mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2025-12-16 08:27:45 +01:00
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
import os
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import sys
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sys.path.append('.')
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import cv2
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import math
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import torch
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import argparse
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import numpy as np
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from torch.nn import functional as F
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from model.pytorch_msssim import ssim_matlab
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from model.RIFE import Model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = Model()
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model.load_model('train_log')
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model.eval()
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model.device()
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path = 'vimeo_interp_test/'
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f = open(path + 'tri_testlist.txt', 'r')
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psnr_list = []
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ssim_list = []
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for i in f:
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name = str(i).strip()
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if(len(name) <= 1):
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continue
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print(path + 'target/' + name + '/im1.png')
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I0 = cv2.imread(path + 'target/' + name + '/im1.png')
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I1 = cv2.imread(path + 'target/' + name + '/im2.png')
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I2 = cv2.imread(path + 'target/' + name + '/im3.png')
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I0 = (torch.tensor(I0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
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I2 = (torch.tensor(I2.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
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mid = model.inference(I0, I2)[0]
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ssim = ssim_matlab(torch.tensor(I1.transpose(2, 0, 1)).to(device).unsqueeze(0) / 255., torch.round(mid * 255).unsqueeze(0) / 255.).detach().cpu().numpy()
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mid = np.round((mid * 255).detach().cpu().numpy()).astype('uint8').transpose(1, 2, 0) / 255.
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I1 = I1 / 255.
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psnr = -10 * math.log10(((I1 - mid) * (I1 - mid)).mean())
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psnr_list.append(psnr)
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ssim_list.append(ssim)
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print("Avg PSNR: {} SSIM: {}".format(np.mean(psnr_list), np.mean(ssim_list)))
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