214 lines
8.2 KiB
Python
214 lines
8.2 KiB
Python
"""Calculates the CLIP Scores
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The CLIP model is a contrasitively learned language-image model. There is
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an image encoder and a text encoder. It is believed that the CLIP model could
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measure the similarity of cross modalities. Please find more information from
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https://github.com/openai/CLIP.
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The CLIP Score measures the Cosine Similarity between two embedded features.
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This repository utilizes the pretrained CLIP Model to calculate
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the mean average of cosine similarities.
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See --help to see further details.
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Code apapted from https://github.com/mseitzer/pytorch-fid and https://github.com/openai/CLIP.
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Copyright 2023 The Hong Kong Polytechnic University
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import os
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import os.path as osp
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from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
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import numpy as np
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import clip
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import torch
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from PIL import Image
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from torch.utils.data import Dataset, DataLoader
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from decord import VideoReader, cpu
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import random
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from pytorchvideo.transforms import ShortSideScale
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from torchvision.io import read_video
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from torchvision.transforms import Lambda, Compose
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from torchvision.transforms._transforms_video import RandomCropVideo
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from eval.cal_lpips import calculate_lpips
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from eval.cal_fvd import calculate_fvd
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from eval.cal_psnr import calculate_psnr
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from eval.cal_ssim import calculate_ssim
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try:
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from tqdm import tqdm
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except ImportError:
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# If tqdm is not available, provide a mock version of it
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def tqdm(x):
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return x
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class VideoDataset(Dataset):
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def __init__(self,
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real_video_dir,
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generated_video_dir,
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num_frames,
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sample_rate = 1,
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crop_size=None,
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resolution=128,
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) -> None:
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super().__init__()
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self.real_video_files = self._combine_without_prefix(real_video_dir)
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self.generated_video_files = self._combine_without_prefix(generated_video_dir)
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self.num_frames = num_frames
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self.sample_rate = sample_rate
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self.crop_size = crop_size
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self.short_size = resolution
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def __len__(self):
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return len(self.real_video_files)
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def __getitem__(self, index):
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if index >= len(self):
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raise IndexError
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real_video_file = self.real_video_files[index]
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generated_video_file = self.generated_video_files[index]
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real_video_tensor = self._load_video(real_video_file)
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generated_video_tensor = self._load_video(generated_video_file)
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return {'real': real_video_tensor, 'generated':generated_video_tensor }
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def _load_video(self, video_path):
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num_frames = self.num_frames
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sample_rate = self.sample_rate
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decord_vr = VideoReader(video_path, ctx=cpu(0))
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total_frames = len(decord_vr)
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sample_frames_len = sample_rate * num_frames
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if total_frames > sample_frames_len:
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s = random.randint(0, total_frames - sample_frames_len - 1)
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e = s + sample_frames_len
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num_frames = num_frames
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else:
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s = 0
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e = total_frames
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num_frames = int(total_frames / sample_frames_len * num_frames)
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print(f'sample_frames_len {sample_frames_len}, only can sample {num_frames * sample_rate}', video_path,
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total_frames)
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frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
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video_data = decord_vr.get_batch(frame_id_list).asnumpy()
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video_data = torch.from_numpy(video_data)
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video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (C, T, H, W)
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return _preprocess(video_data, short_size=self.short_size, crop_size = self.crop_size)
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def _combine_without_prefix(self, folder_path, prefix='.'):
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folder = []
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for name in os.listdir(folder_path):
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if name[0] == prefix:
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continue
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folder.append(osp.join(folder_path, name))
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folder.sort()
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return folder
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def _preprocess(video_data, short_size=128, crop_size=None):
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transform = Compose(
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[
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Lambda(lambda x: ((x / 255.0) - 0.5)),
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ShortSideScale(size=short_size),
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RandomCropVideo(size=crop_size) if crop_size is not None else Lambda(lambda x: x),
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]
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)
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video_outputs = transform(video_data)
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# video_outputs = torch.unsqueeze(video_outputs, 0) # (bz,c,t,h,w)
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return video_outputs
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def calculate_common_metric(args, dataloader,device):
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score_list = []
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for batch_data in tqdm(dataloader): # {'real': real_video_tensor, 'generated':generated_video_tensor }
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real_videos = batch_data['real']
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generated_videos = batch_data['generated']
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if args.metric == 'fvd':
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tmp_list = list(calculate_fvd(real_videos, generated_videos, args.device, method=args.fvd_method)['value'].keys())
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elif args.metric == 'ssim':
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tmp_list = list(calculate_ssim(real_videos, generated_videos)['value'].keys())
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elif args.metric == 'psnr':
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tmp_list = list(calculate_psnr(real_videos, generated_videos)['value'].keys())
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else:
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tmp_list = list(calculate_lpips(real_videos, generated_videos, args.device)['value'].keys())
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score_list += tmp_list
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return np.mean(score_list)
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def main():
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parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
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parser.add_argument('--batch_size', type=int, default=2,
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help='Batch size to use')
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parser.add_argument('--real_video_dir', type=str,
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help=('the path of real videos`'))
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parser.add_argument('--generated_video_dir', type=str,
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help=('the path of generated videos`'))
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parser.add_argument('--device', type=str, default=None,
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help='Device to use. Like cuda, cuda:0 or cpu')
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parser.add_argument('--num-workers', type=int, default=8,
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help=('Number of processes to use for data loading. '
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'Defaults to `min(8, num_cpus)`'))
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parser.add_argument('--sample-fps', type=int, default=30)
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parser.add_argument('--resolution', type=int, default=336)
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parser.add_argument('--crop_size', type=int, default=None)
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parser.add_argument('--num_frames', type=int, default=100)
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parser.add_argument('--sample_rate', type=int, default=1)
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parser.add_argument("--metric", type=str, default="fvd",choices=['fvd','psnr','ssim','lpips'])
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parser.add_argument("--fvd_method", type=str, default='styleganv',choices=['styleganv','videogpt'])
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args = parser.parse_args()
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if args.device is None:
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device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
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else:
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device = torch.device(args.device)
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if args.num_workers is None:
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try:
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num_cpus = len(os.sched_getaffinity(0))
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except AttributeError:
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# os.sched_getaffinity is not available under Windows, use
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# os.cpu_count instead (which may not return the *available* number
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# of CPUs).
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num_cpus = os.cpu_count()
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num_workers = min(num_cpus, 8) if num_cpus is not None else 0
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else:
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num_workers = args.num_workers
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dataset = VideoDataset(args.real_video_dir,
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args.generated_video_dir,
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num_frames = args.num_frames,
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sample_rate = args.sample_rate,
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crop_size=args.crop_size,
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resolution=args.resolution)
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dataloader = DataLoader(dataset, args.batch_size,
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num_workers=num_workers, pin_memory=True)
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metric_score = calculate_common_metric(args, dataloader,device)
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print('metric: ', args.metric, " ",metric_score)
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if __name__ == '__main__':
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main()
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