Update inference_img.py

inference_img.py

@@ -7,113 +7,114 @@ import warnings
 
 
 def main():
+        
+    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, required=True)
+    parser.add_argument('--ratio', default=0, type=float, help='inference ratio between two images with 0 - 1 range')
+    parser.add_argument('--rthreshold', default=0.02, type=float, help='returns image when actual ratio falls in given range threshold')
+    parser.add_argument('--rmaxcycles', default=8, type=int, help='limit max number of bisectional cycles')
+    parser.add_argument('--model', dest='modelDir', type=str, default='train_log', help='directory with trained model files')
+    
+    args = parser.parse_args()
     
-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)
-parser.add_argument('--ratio', default=0, type=float, help='inference ratio between two images with 0 - 1 range')
-parser.add_argument('--rthreshold', default=0.02, type=float, help='returns image when actual ratio falls in given range threshold')
-parser.add_argument('--rmaxcycles', default=8, type=int, help='limit max number of bisectional cycles')
-parser.add_argument('--model', dest='modelDir', type=str, default='train_log', help='directory with trained model files')
-
-args = parser.parse_args()
-
-try:
     try:
         try:
-            from model.RIFE_HDv2 import Model
-            model = Model()
-            model.load_model(args.modelDir, -1)
-            print("Loaded v2.x HD model.")
+            try:
+                from model.RIFE_HDv2 import Model
+                model = Model()
+                model.load_model(args.modelDir, -1)
+                print("Loaded v2.x HD model.")
+            except:
+                from train_log.RIFE_HDv3 import Model
+                model = Model()
+                model.load_model(args.modelDir, -1)
+                print("Loaded v3.x HD model.")
         except:
-            from train_log.RIFE_HDv3 import Model
+            from model.RIFE_HD import Model
             model = Model()
             model.load_model(args.modelDir, -1)
-            print("Loaded v3.x HD model.")
+            print("Loaded v1.x HD model")
     except:
-        from model.RIFE_HD import Model
+        from model.RIFE import Model
         model = Model()
         model.load_model(args.modelDir, -1)
-        print("Loaded v1.x HD model")
-except:
-    from model.RIFE import Model
-    model = Model()
-    model.load_model(args.modelDir, -1)
-    print("Loaded ArXiv-RIFE model")
-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], cv2.IMREAD_UNCHANGED)
-    img1 = cv2.imread(args.img[1], cv2.IMREAD_UNCHANGED)
-    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:
-    img_list = [img0]
-    img0_ratio = 0.0
-    img1_ratio = 1.0
-    if args.ratio <= img0_ratio + args.rthreshold / 2:
-        middle = img0
-    elif args.ratio >= img1_ratio - args.rthreshold / 2:
-        middle = img1
-    else:
-        tmp_img0 = img0
-        tmp_img1 = img1
-        for inference_cycle in range(args.rmaxcycles):
-            middle = model.inference(tmp_img0, tmp_img1)
-            middle_ratio = ( img0_ratio + img1_ratio ) / 2
-            if args.ratio - (args.rthreshold / 2) <= middle_ratio <= args.ratio + (args.rthreshold / 2):
-                break
-            if args.ratio > middle_ratio:
-                tmp_img0 = middle
-                img0_ratio = middle_ratio
-            else:
-                tmp_img1 = middle
-                img1_ratio = middle_ratio
-    img_list.append(middle)
-    img_list.append(img1)
-else:
-    img_list = [img0, img1]
-    for i in range(args.exp):
-        tmp = []
-        for j in range(len(img_list) - 1):
-            mid = model.inference(img_list[j], img_list[j + 1])
-            tmp.append(img_list[j])
-            tmp.append(mid)
-        tmp.append(img1)
-        img_list = tmp
-
-if not os.path.exists('output'):
-    os.mkdir('output')
-for i in range(len(img_list)):
+        print("Loaded ArXiv-RIFE model")
+    model.eval()
+    model.device()
+    
     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])
+        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:
-        cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
-
+        img0 = cv2.imread(args.img[0], cv2.IMREAD_UNCHANGED)
+        img1 = cv2.imread(args.img[1], cv2.IMREAD_UNCHANGED)
+        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:
+        img_list = [img0]
+        img0_ratio = 0.0
+        img1_ratio = 1.0
+        if args.ratio <= img0_ratio + args.rthreshold / 2:
+            middle = img0
+        elif args.ratio >= img1_ratio - args.rthreshold / 2:
+            middle = img1
+        else:
+            tmp_img0 = img0
+            tmp_img1 = img1
+            for inference_cycle in range(args.rmaxcycles):
+                middle = model.inference(tmp_img0, tmp_img1)
+                middle_ratio = ( img0_ratio + img1_ratio ) / 2
+                if args.ratio - (args.rthreshold / 2) <= middle_ratio <= args.ratio + (args.rthreshold / 2):
+                    break
+                if args.ratio > middle_ratio:
+                    tmp_img0 = middle
+                    img0_ratio = middle_ratio
+                else:
+                    tmp_img1 = middle
+                    img1_ratio = middle_ratio
+        img_list.append(middle)
+        img_list.append(img1)
+    else:
+        img_list = [img0, img1]
+        for i in range(args.exp):
+            tmp = []
+            for j in range(len(img_list) - 1):
+                mid = model.inference(img_list[j], img_list[j + 1])
+                tmp.append(img_list[j])
+                tmp.append(mid)
+            tmp.append(img1)
+            img_list = tmp
+    
+    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])
+    
+    cmd_inference_img(args.img,args.exp,args.model)
 
 if __name__ == "__main__":
     main()