Closed z-jiaming closed 1 year ago
It should work without any "fixes" to the tool. For debugging, I would recommend printing out the path to the file that it is trying to read and figuring out why it failed to read that file.
I print "self.sequences[sequence][obj_type]" in self._get_all_elements(sequence, 'mask'). It looks like many "-1", due to "masks.extend([-1] * (len(images) - len(masks)))" in def init()
I simply filter out "-1",
but it also raises an error:
Could you please directly provide your fixed tools? It will help a lot!
Again, I did not apply any "fix". davis2017-evaluation should work out-of-the-box. I just tested it and it still works. From your error message, it seems like the annotation and the segmentation have different shapes. Are you sure you are using the correct directories?
I eval results along the following steps:
1) Download Long-Time Video datasets from [here] which provided in GETTING_STARTED.md, and unzip like:
2) Download your [Precomputed Results] and unzip like:
And delete your provided csv.
3) git clone davis2017-evaluation, fix img and mask directories as:
4) eval as follows:
eval: python evaluation_method.py --davis_path xxx/long_video_set/long_video --task semi-supervised --results_path xxx/LongVideo-1X-r10
eval 3X: python evaluation_method.py --davis_path xxx/long_video_set/long_video_x3 --task semi-supervised --results_path xxx/LongTime-3X-r10
Then, raise the above errors.
Could you please help me to find where is wrong.
--davis_path xxx/long_video_set/long_video_x3
How about you revert (1) and follow (2) as mentioned in our documentation? 😄
Oh, I know it, my mistake! Thanks a lot!!!!
When getting quantitative results on Long-Time Video, we follow the INFERENCE.md to use davis2017-evaluation
We first point --davis_path to long_video_davis or long_video_davis_x3, then remove resolution
But raise error as follow:
Could you please help me to fix this bug, or directly provide your fixed tools? Thanks a lot!