ECCV2022-RIFE/inference_img.py

import os
import cv2
import torch
import argparse
from torch.nn import functional as F
from model.RIFE_HD import Model
import warnings
warnings.filterwarnings("ignore")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
if torch.cuda.is_available():
    torch.backends.cudnn.enabled = True
    torch.backends.cudnn.benchmark = True

parser = argparse.ArgumentParser(description='Interpolation for a pair of images')
parser.add_argument('--img', dest='img', nargs=2, required=True)
parser.add_argument('--exp', default=4, type=int)
args = parser.parse_args()

model = Model()
model.load_model('./train_log', -1)
model.eval()
model.device()
    
img0 = cv2.imread(args.img[0])
img1 = cv2.imread(args.img[1])

img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
n, c, h, w = img0.shape
ph = ((h - 1) // 32 + 1) * 32
pw = ((w - 1) // 32 + 1) * 32
padding = (0, pw - w, 0, ph - h)
img0 = F.pad(img0, padding)
img1 = F.pad(img1, padding)

img_list = [img0, img1]
for i in range(args.exp):
    tmp = []
    for j in range(len(img_list) - 1):
        mid = model.inference(img_list[j], img_list[j + 1])
        tmp.append(img_list[j])
        tmp.append(mid)
    tmp.append(img1)
    img_list = tmp

if not os.path.exists('output'):
    os.mkdir('output')
for i in range(len(img_list)):
    cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])