File size: 1,905 Bytes
04fbff5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import os
import sys
import tqdm
import torch
import argparse
import numpy as np
import os.path as osp
from omegaconf import OmegaConf
sys.path.append('.')
from utils.utils import read, img2tensor
from utils.build_utils import build_from_cfg
from metrics.psnr_ssim import calculate_psnr, calculate_ssim
parser = argparse.ArgumentParser(
prog = 'AMT',
description = 'UCF101 evaluation',
)
parser.add_argument('-c', '--config', default='cfgs/AMT-S.yaml')
parser.add_argument('-p', '--ckpt', default='pretrained/amt-s.pth')
parser.add_argument('-r', '--root', default='data/ucf101_interp_ours')
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cfg_path = args.config
ckpt_path = args.ckpt
root = args.root
network_cfg = OmegaConf.load(cfg_path).network
network_name = network_cfg.name
model = build_from_cfg(network_cfg)
ckpt = torch.load(ckpt_path)
model.load_state_dict(ckpt['state_dict'])
model = model.to(device)
model.eval()
dirs = sorted(os.listdir(root))
psnr_list = []
ssim_list = []
pbar = tqdm.tqdm(dirs, total=len(dirs))
for d in pbar:
dir_path = osp.join(root, d)
I0 = img2tensor(read(osp.join(dir_path, 'frame_00.png'))).to(device)
I1 = img2tensor(read(osp.join(dir_path, 'frame_01_gt.png'))).to(device)
I2 = img2tensor(read(osp.join(dir_path, 'frame_02.png'))).to(device)
embt = torch.tensor(1/2).float().view(1, 1, 1, 1).to(device)
I1_pred = model(I0, I2, embt, eval=True)['imgt_pred']
psnr = calculate_psnr(I1_pred, I1).detach().cpu().numpy()
ssim = calculate_ssim(I1_pred, I1).detach().cpu().numpy()
psnr_list.append(psnr)
ssim_list.append(ssim)
avg_psnr = np.mean(psnr_list)
avg_ssim = np.mean(ssim_list)
desc_str = f'[{network_name}/UCF101] psnr: {avg_psnr:.02f}, ssim: {avg_ssim:.04f}'
pbar.set_description_str(desc_str) |