diff --git a/0303_ACID_FULL_2v/main.log b/0303_ACID_FULL_2v/main.log new file mode 100644 index 0000000000000000000000000000000000000000..85394f693f4e9bafe0ae6720ff23364465f1baf3 --- /dev/null +++ b/0303_ACID_FULL_2v/main.log @@ -0,0 +1,92 @@ +[2026-03-02 17:30:18,235][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + +[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.) + result[selector] = overlay + +[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)`. + +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +[2026-03-02 17:32:39,801][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + +[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.) + result[selector] = overlay + +[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)`. + +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +[2026-03-02 17:33:41,599][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + +[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.) + result[selector] = overlay + +[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)`. + +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +[2026-03-02 17:37:59,587][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + +[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.) + result[selector] = overlay + +[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)`. + +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + diff --git a/0303_ACID_FULL_2v/wandb/run-20260302_173247-8vipx6wd/run-8vipx6wd.wandb b/0303_ACID_FULL_2v/wandb/run-20260302_173247-8vipx6wd/run-8vipx6wd.wandb new file mode 100644 index 0000000000000000000000000000000000000000..5bb6f82956c1a5dd93edfc21d29c230128f400d2 Binary files /dev/null and b/0303_ACID_FULL_2v/wandb/run-20260302_173247-8vipx6wd/run-8vipx6wd.wandb differ diff --git a/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/output.log b/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..c380b1a7eebfd789a9437a2dcc27183f1a4f58f1 --- /dev/null +++ b/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/output.log @@ -0,0 +1,97 @@ +LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] + + | Name | Type | Params | Mode +------------------------------------------------------------------------ +0 | encoder | OurSplat | 888 M | train +1 | density_control_module | DensityControlModule | 514 | train +2 | decoder | DecoderSplattingCUDA | 0 | train +3 | render_losses | ModuleList | 0 | train +4 | density_control_losses | ModuleList | 0 | train +5 | direct_losses | ModuleList | 0 | train +------------------------------------------------------------------------ +888 M Trainable params +0 Non-trainable params +888 M Total params +3,553.936 Total estimated model params size (MB) +1207 Modules in train mode +522 Modules in eval mode +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. + +Validation epoch start on rank 0 +Sanity Checking DataLoader 0: 0%| | 0/1 [00:00, ?it/s]validation step 0; scene = ['fcbd42c6ad4b2529']; +target intrinsic: tensor(0.9452, device='cuda:0') tensor(0.9454, device='cuda:0') +pred intrinsic: tensor(1.5447, device='cuda:0') tensor(1.5103, device='cuda:0') +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. +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. +[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.) + result[selector] = overlay + +[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)`. + +Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off] +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +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]] +Error executing job with overrides: ['+experiment=acid', 'wandb.mode=online', 'wandb.name=0303_ACID_FULL_2v'] +Traceback (most recent call last): + File "/workspace/code/CVPR2026/src/main.py", line 226, in train + trainer.fit(model_wrapper, datamodule=data_module)#, ckpt_path=checkpoint_path) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 561, in fit + call._call_and_handle_interrupt( + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/call.py", line 48, in _call_and_handle_interrupt + return trainer_fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 599, in _fit_impl + self._run(model, ckpt_path=ckpt_path) + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 1012, in _run + results = self._run_stage() + ^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/trainer.py", line 1056, in _run_stage + self.fit_loop.run() + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py", line 216, in run + self.advance() + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/fit_loop.py", line 455, in advance + self.epoch_loop.run(self._data_fetcher) + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 150, in run + self.advance(data_fetcher) + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 322, in advance + batch_output = self.manual_optimization.run(kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/optimization/manual.py", line 94, in run + self.advance(kwargs) + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/loops/optimization/manual.py", line 114, in advance + training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values()) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/trainer/call.py", line 328, in _call_strategy_hook + output = fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/lightning/pytorch/strategies/strategy.py", line 391, in training_step + return self.lightning_module.training_step(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/jaxtyping/_decorator.py", line 562, in wrapped_fn + return wrapped_fn_impl(args, kwargs, bound, memos) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/jaxtyping/_decorator.py", line 486, in wrapped_fn_impl + out = fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/workspace/code/CVPR2026/src/model/model_wrapper.py", line 481, in training_step + all_gs_params_grad = torch.autograd.grad(render_loss, all_gs_params, retain_graph=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/torch/autograd/__init__.py", line 503, in grad + result = _engine_run_backward( + ^^^^^^^^^^^^^^^^^^^^^ + File "/venv/main/lib/python3.12/site-packages/torch/autograd/graph.py", line 829, in _engine_run_backward + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +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). + +Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace. diff --git a/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/wandb-summary.json b/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/wandb-summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8c72bffd57544eea94dc563d6142a002c479d87a --- /dev/null +++ b/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/files/wandb-summary.json @@ -0,0 +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"]}} \ No newline at end of file diff --git a/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/logs/debug-core.log b/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/logs/debug-core.log new file mode 100644 index 0000000000000000000000000000000000000000..4fb87fafeb164cfae181d5e1acc5aaf086f8f562 --- /dev/null +++ b/0303_ACID_FULL_2v/wandb/run-20260302_173806-q9zn619i/logs/debug-core.log @@ -0,0 +1,15 @@ +{"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} +{"time":"2026-03-02T17:38:07.069046292Z","level":"INFO","msg":"server: will exit if parent process dies","ppid":6180} +{"time":"2026-03-02T17:38:07.069032242Z","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-6180-6460-3956421381/socket","Net":"unix"}} +{"time":"2026-03-02T17:38:07.239682516Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"} +{"time":"2026-03-02T17:38:07.25209719Z","level":"INFO","msg":"handleInformInit: received","streamId":"q9zn619i","id":"1(@)"} +{"time":"2026-03-02T17:38:07.742281159Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"q9zn619i","id":"1(@)"} +{"time":"2026-03-02T17:38:13.226363322Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"5pngaa1y82j3"} +{"time":"2026-03-02T17:38:24.944595103Z","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"} +{"time":"2026-03-02T17:38:24.944673915Z","level":"INFO","msg":"connection: closing","id":"1(@)"} +{"time":"2026-03-02T17:38:24.944766956Z","level":"INFO","msg":"server is shutting down"} +{"time":"2026-03-02T17:38:24.944787932Z","level":"INFO","msg":"connection: closed successfully","id":"1(@)"} +{"time":"2026-03-02T17:38:24.944902719Z","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-6180-6460-3956421381/socket","Net":"unix"}} +{"time":"2026-03-02T17:38:25.464657282Z","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"} +{"time":"2026-03-02T17:38:25.46468763Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"} +{"time":"2026-03-02T17:38:25.46469648Z","level":"INFO","msg":"server is closed"} diff --git a/acid/0303_ACID_FULL_2v/.hydra/config.yaml b/acid/0303_ACID_FULL_2v/.hydra/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..16f9d37ad21027e6600e2a4798101f59b179c062 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/.hydra/config.yaml @@ -0,0 +1,188 @@ +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_ACID_FULL_2v + mode: online + tags: + - acid + - 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: null + 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: null + 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/acid + 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: null + 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/acid + 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 diff --git a/acid/0303_ACID_FULL_2v/train_ddp_process_4.log b/acid/0303_ACID_FULL_2v/train_ddp_process_4.log new file mode 100644 index 0000000000000000000000000000000000000000..9646542df5262cc59bab7671da7515ab3813062f --- /dev/null +++ b/acid/0303_ACID_FULL_2v/train_ddp_process_4.log @@ -0,0 +1,324 @@ +[2026-03-02 18:03:28,922][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + diff --git a/acid/0303_ACID_FULL_2v/train_ddp_process_5.log b/acid/0303_ACID_FULL_2v/train_ddp_process_5.log new file mode 100644 index 0000000000000000000000000000000000000000..e7ca5d347d44b17b886d335c1c6072a9c03f767c --- /dev/null +++ b/acid/0303_ACID_FULL_2v/train_ddp_process_5.log @@ -0,0 +1,324 @@ +[2026-03-02 18:03:29,368][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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.) + result[selector] = overlay + +[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. + warnings.warn( # warn only once + diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/output.log b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..5a4aa3cbfc4d5fecc212e4f04d7c98c4e60eefb0 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/output.log @@ -0,0 +1,135 @@ +LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] + + | Name | Type | Params | Mode +------------------------------------------------------------------------ +0 | encoder | OurSplat | 888 M | train +1 | density_control_module | DensityControlModule | 514 | train +2 | decoder | DecoderSplattingCUDA | 0 | train +3 | render_losses | ModuleList | 0 | train +4 | density_control_losses | ModuleList | 0 | train +5 | direct_losses | ModuleList | 0 | train +------------------------------------------------------------------------ +888 M Trainable params +0 Non-trainable params +888 M Total params +3,553.936 Total estimated model params size (MB) +1207 Modules in train mode +522 Modules in eval mode +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. + +Validation epoch start on rank 0 +Sanity Checking DataLoader 0: 0%| | 0/1 [00:00, ?it/s]validation step 0; scene = ['fcbd42c6ad4b2529']; +target intrinsic: tensor(0.9452, device='cuda:0') tensor(0.9454, device='cuda:0') +pred intrinsic: tensor(1.5447, device='cuda:0') tensor(1.5103, device='cuda:0') +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. +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. +[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.) + result[selector] = overlay + +[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)`. + +Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off] +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +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]] +[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.) + result[selector] = overlay + +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +[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.) + result[selector] = overlay + +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +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 +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]] +[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.) + result[selector] = overlay + +Epoch 0: | | 404/? [18:23<00:00, 0.37it/s, v_num=ntcx] diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-metadata.json b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e67bd6ab7f29e3ddf1205e3cb5de80ab73f33809 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-metadata.json @@ -0,0 +1,92 @@ +{ + "os": "Linux-5.15.0-157-generic-x86_64-with-glibc2.39", + "python": "CPython 3.12.12", + "startedAt": "2026-03-02T17:41:24.267732Z", + "args": [ + "+experiment=acid", + "wandb.mode=online", + "wandb.name=0303_ACID_FULL_2v" + ], + "program": "-m src.main", + "git": { + "remote": "git@github.com:K-nowing/CVPR2026.git", + "commit": "9dfce172a0f8c7ce85e763899f7ef741ecffc454" + }, + "email": "dna9041@korea.ac.kr", + "root": "/workspace/code/CVPR2026/outputs/full/acid/0303_ACID_FULL_2v", + "host": "0258ae8f3852", + "executable": "/venv/main/bin/python", + "cpu_count": 112, + "cpu_count_logical": 224, + "gpu": "NVIDIA H200", + "gpu_count": 8, + "disk": { + "/": { + "total": "1170378588160", + "used": "537103478784" + } + }, + "memory": { + "total": "2164193775616" + }, + "gpu_nvidia": [ + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-79687643-93f8-7b36-349a-8f05b89e6678" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-317bba70-b882-ca12-2b8b-173e2db3be03" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-cc84663f-d6cd-d900-0d4c-118462dced2e" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-331b6fb4-1872-8ae5-e5de-e34efc869d56" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-522b1630-b9aa-5aa3-9985-ced479a7780e" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-4c86a636-acfc-e976-3b9e-78425c9c44df" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b" + } + ], + "cudaVersion": "12.8", + "writerId": "vtb6mv5kfj6ggthszg138jy7ckopxu32" +} \ No newline at end of file diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-summary.json b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c79a29c5b8c695cf0fe1d152ccb8f3b79d50cfdd --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/files/wandb-summary.json @@ -0,0 +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} \ No newline at end of file diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/logs/debug-core.log b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/logs/debug-core.log new file mode 100644 index 0000000000000000000000000000000000000000..1d06b64d2acee6f7e4d20d43ce100542d36355f5 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_174124-qfkhntcx/logs/debug-core.log @@ -0,0 +1,107 @@ +{"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} +{"time":"2026-03-02T17:41:24.334867292Z","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-7180-7459-20721055/socket","Net":"unix"}} +{"time":"2026-03-02T17:41:24.335037756Z","level":"INFO","msg":"server: will exit if parent process dies","ppid":7180} +{"time":"2026-03-02T17:41:24.508655225Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"} +{"time":"2026-03-02T17:41:24.519239733Z","level":"INFO","msg":"handleInformInit: received","streamId":"qfkhntcx","id":"1(@)"} +{"time":"2026-03-02T17:41:24.996686178Z","level":"INFO","msg":"handleInformInit: stream started","streamId":"qfkhntcx","id":"1(@)"} +{"time":"2026-03-02T17:41:30.469455269Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"tmp0o1sledbf"} +{"time":"2026-03-02T17:59:59.284332485Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"2(@)"} +{"time":"2026-03-02T17:59:59.284569991Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"3(@)"} +{"time":"2026-03-02T17:59:59.285551435Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"4(@)"} +{"time":"2026-03-02T17:59:59.287836681Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"5(@)"} +{"time":"2026-03-02T17:59:59.288310149Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"6(@)"} +{"time":"2026-03-02T17:59:59.288719529Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"7(@)"} +{"time":"2026-03-02T17:59:59.288762617Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"8(@)"} +{"time":"2026-03-02T17:59:59.288814311Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"9(@)"} +{"time":"2026-03-02T17:59:59.290460205Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"11(@)"} +{"time":"2026-03-02T17:59:59.290588718Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"12(@)"} +{"time":"2026-03-02T17:59:59.290681054Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"15(@)"} +{"time":"2026-03-02T17:59:59.290673411Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"14(@)"} +{"time":"2026-03-02T17:59:59.290777052Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"17(@)"} +{"time":"2026-03-02T17:59:59.290791175Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"13(@)"} +{"time":"2026-03-02T17:59:59.290862451Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"18(@)"} +{"time":"2026-03-02T17:59:59.29082318Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"16(@)"} +{"time":"2026-03-02T17:59:59.290903055Z","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"10(@)"} +{"time":"2026-03-02T18:00:00.052160168Z","level":"INFO","msg":"handleInformFinish: finish message received","streamId":"qfkhntcx","id":"4(@)"} +{"time":"2026-03-02T18:00:00.056643517Z","level":"INFO","msg":"handleInformFinish: stream closed","streamId":"qfkhntcx","id":"4(@)"} +{"time":"2026-03-02T18:00:00.265091444Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"} +{"time":"2026-03-02T18:00:00.265205545Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"} +{"time":"2026-03-02T18:00:00.265668779Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"} +{"time":"2026-03-02T18:00:00.26741403Z","level":"INFO","msg":"connection: cancelling request","id":"1(@)","requestId":"tmp0o1sledbf"} +{"time":"2026-03-02T18:00:00.268154833Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"} +{"time":"2026-03-02T18:00:00.270091219Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"1(@)"} +{"time":"2026-03-02T18:00:00.295715166Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"3(@)"} +{"time":"2026-03-02T18:00:00.296632465Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"2(@)"} +{"time":"2026-03-02T18:00:00.298626077Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"7(@)"} +{"time":"2026-03-02T18:00:00.299943654Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"9(@)"} +{"time":"2026-03-02T18:00:00.29998061Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"12(@)"} +{"time":"2026-03-02T18:00:00.300000162Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"10(@)"} +{"time":"2026-03-02T18:00:00.300755533Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"6(@)"} +{"time":"2026-03-02T18:00:00.300792804Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"14(@)"} +{"time":"2026-03-02T18:00:00.300979482Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"18(@)"} +{"time":"2026-03-02T18:00:00.301443952Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"11(@)"} +{"time":"2026-03-02T18:00:00.301528179Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"17(@)"} +{"time":"2026-03-02T18:00:00.301785579Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"5(@)"} +{"time":"2026-03-02T18:00:00.3018205Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"8(@)"} +{"time":"2026-03-02T18:00:00.302118915Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"16(@)"} +{"time":"2026-03-02T18:00:00.302816248Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"13(@)"} +{"time":"2026-03-02T18:00:00.30294025Z","level":"ERROR","msg":"handleInformRecord: error getting stream","err":"stream qfkhntcx not found","id":"15(@)"} +{"time":"2026-03-02T18:00:02.48915676Z","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"} +{"time":"2026-03-02T18:00:02.489234129Z","level":"INFO","msg":"handleInformTeardown: server shutdown complete","id":"1(@)"} +{"time":"2026-03-02T18:00:02.489249192Z","level":"INFO","msg":"server is shutting down"} +{"time":"2026-03-02T18:00:02.489306958Z","level":"INFO","msg":"connection: closing","id":"11(@)"} +{"time":"2026-03-02T18:00:02.489369792Z","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-7180-7459-20721055/socket","Net":"unix"}} +{"time":"2026-03-02T18:00:02.489431132Z","level":"INFO","msg":"connection: closing","id":"18(@)"} +{"time":"2026-03-02T18:00:02.489441756Z","level":"INFO","msg":"connection: closing","id":"9(@)"} +{"time":"2026-03-02T18:00:02.489493598Z","level":"INFO","msg":"connection: closing","id":"16(@)"} +{"time":"2026-03-02T18:00:02.489468369Z","level":"INFO","msg":"connection: closing","id":"5(@)"} +{"time":"2026-03-02T18:00:02.489565063Z","level":"INFO","msg":"connection: closing","id":"14(@)"} +{"time":"2026-03-02T18:00:02.489573177Z","level":"INFO","msg":"connection: closing","id":"3(@)"} +{"time":"2026-03-02T18:00:02.489610279Z","level":"INFO","msg":"connection: closing","id":"17(@)"} +{"time":"2026-03-02T18:00:02.48966333Z","level":"INFO","msg":"connection: closing","id":"15(@)"} +{"time":"2026-03-02T18:00:02.489544798Z","level":"INFO","msg":"connection: closing","id":"10(@)"} +{"time":"2026-03-02T18:00:02.489681142Z","level":"INFO","msg":"connection: closing","id":"1(@)"} +{"time":"2026-03-02T18:00:02.489705507Z","level":"INFO","msg":"connection: closing","id":"12(@)"} +{"time":"2026-03-02T18:00:02.489671059Z","level":"INFO","msg":"connection: closing","id":"6(@)"} +{"time":"2026-03-02T18:00:02.489747298Z","level":"INFO","msg":"connection: closed successfully","id":"11(@)"} +{"time":"2026-03-02T18:00:02.489762807Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"11(@)"} +{"time":"2026-03-02T18:00:02.489774395Z","level":"INFO","msg":"connection: closed successfully","id":"18(@)"} +{"time":"2026-03-02T18:00:02.489782085Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"18(@)"} +{"time":"2026-03-02T18:00:02.489790255Z","level":"INFO","msg":"connection: closed successfully","id":"16(@)"} +{"time":"2026-03-02T18:00:02.489797193Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"16(@)"} +{"time":"2026-03-02T18:00:02.489806218Z","level":"INFO","msg":"connection: closed successfully","id":"14(@)"} +{"time":"2026-03-02T18:00:02.489731563Z","level":"INFO","msg":"connection: closed successfully","id":"10(@)"} +{"time":"2026-03-02T18:00:02.489813273Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"14(@)"} +{"time":"2026-03-02T18:00:02.489824831Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"10(@)"} +{"time":"2026-03-02T18:00:02.489624247Z","level":"INFO","msg":"connection: closed successfully","id":"3(@)"} +{"time":"2026-03-02T18:00:02.48983519Z","level":"INFO","msg":"connection: closed successfully","id":"15(@)"} +{"time":"2026-03-02T18:00:02.489815437Z","level":"INFO","msg":"connection: closing","id":"7(@)"} +{"time":"2026-03-02T18:00:02.48984933Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"15(@)"} +{"time":"2026-03-02T18:00:02.489646811Z","level":"INFO","msg":"connection: closing","id":"8(@)"} +{"time":"2026-03-02T18:00:02.489859949Z","level":"INFO","msg":"connection: closed successfully","id":"12(@)"} +{"time":"2026-03-02T18:00:02.489857585Z","level":"INFO","msg":"connection: closed successfully","id":"17(@)"} +{"time":"2026-03-02T18:00:02.489872803Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"12(@)"} +{"time":"2026-03-02T18:00:02.489877543Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"17(@)"} +{"time":"2026-03-02T18:00:02.489552094Z","level":"INFO","msg":"connection: closing","id":"4(@)"} +{"time":"2026-03-02T18:00:02.489885256Z","level":"INFO","msg":"connection: closed successfully","id":"6(@)"} +{"time":"2026-03-02T18:00:02.489897775Z","level":"INFO","msg":"connection: closed successfully","id":"8(@)"} +{"time":"2026-03-02T18:00:02.48990122Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"6(@)"} +{"time":"2026-03-02T18:00:02.489907766Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"8(@)"} +{"time":"2026-03-02T18:00:02.489723035Z","level":"INFO","msg":"connection: closed successfully","id":"5(@)"} +{"time":"2026-03-02T18:00:02.489922198Z","level":"INFO","msg":"connection: closed successfully","id":"4(@)"} +{"time":"2026-03-02T18:00:02.489927529Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"5(@)"} +{"time":"2026-03-02T18:00:02.489916435Z","level":"INFO","msg":"connection: closed successfully","id":"7(@)"} +{"time":"2026-03-02T18:00:02.48997986Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"7(@)"} +{"time":"2026-03-02T18:00:02.489552128Z","level":"INFO","msg":"connection: closing","id":"13(@)"} +{"time":"2026-03-02T18:00:02.489729345Z","level":"INFO","msg":"connection: closed successfully","id":"9(@)"} +{"time":"2026-03-02T18:00:02.49002383Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"9(@)"} +{"time":"2026-03-02T18:00:02.489735804Z","level":"INFO","msg":"connection: closed successfully","id":"1(@)"} +{"time":"2026-03-02T18:00:02.490038185Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"1(@)"} +{"time":"2026-03-02T18:00:02.48984709Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"3(@)"} +{"time":"2026-03-02T18:00:02.489637712Z","level":"INFO","msg":"connection: closing","id":"2(@)"} +{"time":"2026-03-02T18:00:02.490058988Z","level":"INFO","msg":"connection: closed successfully","id":"13(@)"} +{"time":"2026-03-02T18:00:02.490069508Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"13(@)"} +{"time":"2026-03-02T18:00:02.489934173Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"4(@)"} +{"time":"2026-03-02T18:00:02.490087101Z","level":"INFO","msg":"connection: closed successfully","id":"2(@)"} +{"time":"2026-03-02T18:00:02.490095905Z","level":"INFO","msg":"connection: ManageConnectionData: connection closed","id":"2(@)"} +{"time":"2026-03-02T18:00:02.490108029Z","level":"INFO","msg":"server is closed"} diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/config.yaml b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..582853640db8a15f03c059f45bca264ecc803bd0 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/config.yaml @@ -0,0 +1,309 @@ +_wandb: + value: + cli_version: 0.25.0 + e: + 8jvz7m9157bluwjfwu52vsf17ozbsnyw: + args: + - +experiment=acid + - wandb.mode=online + - wandb.name=0303_ACID_FULL_2v + cpu_count: 112 + cpu_count_logical: 224 + cudaVersion: "12.8" + disk: + /: + total: "1170378588160" + used: "759094677504" + email: dna9041@korea.ac.kr + executable: /venv/main/bin/python + git: + commit: 9dfce172a0f8c7ce85e763899f7ef741ecffc454 + remote: git@github.com:K-nowing/CVPR2026.git + gpu: NVIDIA H200 + gpu_count: 8 + gpu_nvidia: + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-79687643-93f8-7b36-349a-8f05b89e6678 + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-317bba70-b882-ca12-2b8b-173e2db3be03 + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-cc84663f-d6cd-d900-0d4c-118462dced2e + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210 + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-331b6fb4-1872-8ae5-e5de-e34efc869d56 + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-522b1630-b9aa-5aa3-9985-ced479a7780e + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-4c86a636-acfc-e976-3b9e-78425c9c44df + - architecture: Hopper + cudaCores: 16896 + memoryTotal: "150754820096" + name: NVIDIA H200 + uuid: GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b + host: 0258ae8f3852 + memory: + total: "2164193775616" + os: Linux-5.15.0-157-generic-x86_64-with-glibc2.39 + program: -m src.main + python: CPython 3.12.12 + root: /workspace/code/CVPR2026/outputs/full/acid/0303_ACID_FULL_2v + startedAt: "2026-03-02T18:03:58.051099Z" + writerId: 8jvz7m9157bluwjfwu52vsf17ozbsnyw + m: + - "1": trainer/global_step + "6": + - 3 + "7": [] + - "2": '*' + "5": 1 + "6": + - 1 + "7": [] + python_version: 3.12.12 + t: + "1": + - 1 + - 41 + - 49 + - 50 + - 106 + "2": + - 1 + - 41 + - 49 + - 50 + - 106 + "3": + - 7 + - 13 + - 15 + - 16 + - 66 + "4": 3.12.12 + "5": 0.25.0 + "12": 0.25.0 + "13": linux-x86_64 +checkpointing: + value: + every_n_train_steps: 1875 + load: null + save_top_k: 2 + save_weights_only: false +data_loader: + value: + test: + batch_size: 1 + num_workers: 4 + persistent_workers: false + seed: 2345 + train: + batch_size: 16 + num_workers: 16 + persistent_workers: true + seed: 1234 + val: + batch_size: 1 + num_workers: 1 + persistent_workers: true + seed: 3456 +dataset: + value: + re10k: + augment: true + background_color: + - 0 + - 0 + - 0 + baseline_max: 1e+10 + baseline_min: 0.001 + cameras_are_circular: false + dynamic_context_views: false + input_image_shape: + - 256 + - 256 + make_baseline_1: true + max_context_views_per_gpu: 16 + max_fov: 100 + name: re10k + original_image_shape: + - 360 + - 640 + overfit_to_scene: null + relative_pose: true + roots: + - datasets/acid + skip_bad_shape: true + view_sampler: + initial_max_distance_between_context_views: 25 + initial_min_distance_between_context_views: 25 + max_distance_between_context_views: 90 + min_distance_between_context_views: 45 + min_distance_to_context_views: 0 + name: bounded + num_context_views: 2 + num_target_set: 3 + num_target_views: 4 + same_target_gap: false + target_align: true + warm_up_steps: 9375 +density_control_loss: + value: + error_score: + grad_scale: 10000 + log_scale: false + mode: original + weight: 0.0001 +direct_loss: + value: + l1: + weight: 0.8 + ssim: + weight: 0.2 +mode: + value: train +model: + value: + decoder: + background_color: + - 0 + - 0 + - 0 + make_scale_invariant: false + name: splatting_cuda + density_control: + aggregation_mode: mean + aux_refine: false + grad_mode: absgrad + gs_param_dim: 256 + latent_dim: 128 + mean_dim: 32 + name: density_control_module + num_heads: 1 + num_latents: 64 + num_level: 3 + num_self_attn_per_block: 2 + refine_error: false + refinement_hidden_dim: 32 + refinement_layer_num: 1 + refinement_type: voxelize + score_mode: absgrad + use_depth: false + use_mean_features: true + use_refine_module: false + voxel_size: 0.001 + voxelize_activate: false + encoder: + align_corners: false + gs_param_dim: 256 + head_mode: pcd + input_image_shape: + - 518 + - 518 + name: dcsplat + num_level: 3 + use_voxelize: true +optimizer: + value: + accumulate: 1 + backbone_lr_multiplier: 0.1 + backbone_trainable: T+H + lr: 0.0002 + warm_up_steps: 125 +render_loss: + value: + lpips: + apply_after_step: 0 + weight: 0.05 + mse: + weight: 1 +seed: + value: 111123 +test: + value: + align_pose: false + compute_scores: true + error_threshold: 0.4 + error_threshold_list: + - 0.2 + - 0.4 + - 0.6 + - 0.8 + - 1 + nvs_view_N_list: + - 3 + - 6 + - 16 + - 32 + - 64 + output_path: test/full/acid + pose_align_steps: 100 + pred_intrinsic: false + rot_opt_lr: 0.005 + save_active_mask_image: false + save_compare: false + save_error_score_image: false + save_gs: false + save_image: false + save_sample_wise_metrics: true + save_video: false + threshold_mode: ratio + trans_opt_lr: 0.005 +train: + value: + align_corners: false + beta_dist_param: + - 0.5 + - 4 + cam_scale_mode: sum + camera_loss: 10 + context_view_train: false + ext_scale_detach: false + extended_visualization: false + intrinsic_scaling: false + one_sample_validation: null + print_log_every_n_steps: 10 + scene_scale_reg_loss: 0.01 + train_aux: true + train_gs_num: 1 + train_target_set: true + use_refine_aux: false + verbose: false + vggt_cam_loss: true + vggt_distil: false +trainer: + value: + gradient_clip_val: 0.5 + max_steps: 18751 + num_nodes: 1 + val_check_interval: 500 +wandb: + value: + entity: scene-representation-group + mode: online + name: 0303_ACID_FULL_2v + project: DCSplat + tags: + - acid + - 256x256 diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/requirements.txt b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6be2fb7de27f058fd1a5a7b32679b739f8d96f46 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/requirements.txt @@ -0,0 +1,159 @@ +wheel==0.45.1 +triton==3.4.0 +nvidia-nccl-cu12==2.27.3 +pytz==2025.2 +easydict==1.13 +antlr4-python3-runtime==4.9.3 +wadler_lindig==0.1.7 +packaging==24.2 +urllib3==2.5.0 +tzdata==2025.2 +typing-inspection==0.4.1 +tabulate==0.9.0 +smmap==5.0.2 +opt_einsum==3.4.0 +setuptools==78.1.1 +safetensors==0.5.3 +PyYAML==6.0.2 +PySocks==1.7.1 +pyparsing==3.2.5 +pydantic_core==2.33.2 +pycparser==2.23 +protobuf==6.32.1 +propcache==0.3.2 +proglog==0.1.12 +kiwisolver==1.4.9 +platformdirs==4.4.0 +idna==3.7 +pip==25.2 +pillow==10.4.0 +numpy==1.26.4 +torch==2.8.0+cu128 +ninja==1.13.0 +gmpy2==2.2.1 +networkx==3.4.2 +multidict==6.6.4 +mdurl==0.1.2 +MarkupSafe==3.0.2 +kornia_rs==0.1.9 +imageio-ffmpeg==0.6.0 +hf-xet==1.1.10 +kornia==0.8.1 +fsspec==2024.6.1 +frozenlist==1.7.0 +fonttools==4.60.0 +filelock==3.17.0 +einops==0.8.1 +torchmetrics==1.8.2 +decorator==4.4.2 +torchvision==0.23.0+cu128 +dacite==1.9.2 +cycler==0.12.1 +colorama==0.4.6 +click==8.3.0 +charset-normalizer==3.3.2 +certifi==2025.8.3 +beartype==0.19.0 +opt-einsum-fx==0.1.4 +torchaudio==2.8.0+cu128 +attrs==25.3.0 +async-timeout==5.0.1 +annotated-types==0.7.0 +aiohappyeyeballs==2.6.1 +yarl==1.20.1 +tifffile==2025.5.10 +sentry-sdk==2.39.0 +scipy==1.15.3 +pydantic==2.11.9 +pandas==2.3.2 +opencv-python==4.11.0.86 +omegaconf==2.3.0 +markdown-it-py==4.0.0 +lightning-utilities==0.14.3 +lazy_loader==0.4 +jaxtyping==0.2.37 +imageio==2.37.0 +gitdb==4.0.12 +contourpy==1.3.2 +colorspacious==1.1.2 +cffi==1.17.1 +aiosignal==1.4.0 +scikit-video==1.1.11 +scikit-image==0.25.2 +rich==14.1.0 +moviepy==1.0.3 +matplotlib==3.10.6 +hydra-core==1.3.2 +e3nn==0.6.0 +huggingface-hub==0.35.1 +GitPython==3.1.45 +brotlicffi==1.0.9.2 +aiohttp==3.12.15 +pytorch-lightning==2.5.1 +lpips==0.1.4 +lightning==2.5.1 +gsplat==1.5.3 +torch_scatter==2.1.2+pt28cu128 +plyfile==1.1.3 +wandb==0.25.0 +cuda-bindings==12.9.4 +cuda-pathfinder==1.3.3 +Jinja2==3.1.6 +mpmath==1.3.0 +nvidia-cublas-cu12==12.8.4.1 +nvidia-cuda-cupti-cu12==12.8.90 +nvidia-cuda-nvrtc-cu12==12.8.93 +nvidia-cuda-runtime-cu12==12.8.90 +nvidia-cudnn-cu12==9.10.2.21 +nvidia-cufft-cu12==11.3.3.83 +nvidia-cufile-cu12==1.13.1.3 +nvidia-curand-cu12==10.3.9.90 +nvidia-cusolver-cu12==11.7.3.90 +nvidia-cusparse-cu12==12.5.8.93 +nvidia-cusparselt-cu12==0.7.1 +nvidia-nvjitlink-cu12==12.8.93 +nvidia-nvshmem-cu12==3.4.5 +nvidia-nvtx-cu12==12.8.90 +requests==2.32.5 +sentencepiece==0.2.1 +sympy==1.14.0 +torchcodec==0.10.0 +torchdata==0.10.0 +torchtext==0.6.0 +anyio==4.12.0 +asttokens==3.0.1 +comm==0.2.3 +debugpy==1.8.19 +executing==2.2.1 +h11==0.16.0 +httpcore==1.0.9 +httpx==0.28.1 +ipykernel==7.1.0 +ipython==9.8.0 +ipython_pygments_lexers==1.1.1 +ipywidgets==8.1.8 +jedi==0.19.2 +jupyter_client==8.7.0 +jupyter_core==5.9.1 +jupyterlab_widgets==3.0.16 +matplotlib-inline==0.2.1 +nest-asyncio==1.6.0 +parso==0.8.5 +pexpect==4.9.0 +prompt_toolkit==3.0.52 +psutil==7.2.1 +ptyprocess==0.7.0 +pure_eval==0.2.3 +Pygments==2.19.2 +python-dateutil==2.9.0.post0 +pyzmq==27.1.0 +shellingham==1.5.4 +six==1.17.0 +stack-data==0.6.3 +tornado==6.5.4 +tqdm==4.67.1 +traitlets==5.14.3 +typer-slim==0.21.0 +typing_extensions==4.15.0 +wcwidth==0.2.14 +widgetsnbextension==4.0.15 diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/wandb-summary.json b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/wandb-summary.json new file mode 100644 index 0000000000000000000000000000000000000000..56528b5a9ca46aa0fc1088a78f76f7de2fcadd83 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/files/wandb-summary.json @@ -0,0 +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} \ No newline at end of file diff --git a/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/logs/debug-internal.log b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/logs/debug-internal.log new file mode 100644 index 0000000000000000000000000000000000000000..71700b97213ba36d64e3ceef87d4d3fbdb1d8459 --- /dev/null +++ b/acid/0303_ACID_FULL_2v/wandb/run-20260302_180358-h16yffc1/logs/debug-internal.log @@ -0,0 +1,12 @@ +{"time":"2026-03-02T18:03:58.30488479Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"} +{"time":"2026-03-02T18:03:58.663005908Z","level":"INFO","msg":"stream: created new stream","id":"h16yffc1"} +{"time":"2026-03-02T18:03:58.663134658Z","level":"INFO","msg":"handler: started","stream_id":"h16yffc1"} +{"time":"2026-03-02T18:03:58.663747044Z","level":"INFO","msg":"stream: started","id":"h16yffc1"} +{"time":"2026-03-02T18:03:58.663771823Z","level":"INFO","msg":"writer: started","stream_id":"h16yffc1"} +{"time":"2026-03-02T18:03:58.663827643Z","level":"INFO","msg":"sender: started","stream_id":"h16yffc1"} +{"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
\n\nPlease try again in 30 seconds.\n
\n\n"} +{"time":"2026-03-03T09:16:37.305354887Z","level":"INFO","msg":"stream: closing","id":"h16yffc1"} +{"time":"2026-03-03T09:16:37.749837263Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"} +{"time":"2026-03-03T09:16:37.880324485Z","level":"INFO","msg":"handler: closed","stream_id":"h16yffc1"} +{"time":"2026-03-03T09:16:37.880469696Z","level":"INFO","msg":"sender: closed","stream_id":"h16yffc1"} +{"time":"2026-03-03T09:16:37.880482709Z","level":"INFO","msg":"stream: closed","id":"h16yffc1"} diff --git a/re10k/0303_RE10K_FULL_2v/.hydra/config.yaml b/re10k/0303_RE10K_FULL_2v/.hydra/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..541fe959587da31e500f546a87b70633e189c6aa --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/.hydra/config.yaml @@ -0,0 +1,188 @@ +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: null + 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: null + 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: null + 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 diff --git a/re10k/0303_RE10K_FULL_2v/.hydra/hydra.yaml b/re10k/0303_RE10K_FULL_2v/.hydra/hydra.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b101a581da2bbe87cfc1f78a5864bb4bd7e4ac07 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/.hydra/hydra.yaml @@ -0,0 +1,164 @@ +hydra: + run: + dir: outputs/full/re10k/${wandb.name} + sweep: + dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S} + subdir: ${hydra.job.num} + launcher: + _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: RUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=RUN + task: + - +experiment=re10k + - wandb.mode=online + - wandb.name=0303_RE10K_FULL_2v + job: + name: main + chdir: null + override_dirname: +experiment=re10k,wandb.mode=online,wandb.name=0303_RE10K_FULL_2v + id: ??? + num: ??? + config_name: main + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.3' + cwd: /workspace/code/CVPR2026 + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /workspace/code/CVPR2026/config + schema: file + provider: main + - path: '' + schema: structured + provider: schema + output_dir: /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10K_FULL_2v + choices: + experiment: re10k + dataset@dataset.re10k: re10k + dataset/view_sampler_dataset_specific_config@dataset.re10k.view_sampler: bounded_re10k + dataset/view_sampler@dataset.re10k.view_sampler: bounded + model/density_control: density_control_module + model/decoder: splatting_cuda + model/encoder: dcsplat + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: basic + hydra/output: default + verbose: false diff --git a/re10k/0303_RE10K_FULL_2v/.hydra/overrides.yaml b/re10k/0303_RE10K_FULL_2v/.hydra/overrides.yaml new file mode 100644 index 0000000000000000000000000000000000000000..438958de8c25e9ea941f8fc31bf603df270a58af --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/.hydra/overrides.yaml @@ -0,0 +1,3 @@ +- +experiment=re10k +- wandb.mode=online +- wandb.name=0303_RE10K_FULL_2v diff --git a/re10k/0303_RE10K_FULL_2v/main.log b/re10k/0303_RE10K_FULL_2v/main.log new file mode 100644 index 0000000000000000000000000000000000000000..2bdbd6f9c0ff082c455d842ff5cbe45b505c97f6 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/main.log @@ -0,0 +1,76 @@ +[2026-03-02 17:53:26,172][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + +[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.) + result[selector] = overlay + +[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)`. + +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + +[2026-03-03 09:16:46,710][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + +[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. + warnings.warn( # warn only once + +[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.) + result[selector] = overlay + +[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)`. + +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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.) + return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] + +[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. + +[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. + +[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. + +[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. + +[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. + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_1.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_1.log new file mode 100644 index 0000000000000000000000000000000000000000..a5485c9ade3694ec7deffc2c7d1e91bdfb728350 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_1.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:01,942][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_2.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_2.log new file mode 100644 index 0000000000000000000000000000000000000000..b6da2093646e8deebb4bd5ea1cf77b413c9d088a --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_2.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:02,039][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_3.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_3.log new file mode 100644 index 0000000000000000000000000000000000000000..4555f417a6a2dcfbf76d3793be88aa0d249a50c2 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_3.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:01,837][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_4.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_4.log new file mode 100644 index 0000000000000000000000000000000000000000..b16b39b07cebafa684055871c876eccd54201f04 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_4.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:01,807][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_5.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_5.log new file mode 100644 index 0000000000000000000000000000000000000000..ded7013dc8a996d90c59d7db192d78b79bcee652 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_5.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:01,792][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_6.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_6.log new file mode 100644 index 0000000000000000000000000000000000000000..b9b7429166c86bc691009fb3b80aad615a148b8b --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_6.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:01,811][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/train_ddp_process_7.log b/re10k/0303_RE10K_FULL_2v/train_ddp_process_7.log new file mode 100644 index 0000000000000000000000000000000000000000..81376143cacc30dde24106c950ade3249cc36ae1 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/train_ddp_process_7.log @@ -0,0 +1,21 @@ +[2026-03-03 09:17:01,869][dinov2][INFO] - using MLP layer as FFN +[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. + warnings.warn( + +[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. + warnings.warn(msg) + +[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. + warnings.warn( # warn only once + +[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. +grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256] +bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.) + return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass + +[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.) + result[selector] = overlay + +[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. + warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning) + diff --git a/re10k/0303_RE10K_FULL_2v/wandb/debug-internal.log b/re10k/0303_RE10K_FULL_2v/wandb/debug-internal.log new file mode 100644 index 0000000000000000000000000000000000000000..011b4704f8894a944e94bbf44c039d492cd52f31 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/wandb/debug-internal.log @@ -0,0 +1,6 @@ +{"time":"2026-03-03T09:17:40.960595772Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"} +{"time":"2026-03-03T09:17:41.326404324Z","level":"INFO","msg":"stream: created new stream","id":"d18sudny"} +{"time":"2026-03-03T09:17:41.326554992Z","level":"INFO","msg":"handler: started","stream_id":"d18sudny"} +{"time":"2026-03-03T09:17:41.326775806Z","level":"INFO","msg":"stream: started","id":"d18sudny"} +{"time":"2026-03-03T09:17:41.32697243Z","level":"INFO","msg":"writer: started","stream_id":"d18sudny"} +{"time":"2026-03-03T09:17:41.327074842Z","level":"INFO","msg":"sender: started","stream_id":"d18sudny"} diff --git a/re10k/0303_RE10K_FULL_2v/wandb/debug.log b/re10k/0303_RE10K_FULL_2v/wandb/debug.log new file mode 100644 index 0000000000000000000000000000000000000000..3a3e7cc94e3638b67bfa776bc247a128e57816db --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/wandb/debug.log @@ -0,0 +1,19 @@ +2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_setup.py:_flush():81] Current SDK version is 0.25.0 +2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_setup.py:_flush():81] Configure stats pid to 18776 +2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_setup.py:_flush():81] Loading settings from environment variables +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 +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 +2026-03-03 09:17:40,708 INFO MainThread:18776 [wandb_init.py:init():844] calling init triggers +2026-03-03 09:17:40,709 INFO MainThread:18776 [wandb_init.py:init():849] wandb.init called with sweep_config: {} +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': {}} +2026-03-03 09:17:40,709 INFO MainThread:18776 [wandb_init.py:init():892] starting backend +2026-03-03 09:17:40,950 INFO MainThread:18776 [wandb_init.py:init():895] sending inform_init request +2026-03-03 09:17:40,958 INFO MainThread:18776 [wandb_init.py:init():903] backend started and connected +2026-03-03 09:17:40,960 INFO MainThread:18776 [wandb_init.py:init():973] updated telemetry +2026-03-03 09:17:40,964 INFO MainThread:18776 [wandb_init.py:init():997] communicating run to backend with 90.0 second timeout +2026-03-03 09:17:42,467 INFO MainThread:18776 [wandb_init.py:init():1042] starting run threads in backend +2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_console_start():2524] atexit reg +2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_redirect():2373] redirect: wrap_raw +2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_redirect():2442] Wrapping output streams. +2026-03-03 09:17:42,544 INFO MainThread:18776 [wandb_run.py:_redirect():2465] Redirects installed. +2026-03-03 09:17:42,548 INFO MainThread:18776 [wandb_init.py:init():1082] run started, returning control to user process diff --git a/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-metadata.json b/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..eb07c75943b2f74c4d87d4d3d49e85c9c6f1f2ee --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-metadata.json @@ -0,0 +1,92 @@ +{ + "os": "Linux-5.15.0-157-generic-x86_64-with-glibc2.39", + "python": "CPython 3.12.12", + "startedAt": "2026-03-02T17:53:33.438580Z", + "args": [ + "+experiment=re10k", + "wandb.mode=online", + "wandb.name=0303_RE10K_FULL_2v" + ], + "program": "-m src.main", + "git": { + "remote": "git@github.com:K-nowing/CVPR2026.git", + "commit": "9dfce172a0f8c7ce85e763899f7ef741ecffc454" + }, + "email": "dna9041@korea.ac.kr", + "root": "/workspace/code/CVPR2026/outputs/full/re10k/0303_RE10K_FULL_2v", + "host": "0258ae8f3852", + "executable": "/venv/main/bin/python", + "cpu_count": 112, + "cpu_count_logical": 224, + "gpu": "NVIDIA H200", + "gpu_count": 8, + "disk": { + "/": { + "total": "1170378588160", + "used": "709567029248" + } + }, + "memory": { + "total": "2164193775616" + }, + "gpu_nvidia": [ + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-79687643-93f8-7b36-349a-8f05b89e6678" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-317bba70-b882-ca12-2b8b-173e2db3be03" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-cc84663f-d6cd-d900-0d4c-118462dced2e" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-5fb2a9b9-546c-3788-31a7-dacaa250a210" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-331b6fb4-1872-8ae5-e5de-e34efc869d56" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-522b1630-b9aa-5aa3-9985-ced479a7780e" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-4c86a636-acfc-e976-3b9e-78425c9c44df" + }, + { + "name": "NVIDIA H200", + "memoryTotal": "150754820096", + "cudaCores": 16896, + "architecture": "Hopper", + "uuid": "GPU-bd551ffb-d195-a48e-8095-4c05e0d31c2b" + } + ], + "cudaVersion": "12.8", + "writerId": "nyyynvgq5catl0amasijx0miq9sdne8u" +} \ No newline at end of file diff --git a/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-summary.json b/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-summary.json new file mode 100644 index 0000000000000000000000000000000000000000..13dcb79c0231adf49c95271f12d3a5715397de8c --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/files/wandb-summary.json @@ -0,0 +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} \ No newline at end of file diff --git a/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/logs/debug-internal.log b/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/logs/debug-internal.log new file mode 100644 index 0000000000000000000000000000000000000000..fa9aea0744898a47e71357986c00a13b88f4fb79 --- /dev/null +++ b/re10k/0303_RE10K_FULL_2v/wandb/run-20260302_175333-24m6myoo/logs/debug-internal.log @@ -0,0 +1,50 @@ +{"time":"2026-03-02T17:53:33.692951019Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"} +{"time":"2026-03-02T17:53:34.065688995Z","level":"INFO","msg":"stream: created new stream","id":"24m6myoo"} +{"time":"2026-03-02T17:53:34.06583962Z","level":"INFO","msg":"handler: started","stream_id":"24m6myoo"} +{"time":"2026-03-02T17:53:34.066046233Z","level":"INFO","msg":"stream: started","id":"24m6myoo"} +{"time":"2026-03-02T17:53:34.06628084Z","level":"INFO","msg":"writer: started","stream_id":"24m6myoo"} +{"time":"2026-03-02T17:53:34.06628786Z","level":"INFO","msg":"sender: started","stream_id":"24m6myoo"} +{"time":"2026-03-02T17:59:57.198981838Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.19952135Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.202672482Z","level":"INFO","msg":"flowcontrol: backed up, offloading to disk","recordNumber":565} +{"time":"2026-03-02T17:59:57.483605199Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.483631704Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.483636767Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.48364212Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.483855898Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.483863454Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.483961274Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.483967107Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.484247785Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.484251758Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.484255764Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.484259009Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.484447804Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.484452017Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.484456635Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.484460357Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486098407Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486122217Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486135222Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486140844Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.48628549Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486297255Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486301679Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486313174Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486450601Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486455864Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486465694Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486469967Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486502627Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.486550491Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.486569197Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"} +{"time":"2026-03-02T17:59:57.487174822Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"} +{"time":"2026-03-02T17:59:57.487430202Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:59:57.487444811Z","level":"INFO","msg":"flowcontrol: unblocked","totalOffloaded":24} +{"time":"2026-03-02T17:59:57.487454897Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:59:57.877581157Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"} +{"time":"2026-03-02T17:59:58.079133486Z","level":"INFO","msg":"handler: operation stats","stats":{}} +{"time":"2026-03-02T17:59:58.084611241Z","level":"INFO","msg":"stream: closing","id":"24m6myoo"} +{"time":"2026-03-02T17:59:58.084624435Z","level":"INFO","msg":"handler: closed","stream_id":"24m6myoo"} +{"time":"2026-03-02T17:59:58.084724916Z","level":"INFO","msg":"sender: closed","stream_id":"24m6myoo"} +{"time":"2026-03-02T17:59:58.084732416Z","level":"INFO","msg":"stream: closed","id":"24m6myoo"} diff --git a/re10k/0303_RE10k_FULL_24v/.hydra/config.yaml b/re10k/0303_RE10k_FULL_24v/.hydra/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cb3eabcd562d55f77f1701eeb28b3d09575f39a2 --- /dev/null +++ b/re10k/0303_RE10k_FULL_24v/.hydra/config.yaml @@ -0,0 +1,188 @@ +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: null + 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: null + 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: null + 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 diff --git a/re10k/0303_RE10k_FULL_24v/.hydra/hydra.yaml b/re10k/0303_RE10k_FULL_24v/.hydra/hydra.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c3551144725fe0ab900184a30dad585a2b72e303 --- /dev/null +++ b/re10k/0303_RE10k_FULL_24v/.hydra/hydra.yaml @@ -0,0 +1,164 @@ +hydra: + run: + dir: outputs/full/re10k/${wandb.name} + sweep: + dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S} + subdir: ${hydra.job.num} + launcher: + _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher + sweeper: + _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper + max_batch_size: null + params: null + help: + app_name: ${hydra.job.name} + header: '${hydra.help.app_name} is powered by Hydra. + + ' + footer: 'Powered by Hydra (https://hydra.cc) + + Use --hydra-help to view Hydra specific help + + ' + template: '${hydra.help.header} + + == Configuration groups == + + Compose your configuration from those groups (group=option) + + + $APP_CONFIG_GROUPS + + + == Config == + + Override anything in the config (foo.bar=value) + + + $CONFIG + + + ${hydra.help.footer} + + ' + hydra_help: + template: 'Hydra (${hydra.runtime.version}) + + See https://hydra.cc for more info. + + + == Flags == + + $FLAGS_HELP + + + == Configuration groups == + + Compose your configuration from those groups (For example, append hydra/job_logging=disabled + to command line) + + + $HYDRA_CONFIG_GROUPS + + + Use ''--cfg hydra'' to Show the Hydra config. + + ' + hydra_help: ??? + hydra_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][HYDRA] %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + root: + level: INFO + handlers: + - console + loggers: + logging_example: + level: DEBUG + disable_existing_loggers: false + job_logging: + version: 1 + formatters: + simple: + format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' + handlers: + console: + class: logging.StreamHandler + formatter: simple + stream: ext://sys.stdout + file: + class: logging.FileHandler + formatter: simple + filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log + root: + level: INFO + handlers: + - console + - file + disable_existing_loggers: false + env: {} + mode: RUN + searchpath: [] + callbacks: {} + output_subdir: .hydra + overrides: + hydra: + - hydra.mode=RUN + task: + - +experiment=re10k_24v + - wandb.mode=online + - wandb.name=0303_RE10k_FULL_24v + job: + name: main + chdir: null + override_dirname: +experiment=re10k_24v,wandb.mode=online,wandb.name=0303_RE10k_FULL_24v + id: ??? + num: ??? + config_name: main + env_set: {} + env_copy: [] + config: + override_dirname: + kv_sep: '=' + item_sep: ',' + exclude_keys: [] + runtime: + version: 1.3.2 + version_base: '1.3' + cwd: /workspace/code/CVPR2026 + config_sources: + - path: hydra.conf + schema: pkg + provider: hydra + - path: /workspace/code/CVPR2026/config + schema: file + provider: main + - path: '' + schema: structured + provider: schema + output_dir: /workspace/code/CVPR2026/outputs/full/re10k/0303_RE10k_FULL_24v + choices: + experiment: re10k_24v + dataset@dataset.re10k: re10k + dataset/view_sampler_dataset_specific_config@dataset.re10k.view_sampler: bounded_re10k + dataset/view_sampler@dataset.re10k.view_sampler: bounded + model/density_control: density_control_module + model/decoder: splatting_cuda + model/encoder: dcsplat + hydra/env: default + hydra/callbacks: null + hydra/job_logging: default + hydra/hydra_logging: default + hydra/hydra_help: default + hydra/help: default + hydra/sweeper: basic + hydra/launcher: basic + hydra/output: default + verbose: false diff --git a/re10k/0303_RE10k_FULL_24v/.hydra/overrides.yaml b/re10k/0303_RE10k_FULL_24v/.hydra/overrides.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f6ac3a4f7b38c133e85c65295083b384d6b2e5d7 --- /dev/null +++ b/re10k/0303_RE10k_FULL_24v/.hydra/overrides.yaml @@ -0,0 +1,3 @@ +- +experiment=re10k_24v +- wandb.mode=online +- wandb.name=0303_RE10k_FULL_24v diff --git a/re10k/0303_RE10k_FULL_24v/wandb/debug-internal.log b/re10k/0303_RE10k_FULL_24v/wandb/debug-internal.log new file mode 100644 index 0000000000000000000000000000000000000000..2ab860f868f9aae0afe181c508f27d71f7dcbad4 --- /dev/null +++ b/re10k/0303_RE10k_FULL_24v/wandb/debug-internal.log @@ -0,0 +1,50 @@ +{"time":"2026-03-02T17:34:54.017734281Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"} +{"time":"2026-03-02T17:34:54.390484792Z","level":"INFO","msg":"stream: created new stream","id":"7ul1smti"} +{"time":"2026-03-02T17:34:54.393055945Z","level":"INFO","msg":"stream: started","id":"7ul1smti"} +{"time":"2026-03-02T17:34:54.393159427Z","level":"INFO","msg":"handler: started","stream_id":"7ul1smti"} +{"time":"2026-03-02T17:34:54.393260469Z","level":"INFO","msg":"writer: started","stream_id":"7ul1smti"} +{"time":"2026-03-02T17:34:54.393274583Z","level":"INFO","msg":"sender: started","stream_id":"7ul1smti"} +{"time":"2026-03-02T17:35:54.404488277Z","level":"INFO","msg":"flowcontrol: backed up, offloading to disk","recordNumber":194} +{"time":"2026-03-02T17:35:54.723380765Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.723903957Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.725035725Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.725094746Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.72600883Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.726038861Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.726906187Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.726916179Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.726929825Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.726940046Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.726946025Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.726953664Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.726960686Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.726967766Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.727281805Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.727365903Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.727581651Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.727593656Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.72766555Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.727668326Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.727735186Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.727740979Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.727855582Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.727860456Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.727862278Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.727866553Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.728069007Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.728073576Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.728075459Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.728080037Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.728132582Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.728143993Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:54.728149532Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"} +{"time":"2026-03-02T17:35:54.728419175Z","level":"ERROR","msg":"sender: sendOutputRaw called after exit"} +{"time":"2026-03-02T17:35:54.728485115Z","level":"ERROR","msg":"sender: sendSummary called after exit"} +{"time":"2026-03-02T17:35:54.728490523Z","level":"INFO","msg":"flowcontrol: unblocked","totalOffloaded":33} +{"time":"2026-03-02T17:35:54.72849506Z","level":"WARN","msg":"sender: received Exit record more than once, ignoring"} +{"time":"2026-03-02T17:35:55.062846118Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"} +{"time":"2026-03-02T17:35:55.23103931Z","level":"INFO","msg":"handler: operation stats","stats":{}} +{"time":"2026-03-02T17:35:55.235466397Z","level":"INFO","msg":"stream: closing","id":"7ul1smti"} +{"time":"2026-03-02T17:35:55.235478181Z","level":"INFO","msg":"handler: closed","stream_id":"7ul1smti"} +{"time":"2026-03-02T17:35:55.235510756Z","level":"INFO","msg":"sender: closed","stream_id":"7ul1smti"} +{"time":"2026-03-02T17:35:55.235517876Z","level":"INFO","msg":"stream: closed","id":"7ul1smti"} diff --git a/re10k/0303_RE10k_FULL_24v/wandb/debug.log b/re10k/0303_RE10k_FULL_24v/wandb/debug.log new file mode 100644 index 0000000000000000000000000000000000000000..f848e00b1448c759817ac37e9e169c5846e41b43 --- /dev/null +++ b/re10k/0303_RE10k_FULL_24v/wandb/debug.log @@ -0,0 +1,36166 @@ +2026-03-02 17:34:53,765 INFO MainThread:4123 [wandb_setup.py:_flush():81] Current SDK version is 0.25.0 +2026-03-02 17:34:53,765 INFO MainThread:4123 [wandb_setup.py:_flush():81] Configure stats pid to 4123 +2026-03-02 17:34:53,765 INFO MainThread:4123 [wandb_setup.py:_flush():81] Loading settings from environment variables +2026-03-02 17:34:53,765 INFO MainThread:4123 [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_173453-7ul1smti/logs/debug.log +2026-03-02 17:34:53,765 INFO MainThread:4123 [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_173453-7ul1smti/logs/debug-internal.log +2026-03-02 17:34:53,765 INFO MainThread:4123 [wandb_init.py:init():844] calling init triggers +2026-03-02 17:34:53,766 INFO MainThread:4123 [wandb_init.py:init():849] wandb.init called with sweep_config: {} +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': {}} +2026-03-02 17:34:53,766 INFO MainThread:4123 [wandb_init.py:init():892] starting backend +2026-03-02 17:34:54,007 INFO MainThread:4123 [wandb_init.py:init():895] sending inform_init request +2026-03-02 17:34:54,012 INFO MainThread:4123 [wandb_init.py:init():903] backend started and connected +2026-03-02 17:34:54,014 INFO MainThread:4123 [wandb_init.py:init():973] updated telemetry +2026-03-02 17:34:54,021 INFO MainThread:4123 [wandb_init.py:init():997] communicating run to backend with 90.0 second timeout +2026-03-02 17:34:54,878 INFO MainThread:4123 [wandb_init.py:init():1042] starting run threads in backend +2026-03-02 17:34:54,955 INFO MainThread:4123 [wandb_run.py:_console_start():2524] atexit reg +2026-03-02 17:34:54,955 INFO MainThread:4123 [wandb_run.py:_redirect():2373] redirect: wrap_raw +2026-03-02 17:34:54,955 INFO MainThread:4123 [wandb_run.py:_redirect():2442] Wrapping output streams. +2026-03-02 17:34:54,955 INFO MainThread:4123 [wandb_run.py:_redirect():2465] Redirects installed. +2026-03-02 17:34:54,958 INFO MainThread:4123 [wandb_init.py:init():1082] run started, returning control to user process +2026-03-02 17:35:54,400 INFO MainThread:4678 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,401 INFO MainThread:4678 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,401 INFO MainThread:4678 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,401 INFO MainThread:4678 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,401 INFO MainThread:4686 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,402 INFO MainThread:4686 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,402 INFO MainThread:4686 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,402 INFO MainThread:4686 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,402 INFO MainThread:4672 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,402 INFO MainThread:4698 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4698 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,403 INFO MainThread:4690 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4676 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4698 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,403 INFO MainThread:4698 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,403 INFO MainThread:4682 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4676 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,403 INFO MainThread:4690 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,403 INFO MainThread:4672 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,403 INFO MainThread:4676 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,403 INFO MainThread:4676 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,403 INFO MainThread:4690 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,403 INFO MainThread:4672 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,403 INFO MainThread:4690 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,403 INFO MainThread:4672 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,403 INFO MainThread:4682 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,403 INFO MainThread:4684 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4682 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,403 INFO MainThread:4682 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,403 INFO MainThread:4688 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4684 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,403 INFO MainThread:4684 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,403 INFO MainThread:4688 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,404 INFO MainThread:4684 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,403 INFO MainThread:4701 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,403 INFO MainThread:4696 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,404 INFO MainThread:4688 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,404 INFO MainThread:4688 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,404 INFO MainThread:4701 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,404 INFO MainThread:4692 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,404 INFO MainThread:4696 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,404 INFO MainThread:4701 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,404 INFO MainThread:4701 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,404 INFO MainThread:4696 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,404 INFO MainThread:4692 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,404 INFO MainThread:4696 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,404 INFO MainThread:4694 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,404 INFO MainThread:4692 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,404 INFO MainThread:4692 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,404 INFO MainThread:4703 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,405 INFO MainThread:4694 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,405 INFO MainThread:4694 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,405 INFO MainThread:4694 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,405 INFO MainThread:4703 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,405 INFO MainThread:4703 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,405 INFO MainThread:4703 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,406 INFO MainThread:4654 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,406 INFO MainThread:4674 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,406 INFO MainThread:4654 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,406 INFO MainThread:4654 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,406 INFO MainThread:4654 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,406 INFO MainThread:4674 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,407 INFO MainThread:4674 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,407 INFO MainThread:4674 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,407 INFO MainThread:4680 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,407 INFO MainThread:4680 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,407 INFO MainThread:4680 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,407 INFO MainThread:4680 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,462 INFO MainThread:4123 [wandb_run.py:_finish():2291] finishing run know/DCSplat/7ul1smti +2026-03-02 17:35:54,463 INFO MainThread:4123 [wandb_run.py:_atexit_cleanup():2490] got exitcode: 0 +2026-03-02 17:35:54,463 INFO MainThread:4123 [wandb_run.py:_restore():2472] restore +2026-03-02 17:35:54,463 INFO MainThread:4123 [wandb_run.py:_restore():2478] restore done +2026-03-02 17:35:54,504 ERROR wandb-AsyncioManager-main:4678 [redirect.py:_on_write():662] [all runs] error in stderr callback +Traceback (most recent call last): + File "/venv/main/lib/python3.12/site-packages/wandb/sdk/lib/redirect.py", line 660, in _on_write + cb(written_data) + File "/venv/main/lib/python3.12/site-packages/wandb/sdk/wandb_run.py", line 2452, in