Demo.py

[open-mmlab]

Demo.py

[innerlee]

@dreamerlin maybe we should make a dummy run of demo.py in the test to make sure it works

[innerlee]

Currently demo.py is written for videos, so you need to change the dataset type to video, as in https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb.py#L21 , and also make changes to the testing pipeline to use video loader such as decord, as in https://github.com/open-mmlab/mmaction2/blob/master/configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb.py#L70

[IDayday]

thanks! I will try for that

[dreamerlin]

Rawframe inference in demo scripts will be supported in this.

[IDayday]

Sorry, I still meet a trouble. I know that demo.py is written for videos. So I put my own video named test1.mp4 in the demo folder.
I change the code as:
python demo/demo.py configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb.py demo/checkpoints/tsn_r50_video_1x1x8_100e_kinetics400_rgb_20200702-568cde33.pth demo/test1.mp4 demo/label_map.txt
( I first want to use the SlowFast model, but I can't find any checkpoints with videos in the Modelzoo, so I follow your advice to use TSN.)
But it still can't work.
I have provided the video path in the code such as demo/test1.mp4. Should I modify the config？

[innerlee]

you can run video on models that were trained with rawframes. video/rawframe are input formats and they are not tight with models.

@dreamerlin could u pls check why tsn_r50_video_1x1x8_100e_kinetics400_rgb.py does not work?

[IDayday]

This is the error message I got when I uesd TSN:

Traceback (most recent call last):
File "demo/demo.py", line 35, in
main()
File "demo/demo.py", line 27, in main
results = inference_recognizer(model, args.video, args.label)
File "/dat01/wangbo2/ZT/mmaction2/mmaction/apis/inference.py", line 64, in inference_recognizer
data = collate([data], samples_per_gpu=1)
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 82, in collate
for key in batch[0]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 82, in
for key in batch[0]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in collate
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in collate
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in
.....
( just like above for nearly 2000 lines)
.....
if not isinstance(batch, Sequence):
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/abc.py", line 184, in instancecheck
if subclass in cls._abc_cache:
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/_weakrefset.py", line 75, in contains
return wr in self.data
RecursionError: maximum recursion depth exceeded in comparison

[dreamerlin]

Sorry, I still meet a trouble. I know that demo.py is written for videos. So I put my own video named test1.mp4 in the demo folder.
I change the code as:
python demo/demo.py configs/recognition/tsn/tsn_r50_video_1x1x8_100e_kinetics400_rgb.py demo/checkpoints/tsn_r50_video_1x1x8_100e_kinetics400_rgb_20200702-568cde33.pth demo/test1.mp4 demo/label_map.txt
( I first want to use the SlowFast model, but I can't find any checkpoints with videos in the Modelzoo, so I follow your advice to use TSN.)
But it still can't work.
I have provided the video path in the code such as demo/test1.mp4. Should I modify the config？

You can try to write a tsn_r50_video_inference_1x1x8_100e_kinetics400_rgb.py like tsn_r50_video_inference_1x1x3_100e_kinetics400_rgb.py by using some setting related with testing from the tsn_r50_video_1x1x8_100e_kinetics400_rgb.py.

Since it is for inferencing a single video, There are some hints to modify some params:

• dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]) -> dict(type='Collect', keys=['imgs'], meta_keys=[]), removing label in keys since we don't need to calculate the top_k_accuracy.
• Set ann_file None.
• Set data_prefix to None, since your video filename is in demo/test1.mp4 without directory prefix.

[dreamerlin]

This is the error message I got when I uesd TSN:

Traceback (most recent call last):
File "demo/demo.py", line 35, in
main()
File "demo/demo.py", line 27, in main
results = inference_recognizer(model, args.video, args.label)
File "/dat01/wangbo2/ZT/mmaction2/mmaction/apis/inference.py", line 64, in inference_recognizer
data = collate([data], samples_per_gpu=1)
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 82, in collate
for key in batch[0]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 82, in
for key in batch[0]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in collate
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in collate
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in
.....
( just like above for nearly 2000 lines)
.....
if not isinstance(batch, Sequence):
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/abc.py", line 184, in instancecheck
if subclass in cls._abc_cache:
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/_weakrefset.py", line 75, in contains
return wr in self.data
RecursionError: maximum recursion depth exceeded in comparison

This is due to the dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), you can change it to dict(type='Collect', keys=['imgs'], meta_keys=[]) by removing the unused label. BTW, we will hardcode the label to -1 to avoid this case in #59

Thanks for your report!

[IDayday]

This is the error message I got when I uesd TSN:
Traceback (most recent call last):
File "demo/demo.py", line 35, in
main()
File "demo/demo.py", line 27, in main
results = inference_recognizer(model, args.video, args.label)
File "/dat01/wangbo2/ZT/mmaction2/mmaction/apis/inference.py", line 64, in inference_recognizer
data = collate([data], samples_per_gpu=1)
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 82, in collate
for key in batch[0]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 82, in
for key in batch[0]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in collate
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in collate
return [collate(samples, samples_per_gpu) for samples in transposed]
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/site-packages/mmcv/parallel/collate.py", line 78, in
.....
( just like above for nearly 2000 lines)
.....
if not isinstance(batch, Sequence):
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/abc.py", line 184, in instancecheck
if subclass in cls._abc_cache:
File "/dat01/wangbo2/anaconda3/envs/zt/lib/python3.6/_weakrefset.py", line 75, in contains
return wr in self.data
RecursionError: maximum recursion depth exceeded in comparison

This is due to the dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]), you can change it to dict(type='Collect', keys=['imgs'], meta_keys=[]) by removing the unused label. BTW, we will hardcode the label to -1 to avoid this case in #59

Thanks for your report!

Thank you for answering my doubts. TSN currently work!
I will follow your advice to test other models.

[IDayday]

Another question. How can I get the ouput in video format like the gif ? ( The labels appear in the video)
Dome.py just feedback top-5 recognitions in text. It's useful, but visualization is not good.

[innerlee]

One way to do it is to paint the label into frames using opencv and save it to mp4, and convert mp4 to gif using ffmpeg or online converter.

Maybe supporting mp4 output with label overlay is an option. You may request this feature in the roadmap issue #19

[dreamerlin]

We have already support to output video or gif file in this pr.