RuntimeError: The size of tensor a (600000) must match the size of tensor b (552384) at non-singleton dimension 0

[Abbsalehi]

I trained my colmap dataset with the following config, but I got an error like this. I used "plane.npy" from "tnt" dataset for this process.However, how to compute plane.npy based on our dataset?

Dataset: 115 images (1080*1920)

root_dir = ./ref
dataset_name = colmap
exp_name = building
batch_size = 2048
scale = 8.0
num_epochs = 80
### render a camera path(through interpolation between poses)
render_traj = True
### render camera poses from training dataset
render_train = False
render_rgb = True
render_depth = False
### render derived normal or not
render_normal = True
### render semantic labels or not, set to False if no g.t. semantic labels
render_semantic = True
sem_conf_path = ../mseg_ckps/segformer_mit-b5_8xb1-160k_cityscapes-1024x1024.py
sem_ckpt_path = ../mseg_ckps/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth
styl_img_path = ./styl_img/winter.jpg
### appearance embeddings
embed_a = True
embed_a_len = 8
### mask embeddings
embed_msk = False
random_bg = True
use_skybox = False
## smog
depth_bound = 0.9
sigma = 0.5
rgb_smog = [0.925, 0.906, 0.758]
## flood
depth_path = /home/user/PycharmProjects/Climate_NeRF/scripts/tanks/results/tnt/playground/depth_raw.npy
water_height = 0.0
rgb_water = [0.488, 0.406, 0.32]
refraction_idx = 1.35
gf_r = 5
gf_eps = 0.1
gl_theta = 0.008
gl_sharpness = 500
wave_len = 0.2
wave_ampl = 500000

The error:

File "/home/user/PycharmProjects/Climate_NeRF/models/rendering.py", line 235, in __render_rays_test
    mask = simulator.simulate_after_marching(**sim_kwargs)
  File "/home/user/PycharmProjects/Climate_NeRF/simulate.py", line 260, in simulate_after_marching
    is_water = depth_to_water < depth
RuntimeError: The size of tensor a (600000) must match the size of tensor b (552384) at non-singleton dimension 0