update

utilities/reverse2original.py

@@ -86,7 +86,7 @@ def reverse2wholeimage(b_align_crop_tenor_list,swaped_imgs, mats, crop_size, ori
     # print(mats)
     # print(len(b_align_crop_tenor_list))
     for swaped_img, mat ,source_img in zip(swaped_imgs, mats,b_align_crop_tenor_list):
-        swaped_img = swaped_img.cpu().detach().numpy().transpose((1, 2, 0))
+        swaped_img = swaped_img.cpu().numpy().transpose((1, 2, 0))
         img_white = np.full((crop_size,crop_size), 255, dtype=float)
 
         # inverse the Affine transformation matrix

utilities/sshupload.py

@@ -5,12 +5,18 @@
 # Created Date: Tuesday September 24th 2019
 # Author: Lcx
 # Email: chenxuanhongzju@outlook.com
-# Last Modified:  Friday, 18th February 2022 3:20:14 pm
+# Last Modified:  Thursday, 14th April 2022 12:33:07 pm
 # Modified By: Chen Xuanhong
 # Copyright (c) 2019 Shanghai Jiao Tong University
 #############################################################
 
-import paramiko,os
+try:
+    import paramiko
+except:
+    from pip._internal import main
+    main(['install', 'paramiko'])
+    import paramiko
+import os
 from pathlib import Path
 # ssh传输类：
 

utilities/utilities.py

@@ -5,16 +5,50 @@
 # Created Date: Monday April 6th 2020
 # Author: Chen Xuanhong
 # Email: chenxuanhongzju@outlook.com
-# Last Modified:  Tuesday, 12th October 2021 2:18:05 pm
+# Last Modified:  Thursday, 14th April 2022 11:34:54 am
 # Modified By: Chen Xuanhong
 # Copyright (c) 2020 Shanghai Jiao Tong University
 #############################################################
 
+import os
 import cv2
 import torch
 from PIL import Image
 import numpy as np
 from torchvision import transforms
+from torch.hub import download_url_to_file, get_dir
+from urllib.parse import urlparse
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
+    """Load file form http url, will download models if necessary.
+
+    Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+
+    Args:
+        url (str): URL to be downloaded.
+        model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
+            Default: None.
+        progress (bool): Whether to show the download progress. Default: True.
+        file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
+
+    Returns:
+        str: The path to the downloaded file.
+    """
+    if model_dir is None:  # use the pytorch hub_dir
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    os.makedirs(model_dir, exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(model_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file
 
 # Gram Matrix
 def Gram(tensor: torch.Tensor):