RuntimeError: stack expects each tensor to be equal size & AttributeError: 'NoneType' object has no attribute 'shape'

[bartman081523]

#local run
colmap feature_extractor \
--database_path ./database.db --image_path ./dense/images/

colmap exhaustive_matcher \
--database_path ./database.db

colmap mapper \
--database_path ./database.db \
--image_path ./dense/images \
--output_path ./dense/sparse

colmap image_undistorter \
--image_path ./dense/images \
--input_path ./dense/sparse/0 \
--output_path ./dense \
--output_type COLMAP \
--max_image_size 2000

#colab run

%cd /content/drive/MyDrive/neural-net
!git clone https://github.com/zl548/Neural-Scene-Flow-Fields
%cd Neural-Scene-Flow-Fields
!pip install configargparse
!pip install matplotlib
!pip install opencv
!pip install scikit-image
!pip install scipy
!pip install cupy
!pip install imageio.
!pip install tqdm
!pip install kornia

my Images are 288x512 pixels

%cd /content/drive/MyDrive/neural-net/Neural-Scene-Flow-Fields/nsff_scripts/
    # create camera intrinsics/extrinsic format for NSFF, same as original NeRF where it uses imgs2poses.py script from the LLFF code: https://github.com/Fyusion/LLFF/blob/master/imgs2poses.py
!python save_poses_nerf.py --data_path "/content/drive/MyDrive/neural-net/Neural-Scene-Flow-Fields/nerf_data/bolli/dense"
    # Resize input images and run single view model, 
    # argument resize_height: resized image height for model training, width will be resized based on original aspect ratio
!python run_midas.py --data_path "/content/drive/MyDrive/neural-net/Neural-Scene-Flow-Fields/nerf_data/bolli/dense"  --resize_height 512
!bash ./download_models.sh
    # Run optical flow model
!python run_flows_video.py --model models/raft-things.pth --data_path /content/drive/MyDrive/neural-net/Neural-Scene-Flow-Fields/nerf_data/bolli/dense

Error:

Traceback (most recent call last):
  File "run_flows_video.py", line 448, in <module>
    run_optical_flows(args)
  File "run_flows_video.py", line 350, in run_optical_flows
    images = load_image_list(images)
  File "run_flows_video.py", line 257, in load_image_list
    images = torch.stack(images, dim=0)
RuntimeError: stack expects each tensor to be equal size, but got [3, 512, 288] at entry 0 and [3, 512, 287] at entry 31

So input_w = is not consistent, eventhough my images are all dimensions 288x512

Even if I modify the script:

def load_image(imfile):
    long_dim = 512

    img = np.array(Image.open(imfile)).astype(np.uint8)

    # Portrait Orientation
    if img.shape[0] > img.shape[1]:
        input_h = long_dim
        input_w = 288

The dimensions error is gone, but another error:

...
flow input w 288 h 512
0
/usr/local/lib/python3.7/dist-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2157.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]
Traceback (most recent call last):
  File "run_flows_video.py", line 448, in <module>
    run_optical_flows(args)
  File "run_flows_video.py", line 363, in run_optical_flows
    (img_train.shape[1], img_train.shape[0]), 
AttributeError: 'NoneType' object has no attribute 'shape'

[zhengqili]

This should means somehow you didn't load img_train properly. the code always load the first image in the folder which is called "00000.png", but your image file name might be different