Comments (6)
A quick note: as per dgnn's environment requirements I have munch (2.5.0)
:
/home/kevin/anaconda3/envs/dgnn/lib/python3.9/site-packages
Python finds module munch
as /home/kevin/anaconda3/envs/dgnn/lib/python3.9/site-packages/munch/__init__.py
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I converted the supplied .ply data to .npz rather than using the .npz files above, in case the normal and viewpoint data was not present:
cd ~/dgnn
python ./processing/reconbench/ply2npz.py --user_dir=/home/kevin/dgnn/data
This outputs files as expected, and the feature extraction from the output looks sane, similar to what I logged above.
However, I get the same error noted above when calling run.py
for evaluation.
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Hi, the custom.yaml file was not really up-to-date.
I think you are simply missing this line in it.
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Thanks @raphaelsulzer, I did a hard reset via git and pulled the latest updates, which included the line you noted. I'm able to proceed, but quickly found that I do not understand how to set the inference list in the case of single point cloud.
As above in this thread, I converted a single point cloud via ply2npz
, which I placed in the ‘0’ folder expected in ground truth data, similar to the five model group (0..4) from 'reconbench'. I see run.py
still expects a batch of five point models.
Consider this invocation: python run.py -i -c configs/custom.yaml
, where the .yaml inference settings include:
inference:
dataset: sample
classes: anchor
Based on the code, it looks like the above expects the following directory structure:
...data/
├─ sample/
│ ├─ anchor_0.ply
│ ├─ anchor_0.npz
│ ├─ anchor_0_initial.ply
│ ├─ gt/
│ ├─ ├─0/
│ ├─ ├─├─ anchor_0_labels.npz
│ ├─ ├─├─ anchor_0_* ...
Do I have the .yaml or folder structure wrong?
How can I instruct dgnn
to process the single file group above? For training and validation, the analogue seems to be scan_confs: x
.
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You can structure your dataset any way you want as long as this dictionary is correctly filled. I recommend you to use a python debugger and see if all the paths are correct.
Also your example should probably look like this:
inference:
dataset: sample
classes: ["anchor"]
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Thanks once again for a fast, and kind, response. I had mistakenly switched label data on; once I ran with has_label: 0
I was able to process a single input file as expected. I added a block in dataset.py
to handle inference from my dataset 'sample' shown below:
READ CONFIG FROM /home/kevin/dgnn/configs/custom.yaml
SAVE CONFIG TO /home/kevin/dgnn/data/models/kf96/config.yaml
dataset: sample
class: ['anchor']
Point clouds per class: 0
path: /home/kevin/dgnn/data/sample
filename: anchor_0
scan_conf: 0
gtfile: gt/0/anchor_0
Thanks again @raphaelsulzer for your help!
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