model.eval()
model.device()
path = 'UCF101/ucf101_interp_ours/'
dirs = os.listdir(path)
psnr_list = []
ssim_list = []
print(len(dirs))
for d in dirs:
img0 = (path + d + '/frame_00.png')
img1 = (path + d + '/frame_02.png')
gt = (path + d + '/frame_01_gt.png')
img0 = (torch.tensor(cv2.imread(img0).transpose(2, 0, 1) /
255.)).to(device).float().unsqueeze(0)
img1 = (torch.tensor(cv2.imread(img1).transpose(2, 0, 1) /
255.)).to(device).float().unsqueeze(0)
gt = (torch.tensor(cv2.imread(gt).transpose(2, 0, 1) /
255.)).to(device).float().unsqueeze(0)
pred = model.inference(img0, img1)[0]
ssim = ssim_matlab(gt,
torch.round(pred * 255).unsqueeze(0) /
255.).detach().cpu().numpy()
out = pred.detach().cpu().numpy().transpose(1, 2, 0)
out = np.round(out * 255) / 255.
gt = gt[0].cpu().numpy().transpose(1, 2, 0)
psnr = -10 * math.log10(((gt - out) * (gt - out)).mean())
psnr_list.append(psnr)
ssim_list.append(ssim)
print("Avg PSNR: {} SSIM: {}".format(np.mean(psnr_list),
np.mean(ssim_list)))
model = Model()
model.load_model('./train_log')
model.eval()
model.device()
path = 'vimeo_interp_test/'
f = open(path + 'tri_testlist.txt', 'r')
psnr_list = []
ssim_list = []
for i in f:
name = str(i).strip()
if (len(name) <= 1):
continue
print(path + 'target/' + name + '/im1.png')
I0 = cv2.imread(path + 'target/' + name + '/im1.png')
I1 = cv2.imread(path + 'target/' + name + '/im2.png')
I2 = cv2.imread(path + 'target/' + name + '/im3.png')
I0 = (torch.tensor(I0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
I2 = (torch.tensor(I2.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
mid = model.inference(I0, I2)[0]
ssim = ssim_matlab(
torch.tensor(I1.transpose(2, 0, 1)).to(device).unsqueeze(0) / 255.,
mid.unsqueeze(0)).cpu().numpy()
mid = np.round(
(mid * 255).cpu().numpy()).astype('uint8').transpose(1, 2, 0) / 255.
I1 = I1 / 255.
psnr = -10 * math.log10(((I1 - mid) * (I1 - mid)).mean())
psnr_list.append(psnr)
ssim_list.append(ssim)
print(np.mean(psnr_list), np.mean(ssim_list))
model.eval()
model.device()
path = 'vimeo_interp_test/'
f = open(path + 'tri_testlist.txt', 'r')
psnr_list = []
ssim_list = []
for i in f:
name = str(i).strip()
if (len(name) <= 1):
continue
print(path + 'target/' + name + '/im1.png')
I0 = cv2.imread(path + 'target/' + name + '/im1.png')
I1 = cv2.imread(path + 'target/' + name + '/im2.png')
I2 = cv2.imread(path + 'target/' + name + '/im3.png')
I0 = (torch.tensor(I0.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
I2 = (torch.tensor(I2.transpose(2, 0, 1)).to(device) / 255.).unsqueeze(0)
mid = model.inference(I0, I2)[0]
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()
mid = np.round(
(mid * 255).detach().cpu().numpy()).astype('uint8').transpose(1, 2,
0) / 255.
I1 = I1 / 255.
psnr = -10 * math.log10(((I1 - mid) * (I1 - mid)).mean())
psnr_list.append(psnr)
ssim_list.append(ssim)
print("Avg PSNR: {} SSIM: {}".format(np.mean(psnr_list),
np.mean(ssim_list)))