forked from donydchen/mvsplat
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmore_commands.sh
104 lines (91 loc) · 3.05 KB
/
more_commands.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
#!/usr/bin/env bash
# --- Ablation Models ---
# Table 3: base
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_worefine.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_base \
model.encoder.wo_depth_refine=true
# Table 3: w/o cost volume
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_worefine_wocv.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_wocv \
model.encoder.wo_depth_refine=true \
model.encoder.wo_cost_volume=true
# Table 3: w/o cross-view attention
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_worefine_wobbcrossattn_best.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_wo_backbone_cross_attn \
model.encoder.wo_depth_refine=true \
model.encoder.wo_backbone_cross_attn=true
# Table 3: w/o U-Net
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_worefine_wounet.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_wo_unet \
model.encoder.wo_depth_refine=true \
model.encoder.wo_cost_volume_refine=true
# Table B: w/ Epipolar Transformer
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_worefine_wepitrans.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_w_epipolar_trans \
model.encoder.wo_depth_refine=true \
model.encoder.use_epipolar_trans=true
# Table C: 3 Gaussians per pixel
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_gpp3.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_gpp3 \
model.encoder.gaussians_per_pixel=3
# Table D: w/ random init (300K)
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_wopretrained.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_wo_pretrained
# Table D: w/ random init (450K)
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/ablations/re10k_wopretrained_450k.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true \
wandb.name=abl/re10k_wo_pretrained_450k
# --- Default Final Models ---
# Table 1: re10k
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/re10k.ckpt \
mode=test \
dataset/view_sampler=evaluation \
test.compute_scores=true
# Table 1: acid
python -m src.main +experiment=acid \
checkpointing.load=checkpoints/acid.ckpt \
mode=test \
dataset/view_sampler=evaluation \
dataset.view_sampler.index_path=assets/evaluation_index_acid.json \
test.compute_scores=true
# generate video
python -m src.main +experiment=re10k \
checkpointing.load=checkpoints/re10k.ckpt \
mode=test \
dataset/view_sampler=evaluation \
dataset.view_sampler.index_path=assets/evaluation_index_re10k_video.json \
test.save_video=true \
test.save_image=false \
test.compute_scores=false