reorganize files

eval/cal_fvd.py

@@ -1,89 +0,0 @@
-import numpy as np
-import torch
-from tqdm import tqdm
-
-def trans(x): # requires video to be BCTHW
-    # if greyscale images add channel
-    if x.shape[-3] == 1:
-        x = x.repeat(1, 1, 3, 1, 1)
-
-    # permute BTCHW -> BCTHW
-    x = x.permute(0, 2, 1, 3, 4) 
-
-    return x
-
-
-def calculate_fvd(videos1, videos2, device, method='styleganv'):
-
-    if method == 'styleganv':
-        from fvd.styleganv.fvd import get_fvd_feats, frechet_distance, load_i3d_pretrained
-    elif method == 'videogpt':
-        from fvd.videogpt.fvd import load_i3d_pretrained
-        from fvd.videogpt.fvd import get_fvd_logits as get_fvd_feats
-        from fvd.videogpt.fvd import frechet_distance
-
-    print("calculate_fvd...")
-
-    # videos [batch_size, timestamps, channel, h, w]
-    
-    assert videos1.shape == videos2.shape
-
-    i3d = load_i3d_pretrained(device=device)
-    fvd_results = []
-
-    # support grayscale input, if grayscale -> channel*3
-    # BTCHW -> BCTHW
-    # videos -> [batch_size, channel, timestamps, h, w]
-
-    videos1 = trans(videos1)
-    videos2 = trans(videos2)
-
-    fvd_results = {}
-
-    
-    # for calculate FVD, each clip_timestamp must >= 10
-    for clip_timestamp in tqdm(range(10, videos1.shape[-3]+1)):
-       
-        # get a video clip
-        # videos_clip [batch_size, channel, timestamps[:clip], h, w]
-        videos_clip1 = videos1[:, :, : clip_timestamp]
-        videos_clip2 = videos2[:, :, : clip_timestamp]
-
-        breakpoint()
-        # get FVD features
-        feats1 = get_fvd_feats(videos_clip1, i3d=i3d, device=device)
-        feats2 = get_fvd_feats(videos_clip2, i3d=i3d, device=device)
-
-        breakpoint()
-        # calculate FVD when timestamps[:clip]
-        fvd_results[clip_timestamp] = frechet_distance(feats1, feats2)
-
-    result = {
-        "value": fvd_results,
-        "video_setting": videos1.shape,
-        "video_setting_name": "batch_size, channel, time, heigth, width",
-    }
-
-    return result
-
-# test code / using example
-
-def main():
-    NUMBER_OF_VIDEOS = 8
-    VIDEO_LENGTH = 50
-    CHANNEL = 3
-    SIZE = 64
-    videos1 = torch.zeros(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
-    videos2 = torch.ones(NUMBER_OF_VIDEOS, VIDEO_LENGTH, CHANNEL, SIZE, SIZE, requires_grad=False)
-    device = torch.device("cuda")
-    # device = torch.device("cpu")
-
-    import json
-    result = calculate_fvd(videos1, videos2, device, method='videogpt')
-    print(json.dumps(result, indent=4))
-
-    result = calculate_fvd(videos1, videos2, device, method='styleganv')
-    print(json.dumps(result, indent=4))
-
-if __name__ == "__main__":
-    main()

eval/vae/eval_common_metric.py

@@ -44,7 +44,7 @@ import sys
 sys.path.append(".")
 from cal_lpips import calculate_lpips
 from cal_psnr import calculate_psnr
-# from cal_flolpips import calculate_flolpips
+from cal_flolpips import calculate_flolpips
 from cal_ssim import calculate_ssim
 
 try: