update image demo for multiple models

src/image_demo.py

@@ -11,25 +11,23 @@ import argparse
 class ImageDemoOptions():
     def __init__(self):
         self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-        self.parser.add_argument('--net_model_path', '-n', type=str, default="../models/last_transfered_net.pth", help="Net model path folder")
+        self.parser.add_argument('--model_paths', '-n', nargs='+', type=str, default=["../models/last_transfered_net.pth","../models/last_transfered_lut.pth"], help="Model paths for comparison")
-        self.parser.add_argument('--lut_model_path', '-l', type=str, default="../models/last_transfered_lut.pth", help="Lut model path folder")
         self.parser.add_argument('--hr_image_path', '-a', type=str, default="../data/Set14/HR/monarch.png", help="HR image path")
         self.parser.add_argument('--lr_image_path', '-b', type=str, default="../data/Set14/LR/X4/monarch.png", help="LR image path")
-        self.parser.add_argument('--project_path', type=str, default="../", help="Project path.")  
+        self.parser.add_argument('--output_path', type=str, default="../models/", help="Project path.")  
         self.parser.add_argument('--batch_size', type=int, default=2**10, help="Size of the batch for the input domain values.")  
         self.parser.add_argument('--mirror', action='store_true', default=False) 
 
     def parse_args(self): 
         args = self.parser.parse_args()       
-        args.project_path = Path(args.project_path).resolve() 
+        args.output_path = Path(args.output_path).resolve() 
         args.hr_image_path = Path(args.hr_image_path).resolve()
         args.lr_image_path = Path(args.lr_image_path).resolve()
-        args.net_model_path = Path(args.net_model_path).resolve()
+        args.model_paths = [Path(x).resolve() for x in args.model_paths]
-        args.lut_model_path = Path(args.lut_model_path).resolve()         
         return args
 
     def __repr__(self):
-        config = self.parser.parse_args() 
+        config = self.parse_args() 
         message = ''
         message += '----------------- Options ---------------\n'
         for k, v in sorted(vars(config).items()):
@@ -45,12 +43,10 @@ config_inst = ImageDemoOptions()
 config = config_inst.parse_args()
 
 start_script_time = datetime.now()
-
+print(config_inst)
-net_model = LoadCheckpoint(config.net_model_path).cuda()
+models = [LoadCheckpoint(x).cuda() for x in config.model_paths]
-lut_model = LoadCheckpoint(config.lut_model_path).cuda()
+for m in models:
-
+    print(m)
-print(net_model)
-print(lut_model)
 
 lr_image = cv2.imread(str(config.lr_image_path))[:,:,::-1]
 image_gt = cv2.imread(str(config.hr_image_path))[:,:,::-1]
@@ -62,15 +58,36 @@ image_gt = image_gt.copy()
 
 input_image = torch.tensor(lr_image).type(torch.float32).permute(2,0,1)[None,...].cuda()
 
+predictions = []
+for model in models:
     with torch.inference_mode():
-    net_prediction = net_model(input_image).cpu().type(torch.uint8).squeeze().permute(1,2,0).numpy().copy()
+        prediction = model(input_image).cpu().type(torch.uint8).squeeze().permute(1,2,0).numpy().copy()
-    lut_prediction = lut_model(input_image).cpu().type(torch.uint8).squeeze().permute(1,2,0).numpy().copy()
+        predictions.append(prediction)
-
 
 image_gt = cv2.putText(image_gt, 'GT', org=(20, 50) , fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,255,255), thickness=2, lineType= cv2.LINE_AA) 
-image_net = cv2.putText(net_prediction, net_model.__class__.__name__, org=(20, 50) , fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,255,255), thickness=2, lineType= cv2.LINE_AA) 
+images_predicted = []
-image_lut = cv2.putText(lut_prediction, lut_model.__class__.__name__, org=(20, 50) , fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,255,255), thickness=2, lineType= cv2.LINE_AA) 
+for model_path, model, prediction in zip(config.model_paths, models, predictions):
+    prediction = cv2.putText(prediction, model_path.stem, org=(20, 50) , fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,255,255), thickness=2, lineType= cv2.LINE_AA) 
+    images_predicted.append(prediction)
+
+image_count = 1 + len(images_predicted)
+t = np.sqrt(image_count).astype(np.int32)
+residual = image_count % t
+if residual != 0:
+    column_count = image_count
+    row_count = 1
+else:
+    column_count = image_count // t
+    row_count = t 
+images = [image_gt] + images_predicted
 
-Image.fromarray(np.concatenate([image_gt, image_net, image_lut], 1)).save(config.project_path / "models" / 'last_transfered_demo.png')
+columns = []
+for i in range(row_count):
+    row = []
+    for j in range(column_count):
+        row.append(images[i*column_count + j])
+    columns.append(np.concatenate(row, axis=1))
+canvas = np.concatenate(columns, axis=0).astype(np.uint8)
+Image.fromarray(canvas).save(config.output_path / 'image_demo.png')
 
 print(datetime.now() - start_script_time )