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| """ | |
| Frechet Video Distance (FVD). Matches the original tensorflow implementation from | |
| https://github.com/google-research/google-research/blob/master/frechet_video_distance/frechet_video_distance.py | |
| up to the upsampling operation. Note that this tf.hub I3D model is different from the one released in the I3D repo. | |
| """ | |
| import copy | |
| import numpy as np | |
| import scipy.linalg | |
| from . import metric_utils | |
| #---------------------------------------------------------------------------- | |
| NUM_FRAMES_IN_BATCH = {128: 128, 256: 128, 512: 64, 1024: 32} | |
| #---------------------------------------------------------------------------- | |
| def compute_fvd(opts, max_real: int, num_gen: int, num_frames: int, realdata_subsample_factor: int=3, gendata_subsample_factor: int=1): | |
| # Perfectly reproduced torchscript version of the I3D model, trained on Kinetics-400, used here: | |
| # https://github.com/google-research/google-research/blob/master/frechet_video_distance/frechet_video_distance.py | |
| # Note that the weights on tf.hub (used in the script above) differ from the original released weights | |
| detector_url = 'https://www.dropbox.com/s/ge9e5ujwgetktms/i3d_torchscript.pt?dl=1' | |
| detector_kwargs = dict(rescale=True, resize=True, return_features=True) # Return raw features before the softmax layer. | |
| # real data args | |
| opts = copy.deepcopy(opts) | |
| opts.dataset_kwargs.load_n_consecutive = num_frames | |
| # opts.dataset_kwargs.load_n_consecutive = None | |
| opts.dataset_kwargs.subsample_factor = realdata_subsample_factor | |
| opts.dataset_kwargs.discard_short_videos = True | |
| batch_size = NUM_FRAMES_IN_BATCH[opts.dataset_kwargs.resolution] // num_frames | |
| mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset( | |
| opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs, rel_lo=0, rel_hi=0, | |
| capture_mean_cov=True, max_items=max_real, temporal_detector=True, batch_size=batch_size).get_mean_cov() | |
| if opts.generator_as_dataset: | |
| # fake data args | |
| compute_gen_stats_fn = metric_utils.compute_feature_stats_for_dataset | |
| gen_opts = metric_utils.rewrite_opts_for_gen_dataset(opts) | |
| gen_opts.dataset_kwargs.load_n_consecutive = num_frames | |
| gen_opts.dataset_kwargs.load_n_consecutive_random_offset = False | |
| gen_opts.dataset_kwargs.subsample_factor = gendata_subsample_factor | |
| gen_kwargs = dict() | |
| else: | |
| compute_gen_stats_fn = metric_utils.compute_feature_stats_for_generator | |
| gen_opts = opts | |
| gen_kwargs = dict(num_video_frames=num_frames, subsample_factor=gendata_subsample_factor) | |
| mu_gen, sigma_gen = compute_gen_stats_fn( | |
| opts=gen_opts, detector_url=detector_url, detector_kwargs=detector_kwargs, rel_lo=0, rel_hi=1, capture_mean_cov=True, | |
| max_items=num_gen, temporal_detector=True, batch_size=batch_size, **gen_kwargs).get_mean_cov() | |
| if opts.rank != 0: | |
| return float('nan') | |
| m = np.square(mu_gen - mu_real).sum() | |
| s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member | |
| fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) | |
| return float(fid) | |
| #---------------------------------------------------------------------------- | |