universome / fvd-comparison Goto Github PK

Thanks for your great work!
I try to use your code on my videos. I'm not sure if the way it is used is correct. I am using your code as follows：

1. Read all frames using opencv from real and fake videos. And reshape them to (num_videos, video_len, 224, 224, 3)
2. run compare_metrics.py (without any data pre-processing, such as transforms.Normalize() in pytorch )

Is this correct? @universome

requests.exceptions.ProxyError: HTTPConnectionPool(host='127.0.0.1', port=8080): Max retries exceeded with url: http://zap/JSON/spider/action/scan/?apikey=&url=https%3A%2F%2Fdlq7l0b00rmgi.cloudfront.net (Caused by ProxyError('Cannot connect to proxy.', NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f10a3706e80>: Failed to establish a new connection: [Errno 111] Connection refused')))
90
Error: Process completed with exit code 1.

hi, when i use your code, i found this problem:
requests.exceptions.ProxyError: HTTPSConnectionPool(host='www.dropbox.com', port=443): Max retries exceeded with url: /s/ge9e5ujwgetktms/i3d_torchscript.pt?dl=1 (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))
what should i do?
And i have a another question,the videos resolution must be 224x224?If my video resolution is 128x128,how can i do?
Thanks!

Hi. Thanks for sharing works!

Input value scale

• Is it right input values for torch i3d model would be range in [0,1]?

  • Use "rand" (uniform) function in here

    fvd-comparison/compare_metrics.py

    Line 54 in 5b88a52

    videos_fake = np.random.RandomState(seed_fake).rand(num_videos, video_len, 224, 224, 3).astype(np.float32)

• Meanwhile, It seems input range [-1, 1] used in tensorflow i3d

  • fvd-comparison/their_fvd.py

    Line 67 in 5b88a52

    Expected range is [-1, 1].

• If I use FVD with torch, which one is right between [0,1] and [-1,1] ?

Input resolution

• If I set input resolution above 224, this code returns [batch size, 400, feat_h, feat_w]. If I want to use resolution 256, is it right average [batch size, 400, feat_h, feat_w] to have shape of [batch size, 400] (average across spatial dim) ?
  

  fvd-comparison/compare_metrics.py

  Line 34 in 5b88a52

  detector_kwargs = dict(rescale=False, resize=False, return_features=True) # Return raw features before the softmax layer.