 |
+
fsspec==2024.6.1
|
| 43 |
+
frozenlist==1.7.0
|
| 44 |
+
fonttools==4.60.0
|
| 45 |
+
filelock==3.17.0
|
| 46 |
+
einops==0.8.1
|
| 47 |
+
torchmetrics==1.8.2
|
| 48 |
+
decorator==4.4.2
|
| 49 |
+
torchvision==0.23.0+cu128
|
| 50 |
+
dacite==1.9.2
|
| 51 |
+
cycler==0.12.1
|
| 52 |
+
colorama==0.4.6
|
| 53 |
+
click==8.3.0
|
| 54 |
+
charset-normalizer==3.3.2
|
| 55 |
+
certifi==2025.8.3
|
| 56 |
+
beartype==0.19.0
|
| 57 |
+
opt-einsum-fx==0.1.4
|
| 58 |
+
torchaudio==2.8.0+cu128
|
| 59 |
+
attrs==25.3.0
|
| 60 |
+
async-timeout==5.0.1
|
| 61 |
+
annotated-types==0.7.0
|
| 62 |
+
aiohappyeyeballs==2.6.1
|
| 63 |
+
yarl==1.20.1
|
| 64 |
+
tifffile==2025.5.10
|
| 65 |
+
sentry-sdk==2.39.0
|
| 66 |
+
scipy==1.15.3
|
| 67 |
+
pydantic==2.11.9
|
| 68 |
+
pandas==2.3.2
|
| 69 |
+
opencv-python==4.11.0.86
|
| 70 |
+
omegaconf==2.3.0
|
| 71 |
+
markdown-it-py==4.0.0
|
| 72 |
+
lightning-utilities==0.14.3
|
| 73 |
+
lazy_loader==0.4
|
| 74 |
+
jaxtyping==0.2.37
|
| 75 |
+
imageio==2.37.0
|
| 76 |
+
gitdb==4.0.12
|
| 77 |
+
contourpy==1.3.2
|
| 78 |
+
colorspacious==1.1.2
|
| 79 |
+
cffi==1.17.1
|
| 80 |
+
aiosignal==1.4.0
|
| 81 |
+
scikit-video==1.1.11
|
| 82 |
+
scikit-image==0.25.2
|
| 83 |
+
rich==14.1.0
|
| 84 |
+
moviepy==1.0.3
|
| 85 |
+
matplotlib==3.10.6
|
| 86 |
+
hydra-core==1.3.2
|
| 87 |
+
e3nn==0.6.0
|
| 88 |
+
huggingface-hub==0.35.1
|
| 89 |
+
GitPython==3.1.45
|
| 90 |
+
brotlicffi==1.0.9.2
|
| 91 |
+
aiohttp==3.12.15
|
| 92 |
+
pytorch-lightning==2.5.1
|
| 93 |
+
lpips==0.1.4
|
| 94 |
+
lightning==2.5.1
|
| 95 |
+
gsplat==1.5.3
|
| 96 |
+
torch_scatter==2.1.2+pt28cu128
|
| 97 |
+
plyfile==1.1.3
|
| 98 |
+
wandb==0.25.0
|
| 99 |
+
cuda-bindings==12.9.4
|
| 100 |
+
cuda-pathfinder==1.3.3
|
| 101 |
+
Jinja2==3.1.6
|
| 102 |
+
mpmath==1.3.0
|
| 103 |
+
nvidia-cublas-cu12==12.8.4.1
|
| 104 |
+
nvidia-cuda-cupti-cu12==12.8.90
|
| 105 |
+
nvidia-cuda-nvrtc-cu12==12.8.93
|
| 106 |
+
nvidia-cuda-runtime-cu12==12.8.90
|
| 107 |
+
nvidia-cudnn-cu12==9.10.2.21
|
| 108 |
+
nvidia-cufft-cu12==11.3.3.83
|
| 109 |
+
nvidia-cufile-cu12==1.13.1.3
|
| 110 |
+
nvidia-curand-cu12==10.3.9.90
|
| 111 |
+
nvidia-cusolver-cu12==11.7.3.90
|
| 112 |
+
nvidia-cusparse-cu12==12.5.8.93
|
| 113 |
+
nvidia-cusparselt-cu12==0.7.1
|
| 114 |
+
nvidia-nvjitlink-cu12==12.8.93
|
| 115 |
+
nvidia-nvshmem-cu12==3.4.5
|
| 116 |
+
nvidia-nvtx-cu12==12.8.90
|
| 117 |
+
requests==2.32.5
|
| 118 |
+
sentencepiece==0.2.1
|
| 119 |
+
sympy==1.14.0
|
| 120 |
+
torchcodec==0.10.0
|
| 121 |
+
torchdata==0.10.0
|
| 122 |
+
torchtext==0.6.0
|
| 123 |
+
anyio==4.12.0
|
| 124 |
+
asttokens==3.0.1
|
| 125 |
+
comm==0.2.3
|
| 126 |
+
debugpy==1.8.19
|
| 127 |
+
executing==2.2.1
|
| 128 |
+
h11==0.16.0
|
| 129 |
+
httpcore==1.0.9
|
| 130 |
+
httpx==0.28.1
|
| 131 |
+
ipykernel==7.1.0
|
| 132 |
+
ipython==9.8.0
|
| 133 |
+
ipython_pygments_lexers==1.1.1
|
| 134 |
+
ipywidgets==8.1.8
|
| 135 |
+
jedi==0.19.2
|
| 136 |
+
jupyter_client==8.7.0
|
| 137 |
+
jupyter_core==5.9.1
|
| 138 |
+
jupyterlab_widgets==3.0.16
|
| 139 |
+
matplotlib-inline==0.2.1
|
| 140 |
+
nest-asyncio==1.6.0
|
| 141 |
+
parso==0.8.5
|
| 142 |
+
pexpect==4.9.0
|
| 143 |
+
prompt_toolkit==3.0.52
|
| 144 |
+
psutil==7.2.1
|
| 145 |
+
ptyprocess==0.7.0
|
| 146 |
+
pure_eval==0.2.3
|
| 147 |
+
Pygments==2.19.2
|
| 148 |
+
python-dateutil==2.9.0.post0
|
| 149 |
+
pyzmq==27.1.0
|
| 150 |
+
shellingham==1.5.4
|
| 151 |
+
six==1.17.0
|
| 152 |
+
stack-data==0.6.3
|
| 153 |
+
tornado==6.5.4
|
| 154 |
+
tqdm==4.67.1
|
| 155 |
+
traitlets==5.14.3
|
| 156 |
+
typer-slim==0.21.0
|
| 157 |
+
typing_extensions==4.15.0
|
| 158 |
+
wcwidth==0.2.14
|
| 159 |
+
widgetsnbextension==4.0.15
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/wandb-metadata.json
ADDED
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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 |
+
"os": "Linux-5.15.0-157-generic-x86_64-with-glibc2.39",
|
| 3 |
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|
| 4 |
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"startedAt": "2026-03-02T17:14:46.113597Z",
|
| 5 |
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|
| 6 |
+
"+experiment=re10k_24v",
|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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"program": "-m src.main",
|
| 11 |
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|
| 12 |
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"remote": "git@github.com:K-nowing/CVPR2026.git",
|
| 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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| 47 |
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| 48 |
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| 62 |
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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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"architecture": "Hopper",
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| 73 |
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"uuid": "GPU-522b1630-b9aa-5aa3-9985-ced479a7780e"
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| 74 |
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|
| 75 |
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|
| 76 |
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"name": "NVIDIA H200",
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| 77 |
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| 78 |
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|
| 79 |
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"architecture": "Hopper",
|
| 80 |
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"uuid": "GPU-4c86a636-acfc-e976-3b9e-78425c9c44df"
|
| 81 |
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| 82 |
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{
|
| 83 |
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"name": "NVIDIA H200",
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| 84 |
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| 85 |
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"cudaCores": 16896,
|
| 86 |
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"architecture": "Hopper",
|
| 87 |
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"uuid": "GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b"
|
| 88 |
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|
| 89 |
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| 90 |
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"cudaVersion": "12.8",
|
| 91 |
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"writerId": "q93l51s2fcdx31lzgovdol8wrrqh7ma8"
|
| 92 |
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|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/files/wandb-summary.json
ADDED
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re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug-core.log
ADDED
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@@ -0,0 +1,15 @@
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re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug-internal.log
ADDED
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@@ -0,0 +1,11 @@
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|
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{"time":"2026-03-02T17:14:46.365319792Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
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{"time":"2026-03-02T17:14:46.832460294Z","level":"INFO","msg":"sender: started","stream_id":"8bt5ya2f"}
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{"time":"2026-03-02T17:15:42.207021601Z","level":"INFO","msg":"sender: closed","stream_id":"8bt5ya2f"}
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| 11 |
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{"time":"2026-03-02T17:15:42.207058679Z","level":"INFO","msg":"stream: closed","id":"8bt5ya2f"}
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re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug.log
ADDED
|
@@ -0,0 +1,21 @@
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|
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|
|
|
|
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|
|
|
|
|
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|
|
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|
| 1 |
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_setup.py:_flush():81] Current SDK version is 0.25.0
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_setup.py:_flush():81] Configure stats pid to 5636
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_setup.py:_flush():81] Loading settings from environment variables
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_init.py:setup_run_log_directory():717] Logging user logs to /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug.log
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| 5 |
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_init.py:setup_run_log_directory():718] Logging internal logs to /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171446-8bt5ya2f/logs/debug-internal.log
|
| 6 |
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_init.py:init():844] calling init triggers
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| 7 |
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_init.py:init():849] wandb.init called with sweep_config: {}
|
| 8 |
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config: {'model': {'encoder': {'name': 'dcsplat', 'input_image_shape': [518, 518], 'head_mode': 'pcd', 'num_level': 3, 'gs_param_dim': 256, 'align_corners': False, 'use_voxelize': True}, 'decoder': {'name': 'splatting_cuda', 'background_color': [0.0, 0.0, 0.0], 'make_scale_invariant': False}, 'density_control': {'name': 'density_control_module', 'mean_dim': 32, 'gs_param_dim': 256, 'refinement_layer_num': 1, 'num_level': 3, 'grad_mode': 'absgrad', 'use_mean_features': True, 'refinement_type': 'voxelize', 'refinement_hidden_dim': 32, 'aggregation_mode': 'mean', 'num_heads': 1, 'score_mode': 'absgrad', 'latent_dim': 128, 'num_latents': 64, 'num_self_attn_per_block': 2, 'voxel_size': 0.001, 'aux_refine': False, 'refine_error': False, 'use_refine_module': False, 'voxelize_activate': False, 'use_depth': False}}, 'render_loss': {'mse': {'weight': 1.0}, 'lpips': {'weight': 0.05, 'apply_after_step': 0}}, 'density_control_loss': {'error_score': {'weight': 0.0001, 'log_scale': False, 'grad_scale': 10000.0, 'mode': 'original'}}, 'direct_loss': {'l1': {'weight': 0.8}, 'ssim': {'weight': 0.2}}, 'wandb': {'project': 'DCSplat', 'entity': 'scene-representation-group', 'name': '0303_RE10k_FULL_24v', 'mode': 'online', 'tags': ['re10k', '256x256']}, 'mode': 'train', 'data_loader': {'train': {'num_workers': 16, 'persistent_workers': True, 'batch_size': 16, 'seed': 1234}, 'test': {'num_workers': 4, 'persistent_workers': False, 'batch_size': 1, 'seed': 2345}, 'val': {'num_workers': 1, 'persistent_workers': True, 'batch_size': 1, 'seed': 3456}}, 'optimizer': {'lr': 0.0002, 'warm_up_steps': 125, 'backbone_lr_multiplier': 0.1, 'backbone_trainable': 'T+H', 'accumulate': 1}, 'checkpointing': {'load': None, 'every_n_train_steps': 1500, 'save_top_k': 2, 'save_weights_only': False}, 'train': {'extended_visualization': False, 'print_log_every_n_steps': 10, 'camera_loss': 10.0, 'one_sample_validation': None, 'align_corners': False, 'intrinsic_scaling': False, 'verbose': False, 'beta_dist_param': [0.5, 4.0], 'use_refine_aux': False, 'train_target_set': True, 'train_gs_num': 1, 'ext_scale_detach': False, 'cam_scale_mode': 'sum', 'scene_scale_reg_loss': 0.01, 'train_aux': True, 'vggt_cam_loss': True, 'vggt_distil': False, 'context_view_train': False}, 'test': {'output_path': 'test/full/re10k', 'align_pose': False, 'pose_align_steps': 100, 'rot_opt_lr': 0.005, 'trans_opt_lr': 0.005, 'compute_scores': True, 'save_image': False, 'save_video': False, 'save_active_mask_image': False, 'save_error_score_image': False, 'save_compare': False, 'save_gs': False, 'save_sample_wise_metrics': True, 'pred_intrinsic': False, 'error_threshold': 0.4, 'error_threshold_list': [0.2, 0.4, 0.6, 0.8, 1.0], 'threshold_mode': 'ratio', 'nvs_view_N_list': [3, 6, 16, 32, 64]}, 'seed': 111123, 'trainer': {'max_steps': 15001, 'val_check_interval': 500, 'gradient_clip_val': 0.5, 'num_nodes': 1}, 'dataset': {'re10k': {'make_baseline_1': True, 'relative_pose': True, 'augment': True, 'background_color': [0.0, 0.0, 0.0], 'overfit_to_scene': None, 'skip_bad_shape': True, 'view_sampler': {'name': 'bounded', 'num_target_views': 4, 'num_context_views': 2, 'min_distance_between_context_views': 45, 'max_distance_between_context_views': 90, 'min_distance_to_context_views': 0, 'warm_up_steps': 5000, 'initial_min_distance_between_context_views': 25, 'initial_max_distance_between_context_views': 25, 'same_target_gap': False, 'num_target_set': 3, 'target_align': True}, 'name': 're10k', 'roots': ['datasets/re10k'], 'input_image_shape': [256, 256], 'original_image_shape': [360, 640], 'cameras_are_circular': False, 'baseline_min': 0.001, 'baseline_max': 10000000000.0, 'max_fov': 100.0, 'dynamic_context_views': True, 'max_context_views_per_gpu': 24}}, '_wandb': {}}
|
| 9 |
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2026-03-02 17:14:46,115 INFO MainThread:5636 [wandb_init.py:init():892] starting backend
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| 10 |
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2026-03-02 17:14:46,356 INFO MainThread:5636 [wandb_init.py:init():895] sending inform_init request
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| 11 |
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2026-03-02 17:14:46,361 INFO MainThread:5636 [wandb_init.py:init():903] backend started and connected
|
| 12 |
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2026-03-02 17:14:46,363 INFO MainThread:5636 [wandb_init.py:init():973] updated telemetry
|
| 13 |
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2026-03-02 17:14:46,367 INFO MainThread:5636 [wandb_init.py:init():997] communicating run to backend with 90.0 second timeout
|
| 14 |
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2026-03-02 17:14:47,184 INFO MainThread:5636 [wandb_init.py:init():1042] starting run threads in backend
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| 15 |
+
2026-03-02 17:14:47,257 INFO MainThread:5636 [wandb_run.py:_console_start():2524] atexit reg
|
| 16 |
+
2026-03-02 17:14:47,257 INFO MainThread:5636 [wandb_run.py:_redirect():2373] redirect: wrap_raw
|
| 17 |
+
2026-03-02 17:14:47,257 INFO MainThread:5636 [wandb_run.py:_redirect():2442] Wrapping output streams.
|
| 18 |
+
2026-03-02 17:14:47,257 INFO MainThread:5636 [wandb_run.py:_redirect():2465] Redirects installed.
|
| 19 |
+
2026-03-02 17:14:47,262 INFO MainThread:5636 [wandb_init.py:init():1082] run started, returning control to user process
|
| 20 |
+
2026-03-02 17:15:41,527 INFO wandb-AsyncioManager-main:5636 [service_client.py:_forward_responses():134] Reached EOF.
|
| 21 |
+
2026-03-02 17:15:41,528 INFO wandb-AsyncioManager-main:5636 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/files/output.log
ADDED
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| 1 |
+
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
|
| 2 |
+
|
| 3 |
+
| Name | Type | Params | Mode
|
| 4 |
+
------------------------------------------------------------------------
|
| 5 |
+
0 | encoder | OurSplat | 888 M | train
|
| 6 |
+
1 | density_control_module | DensityControlModule | 514 | train
|
| 7 |
+
2 | decoder | DecoderSplattingCUDA | 0 | train
|
| 8 |
+
3 | render_losses | ModuleList | 0 | train
|
| 9 |
+
4 | density_control_losses | ModuleList | 0 | train
|
| 10 |
+
5 | direct_losses | ModuleList | 0 | train
|
| 11 |
+
------------------------------------------------------------------------
|
| 12 |
+
888 M Trainable params
|
| 13 |
+
0 Non-trainable params
|
| 14 |
+
888 M Total params
|
| 15 |
+
3,553.936 Total estimated model params size (MB)
|
| 16 |
+
1207 Modules in train mode
|
| 17 |
+
522 Modules in eval mode
|
| 18 |
+
Sanity Checking: | | 0/? [00:00<?, ?it/s][2026-03-02 17:16:20,451][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:425: 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=223` in the `DataLoader` to improve performance.
|
| 19 |
+
|
| 20 |
+
Validation epoch start on rank 0
|
| 21 |
+
Sanity Checking DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s]validation step 0; scene = ['306e2b7785657539'];
|
| 22 |
+
target intrinsic: tensor(0.8595, device='cuda:0') tensor(0.8597, device='cuda:0')
|
| 23 |
+
pred intrinsic: tensor(0.8779, device='cuda:0') tensor(0.8773, device='cuda:0')
|
| 24 |
+
W0302 17:16:22.352000 6897 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
|
| 25 |
+
W0302 17:16:22.352000 6897 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
|
| 26 |
+
[2026-03-02 17:16:22,411][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 27 |
+
result[selector] = overlay
|
| 28 |
+
|
| 29 |
+
[2026-03-02 17:16:22,423][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/lightning/pytorch/utilities/data.py:79: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
|
| 30 |
+
|
| 31 |
+
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
|
| 32 |
+
[2026-03-02 17:16:22,424][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
|
| 33 |
+
warnings.warn(
|
| 34 |
+
|
| 35 |
+
[2026-03-02 17:16:22,424][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
|
| 36 |
+
warnings.warn(msg)
|
| 37 |
+
|
| 38 |
+
Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth
|
| 39 |
+
[2026-03-02 17:16:23,821][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/functional.py:554: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /pytorch/aten/src/ATen/native/TensorShape.cpp:4322.)
|
| 40 |
+
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
|
| 41 |
+
|
| 42 |
+
Epoch 0: | | 0/? [00:00<?, ?it/s]context = [[20, 25, 26, 27, 37, 38, 39, 45, 46, 55, 62, 69], [33, 42, 44, 47, 52, 61, 64, 68, 69, 75, 81, 82]]target = [[56, 57, 39, 65, 35, 29, 64, 66, 62, 33, 53, 47], [57, 43, 74, 46, 38, 77, 64, 42, 53, 59, 60, 70]]
|
| 43 |
+
[2026-03-02 17:16:29,377][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 44 |
+
result[selector] = overlay
|
| 45 |
+
|
| 46 |
+
Epoch 0: | | 9/? [00:30<00:00, 0.30it/s, v_num=l9a3]train step 10; scene = [['bfecac3e3bfe37c3'], ['829dca1fdf41c748'], ['888bf64fd7dab7fb'], ['0e312299aada689c'], ['3a53f0f0377f069c'], ['ea2ebd9ebc8d2357']]; loss = 0.865132
|
| 47 |
+
Epoch 0: | | 10/? [00:32<00:00, 0.31it/s, v_num=l9a3]context = [[2, 13, 19, 21, 26, 27], [89, 92, 104, 108, 110, 114], [53, 60, 66, 70, 77, 78], [0, 7, 8, 14, 19, 25]]target = [[15, 17, 23, 3, 25, 13], [97, 103, 108, 106, 96, 93], [55, 73, 64, 68, 60, 71], [20, 24, 9, 21, 23, 12]]
|
| 48 |
+
Epoch 0: | | 19/? [00:54<00:00, 0.35it/s, v_num=l9a3]train step 20; scene = [['97887fa6d823f299'], ['3b6586c3b5c64f35'], ['a886961a446251d8'], ['ae6a3653aeaf508c']]; loss = 0.474846
|
| 49 |
+
Epoch 0: | | 20/? [00:57<00:00, 0.35it/s, v_num=l9a3]context = [[23, 24, 26, 41, 45, 51, 56, 57, 63, 69, 71, 72], [43, 48, 50, 61, 62, 64, 70, 73, 76, 77, 78, 92]]target = [[43, 61, 35, 57, 54, 66, 27, 51, 32, 24, 26, 38], [47, 77, 63, 65, 72, 62, 85, 44, 53, 80, 71, 51]]
|
| 50 |
+
Epoch 0: | | 29/? [01:21<00:00, 0.36it/s, v_num=l9a3]train step 30; scene = [['bb31564840f2db2f'], ['cd2cbe06b65c6337'], ['858a56679ac008f3']]; loss = 0.294881
|
| 51 |
+
Epoch 0: | | 30/? [01:23<00:00, 0.36it/s, v_num=l9a3]context = [[42, 44, 46, 48, 64, 65, 66, 67, 79, 82, 83, 85, 87, 89, 94, 97, 100, 102, 105, 107, 111, 119, 135, 139]]target = [[123, 80, 112, 120, 43, 84, 59, 95, 133, 87, 127, 114, 137, 104, 88, 85, 53, 44, 108, 79, 96, 116, 106, 83]]
|
| 52 |
+
Epoch 0: | | 39/? [01:51<00:00, 0.35it/s, v_num=l9a3]train step 40; scene = [['2efc3baf2ea0c6ed']]; loss = 0.226372
|
| 53 |
+
Epoch 0: | | 40/? [01:55<00:00, 0.35it/s, v_num=l9a3]context = [[54, 55, 62, 67, 76, 79], [10, 17, 18, 28, 33, 35], [12, 15, 20, 21, 26, 37], [32, 36, 40, 43, 44, 57]]target = [[64, 63, 66, 59, 62, 70], [30, 21, 32, 13, 31, 34], [28, 25, 20, 33, 16, 19], [51, 33, 50, 53, 46, 44]]
|
| 54 |
+
Epoch 0: | | 49/? [02:18<00:00, 0.35it/s, v_num=l9a3]train step 50; scene = [['841c18be069dee4a']]; loss = 0.233312
|
| 55 |
+
Epoch 0: | | 50/? [02:21<00:00, 0.35it/s, v_num=l9a3]context = [[23, 24, 28, 36, 40, 44, 45, 46, 51, 63, 66, 72], [3, 4, 5, 9, 12, 21, 22, 26, 36, 47, 48, 52]]target = [[52, 61, 59, 65, 26, 30, 42, 70, 63, 64, 43, 38], [8, 18, 6, 32, 7, 28, 46, 37, 5, 33, 39, 22]]
|
| 56 |
+
Epoch 0: | | 59/? [02:47<00:00, 0.35it/s, v_num=l9a3]train step 60; scene = [['f5a5531ff428e472']]; loss = 0.212860
|
| 57 |
+
Epoch 0: | | 60/? [02:51<00:00, 0.35it/s, v_num=l9a3]context = [[9, 18, 34], [2, 24, 27], [36, 53, 61], [4, 12, 29], [235, 259, 260], [96, 100, 121], [35, 36, 60], [18, 27, 43]]target = [[20, 29, 22], [13, 17, 24], [38, 41, 47], [18, 25, 5], [251, 249, 242], [112, 105, 107], [40, 56, 42], [35, 29, 39]]
|
| 58 |
+
Epoch 0: | | 69/? [03:17<00:00, 0.35it/s, v_num=l9a3]train step 70; scene = [['dd1ebbbc32276ec6']]; loss = 0.141040
|
| 59 |
+
Epoch 0: | | 70/? [03:21<00:00, 0.35it/s, v_num=l9a3]context = [[34, 39, 46, 49, 53, 57, 63, 65, 76, 81, 85, 89, 99, 101, 108, 112, 117, 120, 122, 125, 126, 128, 129, 131]]target = [[44, 45, 35, 61, 60, 51, 73, 66, 54, 58, 96, 86, 120, 55, 48, 104, 111, 94, 64, 87, 69, 80, 67, 92]]
|
| 60 |
+
Epoch 0: | | 79/? [03:47<00:00, 0.35it/s, v_num=l9a3]train step 80; scene = [['ca6c88cc48dbe574'], ['532eb0e37a511234']]; loss = 0.167862
|
| 61 |
+
Epoch 0: | | 80/? [03:50<00:00, 0.35it/s, v_num=l9a3]context = [[213, 216, 217, 220, 228, 229, 245, 246], [60, 69, 70, 74, 77, 88, 92, 93], [25, 26, 30, 34, 35, 46, 55, 58]]target = [[220, 236, 237, 215, 230, 229, 244, 218], [84, 82, 74, 72, 61, 67, 78, 77], [51, 43, 26, 57, 52, 39, 31, 44]]
|
| 62 |
+
Epoch 0: | | 89/? [04:15<00:00, 0.35it/s, v_num=l9a3]train step 90; scene = [['ae4d5e2473edfc8b'], ['6aa7eac0ee8724c7'], ['dcf092738d79d794'], ['d1a73a4f161ca352']]; loss = 0.147083
|
| 63 |
+
Epoch 0: | | 90/? [04:17<00:00, 0.35it/s, v_num=l9a3]context = [[21, 27, 30, 39, 41, 49, 58, 61, 63, 65, 68, 70, 76, 79, 97, 99, 101, 106, 107, 109, 110, 114, 115, 118]]target = [[72, 32, 53, 94, 59, 45, 22, 36, 64, 84, 107, 71, 85, 52, 78, 70, 79, 113, 73, 80, 67, 91, 96, 62]]
|
| 64 |
+
Epoch 0: | | 99/? [04:44<00:00, 0.35it/s, v_num=l9a3]train step 100; scene = [['7f9be765fc88ada1'], ['455616306ec6b0ed'], ['c40aa75b5f7bc9e3']]; loss = 0.167038
|
| 65 |
+
Epoch 0: | | 100/? [04:46<00:00, 0.35it/s, v_num=l9a3]context = [[3, 13, 14, 26, 30, 37, 38, 41, 43, 44, 45, 51, 52, 54, 59, 63, 64, 65, 69, 89, 94, 96, 97, 100]]target = [[66, 56, 50, 88, 46, 75, 58, 11, 40, 25, 19, 97, 43, 9, 16, 24, 48, 87, 80, 14, 74, 18, 26, 61]]
|
| 66 |
+
Epoch 0: | | 109/? [05:11<00:00, 0.35it/s, v_num=l9a3]train step 110; scene = [['f483eb112204d039']]; loss = 0.141983
|
| 67 |
+
Epoch 0: | | 110/? [05:15<00:00, 0.35it/s, v_num=l9a3]context = [[2, 6, 7, 9, 14, 28], [5, 11, 19, 27, 28, 30], [17, 29, 38, 40, 42, 43], [40, 50, 53, 60, 62, 65]]target = [[7, 13, 22, 24, 12, 19], [21, 29, 22, 25, 23, 26], [33, 40, 32, 38, 19, 42], [62, 46, 59, 61, 50, 45]]
|
| 68 |
+
Epoch 0: | | 119/? [05:42<00:00, 0.35it/s, v_num=l9a3]train step 120; scene = [['d93ceeba573fb491'], ['62be563d0f6e9a40'], ['d83de2b77e54dee5'], ['8b7dcc47a8db78b8'], ['188398a54205f797'], ['e70318df450ef2d3'], ['ec3ac5aa298bf2e8'], ['7dce7662605b5320'], ['4f5ef62b501615d3'], ['4a1810079642dcba'], ['36feb99313a2de30'], ['67d2598341a0806d']]; loss = 0.167902
|
| 69 |
+
Epoch 0: | | 120/? [05:44<00:00, 0.35it/s, v_num=l9a3]context = [[37, 46, 52, 54, 57, 61, 71, 79, 80, 81, 82, 85, 86, 95, 99, 101, 107, 117, 118, 120, 126, 127, 130, 134]]target = [[83, 101, 124, 85, 131, 93, 102, 56, 103, 111, 72, 119, 50, 46, 68, 76, 88, 59, 38, 55, 117, 80, 113, 112]]
|
| 70 |
+
Epoch 0: | | 124/? [05:57<00:00, 0.35it/s, v_num=l9a3][2026-03-02 17:22:26,346][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:209: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
|
| 71 |
+
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
|
| 72 |
+
|
| 73 |
+
Epoch 0: | | 129/? [06:11<00:00, 0.35it/s, v_num=l9a3]train step 130; scene = [['58a5dc4980d05a6c'], ['2fb39167f2048739']]; loss = 0.122225
|
| 74 |
+
Epoch 0: | | 130/? [06:13<00:00, 0.35it/s, v_num=l9a3]context = [[196, 197, 204, 207, 213, 221, 222, 229], [35, 39, 44, 46, 51, 57, 64, 68], [155, 161, 163, 165, 168, 173, 182, 188]]target = [[220, 201, 207, 206, 217, 199, 228, 208], [49, 38, 42, 44, 62, 56, 48, 43], [187, 177, 172, 159, 174, 163, 160, 171]]
|
| 75 |
+
Epoch 0: | | 139/? [06:38<00:00, 0.35it/s, v_num=l9a3]train step 140; scene = [['24728560754da352'], ['84e5fce33300942f'], ['8d5e57e2ba2c0ca5'], ['fcef54ea2bb49753'], ['26e3fd38d25851df'], ['9c06937735b5e345']]; loss = 0.154697
|
| 76 |
+
Epoch 0: | | 140/? [06:40<00:00, 0.35it/s, v_num=l9a3]context = [[6, 10, 11, 15, 17, 19, 38, 39, 40, 42, 43, 55], [9, 12, 15, 17, 25, 29, 34, 40, 41, 47, 49, 58]]target = [[48, 10, 53, 9, 19, 46, 44, 51, 37, 52, 45, 20], [41, 12, 50, 27, 21, 37, 47, 38, 48, 19, 11, 43]]
|
| 77 |
+
Epoch 0: | | 149/? [07:02<00:00, 0.35it/s, v_num=l9a3]train step 150; scene = [['386013ba48e47be6'], ['aeb84a5999cba0c0'], ['7f08598eb9ef1c0c']]; loss = 0.146867
|
| 78 |
+
Epoch 0: | | 150/? [07:05<00:00, 0.35it/s, v_num=l9a3]context = [[96, 97, 102, 106, 108, 109, 114, 116, 131, 136, 140, 144, 150, 151, 153, 154, 166, 168, 173, 176, 186, 187, 192, 193]]target = [[139, 149, 185, 183, 108, 128, 155, 97, 135, 130, 133, 187, 138, 161, 160, 123, 109, 176, 166, 181, 148, 98, 182, 113]]
|
| 79 |
+
Epoch 0: | | 159/? [07:29<00:00, 0.35it/s, v_num=l9a3]train step 160; scene = [['556296590f552c92']]; loss = 0.147805
|
| 80 |
+
Epoch 0: | | 160/? [07:33<00:00, 0.35it/s, v_num=l9a3]context = [[83, 90, 92, 108], [91, 94, 112, 117], [142, 146, 162, 168], [172, 185, 190, 198], [185, 191, 198, 210], [83, 93, 99, 109]]target = [[95, 84, 101, 98], [97, 109, 113, 106], [159, 152, 148, 151], [176, 186, 190, 175], [202, 203, 199, 192], [108, 94, 87, 104]]
|
| 81 |
+
Epoch 0: | | 169/? [07:58<00:00, 0.35it/s, v_num=l9a3]train step 170; scene = [['bd988995a964b6f7'], ['3a7ec402de14a010'], ['a77535785a79e60a'], ['fee1c997daddaf5e']]; loss = 0.121860
|
| 82 |
+
Epoch 0: | | 170/? [08:00<00:00, 0.35it/s, v_num=l9a3]context = [[6, 8, 12, 24, 26, 28, 30, 39], [42, 43, 56, 62, 66, 67, 73, 75], [82, 83, 91, 96, 98, 99, 113, 115]]target = [[14, 7, 27, 16, 36, 15, 26, 30], [61, 55, 56, 68, 54, 70, 49, 48], [101, 98, 99, 102, 94, 104, 106, 92]]
|
| 83 |
+
Epoch 0: | | 179/? [08:22<00:00, 0.36it/s, v_num=l9a3]train step 180; scene = [['ba232d0f3f503f0f'], ['18ba931a508cfce6'], ['48eb62d05fc8104c'], ['1d49bc9391005354']]; loss = 0.117397
|
| 84 |
+
Epoch 0: | | 180/? [08:24<00:00, 0.36it/s, v_num=l9a3]context = [[40, 41, 42, 46, 47, 49, 59, 62, 67, 68, 86, 89], [26, 30, 43, 45, 48, 49, 60, 63, 68, 69, 74, 75]]target = [[41, 51, 57, 55, 64, 79, 77, 59, 47, 86, 43, 54], [62, 48, 27, 65, 31, 47, 74, 72, 53, 29, 67, 49]]
|
| 85 |
+
Epoch 0: | | 189/? [08:49<00:00, 0.36it/s, v_num=l9a3]train step 190; scene = [['eaa602e659251e06'], ['430d79082d999336']]; loss = 0.158097
|
| 86 |
+
Epoch 0: | | 190/? [08:52<00:00, 0.36it/s, v_num=l9a3]context = [[8, 12, 22, 26, 30, 33], [20, 30, 33, 40, 42, 47], [2, 5, 10, 14, 27, 29], [25, 30, 34, 40, 47, 51]]target = [[10, 19, 30, 24, 13, 18], [33, 31, 45, 23, 30, 35], [24, 11, 20, 16, 8, 21], [46, 38, 35, 37, 29, 31]]
|
| 87 |
+
Epoch 0: | | 199/? [09:12<00:00, 0.36it/s, v_num=l9a3]train step 200; scene = [['1b7697fd8dfb8717'], ['8f242b96af5a7005']]; loss = 0.093497
|
| 88 |
+
Epoch 0: | | 200/? [09:15<00:00, 0.36it/s, v_num=l9a3]context = [[16, 26, 34, 43], [130, 137, 147, 155], [76, 82, 98, 102], [25, 35, 37, 50], [46, 49, 70, 72], [4, 5, 19, 29]]target = [[19, 35, 22, 23], [133, 145, 143, 153], [84, 87, 99, 89], [32, 46, 49, 26], [71, 68, 47, 69], [20, 21, 15, 12]]
|
| 89 |
+
[2026-03-02 17:25:43,065][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
|
| 90 |
+
result[selector] = overlay
|
| 91 |
+
|
| 92 |
+
Epoch 0: | | 202/? [09:21<00:00, 0.36it/s, v_num=l9a3]
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/files/requirements.txt
ADDED
|
@@ -0,0 +1,159 @@
|
|
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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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|
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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 |
+
wheel==0.45.1
|
| 2 |
+
triton==3.4.0
|
| 3 |
+
nvidia-nccl-cu12==2.27.3
|
| 4 |
+
pytz==2025.2
|
| 5 |
+
easydict==1.13
|
| 6 |
+
antlr4-python3-runtime==4.9.3
|
| 7 |
+
wadler_lindig==0.1.7
|
| 8 |
+
packaging==24.2
|
| 9 |
+
urllib3==2.5.0
|
| 10 |
+
tzdata==2025.2
|
| 11 |
+
typing-inspection==0.4.1
|
| 12 |
+
tabulate==0.9.0
|
| 13 |
+
smmap==5.0.2
|
| 14 |
+
opt_einsum==3.4.0
|
| 15 |
+
setuptools==78.1.1
|
| 16 |
+
safetensors==0.5.3
|
| 17 |
+
PyYAML==6.0.2
|
| 18 |
+
PySocks==1.7.1
|
| 19 |
+
pyparsing==3.2.5
|
| 20 |
+
pydantic_core==2.33.2
|
| 21 |
+
pycparser==2.23
|
| 22 |
+
protobuf==6.32.1
|
| 23 |
+
propcache==0.3.2
|
| 24 |
+
proglog==0.1.12
|
| 25 |
+
kiwisolver==1.4.9
|
| 26 |
+
platformdirs==4.4.0
|
| 27 |
+
idna==3.7
|
| 28 |
+
pip==25.2
|
| 29 |
+
pillow==10.4.0
|
| 30 |
+
numpy==1.26.4
|
| 31 |
+
torch==2.8.0+cu128
|
| 32 |
+
ninja==1.13.0
|
| 33 |
+
gmpy2==2.2.1
|
| 34 |
+
networkx==3.4.2
|
| 35 |
+
multidict==6.6.4
|
| 36 |
+
mdurl==0.1.2
|
| 37 |
+
MarkupSafe==3.0.2
|
| 38 |
+
kornia_rs==0.1.9
|
| 39 |
+
imageio-ffmpeg==0.6.0
|
| 40 |
+
hf-xet==1.1.10
|
| 41 |
+
kornia==0.8.1
|
| 42 |
+
fsspec==2024.6.1
|
| 43 |
+
frozenlist==1.7.0
|
| 44 |
+
fonttools==4.60.0
|
| 45 |
+
filelock==3.17.0
|
| 46 |
+
einops==0.8.1
|
| 47 |
+
torchmetrics==1.8.2
|
| 48 |
+
decorator==4.4.2
|
| 49 |
+
torchvision==0.23.0+cu128
|
| 50 |
+
dacite==1.9.2
|
| 51 |
+
cycler==0.12.1
|
| 52 |
+
colorama==0.4.6
|
| 53 |
+
click==8.3.0
|
| 54 |
+
charset-normalizer==3.3.2
|
| 55 |
+
certifi==2025.8.3
|
| 56 |
+
beartype==0.19.0
|
| 57 |
+
opt-einsum-fx==0.1.4
|
| 58 |
+
torchaudio==2.8.0+cu128
|
| 59 |
+
attrs==25.3.0
|
| 60 |
+
async-timeout==5.0.1
|
| 61 |
+
annotated-types==0.7.0
|
| 62 |
+
aiohappyeyeballs==2.6.1
|
| 63 |
+
yarl==1.20.1
|
| 64 |
+
tifffile==2025.5.10
|
| 65 |
+
sentry-sdk==2.39.0
|
| 66 |
+
scipy==1.15.3
|
| 67 |
+
pydantic==2.11.9
|
| 68 |
+
pandas==2.3.2
|
| 69 |
+
opencv-python==4.11.0.86
|
| 70 |
+
omegaconf==2.3.0
|
| 71 |
+
markdown-it-py==4.0.0
|
| 72 |
+
lightning-utilities==0.14.3
|
| 73 |
+
lazy_loader==0.4
|
| 74 |
+
jaxtyping==0.2.37
|
| 75 |
+
imageio==2.37.0
|
| 76 |
+
gitdb==4.0.12
|
| 77 |
+
contourpy==1.3.2
|
| 78 |
+
colorspacious==1.1.2
|
| 79 |
+
cffi==1.17.1
|
| 80 |
+
aiosignal==1.4.0
|
| 81 |
+
scikit-video==1.1.11
|
| 82 |
+
scikit-image==0.25.2
|
| 83 |
+
rich==14.1.0
|
| 84 |
+
moviepy==1.0.3
|
| 85 |
+
matplotlib==3.10.6
|
| 86 |
+
hydra-core==1.3.2
|
| 87 |
+
e3nn==0.6.0
|
| 88 |
+
huggingface-hub==0.35.1
|
| 89 |
+
GitPython==3.1.45
|
| 90 |
+
brotlicffi==1.0.9.2
|
| 91 |
+
aiohttp==3.12.15
|
| 92 |
+
pytorch-lightning==2.5.1
|
| 93 |
+
lpips==0.1.4
|
| 94 |
+
lightning==2.5.1
|
| 95 |
+
gsplat==1.5.3
|
| 96 |
+
torch_scatter==2.1.2+pt28cu128
|
| 97 |
+
plyfile==1.1.3
|
| 98 |
+
wandb==0.25.0
|
| 99 |
+
cuda-bindings==12.9.4
|
| 100 |
+
cuda-pathfinder==1.3.3
|
| 101 |
+
Jinja2==3.1.6
|
| 102 |
+
mpmath==1.3.0
|
| 103 |
+
nvidia-cublas-cu12==12.8.4.1
|
| 104 |
+
nvidia-cuda-cupti-cu12==12.8.90
|
| 105 |
+
nvidia-cuda-nvrtc-cu12==12.8.93
|
| 106 |
+
nvidia-cuda-runtime-cu12==12.8.90
|
| 107 |
+
nvidia-cudnn-cu12==9.10.2.21
|
| 108 |
+
nvidia-cufft-cu12==11.3.3.83
|
| 109 |
+
nvidia-cufile-cu12==1.13.1.3
|
| 110 |
+
nvidia-curand-cu12==10.3.9.90
|
| 111 |
+
nvidia-cusolver-cu12==11.7.3.90
|
| 112 |
+
nvidia-cusparse-cu12==12.5.8.93
|
| 113 |
+
nvidia-cusparselt-cu12==0.7.1
|
| 114 |
+
nvidia-nvjitlink-cu12==12.8.93
|
| 115 |
+
nvidia-nvshmem-cu12==3.4.5
|
| 116 |
+
nvidia-nvtx-cu12==12.8.90
|
| 117 |
+
requests==2.32.5
|
| 118 |
+
sentencepiece==0.2.1
|
| 119 |
+
sympy==1.14.0
|
| 120 |
+
torchcodec==0.10.0
|
| 121 |
+
torchdata==0.10.0
|
| 122 |
+
torchtext==0.6.0
|
| 123 |
+
anyio==4.12.0
|
| 124 |
+
asttokens==3.0.1
|
| 125 |
+
comm==0.2.3
|
| 126 |
+
debugpy==1.8.19
|
| 127 |
+
executing==2.2.1
|
| 128 |
+
h11==0.16.0
|
| 129 |
+
httpcore==1.0.9
|
| 130 |
+
httpx==0.28.1
|
| 131 |
+
ipykernel==7.1.0
|
| 132 |
+
ipython==9.8.0
|
| 133 |
+
ipython_pygments_lexers==1.1.1
|
| 134 |
+
ipywidgets==8.1.8
|
| 135 |
+
jedi==0.19.2
|
| 136 |
+
jupyter_client==8.7.0
|
| 137 |
+
jupyter_core==5.9.1
|
| 138 |
+
jupyterlab_widgets==3.0.16
|
| 139 |
+
matplotlib-inline==0.2.1
|
| 140 |
+
nest-asyncio==1.6.0
|
| 141 |
+
parso==0.8.5
|
| 142 |
+
pexpect==4.9.0
|
| 143 |
+
prompt_toolkit==3.0.52
|
| 144 |
+
psutil==7.2.1
|
| 145 |
+
ptyprocess==0.7.0
|
| 146 |
+
pure_eval==0.2.3
|
| 147 |
+
Pygments==2.19.2
|
| 148 |
+
python-dateutil==2.9.0.post0
|
| 149 |
+
pyzmq==27.1.0
|
| 150 |
+
shellingham==1.5.4
|
| 151 |
+
six==1.17.0
|
| 152 |
+
stack-data==0.6.3
|
| 153 |
+
tornado==6.5.4
|
| 154 |
+
tqdm==4.67.1
|
| 155 |
+
traitlets==5.14.3
|
| 156 |
+
typer-slim==0.21.0
|
| 157 |
+
typing_extensions==4.15.0
|
| 158 |
+
wcwidth==0.2.14
|
| 159 |
+
widgetsnbextension==4.0.15
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/files/wandb-metadata.json
ADDED
|
@@ -0,0 +1,92 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"os": "Linux-5.15.0-157-generic-x86_64-with-glibc2.39",
|
| 3 |
+
"python": "CPython 3.12.12",
|
| 4 |
+
"startedAt": "2026-03-02T17:16:18.217537Z",
|
| 5 |
+
"args": [
|
| 6 |
+
"+experiment=re10k_24v",
|
| 7 |
+
"wandb.mode=online",
|
| 8 |
+
"wandb.name=0303_RE10k_FULL_24v"
|
| 9 |
+
],
|
| 10 |
+
"program": "-m src.main",
|
| 11 |
+
"git": {
|
| 12 |
+
"remote": "git@github.com:K-nowing/CVPR2026.git",
|
| 13 |
+
"commit": "9dfce172a0f8c7ce85e763899f7ef741ecffc454"
|
| 14 |
+
},
|
| 15 |
+
"email": "dna9041@korea.ac.kr",
|
| 16 |
+
"root": "/workspace/code/CVPR2026/outputs/full/re10k/0303_RE10k_FULL_24v",
|
| 17 |
+
"host": "0258ae8f3852",
|
| 18 |
+
"executable": "/venv/main/bin/python",
|
| 19 |
+
"cpu_count": 112,
|
| 20 |
+
"cpu_count_logical": 224,
|
| 21 |
+
"gpu": "NVIDIA H200",
|
| 22 |
+
"gpu_count": 8,
|
| 23 |
+
"disk": {
|
| 24 |
+
"/": {
|
| 25 |
+
"total": "1170378588160",
|
| 26 |
+
"used": "190019395584"
|
| 27 |
+
}
|
| 28 |
+
},
|
| 29 |
+
"memory": {
|
| 30 |
+
"total": "2164193775616"
|
| 31 |
+
},
|
| 32 |
+
"gpu_nvidia": [
|
| 33 |
+
{
|
| 34 |
+
"name": "NVIDIA H200",
|
| 35 |
+
"memoryTotal": "150754820096",
|
| 36 |
+
"cudaCores": 16896,
|
| 37 |
+
"architecture": "Hopper",
|
| 38 |
+
"uuid": "GPU-79687643-93f8-7b36-349a-8f05b89e6678"
|
| 39 |
+
},
|
| 40 |
+
{
|
| 41 |
+
"name": "NVIDIA H200",
|
| 42 |
+
"memoryTotal": "150754820096",
|
| 43 |
+
"cudaCores": 16896,
|
| 44 |
+
"architecture": "Hopper",
|
| 45 |
+
"uuid": "GPU-317bba70-b882-ca12-2b8b-173e2db3be03"
|
| 46 |
+
},
|
| 47 |
+
{
|
| 48 |
+
"name": "NVIDIA H200",
|
| 49 |
+
"memoryTotal": "150754820096",
|
| 50 |
+
"cudaCores": 16896,
|
| 51 |
+
"architecture": "Hopper",
|
| 52 |
+
"uuid": "GPU-cc84663f-d6cd-d900-0d4c-118462dced2e"
|
| 53 |
+
},
|
| 54 |
+
{
|
| 55 |
+
"name": "NVIDIA H200",
|
| 56 |
+
"memoryTotal": "150754820096",
|
| 57 |
+
"cudaCores": 16896,
|
| 58 |
+
"architecture": "Hopper",
|
| 59 |
+
"uuid": "GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210"
|
| 60 |
+
},
|
| 61 |
+
{
|
| 62 |
+
"name": "NVIDIA H200",
|
| 63 |
+
"memoryTotal": "150754820096",
|
| 64 |
+
"cudaCores": 16896,
|
| 65 |
+
"architecture": "Hopper",
|
| 66 |
+
"uuid": "GPU-331b6fb4-1872-8ae5-e5de-e34efc869d56"
|
| 67 |
+
},
|
| 68 |
+
{
|
| 69 |
+
"name": "NVIDIA H200",
|
| 70 |
+
"memoryTotal": "150754820096",
|
| 71 |
+
"cudaCores": 16896,
|
| 72 |
+
"architecture": "Hopper",
|
| 73 |
+
"uuid": "GPU-522b1630-b9aa-5aa3-9985-ced479a7780e"
|
| 74 |
+
},
|
| 75 |
+
{
|
| 76 |
+
"name": "NVIDIA H200",
|
| 77 |
+
"memoryTotal": "150754820096",
|
| 78 |
+
"cudaCores": 16896,
|
| 79 |
+
"architecture": "Hopper",
|
| 80 |
+
"uuid": "GPU-4c86a636-acfc-e976-3b9e-78425c9c44df"
|
| 81 |
+
},
|
| 82 |
+
{
|
| 83 |
+
"name": "NVIDIA H200",
|
| 84 |
+
"memoryTotal": "150754820096",
|
| 85 |
+
"cudaCores": 16896,
|
| 86 |
+
"architecture": "Hopper",
|
| 87 |
+
"uuid": "GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b"
|
| 88 |
+
}
|
| 89 |
+
],
|
| 90 |
+
"cudaVersion": "12.8",
|
| 91 |
+
"writerId": "rbvja5libjcazn8vlw0jf7aycf36lwlk"
|
| 92 |
+
}
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/logs/debug-core.log
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2026-03-02T17:16:18.285609964Z","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpei8dlxaw/port-6897.txt","pid":6897,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
|
| 2 |
+
{"time":"2026-03-02T17:16:18.286890644Z","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-6897-7175-3365876390/socket","Net":"unix"}}
|
| 3 |
+
{"time":"2026-03-02T17:16:18.287052765Z","level":"INFO","msg":"server: will exit if parent process dies","ppid":6897}
|
| 4 |
+
{"time":"2026-03-02T17:16:18.459956293Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
|
| 5 |
+
{"time":"2026-03-02T17:16:18.471184923Z","level":"INFO","msg":"handleInformInit: received","streamId":"ovail9a3","id":"1(@)"}
|
| 6 |
+
{"time":"2026-03-02T17:16:18.855453481Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"ovail9a3","id":"1(@)"}
|
| 7 |
+
{"time":"2026-03-02T17:16:24.555008002Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"9i8zwo6ci0ps"}
|
| 8 |
+
{"time":"2026-03-02T17:25:50.425512511Z","level":"INFO","msg":"server: parent process exited, terminating service process"}
|
re10k/0303_RE10k_FULL_24v/wandb/run-20260302_171618-ovail9a3/logs/debug-internal.log
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"time":"2026-03-02T17:16:18.47132239Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
|
| 2 |
+
{"time":"2026-03-02T17:16:18.853965261Z","level":"INFO","msg":"stream: created new stream","id":"ovail9a3"}
|
| 3 |
+
{"time":"2026-03-02T17:16:18.855315172Z","level":"INFO","msg":"handler: started","stream_id":"ovail9a3"}
|
| 4 |
+
{"time":"2026-03-02T17:16:18.855394984Z","level":"INFO","msg":"stream: started","id":"ovail9a3"}
|
| 5 |
+
{"time":"2026-03-02T17:16:18.855440247Z","level":"INFO","msg":"sender: started","stream_id":"ovail9a3"}
|
| 6 |
+
{"time":"2026-03-02T17:16:18.855438527Z","level":"INFO","msg":"writer: started","stream_id":"ovail9a3"}
|