def main():
    model = Model()
    loader = model.load_model('./train_log')
    model.eval()
    model.device()

    fixargs(args)
    return
    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])
Exemplo n.º 2
0
import os
import sys
sys.path.append('.')
import cv2
import math
import torch
import argparse
import numpy as np
from torch.nn import functional as F
from model.pytorch_msssim import ssim_matlab
from model.RIFE import Model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = Model()
model.load_model('train_log')
model.eval()
model.device()

name = [
    'Beanbags', 'Dimetrodon', 'DogDance', 'Grove2', 'Grove3', 'Hydrangea',
    'MiniCooper', 'RubberWhale', 'Urban2', 'Urban3', 'Venus', 'Walking'
]
IE_list = []
for i in name:
    i0 = cv2.imread('other-data/{}/frame10.png'.format(i)).transpose(2, 0,
                                                                     1) / 255.
    i1 = cv2.imread('other-data/{}/frame11.png'.format(i)).transpose(2, 0,
                                                                     1) / 255.
    gt = cv2.imread('other-gt-interp/{}/frame10i11.png'.format(i))
    h, w = i0.shape[1], i0.shape[2]
    imgs = torch.zeros([1, 6, 480, 640]).to(device)