about the test data about pytorch-cpn

[GengDavid]

Hi @genius9527
I also used COCO 2017 Keypoint data for training and test.
Sorry I don't know where your problem actually is. Have you modified any code?
Maybe you can make sure you have downloaded the full validation dataset and delete COCO_2017_val.json file(in annotations folder) and run label_transform.py(in ROOT_DIR) again to regenerate it.

[genius9527]

ok,I will try. Can you pass your pre-training model to Baidu Cloud? I can't download anything on Google Cloud Drive.Thanks

[GengDavid]

@genius9527 Sorry to tell that I cannot upload the model to Baidu Cloud due to some limitations. If you still have the problem on downloading the pre-trained model, I can provide you the pre-trained model by Tencent weiyun: COCO.res50.256x192.CPN

[genius9527]

@GengDavid thank you，I used the pre-training model you provided and found that the current test results are normal. The AP exceeded 0.7.But now I have a new problem, I try to use COCO2017/test2017 as the test data, and image_info_test-dev2017.json as the annotations. When I use test.py to test, the following problem occurs:
=> loaded checkpoint 'checkpoint/CPN256x192.pth.tar' (epoch 29)
testing...
0it [00:00, ?it/s]
loading annotations into memory...
Done (t=0.05s)
creating index...
index created!
Loading and preparing results...
Traceback (most recent call last):
File "test.py", line 159, in
main(parser.parse_args())
File "test.py", line 137, in main
eval_dt = eval_gt.loadRes(result_file)
File "/media/uestc/4T_1/pytorch-cpn-master/256.192.model/../cocoapi/PythonAPI/pycocotools/coco.py", line 318, in loadRes
if 'caption' in anns[0]:
IndexError: list index out of range
Have you ever encountered such a problem? Thanks.

[GengDavid]

I have not met this problem before. But it seems that there was something wrong in testing process since the test time was 00:00.

[genius9527]

@GengDavid emmmm，So, have you tested it on the test dataset, how is it tested?

[GengDavid]

The test codes provided are not suit for evaluation on COCO test dataset. If you want to test it on test-dev, you may need to prepare the human detection results on test-dev and save it as the format shown in #2 first. And then you need to comment out line 136-141(which use coco tool to evaluate the results)in test.py. Finally, you can submit the results to COCO submission platform to get the evaluation result.

This is a pipeline for testing on test-dev. Some other changes are also needed. For example, you need to modify test_config to load the right dataset and detection bbox.