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@@ -64,3 +64,4 @@ ABLATION_0225_OURS/wandb/run-20260224_191508-0b125b6z/run-0b125b6z.wandb filter=
 ABLATION_0225_FreqSelect/wandb/run-20260224_222739-y7wvpmyy/run-y7wvpmyy.wandb filter=lfs diff=lfs merge=lfs -text
 ABLATION_0225_targetTrain/wandb/run-20260225_014059-qetzseh9/run-qetzseh9.wandb filter=lfs diff=lfs merge=lfs -text
 ABLATION_0225_targetTrain_SSR/wandb/run-20260225_043637-hluaxp6d/run-hluaxp6d.wandb filter=lfs diff=lfs merge=lfs -text
+ABLATION_0225_ctxTrain_depth_vggtDistl/wandb/run-20260225_143141-pa77a1wq/run-pa77a1wq.wandb filter=lfs diff=lfs merge=lfs -text

ABLATION_0225_ctxTrain_depth_vggtDistl/main.log

@@ -43,3 +43,49 @@
   warnings.warn(msg)
 
 [2026-02-25 14:29:24,307][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,045][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=31` in the `DataLoader` to improve performance.
+
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:31:50,895][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:31:50,904][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)`.
+
+[2026-02-25 14:31:50,905][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(
+
+[2026-02-25 14:31:50,906][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)
+
+[2026-02-25 14:31:52,827][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]
+
+[2026-02-25 14:31:53,133][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.
+
+[2026-02-25 14:31:53,134][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.
+
+[2026-02-25 14:31:53,134][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.
+
+[2026-02-25 14:31:53,135][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.
+
+[2026-02-25 14:31:53,135][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.
+
+[2026-02-25 14:32:07,271][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,422][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
+
+[2026-02-25 14:34:04,202][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)
+
+[2026-02-25 14:47:38,499][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:51:37,911][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_1.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:04,698][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:32:07,271][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,384][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,202][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)
+
+[2026-02-25 14:47:38,498][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_2.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:17,034][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:32:07,277][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,386][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,202][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)
+
+[2026-02-25 14:47:38,498][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_3.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:17,604][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:32:07,273][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,396][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,233][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)
+
+[2026-02-25 14:47:38,498][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_4.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:17,884][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,047][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
+
+[2026-02-25 14:32:07,276][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,389][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,222][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)
+
+[2026-02-25 14:47:38,498][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_5.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:13,767][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:32:06,540][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,384][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,218][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)
+
+[2026-02-25 14:47:38,499][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_6.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:07,824][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:32:06,759][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,391][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,202][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)
+
+[2026-02-25 14:47:38,498][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/train_ddp_process_7.log

@@ -17,3 +17,20 @@
   warnings.warn(msg)
 
 [2026-02-25 14:30:04,401][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:31:48,046][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
+
+[2026-02-25 14:32:06,759][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,388][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:34:04,202][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)
+
+[2026-02-25 14:47:38,498][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+

ABLATION_0225_ctxTrain_depth_vggtDistl/wandb/debug-internal.log

@@ -1,11 +1,6 @@
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-{"time":"2026-02-25T13:55:11.099961565Z","level":"INFO","msg":"sender: closed","stream_id":"g2qj8ry7"}
-{"time":"2026-02-25T13:55:11.099983596Z","level":"INFO","msg":"stream: closed","id":"g2qj8ry7"}
+{"time":"2026-02-25T14:31:41.445785511Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
+{"time":"2026-02-25T14:31:41.986197956Z","level":"INFO","msg":"stream: created new stream","id":"pa77a1wq"}
+{"time":"2026-02-25T14:31:41.986357928Z","level":"INFO","msg":"handler: started","stream_id":"pa77a1wq"}
+{"time":"2026-02-25T14:31:41.986655173Z","level":"INFO","msg":"stream: started","id":"pa77a1wq"}
+{"time":"2026-02-25T14:31:41.986647023Z","level":"INFO","msg":"writer: started","stream_id":"pa77a1wq"}
+{"time":"2026-02-25T14:31:41.986720105Z","level":"INFO","msg":"sender: started","stream_id":"pa77a1wq"}

ABLATION_0225_ctxTrain_depth_vggtDistl/wandb/debug.log

@@ -1,21 +1,19 @@
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-2026-02-25 13:54:41,864 INFO    MainThread:168367 [wandb_init.py:setup_run_log_directory():718] Logging internal logs to /workspace/code/CVPR2026/outputs/ablation/re10k/ABLATION_0225_ctxTrain_depth_vggtDistl/wandb/run-20260225_135441-g2qj8ry7/logs/debug-internal.log
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-config: {'model': {'encoder': {'name': 'dcsplat', 'input_image_shape': [518, 518], 'head_mode': 'depth', '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': True, 'voxelize_activate': True, 'use_depth': True}}, 'render_loss': {'mse': {'weight': 1.0}, 'lpips': {'weight': 0.05, 'apply_after_step': 0}}, 'density_control_loss': {'error_score': {'weight': 0.01, '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': 'ABLATION_0225_ctxTrain_depth_vggtDistl', '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': 25, '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': True, 'context_view_train': True}, 'test': {'output_path': 'test/ablation/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, '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': 3001, 'val_check_interval': 250, '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': 1000, 'initial_min_distance_between_context_views': 25, 'initial_max_distance_between_context_views': 25, 'same_target_gap': False, 'num_target_set': 3}, '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': {}}
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+2026-02-25 14:31:41,152 INFO    MainThread:180369 [wandb_init.py:setup_run_log_directory():718] Logging internal logs to /workspace/code/CVPR2026/outputs/ablation/re10k/ABLATION_0225_ctxTrain_depth_vggtDistl/wandb/run-20260225_143141-pa77a1wq/logs/debug-internal.log
+2026-02-25 14:31:41,152 INFO    MainThread:180369 [wandb_init.py:init():844] calling init triggers
+2026-02-25 14:31:41,153 INFO    MainThread:180369 [wandb_init.py:init():849] wandb.init called with sweep_config: {}
+config: {'model': {'encoder': {'name': 'dcsplat', 'input_image_shape': [518, 518], 'head_mode': 'depth', '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': True, 'voxelize_activate': True, 'use_depth': True}}, 'render_loss': {'mse': {'weight': 1.0}, 'lpips': {'weight': 0.05, 'apply_after_step': 0}, 'depth_consis': {'weight': 1.0, 'sigma_image': None, 'use_second_derivative': False}}, 'density_control_loss': {'error_score': {'weight': 0.01, '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': 'ABLATION_0225_ctxTrain_depth_vggtDistl', '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': 25, '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': True, 'context_view_train': True}, 'test': {'output_path': 'test/ablation/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, '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': 3001, 'val_check_interval': 250, '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': 1000, 'initial_min_distance_between_context_views': 25, 'initial_max_distance_between_context_views': 25, 'same_target_gap': False, 'num_target_set': 3}, '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': {}}
+2026-02-25 14:31:41,153 INFO    MainThread:180369 [wandb_init.py:init():892] starting backend
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ABLATION_0225_ctxTrain_depth_vggtDistl/wandb/run-20260225_143141-pa77a1wq/files/output.log

@@ -0,0 +1,132 @@
+LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
+
+  | Name                   | Type                 | Params | Mode
+------------------------------------------------------------------------
+0 | encoder                | OurSplat             | 888 M  | train
+1 | density_control_module | DensityControlModule | 2.6 M  | 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
+6 | distill_aggregator     | Aggregator           | 909 M  | train
+7 | distill_camera_head    | CameraHead           | 216 M  | train
+8 | distill_depth_head     | DPTHead              | 32.7 M | train
+9 | loss_distill           | DistillLoss          | 0      | train
+------------------------------------------------------------------------
+891 M     Trainable params
+1.2 B     Non-trainable params
+2.0 B     Total params
+8,196.093 Total estimated model params size (MB)
+2779      Modules in train mode
+522       Modules in eval mode
+Sanity Checking: |                                                                                             | 0/? [00:00<?, ?it/s][2026-02-25 14:31:48,045][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=31` in the `DataLoader` to improve performance.
+
+[2026-02-25 14:31:48,046][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
+
+Validation epoch start on rank 0
+Sanity Checking DataLoader 0:   0%|                                                                            | 0/1 [00:00<?, ?it/s]validation step 0; scene = ['306e2b7785657539'];
+target intrinsic:  tensor(0.8595, device='cuda:0') tensor(0.8597, device='cuda:0')
+pred intrinsic:  tensor(0.8779, device='cuda:0') tensor(0.8773, device='cuda:0')
+[rank0]:W0225 14:31:50.831000 180369 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
+[rank0]:W0225 14:31:50.831000 180369 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
+[2026-02-25 14:31:50,895][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:31:50,904][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]
+[2026-02-25 14:31:50,905][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(
+
+[2026-02-25 14:31:50,906][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
+[2026-02-25 14:31:52,827][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]
+
+Sanity Checking DataLoader 0: 100%|████████████████████████████████████████████████████████████████████| 1/1 [00:04<00:00,  0.23it/s][2026-02-25 14:31:53,133][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.
+
+[2026-02-25 14:31:53,134][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.
+
+[2026-02-25 14:31:53,134][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.
+
+[2026-02-25 14:31:53,135][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.
+
+[2026-02-25 14:31:53,135][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.
+
+Epoch 0: |                                                                                                     | 0/? [00:00<?, ?it/s]context = [[34, 36, 50, 53, 54, 60, 63, 70, 76, 78, 79, 80, 81, 88, 92, 94, 102, 110, 112, 114, 122, 125, 126, 131]]target = [[126, 96, 109, 55, 99, 116, 43, 60, 113, 85, 103, 90, 130, 62, 76, 123, 35, 102, 125, 128, 98, 67, 129, 79]]
+[2026-02-25 14:32:07,271][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() = [2, 32, 1, 1], strides() = [32, 1, 32, 32]
+bucket_view.sizes() = [2, 32, 1, 1], strides() = [32, 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
+
+[2026-02-25 14:32:07,422][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
+
+Epoch 0: |                                                                                 | 9/? [00:51<00:00,  0.17it/s, v_num=a1wq]train step 10; scene = [['08c26703c4987851']]; loss = 0.911580
+Epoch 0: |                                                                                | 10/? [00:56<00:00,  0.18it/s, v_num=a1wq]context = [[98, 107, 112, 119, 122, 123], [21, 22, 27, 36, 38, 46], [63, 66, 67, 77, 84, 88], [56, 62, 65, 68, 78, 81]]target = [[105, 110, 116, 112, 111, 101], [29, 37, 36, 22, 40, 45], [73, 66, 75, 87, 83, 64], [79, 69, 75, 58, 61, 62]]
+Epoch 0: |                                                                                | 19/? [01:38<00:00,  0.19it/s, v_num=a1wq]train step 20; scene = [['4012c15c8381568b'], ['af08406c5a9a43a0'], ['9f9f9beffb86fad7'], ['fc8d08df6c875cb0']]; loss = 0.309024
+Epoch 0: |                                                                                | 20/? [01:43<00:00,  0.19it/s, v_num=a1wq]context = [[144, 152, 157, 164, 166, 169, 171, 177], [201, 211, 216, 221, 228, 230, 233, 234], [11, 15, 16, 23, 30, 38, 43, 44]]target = [[153, 170, 149, 169, 145, 174, 165, 157], [229, 216, 205, 206, 203, 213, 233, 215], [37, 38, 39, 35, 15, 24, 19, 25]]
+Epoch 0: |                                                                                | 24/? [02:01<00:00,  0.20it/s, v_num=a1wq][2026-02-25 14:34:04,202][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: |                                                                                | 29/? [02:23<00:00,  0.20it/s, v_num=a1wq]train step 30; scene = [['00980329a3221f1c'], ['1e7c432d2207b6f2'], ['af2748330e5243d0']]; loss = 0.235074
+Epoch 0: |                                                                                | 30/? [02:27<00:00,  0.20it/s, v_num=a1wq]context = [[2, 7, 10, 15, 20, 24, 25, 29, 30, 32, 33, 37, 40, 44, 49, 60, 64, 74, 76, 81, 89, 91, 95, 99]]target = [[79, 84, 35, 43, 89, 44, 63, 58, 48, 13, 65, 7, 96, 27, 51, 20, 60, 15, 85, 59, 22, 66, 17, 62]]
+Epoch 0: |                                                                                | 39/? [03:09<00:00,  0.21it/s, v_num=a1wq]train step 40; scene = [['79a9385753d426bc'], ['593538382d2dc847'], ['c9c67636b9d521be']]; loss = 0.200153
+Epoch 0: |                                                                                | 40/? [03:13<00:00,  0.21it/s, v_num=a1wq]context = [[200, 208, 223, 225], [6, 8, 12, 31], [104, 107, 111, 129], [69, 80, 86, 95], [11, 15, 18, 37], [52, 60, 70, 78]]target = [[213, 203, 214, 201], [29, 11, 15, 28], [111, 122, 120, 115], [77, 90, 89, 78], [18, 24, 29, 14], [64, 67, 63, 74]]
+Epoch 0: |                                                                                | 49/? [03:53<00:00,  0.21it/s, v_num=a1wq]train step 50; scene = [['579a11551b3315d9'], ['c9dd64b7415e788e'], ['6f3fb517d1798d03']]; loss = 0.216190
+Epoch 0: |                                                                                | 50/? [03:58<00:00,  0.21it/s, v_num=a1wq]context = [[20, 22, 25, 28, 36, 47], [10, 13, 15, 22, 26, 35], [31, 47, 49, 51, 52, 56], [109, 110, 115, 116, 125, 135]]target = [[40, 24, 23, 30, 37, 33], [19, 32, 25, 24, 26, 28], [35, 52, 50, 55, 45, 36], [131, 123, 130, 110, 115, 134]]
+Epoch 0: |                                                                                | 59/? [04:38<00:00,  0.21it/s, v_num=a1wq]train step 60; scene = [['07916b8004a8e336'], ['e51ef9945ae527c4'], ['db84f84b1d775bb8'], ['92ed61f8e16b7e67']]; loss = 0.384119
+Epoch 0: |                                                                                | 60/? [04:43<00:00,  0.21it/s, v_num=a1wq]context = [[96, 97, 101, 102, 105, 106, 107, 114, 124, 134, 138, 145], [76, 78, 82, 83, 96, 98, 100, 111, 112, 118, 119, 125]]target = [[144, 124, 137, 102, 107, 130, 119, 129, 118, 123, 133, 126], [98, 108, 96, 100, 113, 123, 77, 78, 112, 109, 80, 102]]
+Epoch 0: |                                                                                | 69/? [05:25<00:00,  0.21it/s, v_num=a1wq]train step 70; scene = [['c34efa1505a0cfaa'], ['a3d0cca9fb57fd85'], ['43d0e6dce7bb1e95'], ['d8c2f0a3734cb493']]; loss = 0.215408
+Epoch 0: |                                                                                | 70/? [05:29<00:00,  0.21it/s, v_num=a1wq]context = [[204, 208, 211, 218, 232, 234, 237, 239, 240, 241, 247, 253], [6, 7, 10, 12, 35, 36, 37, 38, 40, 45, 48, 55]]target = [[230, 236, 222, 227, 220, 252, 216, 221, 213, 217, 249, 240], [18, 24, 25, 12, 9, 42, 34, 52, 41, 33, 43, 48]]
+Epoch 0: |                                                                                | 79/? [06:10<00:00,  0.21it/s, v_num=a1wq]train step 80; scene = [['24d756c820744e31'], ['cd6c21656a85e9b9'], ['f3b24cf238154fc0']]; loss = 0.172991
+Epoch 0: |                                                                                | 80/? [06:14<00:00,  0.21it/s, v_num=a1wq]context = [[4, 30], [52, 79], [61, 87], [12, 40], [83, 109], [3, 29], [221, 249], [198, 227], [9, 38], [46, 72], [0, 26], [123, 150]]target = [[27, 26], [58, 57], [70, 77], [26, 20], [87, 98], [26, 14], [223, 224], [221, 200], [23, 33], [67, 64], [4, 25], [140, 148]]
+Epoch 0: |                                                                                | 89/? [06:56<00:00,  0.21it/s, v_num=a1wq]train step 90; scene = [['617b4bc98d7e0bb6'], ['666e4a9aba27bb64']]; loss = 0.156331
+Epoch 0: |                                                                                | 90/? [07:01<00:00,  0.21it/s, v_num=a1wq]context = [[134, 135, 138, 146, 147, 151, 156, 161, 162, 164, 166, 167, 168, 169, 187, 189, 197, 210, 215, 224, 225, 228, 230, 231]]target = [[143, 149, 191, 182, 151, 226, 165, 140, 208, 171, 179, 223, 168, 136, 194, 207, 227, 144, 187, 185, 145, 218, 139, 170]]
+Epoch 0: |                                                                                | 99/? [07:44<00:00,  0.21it/s, v_num=a1wq]train step 100; scene = [['12fee7f1978d52f1'], ['c963bb60939e2d81']]; loss = 0.152084
+Epoch 0: |                                                                               | 100/? [07:48<00:00,  0.21it/s, v_num=a1wq]context = [[40, 44, 47, 48, 49, 50, 56, 63, 77, 79, 88, 89], [21, 33, 34, 35, 36, 40, 43, 46, 47, 60, 62, 70]]target = [[58, 81, 76, 64, 68, 72, 51, 87, 77, 65, 88, 45], [31, 47, 25, 35, 55, 22, 48, 65, 29, 40, 63, 67]]
+Epoch 0: |                                                                               | 109/? [08:30<00:00,  0.21it/s, v_num=a1wq]train step 110; scene = [['47396d5a5299873e']]; loss = 0.200728
+Epoch 0: |                                                                               | 110/? [08:34<00:00,  0.21it/s, v_num=a1wq]context = [[20, 22, 29, 30, 31, 34, 37, 45, 47, 51, 56, 69], [18, 22, 24, 27, 31, 37, 44, 48, 49, 60, 61, 67]]target = [[49, 22, 36, 59, 63, 60, 45, 66, 38, 28, 26, 64], [61, 65, 25, 20, 63, 60, 26, 22, 33, 45, 37, 35]]
+Epoch 0: |                                                                               | 119/? [09:17<00:00,  0.21it/s, v_num=a1wq]train step 120; scene = [['9bd7044e7cbf8e60'], ['76e44cf6b5658b26']]; loss = 0.090040
+Epoch 0: |                                                                               | 120/? [09:21<00:00,  0.21it/s, v_num=a1wq]context = [[7, 14, 26, 28, 31, 34, 37, 40], [10, 20, 26, 32, 35, 39, 41, 43], [17, 23, 26, 28, 44, 46, 49, 50]]target = [[14, 26, 10, 22, 30, 13, 31, 11], [24, 31, 36, 14, 40, 34, 41, 13], [24, 44, 34, 48, 46, 36, 26, 38]]
+Epoch 0: |                                                                               | 129/? [10:03<00:00,  0.21it/s, v_num=a1wq]train step 130; scene = [['a8cef6a851fbea3c'], ['b6699f4d039a5b06'], ['55cf2bbe9e017ea4'], ['6b0dd861e1ab1fec'], ['14db202c335af709'], ['8b6ff6c5153a7794'], ['b75f3820760d835c'], ['f7dbc855fd2a7669'], ['cfb20f8971e6a591'], ['95f2be7bb8303f50'], ['ff422469e034ae11'], ['5a2ad43377e9d18d']]; loss = 0.172321
+Epoch 0: |                                                                               | 130/? [10:08<00:00,  0.21it/s, v_num=a1wq]context = [[8, 15, 26, 30, 32, 35, 40, 46, 47, 48, 50, 53, 54, 62, 69, 70, 72, 76, 80, 86, 90, 99, 100, 105]]target = [[26, 12, 49, 100, 89, 24, 10, 81, 37, 63, 52, 17, 39, 70, 16, 56, 40, 55, 43, 34, 72, 28, 48, 45]]
+Epoch 0: |                                                                               | 139/? [10:49<00:00,  0.21it/s, v_num=a1wq]train step 140; scene = [['f62a962df5c26a1a'], ['b076420679a04731']]; loss = 0.112418
+Epoch 0: |                                                                               | 140/? [10:53<00:00,  0.21it/s, v_num=a1wq]context = [[90, 95, 97, 104, 118, 121], [14, 29, 34, 35, 36, 44], [12, 14, 22, 27, 34, 41], [6, 10, 16, 18, 34, 35]]target = [[99, 104, 93, 113, 110, 96], [29, 37, 22, 21, 39, 41], [19, 25, 39, 27, 29, 13], [26, 28, 9, 7, 13, 34]]
+Epoch 0: |                                                                               | 149/? [11:36<00:00,  0.21it/s, v_num=a1wq]train step 150; scene = [['a52d26a78b04aebd']]; loss = 0.094999
+Epoch 0: |                                                                               | 150/? [11:41<00:00,  0.21it/s, v_num=a1wq]context = [[115, 132, 134, 145], [16, 32, 39, 44], [57, 63, 78, 88], [9, 11, 23, 36], [15, 26, 28, 44], [1, 8, 22, 33]]target = [[124, 132, 116, 119], [27, 24, 33, 29], [81, 61, 85, 79], [28, 26, 29, 16], [28, 29, 37, 22], [9, 4, 32, 27]]
+Epoch 0: |                                                                               | 159/? [12:22<00:00,  0.21it/s, v_num=a1wq]train step 160; scene = [['268fbffc6c479d5b']]; loss = 0.088229
+Epoch 0: |                                                                               | 160/? [12:27<00:00,  0.21it/s, v_num=a1wq]context = [[18, 25, 26, 37, 42, 46, 49, 51, 53, 64, 65, 67], [69, 75, 78, 79, 82, 84, 94, 95, 104, 108, 117, 118]]target = [[53, 27, 22, 32, 41, 38, 50, 43, 47, 48, 23, 19], [74, 70, 114, 115, 90, 89, 88, 92, 94, 110, 107, 101]]
+Epoch 0: |                                                                               | 169/? [13:10<00:00,  0.21it/s, v_num=a1wq]train step 170; scene = [['719e2e8912e4eed3'], ['a3e51565a737569f']]; loss = 0.169240
+Epoch 0: |                                                                               | 170/? [13:15<00:00,  0.21it/s, v_num=a1wq]context = [[14, 18, 20, 21, 24, 27, 33, 40, 45, 47, 48, 51, 52, 60, 64, 70, 75, 77, 80, 85, 90, 98, 102, 111]]target = [[29, 93, 32, 39, 81, 108, 72, 107, 51, 35, 16, 36, 70, 18, 34, 92, 94, 47, 23, 74, 50, 77, 19, 37]]
+Epoch 0: |                                                                               | 179/? [13:56<00:00,  0.21it/s, v_num=a1wq]train step 180; scene = [['f44b9aa76a94a0a3']]; loss = 0.083026
+Epoch 0: |                                                                               | 180/? [14:01<00:00,  0.21it/s, v_num=a1wq]context = [[0, 6, 17, 22, 26, 28, 33, 41, 50, 55, 56, 57, 71, 76, 79, 81, 84, 85, 86, 87, 89, 95, 96, 97]]target = [[37, 49, 12, 78, 9, 16, 84, 13, 5, 4, 6, 38, 80, 51, 43, 68, 64, 46, 56, 24, 25, 72, 36, 21]]
+Epoch 0: |                                                                               | 189/? [14:44<00:00,  0.21it/s, v_num=a1wq]train step 190; scene = [['71bb669d936a5718'], ['a47203cfd5e0a478'], ['4b009f82cf5c7098']]; loss = 0.124435
+Epoch 0: |                                                                               | 190/? [14:49<00:00,  0.21it/s, v_num=a1wq]context = [[9, 11, 19, 25, 33, 43, 46, 47, 48, 54, 57, 58, 63, 70, 72, 75, 79, 80, 83, 84, 85, 96, 99, 106]]target = [[30, 53, 82, 31, 55, 12, 20, 72, 104, 70, 24, 52, 21, 32, 102, 71, 35, 11, 46, 10, 15, 74, 33, 26]]
+Epoch 0: |                                                                               | 199/? [15:30<00:00,  0.21it/s, v_num=a1wq]train step 200; scene = [['dd5ec950a01c42a0'], ['6d0db0358f7e051e'], ['983fe650a925ec1b']]; loss = 0.127213
+Epoch 0: |                                                                               | 200/? [15:35<00:00,  0.21it/s, v_num=a1wq]context = [[8, 10, 14, 15, 22, 23, 27, 30, 36, 38, 39, 47, 64, 65, 67, 78, 79, 80, 83, 86, 93, 96, 98, 105]]target = [[73, 63, 10, 27, 89, 35, 44, 58, 97, 71, 17, 24, 66, 87, 50, 12, 23, 11, 31, 45, 69, 96, 98, 94]]
+[2026-02-25 14:47:38,499][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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/? [16:24<00:00,  0.21it/s, v_num=a1wq]train step 210; scene = [['9be9b273b3c22c61'], ['4b5883872c9b860c']]; loss = 0.084080
+Epoch 0: |                                                                               | 210/? [16:29<00:00,  0.21it/s, v_num=a1wq]context = [[34, 35, 37, 54, 56, 59, 61, 68, 69, 76, 90, 96, 98, 101, 103, 107, 116, 119, 120, 121, 122, 125, 126, 131]]target = [[95, 50, 119, 121, 84, 107, 72, 52, 80, 42, 127, 94, 79, 98, 46, 128, 73, 75, 106, 92, 37, 110, 96, 56]]
+Epoch 0: |                                                                               | 219/? [17:12<00:00,  0.21it/s, v_num=a1wq]train step 220; scene = [['a3b6faa8d238d993'], ['df9ba36fbe753843']]; loss = 0.142685
+Epoch 0: |                                                                               | 220/? [17:16<00:00,  0.21it/s, v_num=a1wq]context = [[39, 71, 74], [21, 41, 51], [28, 55, 59], [2, 31, 37], [15, 25, 48], [57, 64, 93], [76, 87, 105], [31, 53, 64]]target = [[52, 43, 59], [28, 25, 49], [48, 58, 51], [26, 9, 6], [30, 32, 47], [84, 88, 59], [104, 87, 88], [42, 52, 59]]
+Epoch 0: |                                                                               | 229/? [17:59<00:00,  0.21it/s, v_num=a1wq]train step 230; scene = [['ca04de3c55cd1ca0'], ['3d90d586b33daa63'], ['d1772c09b4b6d95f'], ['03d05f69a1cab4f8'], ['60d296908f43a97a'], ['37c400e282bc481e']]; loss = 0.123628
+Epoch 0: |                                                                               | 230/? [18:03<00:00,  0.21it/s, v_num=a1wq]context = [[203, 204, 208, 209, 210, 233], [41, 42, 43, 52, 60, 76], [2, 8, 9, 23, 29, 31], [70, 74, 92, 97, 98, 100]]target = [[226, 223, 210, 217, 228, 222], [46, 70, 71, 75, 43, 56], [24, 23, 29, 25, 3, 21], [95, 81, 74, 73, 98, 92]]
+Epoch 0: |                                                                               | 239/? [18:46<00:00,  0.21it/s, v_num=a1wq]train step 240; scene = [['9794641b7e015578']]; loss = 0.228188
+Epoch 0: |                                                                               | 240/? [18:51<00:00,  0.21it/s, v_num=a1wq]context = [[62, 65, 68, 71, 80, 86, 87, 96, 99, 101, 103, 111], [134, 138, 139, 142, 151, 163, 171, 172, 173, 174, 181, 183]]target = [[87, 89, 96, 93, 103, 71, 65, 77, 63, 98, 102, 105], [165, 177, 159, 147, 138, 152, 171, 141, 181, 146, 161, 176]]
+Epoch 0: |                                                                               | 249/? [19:33<00:00,  0.21it/s, v_num=a1wq]train step 250; scene = [['93dff1b985f2c7f9']]; loss = 0.084510
+Epoch 0: |                                                                               | 250/? [19:38<00:00,  0.21it/s, v_num=a1wq]Validation epoch start on rank 0
+Validation: |                                                                                                  | 0/? [00:00<?, ?it/s]validation step 250; scene = ['49b8f80c849dc341'];
+target intrinsic:  tensor(0.8891, device='cuda:0') tensor(0.8894, device='cuda:0')                             | 0/1 [00:00<?, ?it/s]
+pred intrinsic:  tensor(0.8701, device='cuda:0') tensor(0.8341, device='cuda:0')
+[2026-02-25 14:51:37,911][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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: |                                                                               | 250/? [19:40<00:00,  0.21it/s, v_num=a1wq]context = [[113, 115, 120, 131, 135, 137, 144, 152], [16, 20, 23, 28, 32, 35, 39, 52], [14, 18, 25, 26, 28, 29, 51, 53]]target = [[149, 139, 118, 151, 137, 141, 121, 130], [45, 51, 28, 50, 35, 37, 27, 23], [29, 26, 22, 51, 24, 34, 47, 45]]
+Epoch 0: |                                                                               | 259/? [20:20<00:00,  0.21it/s, v_num=a1wq]train step 260; scene = [['b2288bf7003d5d4d']]; loss = 0.117824
+Epoch 0: |                                                                               | 260/? [20:25<00:00,  0.21it/s, v_num=a1wq]context = [[206, 208, 212, 216, 221, 236], [58, 60, 68, 69, 70, 93], [15, 19, 26, 34, 41, 44], [15, 18, 23, 34, 42, 44]]target = [[225, 216, 234, 214, 207, 232], [88, 76, 91, 67, 64, 70], [20, 29, 22, 36, 23, 40], [42, 28, 31, 30, 16, 37]]

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+    input_image_shape:
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+    - 518
+    head_mode: depth
+    num_level: 3
+    gs_param_dim: 256
+    align_corners: false
+    use_voxelize: true
+  decoder:
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+    name: density_control_module
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+    gs_param_dim: 256
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+    num_level: 3
+    grad_mode: absgrad
+    use_mean_features: true
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+  - 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: 25
+  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: true
+  context_view_train: true
+test:
+  output_path: test/ablation/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
+  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: 3001
+  val_check_interval: 250
+  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: 1000
+      initial_min_distance_between_context_views: 25
+      initial_max_distance_between_context_views: 25
+      same_target_gap: false
+      num_target_set: 3
+    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

DEBUG/.hydra/hydra.yaml

@@ -0,0 +1,170 @@
+hydra:
+  run:
+    dir: outputs/ablation/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_ablation_24v
+    - dataset.re10k.roots=[datasets/re10k]
+    - wandb.mode=disabled
+    - wandb.name=DEBUG
+    - model.encoder.head_mode=depth
+    - model.density_control.use_depth=true
+    - train.context_view_train=true
+    - dataset.re10k.dynamic_context_views=true
+    - train.vggt_distil=true
+  job:
+    name: main
+    chdir: null
+    override_dirname: +experiment=re10k_ablation_24v,dataset.re10k.dynamic_context_views=true,dataset.re10k.roots=[datasets/re10k],model.density_control.use_depth=true,model.encoder.head_mode=depth,train.context_view_train=true,train.vggt_distil=true,wandb.mode=disabled,wandb.name=DEBUG
+    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/ablation/re10k/DEBUG
+    choices:
+      experiment: re10k_ablation_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

DEBUG/.hydra/overrides.yaml

@@ -0,0 +1,9 @@
+- +experiment=re10k_ablation_24v
+- dataset.re10k.roots=[datasets/re10k]
+- wandb.mode=disabled
+- wandb.name=DEBUG
+- model.encoder.head_mode=depth
+- model.density_control.use_depth=true
+- train.context_view_train=true
+- dataset.re10k.dynamic_context_views=true
+- train.vggt_distil=true

DEBUG/main.log

@@ -0,0 +1,24 @@
+[2026-02-25 14:26:18,200][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:26:24,465][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(
+
+[2026-02-25 14:26:24,465][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)
+
+[2026-02-25 14:26:25,845][dinov2][INFO] - using MLP layer as FFN
+[2026-02-25 14:26:35,621][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=255` in the `DataLoader` to improve performance.
+
+[2026-02-25 14:26:56,525][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(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
+
+[2026-02-25 14:26:56,541][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)`.
+
+[2026-02-25 14:26:56,542][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(
+
+[2026-02-25 14:26:56,542][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)
+
+[2026-02-25 14:26:58,171][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]
+