Update inference_img.py

inference_img.py

@@ -1,118 +1,112 @@
-    import os
-    import cv2
-    import torch
-    import argparse
-    from torch.nn import functional as F
-    import warnings
+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='Interpolation 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('--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:
-        from train_log.RIFE_HDv3 import Model
-        model = Model()
-        model.load_model(args.modelDir, -1)
-        print("Loaded RIFE_HDv3 model.")
-        print("Checkpoint reached!")
-    except:
-        from train_log.IFNet_HDv3 import Model
-        model = Model()
-        model.load_model(args.modelDir, -1)
-        print("Loaded IFNet_HDv3 model.")
-        print("Checkpoint reached!")
-    
-    model.eval()
-    model.device()
+warnings.filterwarnings("ignore")
 
-    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')
-        
-    print("Checkpoint reached!")
+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
 
-    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])
+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=2, 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')
 
-            # Replace this line (or add below your current cv2.imwrite)
-            save_path = 'output/img{}.png'.format(i)
-            success = cv2.imwrite(save_path, (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
-            print(f"Saving to {save_path} → success: {success}")
+args = parser.parse_args()
 
-        else:
-            cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
-            
-            # Replace this line (or add below your current cv2.imwrite)
-            save_path = 'output/img{}.png'.format(i)
-            success = cv2.imwrite(save_path, (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
-            print(f"Saving to {save_path} → success: {success}")
-            
+try:
+    from train_log.RIFE_HDv3 import Model
+    model = Model()
+    model.load_model(args.modelDir, -1)
+    print("Loaded RIFE_HDv3 model.")
     print("Checkpoint reached!")
+except:
+    from train_log.IFNet_HDv3 import Model
+    model = Model()
+    model.load_model(args.modelDir, -1)
+    print("Loaded IFNet_HDv3 model.")
+    print("Checkpoint reached!")
+
+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')
+
+print("Checkpoint reached!")
+
+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])
+        save_path = 'output/img{}.png'.format(i)
+        success = cv2.imwrite(save_path, (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
+        print(f"Saving to {save_path} → success: {success}")
+    else:
+        cv2.imwrite('output/img{}.png'.format(i), (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
+        save_path = 'output/img{}.png'.format(i)
+        success = cv2.imwrite(save_path, (img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w])
+        print(f"Saving to {save_path} → success: {success}")
+
+print("Checkpoint reached!")