2021-02-25 16:47:06 +08:00
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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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2021-06-17 11:31:20 +08:00
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from model.pytorch_msssim import ssim_matlab
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2021-02-25 16:47:06 +08:00
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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 = 'UCF101/ucf101_interp_ours/'
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dirs = os.listdir(path)
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psnr_list = []
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ssim_list = []
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print(len(dirs))
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for d in dirs:
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img0 = (path + d + '/frame_00.png')
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img1 = (path + d + '/frame_02.png')
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gt = (path + d + '/frame_01_gt.png')
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img0 = (torch.tensor(cv2.imread(img0).transpose(2, 0, 1) / 255.)).to(device).float().unsqueeze(0)
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img1 = (torch.tensor(cv2.imread(img1).transpose(2, 0, 1) / 255.)).to(device).float().unsqueeze(0)
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gt = (torch.tensor(cv2.imread(gt).transpose(2, 0, 1) / 255.)).to(device).float().unsqueeze(0)
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pred = model.inference(img0, img1)[0]
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ssim = ssim_matlab(gt, torch.round(pred * 255).unsqueeze(0) / 255.).detach().cpu().numpy()
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out = pred.detach().cpu().numpy().transpose(1, 2, 0)
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out = np.round(out * 255) / 255.
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gt = gt[0].cpu().numpy().transpose(1, 2, 0)
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psnr = -10 * math.log10(((gt - out) * (gt - out)).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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