Update predict.py

1, switched the location of width and height in line 35 because the first dimension in img.shape should be row(height). Also changed their order in dlib.resize_image(), line 41. This do not affect the result, just for clarity.
2, removed redundant line:  new_width, new_height = 628, int( 628 * old_height / old_width)

predict.py

@@ -23,22 +23,23 @@ def rect_to_bb(rect):
 	# return a tuple of (x, y, w, h)
 	return (x, y, w, h)
 
-
-def detect_face(image_paths,  SAVE_DETECTED_AT, default_max_size=800, size = 300, padding = 0.25):
+def detect_face(image_paths,  SAVE_DETECTED_AT, default_max_size=800,size = 300, padding = 0.25):
     cnn_face_detector = dlib.cnn_face_detection_model_v1('dlib_models/mmod_human_face_detector.dat')
     sp = dlib.shape_predictor('dlib_models/shape_predictor_5_face_landmarks.dat')
     base = 2000  # largest width and height
     for index, image_path in enumerate(image_paths):
         if index % 1000 == 0:
             print('---%d/%d---' %(index, len(image_paths)))
         img = dlib.load_rgb_image(image_path)
-        old_width, old_height, _ = img.shape
+
+        old_height, old_width, _ = img.shape
+
         if old_width > old_height:
             new_width, new_height = default_max_size, int(default_max_size * old_height / old_width)
         else:
-            new_width, new_height =  int(default_max_size * old_height / old_width), default_max_size
-        new_width, new_height = 628, int( 628 * old_height / old_width)
-        img = dlib.resize_image(img, new_width, new_height)
+            new_width, new_height =  int(default_max_size * old_width / old_height), default_max_size
+        img = dlib.resize_image(img, rows=new_height, cols=new_width)
+
         dets = cnn_face_detector(img, 1)
         num_faces = len(dets)
         if num_faces == 0:
@@ -62,7 +63,7 @@ def predidct_age_gender_race(save_prediction_at, imgs_path = 'cropped_faces/'):
 
     model_fair_7 = torchvision.models.resnet34(pretrained=True)
     model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18)
-    model_fair_7.load_state_dict(torch.load('fair_face_models/res34_fair_align_multi_7_20190809.pt'))
+    model_fair_7.load_state_dict(torch.load('fair_face_models/fairface_alldata_20191111.pt'))
     model_fair_7 = model_fair_7.to(device)
     model_fair_7.eval()
 
@@ -200,14 +201,18 @@ def ensure_dir(directory):
 
 
 if __name__ == "__main__":
+    #Please create a csv with one column 'img_path', contains the full paths of all images to be analyzed.
+    #Also please change working directory to this file.
     parser = argparse.ArgumentParser()
     parser.add_argument('--csv', dest='input_csv', action='store',
                         help='csv file of image path where col name for image path is "img_path')
+    dlib.DLIB_USE_CUDA = True
     print("using CUDA?: %s" % dlib.DLIB_USE_CUDA)
     args = parser.parse_args()
     SAVE_DETECTED_AT = "detected_faces"
     ensure_dir(SAVE_DETECTED_AT)
     imgs = pd.read_csv(args.input_csv)['img_path']
     detect_face(imgs, SAVE_DETECTED_AT)
     print("detected faces are saved at ", SAVE_DETECTED_AT)
-    predidct_age_gender_race("test_outputs.csv", SAVE_DETECTED_AT)
+    #Please change test_outputs.csv to actual name of output csv. 
+    predidct_age_gender_race("test_outputs.csv", SAVE_DETECTED_AT)