refactor

predict.py

@@ -154,22 +154,23 @@ def plot_diagnosis(predict_image_path, model,label):
 
 if __name__ == "__main__":
     parser = ArgumentParser()
-    #parser.add_argument("--path", type=str, default='/home/Data/generated/seed9984_1.png', help="Path to image to predict")
+    #parser.add_argument("--image_path", type=str, default='/home/Data/generated/seed9984_1.png', help="Path to image to predict")
     parser.add_argument('--seeds', type=num_range, help='List of random seeds Ex. 0-3 or 0,1,2')
     parser.add_argument("--data_path", type=str, default='/workspace/generated-no-valset')
+    parser.add_argument("--model_path", type=str, default='/workspace/stylegan2-ada-pytorch/CNN_trainings/melanoma_model_0_0.9225_16_12_train_reals+15melanoma.pth')
     args = parser.parse_args()
 
     # Setting up GPU for processing or CPU if GPU isn't available
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
     # Load model
     model = load_model()
-    model.load_state_dict(torch.load('/workspace/stylegan2-ada-pytorch/CNN_trainings/melanoma_model_0_0.9225_16_12_train_reals+15melanoma.pth'))
+    model.load_state_dict(torch.load(args.model_path))
     model.eval()
 
 
     if "SAM" in args.data_path:
-        input_images = [str(f) for f in sorted(Path("/workspace/stylegan2-ada-pytorch/processed_dataset_512_SAM").rglob('*jpg')) if os.path.isfile(f)]
+        input_images = [str(f) for f in sorted(Path(args.data_path).rglob('*jpg')) if os.path.isfile(f)]
         y = [1 for i in range(len(input_images))]
         test_df = pd.DataFrame({'image_name': input_images, 'target': y})
     elif "isic" in args.data_path: