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| """Inception Score (IS) from the paper "Improved techniques for training | |
| GANs". Matches the original implementation by Salimans et al. at | |
| https://github.com/openai/improved-gan/blob/master/inception_score/model.py""" | |
| import numpy as np | |
| from . import metric_utils | |
| #---------------------------------------------------------------------------- | |
| NUM_FRAMES_IN_BATCH = {128: 128, 256: 128, 512: 64, 1024: 32} | |
| #---------------------------------------------------------------------------- | |
| def compute_isv(opts, num_gen: int, num_splits: int, backbone: str): | |
| if backbone == 'c3d_ucf101': | |
| # Perfectly reproduced torchscript version of the original chainer checkpoint: | |
| # https://github.com/pfnet-research/tgan2/blob/f892bc432da315d4f6b6ae9448f69d046ef6fe01/tgan2/models/c3d/c3d_ucf101.py | |
| # It is a UCF-101-finetuned C3D model. | |
| detector_url = 'https://www.dropbox.com/s/jxpu7avzdc9n97q/c3d_ucf101.pt?dl=1' | |
| else: | |
| raise NotImplementedError(f'Backbone {backbone} is not supported.') | |
| num_frames = 16 | |
| batch_size = NUM_FRAMES_IN_BATCH[opts.dataset_kwargs.resolution] // num_frames | |
| if opts.generator_as_dataset: | |
| 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 = 1 | |
| 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=1) | |
| gen_probs = compute_gen_stats_fn( | |
| opts=gen_opts, detector_url=detector_url, detector_kwargs={}, | |
| capture_all=True, max_items=num_gen, temporal_detector=True, **gen_kwargs).get_all() # [num_gen, num_classes] | |
| if opts.rank != 0: | |
| return float('nan'), float('nan') | |
| scores = [] | |
| np.random.RandomState(42).shuffle(gen_probs) | |
| for i in range(num_splits): | |
| part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits] | |
| kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True))) | |
| kl = np.mean(np.sum(kl, axis=1)) | |
| scores.append(np.exp(kl)) | |
| return float(np.mean(scores)), float(np.std(scores)) | |
| #---------------------------------------------------------------------------- | |