monniert / unicorn Goto Github PK

CUB and LSUN dataset URL are broken. Help!

hi , I'm new in AI
I'm training the model from scratch i download the dataset and the package

I run in google colab:
!cuda=GPU-f491dbb6-2dc7-a094-5417-a57de28df194 config=p3d_car.yml tag=run_tag /content/unicorn-main\scripts\pipeline.sh

this appears to me:

Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/scipy/io/matlab/mio.py", line 39, in _open_file
return open(file_like, mode), True
FileNotFoundError: [Errno 2] No such file or directory: '/content/unicorn-main/datasets/pascal_3d/ucmr_anno/data/car_train.mat'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/content/unicorn-main/src/trainer.py", line 292, in
trainer = Trainer(cfg, run_dir, seed=seed)
File "/content/unicorn-main/src/utils/init.py", line 104, in wrapper
return f(*args, **kw)
File "/content/unicorn-main/src/trainer.py", line 41, in init
self.train_loader, self.val_loader, self.test_loader = create_train_val_test_loader(cfg, rank, world_size)
File "/content/unicorn-main/src/dataset/init.py", line 16, in create_train_val_test_loader
train = get_dataset(name)(split="train", **kwargs)
File "/content/unicorn-main/src/dataset/p3d_car.py", line 38, in init
self.data = scio.loadmat(str(path), struct_as_record=False, squeeze_me=True)['images']
File "/usr/local/lib/python3.7/dist-packages/scipy/io/matlab/mio.py", line 224, in loadmat
with _open_file_context(file_name, appendmat) as f:
File "/usr/lib/python3.7/contextlib.py", line 112, in enter
return next(self.gen)
File "/usr/local/lib/python3.7/dist-packages/scipy/io/matlab/mio.py", line 17, in _open_file_context
f, opened = _open_file(file_like, appendmat, mode)
File "/usr/local/lib/python3.7/dist-packages/scipy/io/matlab/mio.py", line 45, in _open_file
return open(file_like, mode), True
FileNotFoundError: [Errno 2] No such file or directory: '/content/unicorn-main/datasets/pascal_3d/ucmr_anno/data/car_train.mat'

i just want to know where the location of ucmr_anno file install so i can fix it and know the path

i spend so much time searching ucmr_anno file in colab files

help me please and thank for the work

2023-10-10:

1. dataset is too small ... (objvarse/3d, imagenet-l/2d, ...)
2. the shape generation network is not deep enough, it is diffucult to generate better shape from src_mesh. such as deeper iteration in alphafold, we can image that the shaping is a dynamic processing, step by step.
3. we need better priori stroage, not parameters in functional netowrk, but priori memory network(!), even vecter-db(?), ... such as MPCN.
4. the pose network is too simple, more fowerful network such as robustlock.
5. ......
   Anyway, the is the best design on this 3d recon/gen task, so far.

2023-10-12:
after try, modify, try, modify, try, modify, ...
I think:
if we want to fit a better mesh, these aspects in model we need to optimize from random initialized:
<notice: SINGLE image in, only, here>
a1. input-image: segmentation/mask(SAM) or not #segment background in model or not
a2. input-priori: where? what? #network-parameter, special memeory-priori-netowrk
a*. ......
b1. mesh - morphism #IMPORTANT ball/ring/...; pick up object template; even smplx for human #open/unbound/...
b2. mesh - scale, center, ...
b3. mesh - pose, #IMPORT if we can not predict pose correctly, can not fit a better mesh. (use pose-network? direct fit? or pose related loss)
b*. ......
c1. render-image: background vs foreground/object
c2. render-image: silhouette / shape
c3. render-image: texture
c4. render-light: no light modeling in this paper.
c*. ......

for these fiting aspects, there are different priority to optimzed, there are different composions to optimized.

in this paper, I found that, the pose prediction is not very accurate， so, the final mesh is not good enough.
i think how to do pose prediciton even representation(important, especial for single image in, what is the front-side/start-point of azimuth and eleation), is still an open problem.

I am very appreciated with your work.
I download the images from the website.I found the car, bird, and horse model can not work well with the images.I can get the shape and the texture in the result, but the texture, even the colour is wrong.
And the motobike model works well.

I am currently analyzing the training process of your model.
I recognized that results are only partially reproducible as there seems to be some randomness in the training.

Do you know which parts of the code are influencing the reproducibility? Could it be Pytorch3D, also related to this issue facebookresearch/pytorch3d#659?
It would be great if you could tell me more about it, the tests you might have run, and whether you plan to work on this.

Hi, your work is impressive and much appreciated.

I'm trying to train a model from scratch but, even though in the README appears a tutorial to do so, i think there are no examples of .yml files in which training from scratch is specified so i'm a bit lost. What do i have to include in the .yml to train from scratch?

Dear Authors,

Could you please share how you evaluate the 3D-IoU metric? Thanks...

Hi, I try to retrain the code on muti-gpu by changing the cuda setting. But it still runs on a single gpu, can you help me with this? Thanks