clean code
This commit is contained in:
Kai-46
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commit
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40 changed files with 186 additions and 1069 deletions
3
.gitignore
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.gitignore
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# scripts
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*.sh
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# mac
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.DS_Store
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# pycharm
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.idea/
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23
README.md
23
README.md
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# NeRF++
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Codebase for paper:
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* Work with 360 capture of large-scale unbounded scenes.
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* Support multi-gpu training and inference.
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## Data
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* Download our preprocessed data from [tanks_and_temples](), [lf_data]().
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* Put the data in the code directory.
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* Data format.
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** Each scene consists of 3 splits: train/test/validation.
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** Intrinsics and poses are stored as flattened 4x4 matrices.
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** Opencv camera coordinate system is adopted, i.e., x--->right, y--->down, z--->scene.
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* Scene normalization: move the average camera center to origin, and put all the camera centers inside the unit sphere.
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## Training
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```python
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python ddp_train_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt
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```
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## Testing
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```python
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python ddp_test_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt --render_splits test,camera_path
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```
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BIN
configs/tanks_and_temples/.DS_Store
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configs/tanks_and_temples/.DS_Store
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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scene = tat_intermediate_M60
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expname = tat_intermediate_M60_bg_carve_latest
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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# N_rand = 4096
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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chunk_size = 16384
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# chunk_size = 8192
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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@ -1,49 +0,0 @@
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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scene = tat_intermediate_Playground
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expname = tat_intermediate_Playground_bg_carve_latest
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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# N_rand = 4096
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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chunk_size = 16384
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# chunk_size = 8192
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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scene = tat_intermediate_Playground
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expname = tat_intermediate_Playground_ddp_bignet
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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N_rand = 256
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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# chunk_size = 16384
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chunk_size = 4096
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 512
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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scene = tat_intermediate_Train
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expname = tat_intermediate_Train_bg_carve_latest
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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# N_rand = 4096
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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chunk_size = 16384
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# chunk_size = 8192
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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datadir = ./data/tanks_and_temples
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scene = tat_training_Truck
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expname = tat_training_Truck_ddp_implicit
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expname = tat_training_Truck
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basedir = ./logs
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config = None
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ckpt_path = None
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### TRAINING
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N_iters = 1250001
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# N_rand = 512
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N_rand = 1024
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 50000000
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### implicit
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use_implicit = True
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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# chunk_size = 16384
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# chunk_size = 4096
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chunk_size = 8192
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### RENDERING
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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@ -1,48 +0,0 @@
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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scene = tat_training_Truck
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expname = tat_training_Truck_ddp_bignet
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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N_rand = 256
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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# chunk_size = 16384
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chunk_size = 4096
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 512
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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@ -1,47 +0,0 @@
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
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scene = tat_training_Truck_subset
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expname = tat_training_Truck_subset_bg_carvenew
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 250001
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,64
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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chunk_size = 8192
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = False
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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@ -1,54 +0,0 @@
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
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scene = tat_intermediate_Playground
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expname = tat_intermediate_Playground_ddp_sparse_addcarve
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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# N_rand = 4096
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### implicit
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use_implicit = True
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load_min_depth = True
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regularize_weight = 0.1
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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chunk_size = 16384
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# chunk_size = 8192
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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@ -1,54 +0,0 @@
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
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scene = tat_intermediate_Playground
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expname = tat_intermediate_Playground_ddp_sparse_addparam
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basedir = ./logs
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config = None
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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# N_rand = 4096
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### implicit
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use_implicit = True
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load_min_depth = False
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regularize_weight = 0.
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### CASCADE
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cascade_level = 2
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cascade_samples = 64,128
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near_depth = 0.
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far_depth = 1.
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### TESTING
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render_only = False
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render_test = False
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render_train = False
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chunk_size = 16384
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# chunk_size = 8192
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### RENDERING
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det = False
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max_freq_log2 = 10
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max_freq_log2_viewdirs = 4
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netdepth = 8
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netwidth = 256
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raw_noise_std = 1.0
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N_iters_perturb = 1000
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inv_uniform = False
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use_viewdirs = True
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white_bkgd = False
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### CONSOLE AND TENSORBOARD
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i_img = 2000
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i_print = 100
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i_testset = 5000000
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i_video = 5000000
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i_weights = 5000
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@ -1,54 +0,0 @@
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### INPUT
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datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
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scene = tat_intermediate_Playground
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expname = tat_intermediate_Playground_ddp_sparse_addregularize_pretrain
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basedir = ./logs
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config = /home/zhangka2/gernot_experi/nerf_bg_latest_ddp/logs/tat_intermediate_Playground_ddp_sparse_addparam/model_210000.pth
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ckpt_path = None
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no_reload = False
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testskip = 1
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### TRAINING
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N_iters = 1250001
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# N_rand = 4096
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N_rand = 2048
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lrate = 0.0005
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lrate_decay_factor = 0.1
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lrate_decay_steps = 500000
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### implicit
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use_implicit = True
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load_min_depth = False
|
||||
regularize_weight = 0.1
|
||||
|
||||
### CASCADE
|
||||
cascade_level = 2
|
||||
cascade_samples = 64,128
|
||||
near_depth = 0.
|
||||
far_depth = 1.
|
||||
|
||||
### TESTING
|
||||
render_only = False
|
||||
render_test = False
|
||||
render_train = False
|
||||
chunk_size = 16384
|
||||
# chunk_size = 8192
|
||||
|
||||
### RENDERING
|
||||
det = False
|
||||
max_freq_log2 = 10
|
||||
max_freq_log2_viewdirs = 4
|
||||
netdepth = 8
|
||||
netwidth = 256
|
||||
raw_noise_std = 1.0
|
||||
N_iters_perturb = 1000
|
||||
inv_uniform = False
|
||||
use_viewdirs = True
|
||||
white_bkgd = False
|
||||
|
||||
### CONSOLE AND TENSORBOARD
|
||||
i_img = 2000
|
||||
i_print = 100
|
||||
i_testset = 5000000
|
||||
i_video = 5000000
|
||||
i_weights = 5000
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
### INPUT
|
||||
datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
|
||||
scene = tat_training_Truck
|
||||
expname = tat_training_Truck_ddp_sparse_addcarve
|
||||
basedir = ./logs
|
||||
config = None
|
||||
ckpt_path = None
|
||||
no_reload = False
|
||||
testskip = 1
|
||||
|
||||
### TRAINING
|
||||
N_iters = 1250001
|
||||
# N_rand = 512
|
||||
N_rand = 1024
|
||||
lrate = 0.0005
|
||||
lrate_decay_factor = 0.1
|
||||
lrate_decay_steps = 50000000
|
||||
|
||||
### implicit
|
||||
use_implicit = True
|
||||
load_min_depth = True
|
||||
regularize_weight = 0.1
|
||||
|
||||
### CASCADE
|
||||
cascade_level = 2
|
||||
cascade_samples = 64,128
|
||||
near_depth = 0.
|
||||
far_depth = 1.
|
||||
|
||||
### TESTING
|
||||
render_only = False
|
||||
render_test = False
|
||||
render_train = False
|
||||
# chunk_size = 16384
|
||||
# chunk_size = 4096
|
||||
chunk_size = 8192
|
||||
|
||||
### RENDERING
|
||||
det = False
|
||||
max_freq_log2 = 10
|
||||
max_freq_log2_viewdirs = 4
|
||||
netdepth = 8
|
||||
netwidth = 256
|
||||
raw_noise_std = 1.0
|
||||
N_iters_perturb = 1000
|
||||
inv_uniform = False
|
||||
use_viewdirs = True
|
||||
white_bkgd = False
|
||||
|
||||
### CONSOLE AND TENSORBOARD
|
||||
i_img = 2000
|
||||
i_print = 100
|
||||
i_testset = 5000000
|
||||
i_video = 5000000
|
||||
i_weights = 5000
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
### INPUT
|
||||
datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
|
||||
scene = tat_training_Truck
|
||||
expname = tat_training_Truck_ddp_sparse_addparam
|
||||
basedir = ./logs
|
||||
config = None
|
||||
ckpt_path = None
|
||||
no_reload = False
|
||||
testskip = 1
|
||||
|
||||
### TRAINING
|
||||
N_iters = 1250001
|
||||
# N_rand = 512
|
||||
N_rand = 1024
|
||||
lrate = 0.0005
|
||||
lrate_decay_factor = 0.1
|
||||
lrate_decay_steps = 50000000
|
||||
|
||||
### implicit
|
||||
use_implicit = True
|
||||
load_min_depth = False
|
||||
regularize_weight = 0.
|
||||
|
||||
### CASCADE
|
||||
cascade_level = 2
|
||||
cascade_samples = 64,128
|
||||
near_depth = 0.
|
||||
far_depth = 1.
|
||||
|
||||
### TESTING
|
||||
render_only = False
|
||||
render_test = False
|
||||
render_train = False
|
||||
# chunk_size = 16384
|
||||
# chunk_size = 4096
|
||||
chunk_size = 8192
|
||||
|
||||
### RENDERING
|
||||
det = False
|
||||
max_freq_log2 = 10
|
||||
max_freq_log2_viewdirs = 4
|
||||
netdepth = 8
|
||||
netwidth = 256
|
||||
raw_noise_std = 1.0
|
||||
N_iters_perturb = 1000
|
||||
inv_uniform = False
|
||||
use_viewdirs = True
|
||||
white_bkgd = False
|
||||
|
||||
### CONSOLE AND TENSORBOARD
|
||||
i_img = 2000
|
||||
i_print = 100
|
||||
i_testset = 5000000
|
||||
i_video = 5000000
|
||||
i_weights = 5000
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
### INPUT
|
||||
datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
|
||||
scene = tat_training_Truck
|
||||
expname = tat_training_Truck_ddp_sparse_addregularize_pretrain
|
||||
basedir = ./logs
|
||||
config = None
|
||||
ckpt_path = /home/zhangka2/gernot_experi/nerf_bg_latest_ddp/logs/tat_training_Truck_ddp_sparse_addparam/model_245000.pth
|
||||
no_reload = False
|
||||
testskip = 1
|
||||
|
||||
### TRAINING
|
||||
N_iters = 1250001
|
||||
# N_rand = 512
|
||||
N_rand = 1024
|
||||
lrate = 0.0005
|
||||
lrate_decay_factor = 0.1
|
||||
lrate_decay_steps = 50000000
|
||||
|
||||
### implicit
|
||||
use_implicit = True
|
||||
load_min_depth = False
|
||||
regularize_weight = 0.1
|
||||
|
||||
### CASCADE
|
||||
cascade_level = 2
|
||||
cascade_samples = 64,128
|
||||
near_depth = 0.
|
||||
far_depth = 1.
|
||||
|
||||
### TESTING
|
||||
render_only = False
|
||||
render_test = False
|
||||
render_train = False
|
||||
# chunk_size = 16384
|
||||
# chunk_size = 4096
|
||||
chunk_size = 8192
|
||||
|
||||
### RENDERING
|
||||
det = False
|
||||
max_freq_log2 = 10
|
||||
max_freq_log2_viewdirs = 4
|
||||
netdepth = 8
|
||||
netwidth = 256
|
||||
raw_noise_std = 1.0
|
||||
N_iters_perturb = 1000
|
||||
inv_uniform = False
|
||||
use_viewdirs = True
|
||||
white_bkgd = False
|
||||
|
||||
### CONSOLE AND TENSORBOARD
|
||||
i_img = 2000
|
||||
i_print = 100
|
||||
i_testset = 5000000
|
||||
i_video = 5000000
|
||||
i_weights = 5000
|
||||
|
|
@ -24,16 +24,22 @@ def find_files(dir, exts):
|
|||
return []
|
||||
|
||||
|
||||
def load_data_split(basedir, scene, split, skip=1, try_load_min_depth=True):
|
||||
def load_data_split(basedir, scene, split, skip=1, try_load_min_depth=True, only_img_files=False):
|
||||
|
||||
def parse_txt(filename):
|
||||
assert os.path.isfile(filename)
|
||||
nums = open(filename).read().split()
|
||||
return np.array([float(x) for x in nums]).reshape([4, 4]).astype(np.float32)
|
||||
|
||||
split_dir = '{}/{}/{}'.format(basedir, scene, split)
|
||||
|
||||
if only_img_files:
|
||||
img_files = find_files('{}/rgb'.format(split_dir), exts=['*.png', '*.jpg'])
|
||||
return img_files
|
||||
|
||||
# camera parameters files
|
||||
intrinsics_files = find_files('{}/intrinsics'.format(split_dir), exts=['*.txt'])
|
||||
pose_files = find_files('{}/pose'.format(split_dir), exts=['*.txt'])
|
||||
|
||||
logger.info('raw intrinsics_files: {}'.format(len(intrinsics_files)))
|
||||
logger.info('raw pose_files: {}'.format(len(pose_files)))
|
||||
|
||||
|
|
@ -49,6 +55,7 @@ def load_data_split(basedir, scene, split, skip=1, try_load_min_depth=True):
|
|||
assert(len(img_files) == cam_cnt)
|
||||
else:
|
||||
img_files = [None, ] * cam_cnt
|
||||
|
||||
# mask files
|
||||
mask_files = find_files('{}/mask'.format(split_dir), exts=['*.png', '*.jpg'])
|
||||
if len(mask_files) > 0:
|
||||
|
|
@ -67,11 +74,12 @@ def load_data_split(basedir, scene, split, skip=1, try_load_min_depth=True):
|
|||
else:
|
||||
mindepth_files = [None, ] * cam_cnt
|
||||
|
||||
# assume all images have the same size
|
||||
# assume all images have the same size as training image
|
||||
train_imgfile = find_files('{}/{}/train/rgb'.format(basedir, scene), exts=['*.png', '*.jpg'])[0]
|
||||
train_im = imageio.imread(train_imgfile)
|
||||
H, W = train_im.shape[:2]
|
||||
|
||||
# create ray samplers
|
||||
ray_samplers = []
|
||||
for i in range(cam_cnt):
|
||||
intrinsics = parse_txt(intrinsics_files[i])
|
||||
|
|
|
|||
56
ddp_model.py
56
ddp_model.py
|
|
@ -5,6 +5,9 @@ import torch.nn as nn
|
|||
from utils import TINY_NUMBER, HUGE_NUMBER
|
||||
from collections import OrderedDict
|
||||
from nerf_network import Embedder, MLPNet
|
||||
import os
|
||||
import logging
|
||||
logger = logging.getLogger(__package__)
|
||||
|
||||
|
||||
######################################################################################
|
||||
|
|
@ -44,14 +47,6 @@ def depth2pts_outside(ray_o, ray_d, depth):
|
|||
|
||||
class NerfNet(nn.Module):
|
||||
def __init__(self, args):
|
||||
'''
|
||||
:param D: network depth
|
||||
:param W: network width
|
||||
:param input_ch: input channels for encodings of (x, y, z)
|
||||
:param input_ch_viewdirs: input channels for encodings of view directions
|
||||
:param skips: skip connection in network
|
||||
:param use_viewdirs: if True, will use the view directions as input
|
||||
'''
|
||||
super().__init__()
|
||||
# foreground
|
||||
self.fg_embedder_position = Embedder(input_dim=3,
|
||||
|
|
@ -146,3 +141,48 @@ class NerfNet(nn.Module):
|
|||
('bg_depth', bg_depth_map),
|
||||
('bg_lambda', bg_lambda)])
|
||||
return ret
|
||||
|
||||
|
||||
def remap_name(name):
|
||||
name = name.replace('.', '-') # dot is not allowed by pytorch
|
||||
if name[-1] == '/':
|
||||
name = name[:-1]
|
||||
idx = name.rfind('/')
|
||||
for i in range(2):
|
||||
if idx >= 0:
|
||||
idx = name[:idx].rfind('/')
|
||||
return name[idx + 1:]
|
||||
|
||||
|
||||
class NerfNetWithAutoExpo(nn.Module):
|
||||
def __init__(self, args, optim_autoexpo=False, img_names=None):
|
||||
super().__init__()
|
||||
self.nerf_net = NerfNet(args)
|
||||
|
||||
self.optim_autoexpo = optim_autoexpo
|
||||
if self.optim_autoexpo:
|
||||
assert(img_names is not None)
|
||||
logger.info('Optimizing autoexposure!')
|
||||
|
||||
self.img_names = [remap_name(x) for x in img_names]
|
||||
logger.info('\n'.join(self.img_names))
|
||||
self.autoexpo_params = nn.ParameterDict(OrderedDict([(x, nn.Parameter(torch.Tensor([0.5, 0.]))) for x in self.img_names]))
|
||||
|
||||
def forward(self, ray_o, ray_d, fg_z_max, fg_z_vals, bg_z_vals, img_name=None):
|
||||
'''
|
||||
:param ray_o, ray_d: [..., 3]
|
||||
:param fg_z_max: [...,]
|
||||
:param fg_z_vals, bg_z_vals: [..., N_samples]
|
||||
:return
|
||||
'''
|
||||
ret = self.nerf_net(ray_o, ray_d, fg_z_max, fg_z_vals, bg_z_vals)
|
||||
|
||||
if img_name is not None:
|
||||
img_name = remap_name(img_name)
|
||||
if self.optim_autoexpo and (img_name in self.autoexpo_params):
|
||||
autoexpo = self.autoexpo_params[img_name]
|
||||
scale = torch.abs(autoexpo[0]) + 0.5 # make sure scale is always positive
|
||||
shift = autoexpo[1]
|
||||
ret['autoexpo'] = (scale, shift)
|
||||
|
||||
return ret
|
||||
|
|
|
|||
|
|
@ -2,17 +2,17 @@ import torch
|
|||
# import torch.nn as nn
|
||||
import torch.optim
|
||||
import torch.distributed
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
# from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
import torch.multiprocessing
|
||||
import numpy as np
|
||||
import os
|
||||
from collections import OrderedDict
|
||||
from ddp_model import NerfNet
|
||||
# from collections import OrderedDict
|
||||
# from ddp_model import NerfNet
|
||||
import time
|
||||
from data_loader_split import load_data_split
|
||||
from utils import mse2psnr, colorize_np, to8b
|
||||
import imageio
|
||||
from ddp_run_nerf import config_parser, setup_logger, setup, cleanup, render_single_image
|
||||
from ddp_train_nerf import config_parser, setup_logger, setup, cleanup, render_single_image, create_nerf
|
||||
import logging
|
||||
|
||||
|
||||
|
|
@ -37,46 +37,7 @@ def ddp_test_nerf(rank, args):
|
|||
args.chunk_size = 4096
|
||||
|
||||
###### create network and wrap in ddp; each process should do this
|
||||
# fix random seed just to make sure the network is initialized with same weights at different processes
|
||||
torch.manual_seed(777)
|
||||
# very important!!! otherwise it might introduce extra memory in rank=0 gpu
|
||||
torch.cuda.set_device(rank)
|
||||
|
||||
models = OrderedDict()
|
||||
models['cascade_level'] = args.cascade_level
|
||||
models['cascade_samples'] = [int(x.strip()) for x in args.cascade_samples.split(',')]
|
||||
for m in range(models['cascade_level']):
|
||||
net = NerfNet(args).to(rank)
|
||||
net = DDP(net, device_ids=[rank], output_device=rank)
|
||||
optim = torch.optim.Adam(net.parameters(), lr=args.lrate)
|
||||
models['net_{}'.format(m)] = net
|
||||
models['optim_{}'.format(m)] = optim
|
||||
|
||||
start = -1
|
||||
|
||||
###### load pretrained weights; each process should do this
|
||||
if (args.ckpt_path is not None) and (os.path.isfile(args.ckpt_path)):
|
||||
ckpts = [args.ckpt_path]
|
||||
else:
|
||||
ckpts = [os.path.join(args.basedir, args.expname, f)
|
||||
for f in sorted(os.listdir(os.path.join(args.basedir, args.expname))) if f.endswith('.pth')]
|
||||
def path2iter(path):
|
||||
tmp = os.path.basename(path)[:-4]
|
||||
idx = tmp.rfind('_')
|
||||
return int(tmp[idx + 1:])
|
||||
ckpts = sorted(ckpts, key=path2iter)
|
||||
logger.info('Found ckpts: {}'.format(ckpts))
|
||||
if len(ckpts) > 0 and not args.no_reload:
|
||||
fpath = ckpts[-1]
|
||||
logger.info('Reloading from: {}'.format(fpath))
|
||||
start = path2iter(fpath)
|
||||
# configure map_location properly for different processes
|
||||
map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
|
||||
to_load = torch.load(fpath, map_location=map_location)
|
||||
for m in range(models['cascade_level']):
|
||||
for name in ['net_{}'.format(m), 'optim_{}'.format(m)]:
|
||||
models[name].load_state_dict(to_load[name])
|
||||
models[name].load_state_dict(to_load[name])
|
||||
start, models = create_nerf(rank, args)
|
||||
|
||||
render_splits = [x.strip() for x in args.render_splits.strip().split(',')]
|
||||
# start testing
|
||||
|
|
@ -157,4 +118,3 @@ if __name__ == '__main__':
|
|||
setup_logger()
|
||||
test()
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,18 +1,20 @@
|
|||
import torch
|
||||
# import torch.nn as nn
|
||||
import torch.nn as nn
|
||||
import torch.optim
|
||||
import torch.distributed
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
import torch.multiprocessing
|
||||
import os
|
||||
from collections import OrderedDict
|
||||
from ddp_model import NerfNet
|
||||
from ddp_model import NerfNetWithAutoExpo
|
||||
import time
|
||||
from data_loader_split import load_data_split
|
||||
import numpy as np
|
||||
from tensorboardX import SummaryWriter
|
||||
from utils import img2mse, mse2psnr, img_HWC2CHW, colorize, TINY_NUMBER
|
||||
import logging
|
||||
import json
|
||||
|
||||
|
||||
logger = logging.getLogger(__package__)
|
||||
|
||||
|
|
@ -274,6 +276,59 @@ def cleanup():
|
|||
torch.distributed.destroy_process_group()
|
||||
|
||||
|
||||
def create_nerf(rank, args):
|
||||
###### create network and wrap in ddp; each process should do this
|
||||
# fix random seed just to make sure the network is initialized with same weights at different processes
|
||||
torch.manual_seed(777)
|
||||
# very important!!! otherwise it might introduce extra memory in rank=0 gpu
|
||||
torch.cuda.set_device(rank)
|
||||
|
||||
models = OrderedDict()
|
||||
models['cascade_level'] = args.cascade_level
|
||||
models['cascade_samples'] = [int(x.strip()) for x in args.cascade_samples.split(',')]
|
||||
for m in range(models['cascade_level']):
|
||||
img_names = None
|
||||
if args.optim_autoexpo:
|
||||
# load training image names for autoexposure
|
||||
f = os.path.join(args.basedir, args.expname, 'train_images.json')
|
||||
with open(f) as file:
|
||||
img_names = json.load(file)
|
||||
net = NerfNetWithAutoExpo(args, optim_autoexpo=args.optim_autoexpo, img_names=img_names).to(rank)
|
||||
net = DDP(net, device_ids=[rank], output_device=rank, find_unused_parameters=True)
|
||||
# net = DDP(net, device_ids=[rank], output_device=rank)
|
||||
optim = torch.optim.Adam(net.parameters(), lr=args.lrate)
|
||||
models['net_{}'.format(m)] = net
|
||||
models['optim_{}'.format(m)] = optim
|
||||
|
||||
start = -1
|
||||
|
||||
###### load pretrained weights; each process should do this
|
||||
if (args.ckpt_path is not None) and (os.path.isfile(args.ckpt_path)):
|
||||
ckpts = [args.ckpt_path]
|
||||
else:
|
||||
ckpts = [os.path.join(args.basedir, args.expname, f)
|
||||
for f in sorted(os.listdir(os.path.join(args.basedir, args.expname))) if f.endswith('.pth')]
|
||||
def path2iter(path):
|
||||
tmp = os.path.basename(path)[:-4]
|
||||
idx = tmp.rfind('_')
|
||||
return int(tmp[idx + 1:])
|
||||
ckpts = sorted(ckpts, key=path2iter)
|
||||
logger.info('Found ckpts: {}'.format(ckpts))
|
||||
if len(ckpts) > 0 and not args.no_reload:
|
||||
fpath = ckpts[-1]
|
||||
logger.info('Reloading from: {}'.format(fpath))
|
||||
start = path2iter(fpath)
|
||||
# configure map_location properly for different processes
|
||||
map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
|
||||
to_load = torch.load(fpath, map_location=map_location)
|
||||
for m in range(models['cascade_level']):
|
||||
for name in ['net_{}'.format(m), 'optim_{}'.format(m)]:
|
||||
models[name].load_state_dict(to_load[name])
|
||||
models[name].load_state_dict(to_load[name])
|
||||
|
||||
return start, models
|
||||
|
||||
|
||||
def ddp_train_nerf(rank, args):
|
||||
###### set up multi-processing
|
||||
setup(rank, args.world_size)
|
||||
|
|
@ -306,50 +361,20 @@ def ddp_train_nerf(rank, args):
|
|||
file.write(open(args.config, 'r').read())
|
||||
torch.distributed.barrier()
|
||||
|
||||
ray_samplers = load_data_split(args.datadir, args.scene, split='train', try_load_min_depth=args.load_min_depth)
|
||||
val_ray_samplers = load_data_split(args.datadir, args.scene, split='validation', try_load_min_depth=args.load_min_depth)
|
||||
ray_samplers = load_data_split(args.datadir, args.scene, split='train',
|
||||
try_load_min_depth=args.load_min_depth)
|
||||
val_ray_samplers = load_data_split(args.datadir, args.scene, split='validation',
|
||||
try_load_min_depth=args.load_min_depth, skip=args.testskip)
|
||||
|
||||
# write training image names for autoexposure
|
||||
if args.optim_autoexpo:
|
||||
f = os.path.join(args.basedir, args.expname, 'train_images.json')
|
||||
with open(f, 'w') as file:
|
||||
img_names = [ray_samplers[i].img_path for i in range(len(ray_samplers))]
|
||||
json.dump(img_names, file, indent=2)
|
||||
|
||||
###### create network and wrap in ddp; each process should do this
|
||||
# fix random seed just to make sure the network is initialized with same weights at different processes
|
||||
torch.manual_seed(777)
|
||||
# very important!!! otherwise it might introduce extra memory in rank=0 gpu
|
||||
torch.cuda.set_device(rank)
|
||||
|
||||
models = OrderedDict()
|
||||
models['cascade_level'] = args.cascade_level
|
||||
models['cascade_samples'] = [int(x.strip()) for x in args.cascade_samples.split(',')]
|
||||
for m in range(models['cascade_level']):
|
||||
net = NerfNet(args).to(rank)
|
||||
net = DDP(net, device_ids=[rank], output_device=rank)
|
||||
optim = torch.optim.Adam(net.parameters(), lr=args.lrate)
|
||||
models['net_{}'.format(m)] = net
|
||||
models['optim_{}'.format(m)] = optim
|
||||
|
||||
start = -1
|
||||
|
||||
###### load pretrained weights; each process should do this
|
||||
if (args.ckpt_path is not None) and (os.path.isfile(args.ckpt_path)):
|
||||
ckpts = [args.ckpt_path]
|
||||
else:
|
||||
ckpts = [os.path.join(args.basedir, args.expname, f)
|
||||
for f in sorted(os.listdir(os.path.join(args.basedir, args.expname))) if f.endswith('.pth')]
|
||||
def path2iter(path):
|
||||
tmp = os.path.basename(path)[:-4]
|
||||
idx = tmp.rfind('_')
|
||||
return int(tmp[idx + 1:])
|
||||
ckpts = sorted(ckpts, key=path2iter)
|
||||
logger.info('Found ckpts: {}'.format(ckpts))
|
||||
if len(ckpts) > 0 and not args.no_reload:
|
||||
fpath = ckpts[-1]
|
||||
logger.info('Reloading from: {}'.format(fpath))
|
||||
start = path2iter(fpath)
|
||||
# configure map_location properly for different processes
|
||||
map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
|
||||
to_load = torch.load(fpath, map_location=map_location)
|
||||
for m in range(models['cascade_level']):
|
||||
for name in ['net_{}'.format(m), 'optim_{}'.format(m)]:
|
||||
models[name].load_state_dict(to_load[name])
|
||||
models[name].load_state_dict(to_load[name])
|
||||
start, models = create_nerf(rank, args)
|
||||
|
||||
##### important!!!
|
||||
# make sure different processes sample different rays
|
||||
|
|
@ -416,13 +441,23 @@ def ddp_train_nerf(rank, args):
|
|||
bg_depth, _ = torch.sort(torch.cat((bg_depth, bg_depth_samples), dim=-1))
|
||||
|
||||
optim.zero_grad()
|
||||
ret = net(ray_batch['ray_o'], ray_batch['ray_d'], fg_far_depth, fg_depth, bg_depth)
|
||||
ret = net(ray_batch['ray_o'], ray_batch['ray_d'], fg_far_depth, fg_depth, bg_depth, img_name=ray_batch['img_name'])
|
||||
all_rets.append(ret)
|
||||
|
||||
rgb_gt = ray_batch['rgb'].to(rank)
|
||||
loss = img2mse(ret['rgb'], rgb_gt)
|
||||
scalars_to_log['level_{}/loss'.format(m)] = loss.item()
|
||||
scalars_to_log['level_{}/pnsr'.format(m)] = mse2psnr(loss.item())
|
||||
if 'autoexpo' in ret:
|
||||
scale, shift = ret['autoexpo']
|
||||
scalars_to_log['level_{}/autoexpo_scale'.format(m)] = scale.item()
|
||||
scalars_to_log['level_{}/autoexpo_shift'.format(m)] = shift.item()
|
||||
# rgb_gt = scale * rgb_gt + shift
|
||||
rgb_pred = (ret['rgb'] - shift) / scale
|
||||
rgb_loss = img2mse(rgb_pred, rgb_gt)
|
||||
loss = rgb_loss + args.lambda_autoexpo * (torch.abs(scale-1.)+torch.abs(shift))
|
||||
else:
|
||||
rgb_loss = img2mse(ret['rgb'], rgb_gt)
|
||||
loss = rgb_loss
|
||||
scalars_to_log['level_{}/loss'.format(m)] = rgb_loss.item()
|
||||
scalars_to_log['level_{}/pnsr'.format(m)] = mse2psnr(rgb_loss.item())
|
||||
loss.backward()
|
||||
optim.step()
|
||||
|
||||
|
|
@ -462,7 +497,7 @@ def ddp_train_nerf(rank, args):
|
|||
logger.info('Logged a random training view in {} seconds'.format(dt))
|
||||
log_view_to_tb(writer, global_step, log_data, gt_img=ray_samplers[idx].get_img(), mask=None, prefix='train/')
|
||||
|
||||
log_data = None
|
||||
del log_data
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
if rank == 0 and (global_step % args.i_weights == 0 and global_step > 0):
|
||||
|
|
@ -523,6 +558,11 @@ def config_parser():
|
|||
# multiprocess learning
|
||||
parser.add_argument("--world_size", type=int, default='-1',
|
||||
help='number of processes')
|
||||
# optimize autoexposure
|
||||
parser.add_argument("--optim_autoexpo", action='store_true',
|
||||
help='optimize autoexposure parameters')
|
||||
parser.add_argument("--lambda_autoexpo", type=float, default=1., help='regularization weight for autoexposure')
|
||||
|
||||
# learning rate options
|
||||
parser.add_argument("--lrate", type=float, default=5e-4, help='learning rate')
|
||||
parser.add_argument("--lrate_decay_factor", type=float, default=0.1,
|
||||
|
|
@ -530,8 +570,6 @@ def config_parser():
|
|||
parser.add_argument("--lrate_decay_steps", type=int, default=5000,
|
||||
help='decay learning rate by a factor every specified number of steps')
|
||||
# rendering options
|
||||
parser.add_argument("--inv_uniform", action='store_true',
|
||||
help='if True, will uniformly sample inverse depths')
|
||||
parser.add_argument("--det", action='store_true', help='deterministic sampling for coarse and fine samples')
|
||||
parser.add_argument("--max_freq_log2", type=int, default=10,
|
||||
help='log2 of max freq for positional encoding (3D location)')
|
||||
|
|
@ -172,11 +172,12 @@ class RaySamplerSingleImage(object):
|
|||
('depth', depth),
|
||||
('rgb', rgb),
|
||||
('mask', mask),
|
||||
('min_depth', min_depth)
|
||||
('min_depth', min_depth),
|
||||
('img_name', self.img_path)
|
||||
])
|
||||
# return torch tensors
|
||||
for k in ret:
|
||||
if ret[k] is not None:
|
||||
if isinstance(ret[k], np.ndarray):
|
||||
ret[k] = torch.from_numpy(ret[k])
|
||||
|
||||
return ret
|
||||
|
|
|
|||
|
|
@ -1,19 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C pascal
|
||||
#SBATCH --mem=40G
|
||||
#SBATCH --time=24:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
#SBATCH --qos=high
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
#$PYTHON -u $CODE_DIR/ddp_test_nerf.py --config $CODE_DIR/configs/lf_data/lf_africa.txt
|
||||
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_test_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck.txt
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:3
|
||||
#SBATCH -c 8
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=16G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest
|
||||
echo $CODE_DIR
|
||||
|
||||
#$PYTHON -u $CODE_DIR/run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_addregularize.txt
|
||||
#$PYTHON -u $CODE_DIR/nerf_render_path.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_addregularize.txt
|
||||
#$PYTHON -u $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_addregularize.txt
|
||||
|
||||
$PYTHON -u $CODE_DIR/nerf_render_path.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_playground.txt
|
||||
$PYTHON -u $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_playground.txt
|
||||
|
||||
#$PYTHON -u $CODE_DIR/nerf_render_path.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_addregularize.txt
|
||||
#$PYTHON -u $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_addregularize.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=60G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples_sparse/tat_intermediate_playground_addparam.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=60G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples_sparse/tat_intermediate_playground_addcarve.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=60G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples_sparse/tat_intermediate_playground_addregularize.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=60G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples_sparse/tat_training_truck_addparam.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
####SBATCH -C turing
|
||||
#SBATCH --mem=60G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples_sparse/tat_training_truck_addcarve.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=60G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples_sparse/tat_training_truck_addregularize.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=80G
|
||||
#SBATCH --time=24:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
#SBATCH --qos=high
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/lf_data/lf_africa.txt
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=100G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
######## #SBATCH --qos=high
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/lf_data/lf_basket.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=80G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
#SBATCH --qos=normal
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/lf_data/lf_ship.txt
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=80G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
#SBATCH --qos=normal
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/lf_data/lf_torch.txt
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:3
|
||||
#SBATCH -c 8
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=16G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_m60.txt
|
||||
$PYTHON -u $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_m60.txt
|
||||
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:3
|
||||
#SBATCH -c 8
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=16G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_playground.txt
|
||||
$PYTHON -u $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_playground.txt
|
||||
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH -c 25
|
||||
#SBATCH -C turing
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_playground_bignet.txt
|
||||
|
|
@ -1,18 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:3
|
||||
#SBATCH -c 8
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=16G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_train.txt
|
||||
$PYTHON -u $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_intermediate_train.txt
|
||||
|
||||
|
|
@ -1,16 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p q6
|
||||
#SBATCH --gres=gpu:4
|
||||
#SBATCH -c 10
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=50G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck.txt
|
||||
|
|
@ -1,15 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:8
|
||||
#SBATCH -c 25
|
||||
#SBATCH -C turing
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf-ddp/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg_latest_ddp
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON -u $CODE_DIR/ddp_run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_bignet.txt
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
#!/bin/bash
|
||||
#SBATCH -p gpu
|
||||
#SBATCH --gres=gpu:3
|
||||
#SBATCH -c 8
|
||||
#SBATCH -C turing
|
||||
#SBATCH --mem=16G
|
||||
#SBATCH --time=48:00:00
|
||||
#SBATCH --output=slurm_%A.out
|
||||
#SBATCH --exclude=isl-gpu17
|
||||
|
||||
|
||||
PYTHON=/home/zhangka2/anaconda3/envs/nerf/bin/python
|
||||
|
||||
CODE_DIR=/home/zhangka2/gernot_experi/nerf_bg
|
||||
echo $CODE_DIR
|
||||
|
||||
$PYTHON $CODE_DIR/run_nerf.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_subset.txt
|
||||
$PYTHON $CODE_DIR/nerf_render_image.py --config $CODE_DIR/configs/tanks_and_temples/tat_training_truck_subset.txt
|
||||
|
||||
Loading…
Reference in a new issue