Add SR code

inference_img_SR.py

@@ -0,0 +1,69 @@
+import os
+import cv2
+import torch
+import argparse
+from torch.nn import functional as F
+import warnings
+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='STVSR for a pair of images')
+parser.add_argument('--img', dest='img', nargs=2, required=True)
+parser.add_argument('--exp', default=2, type=int)
+parser.add_argument('--ratio', default=0, type=float, help='inference ratio between two images with 0 - 1 range')
+parser.add_argument('--model', dest='modelDir', type=str, default='train_log', help='directory with trained model files')
+
+args = parser.parse_args()
+
+from train_log.model import Model
+model = Model()
+model.device()
+model.load_model('train_log')
+model.eval()
+
+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 = cv2.resize(img0, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
+    img1 = cv2.resize(img1, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
+    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], cv2.IMREAD_UNCHANGED)
+    img1 = cv2.imread(args.img[1], cv2.IMREAD_UNCHANGED)
+    img0 = cv2.resize(img0, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
+    img1 = cv2.resize(img1, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
+    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)
+
+if args.ratio:
+    print('ratio={}'.format(args.ratio))
+    img_list = model.inference(img0, img1, timestep=args.ratio)
+else:
+    n = 2 ** args.exp - 1    
+    time_list = [0]
+    for i in range(n):
+        time_list.append((i+1) * 1. / (n+1))
+    time_list.append(1)
+    print(time_list)
+    img_list = model.inference(img0, img1, timestep=time_list)
+
+if not os.path.exists('output'):
+    os.mkdir('output')
+for i in range(len(img_list)):
+    if args.img[0].endswith('.exr') and args.img[1].endswith('.exr'):
+        cv2.imwrite('output/img{}.exr'.format(i), (img_list[i][0]).cpu().numpy().transpose(1, 2, 0)[:h, :w], [cv2.IMWRITE_EXR_TYPE, cv2.IMWRITE_EXR_TYPE_HALF])
+    else:
+        cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])