while (IOThreadsFlag):
time.sleep(0.01)
pbar.close() # type: ignore
pbar_dup.close()
elif torch.cuda.is_available() and not args.cpu:
# Process on GPU
if 'v1.8.model' in args.model:
from model.RIFE_HD import Model # type: ignore
else:
from model.RIFE_HDv2 import Model # type: ignore
model = Model()
model.load_model(args.model, -1)
model.eval()
model.device()
print('Trained model loaded: %s' % args.model)
first_image = cv2.imread(os.path.join(args.input,
files_list[0]), cv2.IMREAD_COLOR
| cv2.IMREAD_ANYDEPTH)[:, :, ::-1].copy()
h, w, _ = first_image.shape
ph = ((h - 1) // 64 + 1) * 64
pw = ((w - 1) // 64 + 1) * 64
padding = (0, pw - w, 0, ph - h)
device = torch.device("cuda")
torch.set_grad_enabled(False)
torch.backends.cudnn.enabled = True
warnings.filterwarnings("ignore")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
torch.set_grad_enabled(False)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser(
description='Interpolation for a pair of images')
parser.add_argument('--img', dest='img', nargs=2, required=True)
parser.add_argument('--exp', default=4, type=int)
args = parser.parse_args()
model = Model()
model.load_model('./train_log', -1)
model.eval()
model.device()
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)
warnings.filterwarnings("ignore")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_grad_enabled(False)
if torch.cuda.is_available():
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser(
description='Interpolation for a pair of images')
parser.add_argument('--img', dest='img', nargs=2, required=True)
parser.add_argument('--exp', default=4, type=int)
args = parser.parse_args()
model = Model()
model.load_model(
os.path.join(os.path.dirname(os.path.realpath(__file__)), 'train_log'), -1)
model.eval()
model.device()
if args.img[0].endswith('.exr') and args.img[1].endswith('.exr'):
img0 = cv2.imread(args.img[0], cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH)
img1 = cv2.imread(args.img[1], cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH)
img0 = (torch.tensor(img0.transpose(2, 0, 1)).to(device)).unsqueeze(0)
img1 = (torch.tensor(img1.transpose(2, 0, 1)).to(device)).unsqueeze(0)
else:
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) /
