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  1. ABLATION_0302_LapFreqSelect/main.log +7 -0
  2. ABLATION_0302_LapFreqSelect/train_ddp_process_1.log +1 -0
  3. ABLATION_0302_LapFreqSelect/train_ddp_process_2.log +1 -0
  4. ABLATION_0302_LapFreqSelect/train_ddp_process_3.log +1 -0
  5. ABLATION_0302_LapFreqSelect/train_ddp_process_4.log +1 -0
  6. ABLATION_0302_LapFreqSelect/train_ddp_process_6.log +1 -0
  7. ABLATION_0302_LapFreqSelect/train_ddp_process_7.log +1 -0
  8. ABLATION_0302_noTgtAlign/.hydra/config.yaml +1 -1
  9. ABLATION_0302_noTgtAlign/.hydra/hydra.yaml +1 -2
  10. ABLATION_0302_noTgtAlign/.hydra/overrides.yaml +0 -1
  11. ABLATION_0302_noTgtAlign/peak_vram_memory.json +6 -0
  12. ABLATION_0302_noTgtAlign/train_ddp_process_4.log +75 -0
  13. ABLATION_0302_noTgtAlign/wandb/debug-internal.log +11 -6
  14. ABLATION_0302_noTgtAlign/wandb/debug.log +21 -19
  15. ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/files/config.yaml +313 -0
  16. ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/files/output.log +278 -0
  17. ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/files/requirements.txt +173 -0
  18. ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/files/wandb-metadata.json +93 -0
  19. ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/logs/debug-core.log +84 -0
  20. ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/logs/debug.log +0 -0
ABLATION_0302_LapFreqSelect/main.log ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ [2026-03-04 01:26:41,277][dinov2][INFO] - using MLP layer as FFN
2
+ [2026-03-04 01:26:47,317][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
3
+ warnings.warn(
4
+
5
+ [2026-03-04 01:26:47,317][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
6
+ warnings.warn(msg)
7
+
ABLATION_0302_LapFreqSelect/train_ddp_process_1.log ADDED
@@ -0,0 +1 @@
 
 
1
+ [2026-03-04 01:26:54,799][dinov2][INFO] - using MLP layer as FFN
ABLATION_0302_LapFreqSelect/train_ddp_process_2.log ADDED
@@ -0,0 +1 @@
 
 
1
+ [2026-03-04 01:26:54,812][dinov2][INFO] - using MLP layer as FFN
ABLATION_0302_LapFreqSelect/train_ddp_process_3.log ADDED
@@ -0,0 +1 @@
 
 
1
+ [2026-03-04 01:26:54,820][dinov2][INFO] - using MLP layer as FFN
ABLATION_0302_LapFreqSelect/train_ddp_process_4.log ADDED
@@ -0,0 +1 @@
 
 
1
+ [2026-03-04 01:26:54,913][dinov2][INFO] - using MLP layer as FFN
ABLATION_0302_LapFreqSelect/train_ddp_process_6.log ADDED
@@ -0,0 +1 @@
 
 
1
+ [2026-03-04 01:26:54,939][dinov2][INFO] - using MLP layer as FFN
ABLATION_0302_LapFreqSelect/train_ddp_process_7.log ADDED
@@ -0,0 +1 @@
 
 
1
+ [2026-03-04 01:26:54,765][dinov2][INFO] - using MLP layer as FFN
ABLATION_0302_noTgtAlign/.hydra/config.yaml CHANGED
@@ -28,7 +28,7 @@ model:
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  refinement_hidden_dim: 32
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  aggregation_mode: mean
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  num_heads: 1
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- score_mode: random
32
  latent_dim: 128
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  num_latents: 64
34
  num_self_attn_per_block: 2
 
28
  refinement_hidden_dim: 32
29
  aggregation_mode: mean
30
  num_heads: 1
31
+ score_mode: absgrad
32
  latent_dim: 128
33
  num_latents: 64
34
  num_self_attn_per_block: 2
ABLATION_0302_noTgtAlign/.hydra/hydra.yaml CHANGED
@@ -115,11 +115,10 @@ hydra:
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  - +experiment=re10k_ablation_24v
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  - wandb.mode=online
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  - wandb.name=ABLATION_0302_noTgtAlign
118
- - model.density_control.score_mode=random
119
  job:
120
  name: main
121
  chdir: null
122
- override_dirname: +experiment=re10k_ablation_24v,model.density_control.score_mode=random,wandb.mode=online,wandb.name=ABLATION_0302_noTgtAlign
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  id: ???
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  num: ???
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  config_name: main
 
115
  - +experiment=re10k_ablation_24v
116
  - wandb.mode=online
117
  - wandb.name=ABLATION_0302_noTgtAlign
 
118
  job:
119
  name: main
120
  chdir: null
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+ override_dirname: +experiment=re10k_ablation_24v,wandb.mode=online,wandb.name=ABLATION_0302_noTgtAlign
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  id: ???
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  num: ???
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  config_name: main
ABLATION_0302_noTgtAlign/.hydra/overrides.yaml CHANGED
@@ -1,4 +1,3 @@
1
  - +experiment=re10k_ablation_24v
2
  - wandb.mode=online
3
  - wandb.name=ABLATION_0302_noTgtAlign
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- - model.density_control.score_mode=random
 
1
  - +experiment=re10k_ablation_24v
2
  - wandb.mode=online
3
  - wandb.name=ABLATION_0302_noTgtAlign
 
ABLATION_0302_noTgtAlign/peak_vram_memory.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
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+ "peak_memory_allocated_gb": 95.862,
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+ "peak_memory_reserved_gb": 110.58,
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+ "total_elapsed_hours": 3.11,
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+ "mode": "train"
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+ }
ABLATION_0302_noTgtAlign/train_ddp_process_4.log CHANGED
@@ -42,3 +42,78 @@ bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered int
42
  [2026-03-03 17:48:34,775][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.)
43
  result[selector] = overlay
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42
  [2026-03-03 17:48:34,775][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.)
43
  result[selector] = overlay
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45
+ [2026-03-03 18:01:02,021][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
46
+ result[selector] = overlay
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+
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+ [2026-03-03 18:13:25,680][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 18:25:47,338][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
54
+ [2026-03-03 18:31:57,697][dinov2][INFO] - using MLP layer as FFN
55
+ [2026-03-03 18:32:27,497][py.warnings][WARNING] - /venv/main/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
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+ warnings.warn(
57
+
58
+ [2026-03-03 18:32:27,501][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.
59
+ warnings.warn(msg)
60
+
61
+ [2026-03-03 18:32:42,134][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.
62
+ warnings.warn( # warn only once
63
+
64
+ [2026-03-03 18:33:01,313][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.
65
+ grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
66
+ bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
67
+ return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
68
+
69
+ [2026-03-03 18:33:01,426][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.)
70
+ result[selector] = overlay
71
+
72
+ [2026-03-03 18:34:34,275][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.
73
+ warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
74
+
75
+ [2026-03-03 18:45:20,241][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
76
+ result[selector] = overlay
77
+
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+ [2026-03-03 18:57:44,444][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
79
+ result[selector] = overlay
80
+
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+ [2026-03-03 19:10:06,141][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
82
+ result[selector] = overlay
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+
84
+ [2026-03-03 19:22:26,646][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 19:34:44,817][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.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 19:47:04,316][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 19:59:23,283][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 20:11:55,578][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 20:24:20,974][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.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 20:36:39,577][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 20:49:01,141][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 21:01:27,559][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 21:13:42,613][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 21:26:01,866][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.)
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+ result[selector] = overlay
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+
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+ [2026-03-03 21:38:35,396][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.)
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+ result[selector] = overlay
119
+
ABLATION_0302_noTgtAlign/wandb/debug-internal.log CHANGED
@@ -1,6 +1,11 @@
1
- {"time":"2026-03-03T17:35:55.708502915Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
2
- {"time":"2026-03-03T17:35:56.156384015Z","level":"INFO","msg":"stream: created new stream","id":"et94mfhp"}
3
- {"time":"2026-03-03T17:35:56.156522256Z","level":"INFO","msg":"handler: started","stream_id":"et94mfhp"}
4
- {"time":"2026-03-03T17:35:56.156785449Z","level":"INFO","msg":"stream: started","id":"et94mfhp"}
5
- {"time":"2026-03-03T17:35:56.156813659Z","level":"INFO","msg":"sender: started","stream_id":"et94mfhp"}
6
- {"time":"2026-03-03T17:35:56.156835559Z","level":"INFO","msg":"writer: started","stream_id":"et94mfhp"}
 
 
 
 
 
 
1
+ {"time":"2026-03-03T18:32:36.416950124Z","level":"INFO","msg":"stream: starting","core version":"0.25.0"}
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3
+ {"time":"2026-03-03T18:32:36.896847584Z","level":"INFO","msg":"handler: started","stream_id":"bjvibjti"}
4
+ {"time":"2026-03-03T18:32:36.897054806Z","level":"INFO","msg":"stream: started","id":"bjvibjti"}
5
+ {"time":"2026-03-03T18:32:36.897086516Z","level":"INFO","msg":"sender: started","stream_id":"bjvibjti"}
6
+ {"time":"2026-03-03T18:32:36.897099556Z","level":"INFO","msg":"writer: started","stream_id":"bjvibjti"}
7
+ {"time":"2026-03-03T21:38:44.775871388Z","level":"INFO","msg":"stream: closing","id":"bjvibjti"}
8
+ {"time":"2026-03-03T21:38:45.836130953Z","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
9
+ {"time":"2026-03-03T21:38:46.109942009Z","level":"INFO","msg":"handler: closed","stream_id":"bjvibjti"}
10
+ {"time":"2026-03-03T21:38:46.1101932Z","level":"INFO","msg":"sender: closed","stream_id":"bjvibjti"}
11
+ {"time":"2026-03-03T21:38:46.110223611Z","level":"INFO","msg":"stream: closed","id":"bjvibjti"}
ABLATION_0302_noTgtAlign/wandb/debug.log CHANGED
@@ -1,19 +1,21 @@
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+ warm_up_steps: 25
238
+ render_loss:
239
+ value:
240
+ lpips:
241
+ apply_after_step: 0
242
+ weight: 0.05
243
+ mse:
244
+ weight: 1
245
+ seed:
246
+ value: 111123
247
+ test:
248
+ value:
249
+ align_pose: false
250
+ compute_scores: true
251
+ error_threshold: 0.4
252
+ error_threshold_list:
253
+ - 0.2
254
+ - 0.4
255
+ - 0.6
256
+ - 0.8
257
+ - 1
258
+ nvs_view_N_list:
259
+ - 3
260
+ - 6
261
+ - 16
262
+ - 32
263
+ - 64
264
+ output_path: test/ablation/re10k
265
+ pose_align_steps: 100
266
+ pred_intrinsic: false
267
+ rot_opt_lr: 0.005
268
+ save_active_mask_image: false
269
+ save_compare: false
270
+ save_error_score_image: false
271
+ save_gs: false
272
+ save_image: false
273
+ save_sample_wise_metrics: true
274
+ save_video: false
275
+ threshold_mode: ratio
276
+ trans_opt_lr: 0.005
277
+ train:
278
+ value:
279
+ align_corners: false
280
+ beta_dist_param:
281
+ - 0.5
282
+ - 4
283
+ cam_scale_mode: sum
284
+ camera_loss: 10
285
+ context_view_train: false
286
+ ext_scale_detach: false
287
+ extended_visualization: false
288
+ intrinsic_scaling: false
289
+ one_sample_validation: null
290
+ print_log_every_n_steps: 10
291
+ scene_scale_reg_loss: 0.01
292
+ train_aux: true
293
+ train_gs_num: 1
294
+ train_target_set: true
295
+ use_refine_aux: false
296
+ verbose: false
297
+ vggt_cam_loss: true
298
+ vggt_distil: false
299
+ trainer:
300
+ value:
301
+ gradient_clip_val: 0.5
302
+ max_steps: 3001
303
+ num_nodes: 1
304
+ val_check_interval: 250
305
+ wandb:
306
+ value:
307
+ entity: scene-representation-group
308
+ mode: online
309
+ name: ABLATION_0302_noTgtAlign
310
+ project: DCSplat
311
+ tags:
312
+ - re10k
313
+ - 256x256
ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/files/output.log ADDED
@@ -0,0 +1,278 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
2
+
3
+ | Name | Type | Params | Mode
4
+ ------------------------------------------------------------------------
5
+ 0 | encoder | OurSplat | 888 M | train
6
+ 1 | density_control_module | DensityControlModule | 0 | train
7
+ 2 | decoder | DecoderSplattingCUDA | 0 | train
8
+ 3 | render_losses | ModuleList | 0 | train
9
+ 4 | density_control_losses | ModuleList | 0 | train
10
+ 5 | direct_losses | ModuleList | 0 | train
11
+ ------------------------------------------------------------------------
12
+ 888 M Trainable params
13
+ 0 Non-trainable params
14
+ 888 M Total params
15
+ 3,553.933 Total estimated model params size (MB)
16
+ 1202 Modules in train mode
17
+ 522 Modules in eval mode
18
+ Sanity Checking: | | 0/? [00:00<?, ?it/s][2026-03-03 17:35:58,952][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.
19
+
20
+ [2026-03-03 17:35:58,954][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.
21
+ warnings.warn( # warn only once
22
+
23
+ Validation epoch start on rank 0
24
+ Sanity Checking DataLoader 0: 0%| | 0/1 [00:00<?, ?it/s]validation step 0; scene = ['306e2b7785657539'];
25
+ target intrinsic: tensor(0.8595, device='cuda:0') tensor(0.8597, device='cuda:0')
26
+ pred intrinsic: tensor(0.8779, device='cuda:0') tensor(0.8773, device='cuda:0')
27
+ [rank0]:W0303 17:36:01.277000 870906 site-packages/torch/utils/cpp_extension.py:2425] TORCH_CUDA_ARCH_LIST is not set, all archs for visible cards are included for compilation.
28
+ [rank0]:W0303 17:36:01.277000 870906 site-packages/torch/utils/cpp_extension.py:2425] If this is not desired, please set os.environ['TORCH_CUDA_ARCH_LIST'] to specific architectures.
29
+ [2026-03-03 17:36:01,340][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
30
+ result[selector] = overlay
31
+
32
+ [2026-03-03 17:36:01,350][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)`.
33
+
34
+ Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
35
+ [2026-03-03 17:36:01,350][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.
36
+ warnings.warn(
37
+
38
+ [2026-03-03 17:36:01,351][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.
39
+ warnings.warn(msg)
40
+
41
+ Loading model from: /venv/main/lib/python3.12/site-packages/lpips/weights/v0.1/vgg.pth
42
+ [2026-03-03 17:36:03,034][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.)
43
+ return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
44
+
45
+ Sanity Checking DataLoader 0: 100%|████████████████████████████████████████████████████████████████████| 1/1 [00:03<00:00, 0.27it/s][2026-03-03 17:36:03,326][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.
46
+
47
+ [2026-03-03 17:36:03,327][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.
48
+
49
+ [2026-03-03 17:36:03,328][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.
50
+
51
+ [2026-03-03 17:36:03,328][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.
52
+
53
+ [2026-03-03 17:36:03,328][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.
54
+
55
+ 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]]
56
+ [2026-03-03 17:36:13,075][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.
57
+ grad.sizes() = [57, 256, 1, 1], strides() = [256, 1, 256, 256]
58
+ bucket_view.sizes() = [57, 256, 1, 1], strides() = [256, 1, 1, 1] (Triggered internally at /pytorch/torch/csrc/distributed/c10d/reducer.cpp:334.)
59
+ return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
60
+
61
+ [2026-03-03 17:36:13,149][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
62
+ result[selector] = overlay
63
+
64
+ Epoch 0: | | 9/? [00:41<00:00, 0.22it/s, v_num=mfhp]train step 10; scene = [['08c26703c4987851']]; loss = 0.814219
65
+ Epoch 0: | | 10/? [00:45<00:00, 0.22it/s, v_num=mfhp]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]]
66
+ Epoch 0: | | 19/? [01:18<00:00, 0.24it/s, v_num=mfhp]train step 20; scene = [['4012c15c8381568b'], ['af08406c5a9a43a0'], ['9f9f9beffb86fad7'], ['fc8d08df6c875cb0']]; loss = 0.247813
67
+ Epoch 0: | | 20/? [01:21<00:00, 0.24it/s, v_num=mfhp]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]]
68
+ Epoch 0: | | 24/? [01:35<00:00, 0.25it/s, v_num=mfhp][2026-03-03 17:37:46,051][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.
69
+ warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
70
+
71
+ Epoch 0: | | 29/? [01:53<00:00, 0.25it/s, v_num=mfhp]train step 30; scene = [['00980329a3221f1c'], ['1e7c432d2207b6f2'], ['af2748330e5243d0']]; loss = 0.187076
72
+ Epoch 0: | | 30/? [01:57<00:00, 0.26it/s, v_num=mfhp]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]]
73
+ Epoch 0: | | 39/? [02:30<00:00, 0.26it/s, v_num=mfhp]train step 40; scene = [['79a9385753d426bc'], ['593538382d2dc847'], ['c9c67636b9d521be']]; loss = 0.155894
74
+ Epoch 0: | | 40/? [02:34<00:00, 0.26it/s, v_num=mfhp]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]]
75
+ Epoch 0: | | 49/? [03:06<00:00, 0.26it/s, v_num=mfhp]train step 50; scene = [['579a11551b3315d9'], ['c9dd64b7415e788e'], ['6f3fb517d1798d03']]; loss = 0.144112
76
+ Epoch 0: | | 50/? [03:09<00:00, 0.26it/s, v_num=mfhp]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]]
77
+ Epoch 0: | | 59/? [03:43<00:00, 0.26it/s, v_num=mfhp]train step 60; scene = [['07916b8004a8e336'], ['e51ef9945ae527c4'], ['db84f84b1d775bb8'], ['92ed61f8e16b7e67']]; loss = 0.142657
78
+ Epoch 0: | | 60/? [03:46<00:00, 0.26it/s, v_num=mfhp]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]]
79
+ Epoch 0: | | 69/? [04:20<00:00, 0.26it/s, v_num=mfhp]train step 70; scene = [['c34efa1505a0cfaa'], ['a3d0cca9fb57fd85'], ['43d0e6dce7bb1e95'], ['d8c2f0a3734cb493']]; loss = 0.100209
80
+ Epoch 0: | | 70/? [04:23<00:00, 0.27it/s, v_num=mfhp]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]]
81
+ Epoch 0: | | 79/? [04:56<00:00, 0.27it/s, v_num=mfhp]train step 80; scene = [['24d756c820744e31'], ['cd6c21656a85e9b9'], ['f3b24cf238154fc0']]; loss = 0.096154
82
+ Epoch 0: | | 80/? [05:00<00:00, 0.27it/s, v_num=mfhp]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]]
83
+ Epoch 0: | | 89/? [05:34<00:00, 0.27it/s, v_num=mfhp]train step 90; scene = [['617b4bc98d7e0bb6'], ['666e4a9aba27bb64']]; loss = 0.099716
84
+ Epoch 0: | | 90/? [05:37<00:00, 0.27it/s, v_num=mfhp]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]]
85
+ Epoch 0: | | 99/? [06:11<00:00, 0.27it/s, v_num=mfhp]train step 100; scene = [['12fee7f1978d52f1'], ['c963bb60939e2d81']]; loss = 0.108745
86
+ Epoch 0: | | 100/? [06:15<00:00, 0.27it/s, v_num=mfhp]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]]
87
+ Epoch 0: | | 109/? [06:48<00:00, 0.27it/s, v_num=mfhp]train step 110; scene = [['47396d5a5299873e']]; loss = 0.127887
88
+ Epoch 0: | | 110/? [06:52<00:00, 0.27it/s, v_num=mfhp]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]]
89
+ Epoch 0: | | 119/? [07:25<00:00, 0.27it/s, v_num=mfhp]train step 120; scene = [['9bd7044e7cbf8e60'], ['76e44cf6b5658b26']]; loss = 0.085511
90
+ Epoch 0: | | 120/? [07:29<00:00, 0.27it/s, v_num=mfhp]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]]
91
+ Epoch 0: | | 129/? [08:02<00:00, 0.27it/s, v_num=mfhp]train step 130; scene = [['a8cef6a851fbea3c'], ['b6699f4d039a5b06'], ['55cf2bbe9e017ea4'], ['6b0dd861e1ab1fec'], ['14db202c335af709'], ['8b6ff6c5153a7794'], ['b75f3820760d835c'], ['f7dbc855fd2a7669'], ['cfb20f8971e6a591'], ['95f2be7bb8303f50'], ['ff422469e034ae11'], ['5a2ad43377e9d18d']]; loss = 0.110932
92
+ Epoch 0: | | 130/? [08:06<00:00, 0.27it/s, v_num=mfhp]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]]
93
+ Epoch 0: | | 139/? [08:38<00:00, 0.27it/s, v_num=mfhp]train step 140; scene = [['f62a962df5c26a1a'], ['b076420679a04731']]; loss = 0.078446
94
+ Epoch 0: | | 140/? [08:42<00:00, 0.27it/s, v_num=mfhp]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]]
95
+ Epoch 0: | | 149/? [09:16<00:00, 0.27it/s, v_num=mfhp]train step 150; scene = [['a52d26a78b04aebd']]; loss = 0.071345
96
+ Epoch 0: | | 150/? [09:19<00:00, 0.27it/s, v_num=mfhp]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]]
97
+ Epoch 0: | | 159/? [09:52<00:00, 0.27it/s, v_num=mfhp]train step 160; scene = [['268fbffc6c479d5b']]; loss = 0.069589
98
+ Epoch 0: | | 160/? [09:56<00:00, 0.27it/s, v_num=mfhp]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]]
99
+ Epoch 0: | | 169/? [10:30<00:00, 0.27it/s, v_num=mfhp]train step 170; scene = [['719e2e8912e4eed3'], ['a3e51565a737569f']]; loss = 0.156401
100
+ Epoch 0: | | 170/? [10:34<00:00, 0.27it/s, v_num=mfhp]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]]
101
+ Epoch 0: | | 179/? [11:07<00:00, 0.27it/s, v_num=mfhp]train step 180; scene = [['f44b9aa76a94a0a3']]; loss = 0.112078
102
+ Epoch 0: | | 180/? [11:10<00:00, 0.27it/s, v_num=mfhp]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]]
103
+ Epoch 0: | | 189/? [11:44<00:00, 0.27it/s, v_num=mfhp]train step 190; scene = [['71bb669d936a5718'], ['a47203cfd5e0a478'], ['4b009f82cf5c7098']]; loss = 0.108068
104
+ Epoch 0: | | 190/? [11:48<00:00, 0.27it/s, v_num=mfhp]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]]
105
+ Epoch 0: | | 199/? [12:20<00:00, 0.27it/s, v_num=mfhp]train step 200; scene = [['dd5ec950a01c42a0'], ['6d0db0358f7e051e'], ['983fe650a925ec1b']]; loss = 0.115138
106
+ Epoch 0: | | 200/? [12:24<00:00, 0.27it/s, v_num=mfhp]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]]
107
+ [2026-03-03 17:48:34,774][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
108
+ result[selector] = overlay
109
+
110
+ Epoch 0: | | 209/? [13:04<00:00, 0.27it/s, v_num=mfhp]train step 210; scene = [['9be9b273b3c22c61'], ['4b5883872c9b860c']]; loss = 0.090815
111
+ Epoch 0: | | 210/? [13:07<00:00, 0.27it/s, v_num=mfhp]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]]
112
+ Epoch 0: | | 219/? [13:41<00:00, 0.27it/s, v_num=mfhp]train step 220; scene = [['a3b6faa8d238d993'], ['df9ba36fbe753843']]; loss = 0.064624
113
+ Epoch 0: | | 220/? [13:45<00:00, 0.27it/s, v_num=mfhp]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]]
114
+ Epoch 0: | | 229/? [14:18<00:00, 0.27it/s, v_num=mfhp]train step 230; scene = [['ca04de3c55cd1ca0'], ['3d90d586b33daa63'], ['d1772c09b4b6d95f'], ['03d05f69a1cab4f8'], ['60d296908f43a97a'], ['37c400e282bc481e']]; loss = 0.075824
115
+ Epoch 0: | | 230/? [14:22<00:00, 0.27it/s, v_num=mfhp]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]]
116
+ Epoch 0: | | 239/? [14:56<00:00, 0.27it/s, v_num=mfhp]train step 240; scene = [['9794641b7e015578']]; loss = 0.116614
117
+ Epoch 0: | | 240/? [14:59<00:00, 0.27it/s, v_num=mfhp]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]]
118
+ Epoch 0: | | 249/? [15:33<00:00, 0.27it/s, v_num=mfhp]train step 250; scene = [['93dff1b985f2c7f9']]; loss = 0.094535
119
+ Epoch 0: | | 250/? [15:37<00:00, 0.27it/s, v_num=mfhp]Validation epoch start on rank 0
120
+ Validation: | | 0/? [00:00<?, ?it/s]validation step 250; scene = ['49b8f80c849dc341'];
121
+ target intrinsic: tensor(0.8891, device='cuda:0') tensor(0.8894, device='cuda:0') | 0/1 [00:00<?, ?it/s]
122
+ pred intrinsic: tensor(0.8626, device='cuda:0') tensor(0.8625, device='cuda:0')
123
+ [2026-03-03 17:51:44,568][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
124
+ result[selector] = overlay
125
+
126
+ Epoch 0: | | 250/? [15:38<00:00, 0.27it/s, v_num=mfhp]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]]
127
+ Epoch 0: | | 259/? [16:10<00:00, 0.27it/s, v_num=mfhp]train step 260; scene = [['b2288bf7003d5d4d']]; loss = 0.079294
128
+ Epoch 0: | | 260/? [16:13<00:00, 0.27it/s, v_num=mfhp]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]]
129
+ Epoch 0: | | 269/? [16:47<00:00, 0.27it/s, v_num=mfhp]train step 270; scene = [['013ec74a4fde6737'], ['78e816776b064fc4'], ['1b778f72bbee1f27'], ['c71549de92ecb2e4'], ['8e16c8644efeec52'], ['35c5fc80e85db7cd'], ['34c8c62d878eca66'], ['203a5fd3a45ac4a7']]; loss = 0.062206
130
+ Epoch 0: | | 270/? [16:51<00:00, 0.27it/s, v_num=mfhp]context = [[160, 174, 177, 178, 192, 200], [34, 52, 54, 56, 58, 67], [78, 85, 101, 102, 109, 118], [123, 135, 136, 144, 150, 155]]target = [[169, 195, 175, 174, 194, 189], [55, 53, 57, 48, 40, 66], [90, 108, 110, 84, 109, 83], [131, 130, 127, 124, 139, 133]]
131
+ Epoch 0: | | 279/? [17:25<00:00, 0.27it/s, v_num=mfhp]train step 280; scene = [['75335793f866b96d'], ['e9d9dc952f5bbd83']]; loss = 0.040364
132
+ Epoch 0: | | 280/? [17:29<00:00, 0.27it/s, v_num=mfhp]context = [[2, 31], [39, 73], [69, 98], [58, 92], [109, 146], [27, 59], [83, 114], [199, 231], [13, 50], [77, 109], [72, 105], [7, 45]]target = [[20, 26], [71, 57], [92, 77], [61, 72], [125, 129], [44, 51], [110, 101], [211, 202], [27, 17], [98, 104], [80, 89], [36, 37]]
133
+ Epoch 0: | | 289/? [18:00<00:00, 0.27it/s, v_num=mfhp]train step 290; scene = [['51252022ddf74fb9'], ['8dd73309b133b8bf'], ['9e8db62a9b3cbd5e'], ['d41e59ee023e977b'], ['ce1a9465dc08ef4c'], ['e7887dec76685627']]; loss = 0.071847
134
+ Epoch 0: | | 290/? [18:04<00:00, 0.27it/s, v_num=mfhp]context = [[74, 83, 85, 88, 89, 93, 100, 101, 108, 119, 122, 123], [17, 20, 23, 24, 28, 30, 32, 35, 48, 52, 64, 66]]target = [[110, 79, 95, 92, 111, 109, 99, 89, 77, 98, 81, 102], [28, 29, 52, 36, 54, 64, 49, 23, 40, 58, 31, 27]]
135
+ Epoch 0: | | 299/? [18:38<00:00, 0.27it/s, v_num=mfhp]train step 300; scene = [['0c5d83212982c0ec'], ['00793a8a3b268d7c'], ['47a9b1e96499a466'], ['a1fb990016d7b3af']]; loss = 0.051479
136
+ Epoch 0: | | 300/? [18:42<00:00, 0.27it/s, v_num=mfhp]context = [[83, 88, 89, 91, 92, 100, 102, 104, 107, 111, 113, 115, 129, 132, 134, 135, 140, 142, 145, 150, 154, 173, 175, 180]]target = [[115, 140, 130, 169, 114, 163, 175, 177, 104, 152, 151, 89, 154, 123, 132, 85, 122, 119, 155, 91, 117, 171, 158, 157]]
137
+ Epoch 0: | | 309/? [19:14<00:00, 0.27it/s, v_num=mfhp]train step 310; scene = [['9b73ab94b5c43711'], ['8c845b940aa8244c'], ['b2789c1a5c127a02'], ['3db6c0e172d18826']]; loss = 0.066993
138
+ Epoch 0: | | 310/? [19:18<00:00, 0.27it/s, v_num=mfhp]context = [[30, 47, 66], [16, 31, 46], [10, 28, 40], [43, 68, 75], [21, 43, 63], [15, 29, 46], [36, 44, 66], [17, 39, 59]]target = [[51, 41, 31], [29, 43, 44], [23, 35, 33], [64, 71, 63], [48, 59, 27], [45, 29, 27], [60, 42, 50], [34, 26, 47]]
139
+ Epoch 0: | | 319/? [19:52<00:00, 0.27it/s, v_num=mfhp]train step 320; scene = [['591cd9d079cd7842'], ['3dd7802a2c93a865']]; loss = 0.084790
140
+ Epoch 0: | | 320/? [19:56<00:00, 0.27it/s, v_num=mfhp]context = [[4, 22, 28, 31, 34, 37, 45, 46], [26, 28, 38, 41, 46, 55, 57, 59], [129, 139, 145, 150, 151, 158, 167, 169]]target = [[7, 41, 13, 5, 20, 27, 32, 44], [29, 32, 54, 28, 40, 52, 43, 33], [137, 143, 146, 152, 130, 131, 145, 133]]
141
+ Epoch 0: | | 329/? [20:30<00:00, 0.27it/s, v_num=mfhp]train step 330; scene = [['30d9f6321281dade'], ['2a08fac923c9e50d']]; loss = 0.063144
142
+ Epoch 0: | | 330/? [20:34<00:00, 0.27it/s, v_num=mfhp]context = [[4, 6, 12, 14, 24, 41, 42, 44, 45, 48, 49, 53], [102, 106, 112, 113, 118, 122, 125, 130, 134, 140, 149, 151]]target = [[50, 42, 7, 28, 11, 33, 20, 45, 25, 8, 22, 16], [135, 134, 113, 128, 139, 112, 117, 108, 122, 127, 138, 120]]
143
+ Epoch 0: | | 339/? [21:06<00:00, 0.27it/s, v_num=mfhp]train step 340; scene = [['bd9f2096d355b1b8'], ['07d3325178e7a790'], ['8204d757ce43dda8']]; loss = 0.062880
144
+ Epoch 0: | | 340/? [21:10<00:00, 0.27it/s, v_num=mfhp]context = [[87, 88, 93, 105, 107, 111, 116, 118, 123, 131, 132, 136], [104, 111, 116, 121, 122, 127, 131, 132, 136, 137, 142, 153]]target = [[114, 108, 107, 130, 95, 129, 118, 104, 116, 96, 109, 99], [127, 106, 145, 129, 114, 109, 143, 150, 111, 133, 146, 122]]
145
+ Epoch 0: | | 349/? [21:44<00:00, 0.27it/s, v_num=mfhp]train step 350; scene = [['9d0bfbe5b7f98545'], ['06a16655c8e8ad9c']]; loss = 0.105639
146
+ Epoch 0: | | 350/? [21:48<00:00, 0.27it/s, v_num=mfhp]context = [[33, 52, 53, 56, 58, 64, 73, 75], [100, 102, 115, 117, 130, 134, 139, 142], [223, 225, 240, 242, 255, 256, 263, 267]]target = [[41, 60, 53, 63, 72, 38, 37, 73], [122, 141, 116, 138, 110, 119, 118, 135], [260, 237, 261, 264, 238, 255, 243, 253]]
147
+ Epoch 0: | | 359/? [22:21<00:00, 0.27it/s, v_num=mfhp]train step 360; scene = [['73b27f4f150327af'], ['169aaaf51ef3849c'], ['068a8406f1a383d8'], ['a9936b77895f33b3']]; loss = 0.078213
148
+ Epoch 0: | | 360/? [22:25<00:00, 0.27it/s, v_num=mfhp]context = [[0, 14, 18, 23, 25, 32, 42, 45, 46, 49, 53, 55, 59, 60, 61, 74, 78, 80, 84, 85, 89, 90, 92, 97]]target = [[50, 81, 69, 64, 38, 86, 91, 16, 27, 33, 44, 90, 48, 23, 8, 79, 6, 39, 42, 36, 82, 78, 59, 54]]
149
+ Epoch 0: | | 369/? [22:57<00:00, 0.27it/s, v_num=mfhp]train step 370; scene = [['8673faf0a9d48165'], ['99a0790d72e6c2af'], ['6cbbe9075b0d2138']]; loss = 0.060302
150
+ Epoch 0: | | 370/? [23:01<00:00, 0.27it/s, v_num=mfhp]context = [[61, 63, 65, 72, 73, 74, 76, 89, 92, 94, 100, 101, 120, 124, 126, 127, 136, 140, 144, 145, 150, 152, 156, 158]]target = [[121, 100, 147, 98, 148, 143, 126, 63, 73, 141, 79, 119, 115, 106, 153, 101, 120, 71, 91, 62, 105, 84, 151, 145]]
151
+ Epoch 0: | | 379/? [23:35<00:00, 0.27it/s, v_num=mfhp]train step 380; scene = [['656330f47c5df010'], ['6dfb89a98e14ca66']]; loss = 0.060539
152
+ Epoch 0: | | 380/? [23:38<00:00, 0.27it/s, v_num=mfhp]context = [[210, 215, 223, 225, 231, 234, 235, 249], [28, 32, 35, 53, 54, 56, 57, 68], [149, 150, 154, 155, 174, 177, 181, 183]]target = [[241, 236, 218, 230, 223, 228, 239, 227], [55, 46, 44, 49, 30, 33, 60, 45], [157, 160, 161, 164, 170, 181, 168, 166]]
153
+ Epoch 0: | | 389/? [24:12<00:00, 0.27it/s, v_num=mfhp]train step 390; scene = [['723f94d150ab09f2'], ['393cdfb7e832d285'], ['14900b71ac66b7bd'], ['452625cd6b071b87'], ['281599bbab3e73dd'], ['0a2b42e240751d33']]; loss = 0.071231
154
+ Epoch 0: | | 390/? [24:15<00:00, 0.27it/s, v_num=mfhp]context = [[48, 52, 80], [14, 47, 60], [142, 159, 187], [78, 120, 121], [6, 24, 45], [47, 68, 90], [15, 19, 48], [9, 20, 52]]target = [[51, 50, 70], [45, 15, 56], [172, 148, 157], [120, 98, 109], [20, 33, 11], [83, 84, 73], [45, 30, 32], [16, 36, 45]]
155
+ Epoch 0: | | 399/? [24:47<00:00, 0.27it/s, v_num=mfhp]train step 400; scene = [['4303746d8f23f16b'], ['0fe8246bb7e2fe40'], ['b7d77240852d6a52'], ['6e5505414fd63528'], ['44985936f68c3a36'], ['1550f1b4fff1f2a4'], ['cea3d842c3285c65'], ['b34bb5f53856d34f']]; loss = 0.088220
156
+ Epoch 0: | | 400/? [24:51<00:00, 0.27it/s, v_num=mfhp]context = [[9, 20, 28, 31, 34, 53], [189, 196, 216, 223, 230, 232], [74, 77, 86, 88, 98, 118], [132, 145, 157, 168, 179, 181]]target = [[51, 32, 14, 50, 45, 34], [212, 226, 201, 205, 215, 222], [93, 113, 107, 94, 83, 79], [161, 139, 177, 151, 156, 172]]
157
+ [2026-03-03 18:01:02,020][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
158
+ result[selector] = overlay
159
+
160
+ Epoch 0: | | 409/? [25:25<00:00, 0.27it/s, v_num=mfhp]train step 410; scene = [['144e1ec915e46d29'], ['b290b6a0afa1dac7'], ['b3d84dba6581c3d9']]; loss = 0.065141
161
+ Epoch 0: | | 410/? [25:29<00:00, 0.27it/s, v_num=mfhp]context = [[26, 40, 41, 50, 55, 58], [159, 174, 179, 192, 198, 201], [2, 3, 5, 7, 24, 40], [187, 204, 205, 208, 217, 221]]target = [[38, 32, 44, 52, 42, 27], [198, 191, 196, 162, 163, 169], [33, 17, 23, 37, 15, 39], [214, 211, 195, 220, 210, 192]]
162
+ Epoch 0: | | 419/? [26:02<00:00, 0.27it/s, v_num=mfhp]train step 420; scene = [['a1dff9c50d92dc9c']]; loss = 0.056085
163
+ Epoch 0: | | 420/? [26:06<00:00, 0.27it/s, v_num=mfhp]context = [[11, 13, 14, 26, 27, 41, 46, 51], [151, 155, 161, 167, 172, 176, 191, 195], [49, 52, 55, 57, 59, 74, 79, 90]]target = [[25, 44, 17, 33, 37, 24, 38, 40], [193, 191, 194, 162, 158, 176, 163, 171], [87, 85, 67, 58, 86, 68, 83, 78]]
164
+ Epoch 0: | | 429/? [26:39<00:00, 0.27it/s, v_num=mfhp]train step 430; scene = [['36664e22fd10a141'], ['0474328f4cefd619']]; loss = 0.051113
165
+ Epoch 0: | | 430/? [26:42<00:00, 0.27it/s, v_num=mfhp]context = [[123, 130, 131, 132, 136, 154, 169, 173], [35, 36, 47, 49, 65, 67, 76, 86], [33, 34, 39, 41, 59, 64, 65, 73]]target = [[145, 147, 167, 130, 146, 170, 138, 137], [59, 38, 49, 42, 70, 76, 39, 40], [56, 44, 36, 35, 38, 45, 48, 58]]
166
+ Epoch 0: | | 439/? [27:17<00:00, 0.27it/s, v_num=mfhp]train step 440; scene = [['342099a48847f4f6'], ['5ad0327426e3718b'], ['c25b314716aa6b10'], ['c91e2b5399b14430'], ['e1d9ade67e615bd8'], ['46df912c9748215b']]; loss = 0.066512
167
+ Epoch 0: | | 440/? [27:20<00:00, 0.27it/s, v_num=mfhp]context = [[0, 33], [10, 57], [36, 82], [46, 79], [62, 99], [33, 79], [11, 52], [1, 34], [0, 37], [26, 62], [18, 67], [96, 138]]target = [[22, 18], [37, 23], [44, 54], [69, 50], [68, 69], [69, 56], [49, 41], [27, 30], [31, 17], [42, 32], [59, 54], [108, 115]]
168
+ Epoch 0: | | 449/? [27:54<00:00, 0.27it/s, v_num=mfhp]train step 450; scene = [['e19c6facac1c9624'], ['5244830b7357365b'], ['b80c2522b1070e2f'], ['6ea0ff32c8ea695c'], ['2f311b2bbbeb5940'], ['3f7992e72a096099']]; loss = 0.069044
169
+ Epoch 0: | | 450/? [27:57<00:00, 0.27it/s, v_num=mfhp]context = [[41, 42, 44, 49, 57, 66, 68, 73, 75, 76, 83, 91], [4, 5, 15, 24, 32, 35, 37, 38, 45, 47, 49, 56]]target = [[79, 77, 90, 46, 81, 83, 56, 68, 89, 70, 84, 57], [7, 8, 40, 37, 32, 10, 25, 35, 23, 43, 48, 52]]
170
+ Epoch 0: | | 459/? [28:31<00:00, 0.27it/s, v_num=mfhp]train step 460; scene = [['46fb6702ed1b9967'], ['bdc3f978b0d3aa8f']]; loss = 0.056139
171
+ Epoch 0: | | 460/? [28:35<00:00, 0.27it/s, v_num=mfhp]context = [[65, 66, 69, 78, 84, 85, 90, 91, 99, 103, 112, 116], [39, 40, 52, 63, 64, 68, 72, 73, 83, 87, 90, 92]]target = [[99, 86, 101, 76, 84, 77, 105, 66, 89, 75, 100, 106], [84, 61, 68, 82, 59, 47, 51, 71, 85, 56, 57, 50]]
172
+ Epoch 0: | | 469/? [29:07<00:00, 0.27it/s, v_num=mfhp]train step 470; scene = [['2c88995e05a17d17'], ['2b1f47da224557a3'], ['62216d162b71b5b4'], ['61d39a97cb69d99f'], ['42000d5a83b48ee4'], ['cc8480640599f9f3']]; loss = 0.065505
173
+ Epoch 0: | | 470/? [29:10<00:00, 0.27it/s, v_num=mfhp]context = [[1, 6, 15, 26, 28, 41, 46, 52], [0, 1, 3, 5, 14, 26, 32, 37], [52, 56, 58, 79, 87, 88, 93, 97]]target = [[24, 22, 32, 14, 42, 21, 12, 37], [14, 10, 11, 25, 12, 34, 7, 32], [62, 60, 80, 87, 63, 55, 78, 88]]
174
+ Epoch 0: | | 479/? [29:44<00:00, 0.27it/s, v_num=mfhp]train step 480; scene = [['2e3bb7fb33e1ed30'], ['7460f503eb18fa6a'], ['bde49071d2088850'], ['e80016be3043dfa4']]; loss = 0.097861
175
+ Epoch 0: | | 480/? [29:47<00:00, 0.27it/s, v_num=mfhp]context = [[9, 15, 37, 47], [0, 5, 7, 35], [101, 113, 134, 153], [64, 88, 93, 107], [18, 44, 56, 62], [31, 46, 58, 75]]target = [[45, 27, 28, 18], [10, 8, 14, 3], [128, 143, 110, 129], [87, 84, 90, 94], [29, 31, 40, 36], [52, 72, 33, 56]]
176
+ Epoch 0: | | 489/? [30:21<00:00, 0.27it/s, v_num=mfhp]train step 490; scene = [['83085493f4bc18d2']]; loss = 0.092899
177
+ Epoch 0: | | 490/? [30:25<00:00, 0.27it/s, v_num=mfhp]context = [[0, 3, 7, 12, 38, 40, 45, 48], [25, 28, 57, 62, 69, 70, 76, 78], [25, 34, 37, 39, 41, 53, 65, 67]]target = [[15, 7, 39, 47, 42, 26, 25, 5], [63, 40, 59, 31, 75, 34, 47, 39], [58, 35, 53, 33, 65, 44, 38, 31]]
178
+ Epoch 0: | | 499/? [30:58<00:00, 0.27it/s, v_num=mfhp]train step 500; scene = [['1241bcb5732a9502'], ['d33a9e90e1416efb']]; loss = 0.044272
179
+ Epoch 0: | | 500/? [31:02<00:00, 0.27it/s, v_num=mfhp]Validation epoch start on rank 0
180
+ Validation: | | 0/? [00:00<?, ?it/s]validation step 500; scene = ['73d6f935f31b3fd4'];
181
+ target intrinsic: tensor(0.8576, device='cuda:0') tensor(0.8579, device='cuda:0') | 0/1 [00:00<?, ?it/s]
182
+ pred intrinsic: tensor(0.8589, device='cuda:0') tensor(0.8561, device='cuda:0')
183
+ [2026-03-03 18:07:09,779][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
184
+ result[selector] = overlay
185
+
186
+ Epoch 0: | | 500/? [31:03<00:00, 0.27it/s, v_num=mfhp]context = [[1, 21, 23, 29, 39, 48, 49, 52], [17, 18, 20, 28, 34, 39, 45, 51], [99, 103, 106, 110, 116, 136, 138, 140]]target = [[4, 46, 41, 43, 40, 37, 39, 25], [30, 48, 22, 42, 44, 31, 38, 37], [112, 108, 133, 116, 111, 125, 123, 127]]
187
+ Epoch 0: | | 509/? [31:35<00:00, 0.27it/s, v_num=mfhp]train step 510; scene = [['0eed4548041bea8e'], ['277a96ce456580f4']]; loss = 0.051501
188
+ Epoch 0: | | 510/? [31:39<00:00, 0.27it/s, v_num=mfhp]context = [[3, 13, 21, 24, 27, 28, 30, 37, 42, 44, 46, 55, 62, 65, 69, 71, 73, 80, 83, 84, 89, 95, 97, 100]]target = [[51, 81, 54, 89, 55, 45, 10, 19, 21, 68, 48, 67, 33, 69, 8, 25, 64, 83, 37, 29, 24, 82, 99, 46]]
189
+ Epoch 0: | | 519/? [32:13<00:00, 0.27it/s, v_num=mfhp]train step 520; scene = [['625e3aa0ff734714'], ['395802511d26f32e'], ['39343936591c28de']]; loss = 0.137702
190
+ Epoch 0: | | 520/? [32:17<00:00, 0.27it/s, v_num=mfhp]context = [[13, 16, 17, 22, 25, 30, 35, 39, 53, 56, 62, 68, 78, 87, 90, 92, 93, 95, 99, 100, 103, 106, 107, 110]]target = [[39, 96, 58, 28, 16, 59, 44, 17, 26, 83, 103, 31, 57, 35, 107, 51, 27, 77, 46, 30, 100, 91, 93, 97]]
191
+ Epoch 0: | | 529/? [32:49<00:00, 0.27it/s, v_num=mfhp]train step 530; scene = [['0c199c575b699444'], ['70d878da47f984e4'], ['15f77c76ea744f99'], ['e54b5eec8cc47776'], ['1969ed97e68d83d9'], ['c7cf9b63dc3e5830'], ['bcef3076b93012b1'], ['ab2680bf91942e23']]; loss = 0.082492
192
+ Epoch 0: | | 530/? [32:53<00:00, 0.27it/s, v_num=mfhp]context = [[78, 84, 85, 92, 124, 127], [14, 15, 24, 52, 64, 68], [1, 6, 16, 20, 22, 40], [33, 51, 61, 65, 66, 70]]target = [[104, 103, 94, 96, 88, 82], [15, 53, 58, 47, 56, 60], [31, 10, 18, 26, 23, 24], [37, 69, 62, 65, 49, 64]]
193
+ Epoch 0: | | 539/? [33:26<00:00, 0.27it/s, v_num=mfhp]train step 540; scene = [['a071d9276f6a9272']]; loss = 0.114086
194
+ Epoch 0: | | 540/? [33:30<00:00, 0.27it/s, v_num=mfhp]context = [[35, 46, 47, 49, 52, 54, 55, 56, 57, 65, 66, 85], [51, 54, 55, 59, 68, 77, 82, 86, 87, 93, 105, 106]]target = [[69, 68, 75, 48, 44, 79, 53, 74, 60, 81, 72, 71], [77, 73, 88, 59, 67, 61, 102, 62, 93, 75, 95, 105]]
195
+ Epoch 0: | | 549/? [34:04<00:00, 0.27it/s, v_num=mfhp]train step 550; scene = [['836250796ea45b6c']]; loss = 0.085734
196
+ Epoch 0: | | 550/? [34:08<00:00, 0.27it/s, v_num=mfhp]context = [[30, 34, 39, 45, 47, 79, 80, 83], [16, 21, 22, 24, 33, 42, 51, 60], [115, 128, 129, 138, 141, 150, 156, 163]]target = [[79, 51, 62, 77, 46, 54, 33, 49], [27, 59, 20, 41, 21, 48, 58, 30], [133, 125, 124, 159, 122, 129, 155, 119]]
197
+ Epoch 0: | | 559/? [34:42<00:00, 0.27it/s, v_num=mfhp]train step 560; scene = [['d70ca840b3c5aec9'], ['65c3f29c43dd1e63'], ['d3917d0a1eda2a1f'], ['5c83dfc8f9ab44fa']]; loss = 0.060085
198
+ Epoch 0: | | 560/? [34:45<00:00, 0.27it/s, v_num=mfhp]context = [[62, 66, 67, 68, 71, 87, 88, 92, 100, 108, 110, 122], [153, 160, 165, 168, 171, 177, 178, 181, 183, 185, 200, 205]]target = [[95, 104, 83, 68, 115, 81, 74, 106, 119, 79, 90, 72], [204, 165, 196, 185, 197, 198, 195, 187, 155, 188, 154, 201]]
199
+ Epoch 0: | | 569/? [35:19<00:00, 0.27it/s, v_num=mfhp]train step 570; scene = [['9d8ddcdbe1f7ac42'], ['721df0f45094ca34'], ['fdbfe35f5940d3ad']]; loss = 0.045826
200
+ Epoch 0: | | 570/? [35:23<00:00, 0.27it/s, v_num=mfhp]context = [[23, 34, 41, 42, 48, 54, 56, 59, 60, 63, 66, 71, 74, 79, 88, 90, 92, 93, 95, 97, 110, 111, 119, 120]]target = [[30, 27, 28, 53, 31, 75, 85, 54, 77, 111, 76, 25, 44, 52, 33, 41, 69, 89, 73, 68, 26, 93, 83, 119]]
201
+ Epoch 0: | | 579/? [35:57<00:00, 0.27it/s, v_num=mfhp]train step 580; scene = [['88a0267e41b851f0'], ['df71fbb70b19cbc3'], ['1c713c10ecf5a0c9']]; loss = 0.066170
202
+ Epoch 0: | | 580/? [36:00<00:00, 0.27it/s, v_num=mfhp]context = [[9, 22, 36, 39, 43, 68], [70, 85, 95, 98, 116, 121], [10, 28, 45, 47, 50, 57], [132, 144, 154, 158, 168, 188]]target = [[67, 45, 11, 46, 50, 30], [114, 94, 71, 104, 117, 76], [22, 12, 24, 51, 50, 13], [180, 169, 183, 173, 182, 171]]
203
+ Epoch 0: | | 589/? [36:34<00:00, 0.27it/s, v_num=mfhp]train step 590; scene = [['3f732b63cdd0729e'], ['9be3165beb073d95'], ['42a6c835ff830674'], ['f928d960cbfae15a'], ['140b10a4f6bb5aa5'], ['cc8e19c8ad1846f4']]; loss = 0.089595
204
+ Epoch 0: | | 590/? [36:37<00:00, 0.27it/s, v_num=mfhp]context = [[198, 200, 201, 204, 209, 210, 224, 234], [6, 14, 18, 23, 25, 27, 58, 59], [13, 29, 40, 51, 55, 66, 70, 71]]target = [[203, 208, 227, 217, 222, 223, 200, 211], [11, 46, 45, 19, 20, 18, 47, 7], [16, 51, 40, 15, 55, 44, 23, 61]]
205
+ Epoch 0: | | 599/? [37:11<00:00, 0.27it/s, v_num=mfhp]train step 600; scene = [['cb734fdc69e9900e']]; loss = 0.055887
206
+ Epoch 0: | | 600/? [37:15<00:00, 0.27it/s, v_num=mfhp]context = [[10, 16, 24, 31, 33, 34, 36, 41, 49, 59, 63, 67], [160, 162, 167, 172, 173, 180, 183, 187, 189, 205, 212, 214]]target = [[32, 54, 42, 14, 39, 60, 66, 33, 62, 22, 47, 49], [182, 199, 163, 179, 203, 200, 174, 209, 164, 172, 194, 167]]
207
+ [2026-03-03 18:13:25,680][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
208
+ result[selector] = overlay
209
+
210
+ Epoch 0: | | 609/? [37:49<00:00, 0.27it/s, v_num=mfhp]train step 610; scene = [['ed9409fa128e193b'], ['a5c03b0c5fb7203e']]; loss = 0.042144
211
+ Epoch 0: | | 610/? [37:53<00:00, 0.27it/s, v_num=mfhp]context = [[36, 37, 39, 50, 51, 52, 57, 59, 72, 73, 86, 94], [45, 46, 54, 57, 66, 75, 77, 81, 83, 89, 97, 101]]target = [[47, 37, 89, 79, 92, 78, 81, 90, 38, 56, 84, 69], [84, 91, 85, 69, 79, 53, 55, 48, 87, 49, 92, 63]]
212
+ Epoch 0: | | 619/? [38:26<00:00, 0.27it/s, v_num=mfhp]train step 620; scene = [['7898a828b7203ca4'], ['9c269fce78f0dd27'], ['e1e317857deb7afc'], ['30124191dafb3383'], ['c39f1a9a73797efe'], ['a640a55439a43108']]; loss = 0.048999
213
+ Epoch 0: | | 620/? [38:30<00:00, 0.27it/s, v_num=mfhp]context = [[2, 15, 16, 21, 26, 29, 47, 56], [6, 12, 15, 37, 45, 47, 50, 68], [9, 18, 23, 26, 39, 48, 50, 60]]target = [[38, 7, 32, 36, 16, 21, 53, 22], [50, 67, 28, 43, 15, 41, 44, 23], [12, 54, 16, 24, 58, 32, 30, 47]]
214
+ Epoch 0: | | 629/? [39:03<00:00, 0.27it/s, v_num=mfhp]train step 630; scene = [['dd3bbf1f7f832e83'], ['0ff7277275e55096'], ['5f45c360d76a3b12']]; loss = 0.040934
215
+ Epoch 0: | | 630/? [39:07<00:00, 0.27it/s, v_num=mfhp]context = [[29, 42, 44, 45, 76, 81, 87, 93], [77, 84, 94, 96, 100, 102, 121, 125], [67, 68, 70, 89, 90, 91, 96, 107]]target = [[83, 55, 36, 89, 45, 62, 90, 63], [106, 92, 114, 99, 117, 82, 119, 107], [99, 68, 75, 92, 79, 85, 101, 90]]
216
+ Epoch 0: | | 639/? [39:41<00:00, 0.27it/s, v_num=mfhp]train step 640; scene = [['867edbda9bb8ef59'], ['1d83764e77e159d8'], ['e318dafa4071cef9'], ['169f09c33ee35289']]; loss = 0.091318
217
+ Epoch 0: | | 640/? [39:45<00:00, 0.27it/s, v_num=mfhp]context = [[52, 57, 67, 75, 77, 80, 85, 86, 99, 105, 112, 117], [6, 11, 22, 23, 32, 39, 42, 47, 48, 53, 63, 64]]target = [[114, 60, 111, 110, 57, 69, 68, 80, 62, 99, 77, 90], [27, 15, 46, 56, 51, 60, 41, 63, 49, 16, 9, 43]]
218
+ Epoch 0: | | 649/? [40:18<00:00, 0.27it/s, v_num=mfhp]train step 650; scene = [['23668135f32e0126'], ['daca15248046e480'], ['174ebd189316bd92']]; loss = 0.049692
219
+ Epoch 0: | | 650/? [40:22<00:00, 0.27it/s, v_num=mfhp]context = [[6, 23, 25, 26, 28, 32, 37, 38, 39, 40, 50, 57], [0, 2, 3, 11, 17, 19, 24, 25, 38, 40, 45, 53]]target = [[15, 47, 23, 31, 12, 30, 20, 55, 33, 11, 22, 10], [51, 9, 13, 34, 52, 30, 26, 45, 38, 27, 11, 14]]
220
+ Epoch 0: | | 659/? [40:55<00:00, 0.27it/s, v_num=mfhp]train step 660; scene = [['60499200285c9abe']]; loss = 0.073265
221
+ Epoch 0: | | 660/? [40:58<00:00, 0.27it/s, v_num=mfhp]context = [[57, 65, 76, 86, 93, 99, 100, 122], [31, 34, 42, 47, 60, 80, 81, 83], [13, 16, 19, 30, 33, 49, 50, 55]]target = [[85, 75, 88, 114, 107, 70, 116, 79], [82, 35, 67, 56, 68, 61, 74, 58], [54, 44, 23, 36, 31, 39, 45, 28]]
222
+ Epoch 0: | | 669/? [41:31<00:00, 0.27it/s, v_num=mfhp]train step 670; scene = [['7665ff641f430aa5']]; loss = 0.038339
223
+ Epoch 0: | | 670/? [41:34<00:00, 0.27it/s, v_num=mfhp]context = [[50, 57, 61, 69, 82, 87, 98, 101], [26, 33, 44, 47, 53, 56, 62, 66], [11, 33, 39, 48, 56, 67, 75, 78]]target = [[67, 96, 71, 75, 73, 57, 55, 89], [65, 39, 63, 59, 60, 47, 55, 41], [18, 56, 21, 64, 32, 59, 33, 25]]
224
+ Epoch 0: | | 679/? [42:08<00:00, 0.27it/s, v_num=mfhp]train step 680; scene = [['43c939b11c5fed4a']]; loss = 0.084846
225
+ Epoch 0: | | 680/? [42:12<00:00, 0.27it/s, v_num=mfhp]context = [[47, 54, 60, 64, 79, 80, 83, 93], [12, 13, 21, 30, 34, 45, 53, 55], [57, 75, 78, 82, 96, 103, 113, 117]]target = [[80, 62, 75, 54, 57, 92, 53, 58], [38, 32, 15, 44, 42, 51, 27, 39], [81, 87, 106, 94, 99, 103, 73, 79]]
226
+ Epoch 0: | | 689/? [42:44<00:00, 0.27it/s, v_num=mfhp]train step 690; scene = [['1848b8b363d0d2b9'], ['afe6b05d0554a880']]; loss = 0.068950
227
+ Epoch 0: | | 690/? [42:48<00:00, 0.27it/s, v_num=mfhp]context = [[12, 18, 20, 27, 36, 37, 38, 43, 48, 49, 52, 58, 59, 62, 67, 81, 83, 86, 93, 95, 101, 104, 108, 109]]target = [[34, 24, 43, 31, 87, 30, 51, 54, 52, 94, 86, 21, 44, 97, 61, 95, 38, 60, 49, 73, 41, 19, 65, 67]]
228
+ Epoch 0: | | 699/? [43:20<00:00, 0.27it/s, v_num=mfhp]train step 700; scene = [['674ef9fb9cf20f9f'], ['8624ee0839cb6e4c'], ['caed302f388b799f']]; loss = 0.052812
229
+ Epoch 0: | | 700/? [43:23<00:00, 0.27it/s, v_num=mfhp]context = [[31, 32, 41, 46, 53, 54, 55, 57, 65, 68, 73, 74, 80, 85, 100, 105, 108, 109, 113, 114, 116, 118, 126, 128]]target = [[117, 52, 85, 57, 37, 45, 78, 100, 125, 35, 113, 66, 105, 103, 61, 83, 88, 40, 116, 60, 79, 32, 102, 107]]
230
+ Epoch 0: | | 709/? [43:57<00:00, 0.27it/s, v_num=mfhp]train step 710; scene = [['db6cd90de8fee2ff'], ['7a20ba81fb778529'], ['970350268b239272']]; loss = 0.047345
231
+ Epoch 0: | | 710/? [44:01<00:00, 0.27it/s, v_num=mfhp]context = [[3, 12, 16, 31, 41, 45, 49, 53, 54, 55, 67, 69], [208, 211, 217, 221, 222, 227, 230, 231, 239, 247, 252, 267]]target = [[38, 34, 68, 25, 44, 63, 48, 7, 42, 6, 64, 28], [228, 253, 259, 266, 244, 238, 249, 234, 232, 241, 220, 213]]
232
+ Epoch 0: | | 719/? [44:34<00:00, 0.27it/s, v_num=mfhp]train step 720; scene = [['f63d2df8871ce70c'], ['0fdeda15097ed4a4']]; loss = 0.043177
233
+ Epoch 0: | | 720/? [44:38<00:00, 0.27it/s, v_num=mfhp]context = [[55, 57, 71, 72, 75, 85, 90, 92, 98, 99, 102, 108, 112, 116, 125, 126, 130, 131, 135, 141, 144, 145, 146, 152]]target = [[61, 145, 86, 66, 62, 119, 100, 139, 105, 125, 58, 101, 140, 132, 118, 128, 65, 141, 151, 78, 104, 107, 150, 138]]
234
+ Epoch 0: | | 729/? [45:11<00:00, 0.27it/s, v_num=mfhp]train step 730; scene = [['232abb354c423e81'], ['d34926c73ae1277e']]; loss = 0.032255
235
+ Epoch 0: | | 730/? [45:15<00:00, 0.27it/s, v_num=mfhp]context = [[45, 50, 54, 58, 66, 70, 72, 78, 85, 88, 94, 105, 109, 110, 111, 117, 120, 122, 126, 127, 128, 133, 135, 142]]target = [[95, 96, 50, 114, 135, 124, 104, 100, 49, 119, 139, 62, 92, 123, 58, 46, 57, 112, 116, 90, 54, 101, 85, 81]]
236
+ Epoch 0: | | 739/? [45:48<00:00, 0.27it/s, v_num=mfhp]train step 740; scene = [['19f7966006ad778d'], ['dde0212418df7ca9'], ['ad75e36b74f6b033'], ['ea97e5ae55e56208'], ['9d29b0289133ab4e'], ['282938f90821bdef']]; loss = 0.119270
237
+ Epoch 0: | | 740/? [45:52<00:00, 0.27it/s, v_num=mfhp]context = [[8, 13, 19, 22, 30, 31, 36, 45, 46, 48, 54, 57, 58, 59, 60, 61, 65, 73, 77, 83, 86, 88, 93, 105]]target = [[46, 101, 90, 61, 31, 23, 37, 95, 18, 67, 32, 100, 93, 35, 89, 45, 10, 70, 60, 97, 85, 81, 66, 79]]
238
+ Epoch 0: | | 749/? [46:26<00:00, 0.27it/s, v_num=mfhp]train step 750; scene = [['f85921f42c5d98d7'], ['a95dacbd3ea3db36']]; loss = 0.045635
239
+ Epoch 0: | | 750/? [46:30<00:00, 0.27it/s, v_num=mfhp]Validation epoch start on rank 0
240
+ Validation: | | 0/? [00:00<?, ?it/s]validation step 750; scene = ['91fda69e1cda4602'];
241
+ target intrinsic: tensor(0.8937, device='cuda:0') tensor(0.8939, device='cuda:0') | 0/1 [00:00<?, ?it/s]
242
+ pred intrinsic: tensor(0.9293, device='cuda:0') tensor(0.9274, device='cuda:0')
243
+ [2026-03-03 18:22:37,515][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
244
+ result[selector] = overlay
245
+
246
+ Epoch 0: | | 750/? [46:31<00:00, 0.27it/s, v_num=mfhp]context = [[1, 38, 46, 49, 52, 59], [213, 218, 227, 233, 252, 257], [99, 105, 109, 124, 125, 153], [8, 17, 20, 37, 62, 65]]target = [[54, 19, 39, 57, 7, 4], [252, 219, 223, 216, 248, 237], [148, 136, 102, 103, 149, 132], [12, 52, 13, 44, 63, 39]]
247
+ Epoch 0: | | 759/? [47:04<00:00, 0.27it/s, v_num=mfhp]train step 760; scene = [['75617c97bff1e873'], ['ff02f88545dfa566']]; loss = 0.033483
248
+ Epoch 0: | | 760/? [47:08<00:00, 0.27it/s, v_num=mfhp]context = [[71, 73, 111], [2, 14, 56], [29, 82, 88], [120, 144, 168], [181, 214, 250], [0, 10, 71], [12, 53, 54], [14, 27, 87]]target = [[101, 74, 84], [43, 51, 38], [82, 55, 72], [156, 139, 147], [198, 214, 207], [22, 5, 40], [36, 13, 39], [68, 35, 70]]
249
+ Epoch 0: | | 769/? [47:42<00:00, 0.27it/s, v_num=mfhp]train step 770; scene = [['62b0d4ee613af70f'], ['f7926eb1096de201'], ['c63b37ec347f0d0e'], ['b43d9f7c70f5caa0']]; loss = 0.074525
250
+ Epoch 0: | | 770/? [47:45<00:00, 0.27it/s, v_num=mfhp]context = [[13, 29, 30, 31, 32, 34, 41, 42, 46, 52, 63, 66], [146, 149, 157, 166, 177, 180, 181, 193, 197, 204, 211, 213]]target = [[35, 55, 52, 59, 24, 51, 63, 18, 42, 44, 33, 61], [183, 158, 171, 175, 163, 196, 177, 181, 210, 167, 187, 174]]
251
+ Epoch 0: | | 779/? [48:19<00:00, 0.27it/s, v_num=mfhp]train step 780; scene = [['b41f4db8b8a42a71']]; loss = 0.089240
252
+ Epoch 0: | | 780/? [48:23<00:00, 0.27it/s, v_num=mfhp]context = [[43, 50, 53, 55, 57, 58, 59, 60, 68, 81, 94, 96, 102, 106, 108, 110, 112, 121, 125, 126, 130, 131, 136, 140]]target = [[74, 122, 93, 98, 70, 84, 49, 136, 77, 117, 135, 138, 123, 89, 119, 45, 129, 105, 50, 58, 63, 103, 82, 121]]
253
+ Epoch 0: | | 789/? [48:56<00:00, 0.27it/s, v_num=mfhp]train step 790; scene = [['d79666d294813d8e']]; loss = 0.259840
254
+ Epoch 0: | | 790/? [48:59<00:00, 0.27it/s, v_num=mfhp]context = [[59, 79, 88, 93, 100, 107], [3, 16, 19, 31, 46, 51], [41, 49, 54, 56, 72, 83], [226, 229, 235, 244, 255, 272]]target = [[103, 98, 81, 75, 74, 72], [48, 44, 20, 31, 15, 46], [64, 82, 70, 50, 47, 60], [251, 249, 271, 250, 259, 260]]
255
+ Epoch 0: | | 799/? [49:33<00:00, 0.27it/s, v_num=mfhp]train step 800; scene = [['cb797cd30542e55c']]; loss = 0.091464
256
+ Epoch 0: | | 800/? [49:36<00:00, 0.27it/s, v_num=mfhp]context = [[10, 19, 20, 39, 42, 55], [1, 6, 11, 20, 30, 42], [0, 3, 4, 19, 42, 45], [21, 35, 52, 82, 85, 88]]target = [[38, 39, 26, 21, 37, 40], [11, 40, 18, 27, 3, 24], [43, 33, 8, 24, 5, 19], [83, 71, 66, 32, 63, 47]]
257
+ [2026-03-03 18:25:47,338][py.warnings][WARNING] - /workspace/code/CVPR2026/src/visualization/layout.py:105: UserWarning: Using a non-tuple sequence for multidimensional indexing is deprecated and will be changed in pytorch 2.9; use x[tuple(seq)] instead of x[seq]. In pytorch 2.9 this will be interpreted as tensor index, x[torch.tensor(seq)], which will result either in an error or a different result (Triggered internally at /pytorch/torch/csrc/autograd/python_variable_indexing.cpp:316.)
258
+ result[selector] = overlay
259
+
260
+ Epoch 0: | | 809/? [50:10<00:00, 0.27it/s, v_num=mfhp]train step 810; scene = [['9bd08fc9288bef8b']]; loss = 0.096803
261
+ Epoch 0: | | 810/? [50:14<00:00, 0.27it/s, v_num=mfhp]context = [[5, 6, 14, 17, 19, 23, 30, 37, 40, 49, 53, 55], [3, 6, 15, 17, 27, 34, 38, 40, 42, 46, 56, 65]]target = [[16, 8, 48, 23, 44, 49, 10, 7, 18, 50, 22, 15], [34, 18, 9, 42, 31, 38, 45, 26, 43, 6, 49, 44]]
262
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263
+ Epoch 0: | | 820/? [50:51<00:00, 0.27it/s, v_num=mfhp]context = [[22, 23, 26, 28, 31, 43, 51, 53, 57, 65, 67, 68, 71, 76, 77, 78, 80, 84, 88, 90, 107, 112, 116, 119]]target = [[35, 45, 31, 72, 24, 71, 89, 54, 25, 61, 44, 97, 56, 47, 82, 87, 50, 110, 108, 75, 103, 67, 86, 117]]
264
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265
+ Epoch 0: | | 830/? [51:27<00:00, 0.27it/s, v_num=mfhp]context = [[42, 67, 90, 119], [18, 54, 72, 90], [43, 67, 73, 102], [2, 22, 40, 43], [23, 40, 52, 67], [19, 37, 75, 78]]target = [[86, 112, 87, 94], [59, 40, 20, 47], [58, 97, 66, 77], [34, 19, 32, 17], [62, 37, 35, 54], [54, 22, 31, 50]]
266
+ Epoch 0: | | 839/? [51:59<00:00, 0.27it/s, v_num=mfhp]train step 840; scene = [['c51c7bc0c8151abb'], ['a0d16e79ab441c4f']]; loss = 0.133728
267
+ Epoch 0: | | 840/? [52:03<00:00, 0.27it/s, v_num=mfhp]context = [[1, 18, 22, 27, 29, 35, 38, 49], [3, 12, 30, 36, 43, 47, 54, 55], [17, 36, 55, 65, 72, 77, 86, 88]]target = [[5, 36, 48, 11, 2, 39, 35, 37], [6, 54, 43, 18, 30, 15, 10, 11], [52, 59, 56, 77, 25, 68, 22, 27]]
268
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269
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270
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271
+ Epoch 0: | | 860/? [53:17<00:00, 0.27it/s, v_num=mfhp]context = [[147, 155, 165, 189, 194, 195], [9, 15, 44, 47, 49, 82], [5, 22, 23, 51, 61, 64], [97, 98, 140, 149, 160, 170]]target = [[194, 192, 155, 151, 174, 158], [22, 27, 54, 18, 81, 31], [25, 27, 29, 23, 6, 59], [137, 162, 98, 111, 152, 164]]
272
+ Epoch 0: | | 869/? [53:51<00:00, 0.27it/s, v_num=mfhp]train step 870; scene = [['c357fbd8aca05570']]; loss = 0.051317
273
+ Epoch 0: | | 870/? [53:55<00:00, 0.27it/s, v_num=mfhp]context = [[44, 45, 82, 100], [70, 93, 96, 147], [144, 176, 184, 205], [0, 51, 65, 67], [105, 106, 130, 152], [5, 6, 55, 60]]target = [[81, 66, 67, 97], [116, 118, 137, 91], [200, 193, 197, 176], [11, 13, 22, 32], [120, 117, 127, 130], [17, 14, 58, 27]]
274
+ Epoch 0: | | 879/? [54:28<00:00, 0.27it/s, v_num=mfhp]train step 880; scene = [['c672fa3960b73528'], ['a60e4127f167ac93'], ['a9739ec3a34012af']]; loss = 0.079480
275
+ Epoch 0: | | 880/? [54:32<00:00, 0.27it/s, v_num=mfhp]context = [[15, 85, 91], [0, 68, 74], [73, 85, 121], [4, 53, 80], [15, 36, 63], [42, 81, 92], [0, 45, 49], [65, 97, 132]]target = [[49, 21, 35], [47, 44, 69], [119, 89, 106], [57, 76, 6], [28, 32, 55], [62, 43, 49], [18, 44, 24], [90, 104, 71]]
276
+ Epoch 0: | | 889/? [55:04<00:00, 0.27it/s, v_num=mfhp]train step 890; scene = [['40a3f4f9389dd20c'], ['b14ec6f019932d8d'], ['4b9ed7532c875dab'], ['10c36bd5ef5f5a6b'], ['bc9a64096787007d'], ['d58a26d24f2776b2'], ['46f2228076e6f3f7'], ['399668567ff33ad7']]; loss = 0.065856
277
+ Epoch 0: | | 890/? [55:08<00:00, 0.27it/s, v_num=mfhp]context = [[32, 34, 35, 37, 39, 44, 52, 59, 73, 84, 87, 93], [20, 21, 22, 34, 43, 47, 52, 57, 64, 69, 72, 96]]target = [[87, 80, 41, 73, 35, 37, 86, 66, 72, 39, 50, 83], [28, 22, 50, 33, 26, 79, 75, 73, 29, 77, 60, 37]]
278
+ Epoch 0: | | 893/? [55:19<00:00, 0.27it/s, v_num=mfhp]
ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/files/requirements.txt ADDED
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ABLATION_0302_noTgtAlign/wandb/run-20260303_173555-et94mfhp/logs/debug.log ADDED
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