ECCV2022-RIFE/inference.py

import cv2
import torch
import argparse
from torch.nn import functional as F
from model.RIFE import Model

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

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

model = Model()
model.load_model('./train_log')
model.eval()
model.device()
img0 = cv2.imread(args.img[0])
img1 = cv2.imread(args.img[1])
h, w, _ = img0.shape
ph = h // 32 * 32
pw = w // 32 * 32
padding = (0, pw - w, 0, ph - h)
img0 = torch.tensor(img0.transpose(2, 0, 1)).to(device) / 255.
img1 = torch.tensor(img1.transpose(2, 0, 1)).to(device) / 255.
imgs = F.pad(torch.cat((img0, img1), 0).float(), padding)
with torch.no_grad():
    res = model.inference(imgs.unsqueeze(0)) * 255
cv2.imwrite('output.png', res[0].numpy().transpose(1, 2, 0)[:h, :w])