clean code

.gitignore

@@ -1,6 +1,9 @@
 # scripts
 *.sh
 
+# mac
+.DS_Store
+
 # pycharm
 .idea/
 
@@ -141,4 +144,4 @@ dmypy.json
 .pytype/
 
 # Cython debug symbols
-cython_debug/
+cython_debug/

README.md

@@ -0,0 +1,23 @@
+# NeRF++
+Codebase for paper: 
+* Work with 360 capture of large-scale unbounded scenes.
+* Support multi-gpu training and inference.
+
+## Data
+* Download our preprocessed data from [tanks_and_temples](), [lf_data]().
+* Put the data in the code directory.
+* Data format. 
+** Each scene consists of 3 splits: train/test/validation. 
+** Intrinsics and poses are stored as flattened 4x4 matrices.
+** Opencv camera coordinate system is adopted, i.e., x--->right, y--->down, z--->scene.
+* Scene normalization: move the average camera center to origin, and put all the camera centers inside the unit sphere.
+
+## Training
+```python
+python ddp_train_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt
+```
+
+## Testing
+```python
+python ddp_test_nerf.py --config configs/tanks_and_temples/tat_training_truck.txt --render_splits test,camera_path
+```

configs/tanks_and_temples/tat_intermediate_m60.txt

@@ -1,49 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
-scene = tat_intermediate_M60
-expname = tat_intermediate_M60_bg_carve_latest
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-# N_rand = 4096
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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

configs/tanks_and_temples/tat_intermediate_playground.txt

@@ -1,49 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
-scene = tat_intermediate_Playground
-expname = tat_intermediate_Playground_bg_carve_latest
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-# N_rand = 4096
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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

configs/tanks_and_temples/tat_intermediate_playground_bignet.txt

@@ -1,48 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
-scene = tat_intermediate_Playground
-expname = tat_intermediate_Playground_ddp_bignet
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-N_rand = 256
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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
-
-### RENDERING
-det = False
-max_freq_log2 = 10
-max_freq_log2_viewdirs = 4
-netdepth = 8
-netwidth = 512
-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

configs/tanks_and_temples/tat_intermediate_train.txt

@@ -1,49 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
-scene = tat_intermediate_Train
-expname = tat_intermediate_Train_bg_carve_latest
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-# N_rand = 4096
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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

configs/tanks_and_temples/tat_training_truck.txt

@@ -1,7 +1,7 @@
 ### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
+datadir = ./data/tanks_and_temples
 scene = tat_training_Truck
-expname = tat_training_Truck_ddp_implicit
+expname = tat_training_Truck
 basedir = ./logs
 config = None
 ckpt_path = None
@@ -10,27 +10,16 @@ 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
-
 ### 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
@@ -39,15 +28,10 @@ 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

configs/tanks_and_temples/tat_training_truck_bignet.txt

@@ -1,48 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
-scene = tat_training_Truck
-expname = tat_training_Truck_ddp_bignet
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-N_rand = 256
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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
-
-### RENDERING
-det = False
-max_freq_log2 = 10
-max_freq_log2_viewdirs = 4
-netdepth = 8
-netwidth = 512
-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

configs/tanks_and_temples/tat_training_truck_subset.txt

@@ -1,47 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere
-scene = tat_training_Truck_subset
-expname = tat_training_Truck_subset_bg_carvenew
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 250001
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### CASCADE
-cascade_level = 2
-cascade_samples = 64,64
-near_depth = 0.
-far_depth = 1.
-
-### TESTING
-render_only = False
-render_test = False
-render_train = False
-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 = False
-white_bkgd = False
-
-### CONSOLE AND TENSORBOARD
-i_img = 2000
-i_print = 100
-i_testset = 5000000
-i_video = 5000000
-i_weights = 5000

configs/tanks_and_temples_sparse/tat_intermediate_playground_addcarve.txt

@@ -1,54 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
-scene = tat_intermediate_Playground
-expname = tat_intermediate_Playground_ddp_sparse_addcarve
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-# N_rand = 4096
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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 = 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

configs/tanks_and_temples_sparse/tat_intermediate_playground_addparam.txt

@@ -1,54 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
-scene = tat_intermediate_Playground
-expname = tat_intermediate_Playground_ddp_sparse_addparam
-basedir = ./logs
-config = None
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-# N_rand = 4096
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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 = 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

configs/tanks_and_temples_sparse/tat_intermediate_playground_addregularize.txt

@@ -1,54 +0,0 @@
-### INPUT
-datadir = /home/zhangka2/gernot_experi/gernot_data/gernot_nerf_sphere_sparse
-scene = tat_intermediate_Playground
-expname = tat_intermediate_Playground_ddp_sparse_addregularize_pretrain
-basedir = ./logs
-config = /home/zhangka2/gernot_experi/nerf_bg_latest_ddp/logs/tat_intermediate_Playground_ddp_sparse_addparam/model_210000.pth
-ckpt_path = None
-no_reload = False
-testskip = 1
-
-### TRAINING
-N_iters = 1250001
-# N_rand = 4096
-N_rand = 2048
-lrate = 0.0005
-lrate_decay_factor = 0.1
-lrate_decay_steps = 500000
-
-### 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 = 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

configs/tanks_and_temples_sparse/tat_training_truck_addcarve.txt

@@ -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

configs/tanks_and_temples_sparse/tat_training_truck_addparam.txt

@@ -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

configs/tanks_and_temples_sparse/tat_training_truck_addregularize.txt

@@ -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

data_loader_split.py

@@ -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])

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

ddp_test_nerf.py

@@ -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 collections import OrderedDict
-from ddp_model import NerfNet
+# 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
+    start, models = create_nerf(rank, args)
-    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])
 
     render_splits = [x.strip() for x in args.render_splits.strip().split(',')]
     # start testing
@@ -157,4 +118,3 @@ if __name__ == '__main__':
     setup_logger()
     test()
 
-

ddp_train_nerf.py

@@ -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)
+    ray_samplers = load_data_split(args.datadir, args.scene, split='train',
-    val_ray_samplers = load_data_split(args.datadir, args.scene, split='validation', try_load_min_depth=args.load_min_depth)
+                                   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
+    start, models = create_nerf(rank, args)
-    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])
 
     ##### 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)
+            if 'autoexpo' in ret:
-            scalars_to_log['level_{}/loss'.format(m)] = loss.item()
+                scale, shift = ret['autoexpo']
-            scalars_to_log['level_{}/pnsr'.format(m)] = mse2psnr(loss.item())
+                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)')

nerf_sample_ray_split.py

@@ -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

render_all.sh

@@ -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

render_tat_training_truck.sh

@@ -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

sparse_playground.sh

@@ -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

sparse_playground_addcarve.sh

@@ -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

sparse_playground_addregularize.sh

@@ -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

sparse_truck.sh

@@ -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

sparse_truck_addcarve.sh

@@ -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

sparse_truck_addregularize.sh

@@ -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

train_lf_africa.sh

@@ -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

train_lf_basket.sh

@@ -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

train_lf_ship.sh

@@ -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

train_lf_torch.sh

@@ -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

train_tat_intermediate_m60.sh

@@ -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
-

train_tat_intermediate_playground.sh

@@ -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
-

train_tat_intermediate_playground_bignet.sh

@@ -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

train_tat_intermediate_train.sh

@@ -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
-

train_tat_training_truck.sh

@@ -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

train_tat_training_truck_bignet.sh

@@ -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

train_tat_truck_sphere_subset.sh

@@ -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
-