split convert_test and inference_test

Author: grimoire

tests/model_test.py

@@ -14,21 +14,14 @@
 logger = logging.getLogger("mmdet2trt")
 
 
-def model_test(test_folder,
-               cfg_path,
-               checkpoint,
-               save_folder,
-               opt_shape_param=None,
-               max_workspace_size=1 << 25,
-               device="cuda:0",
-               score_thr=0.3,
-               fp16=True,
-               enable_mask=False):
-
-    if not osp.exists(save_folder):
-        os.mkdir(save_folder)
-    trt_model_path = osp.join(save_folder, 'trt_model.pth')
-
+def convert_test(cfg_path,
+                 checkpoint,
+                 trt_model_path,
+                 opt_shape_param=None,
+                 max_workspace_size=1 << 25,
+                 device="cuda:0",
+                 fp16=True,
+                 enable_mask=False):
     logger.info("creating {} trt model.".format(cfg_path))
     trt_model = mmdet2trt(cfg_path,
                           checkpoint,
@@ -39,9 +32,15 @@ def model_test(test_folder,
                           enable_mask=enable_mask)
     logger.info("finish, save trt_model in {}".format(trt_model_path))
     torch.save(trt_model.state_dict(), trt_model_path)
+    return trt_model
 
-    trt_model = init_detector(trt_model_path)
 
+def inference_test(trt_model,
+                   cfg_path,
+                   device,
+                   test_folder,
+                   save_folder,
+                   score_thr=0.3):
     file_list = os.listdir(test_folder)
 
     for file_name in tqdm.tqdm(file_list):
@@ -74,6 +73,9 @@ def model_test(test_folder,
         cv2.imwrite(osp.join(save_folder, file_name), image)
 
 
+TEST_MODE_DICT = {'convert': 1, 'inference': 1 << 1, 'all': 0b11}
+
+
 def main():
     parser = ArgumentParser()
     parser.add_argument('test_folder', help='folder contain test images')
@@ -82,6 +84,10 @@ def main():
     parser.add_argument(
         'save_folder',
         help='tensorrt model and test images results save folder')
+    parser.add_argument('--trt_model_path',
+                        default='',
+                        help='save and inference model. '
+                        'default [save_folder]/trt_model.pth')
     parser.add_argument(
         '--opt_shape_param',
         default='[ [ [1,3,800,800], [1,3,800,1344], [1,3,1344,1344] ] ]',
@@ -102,17 +108,40 @@ def main():
     parser.add_argument('--enable_mask',
                         action='store_true',
                         help="enable mask output")
+    parser.add_argument('--test-mode',
+                        default='all',
+                        help='what to do in the test',
+                        choices=['convert', 'inference', 'all'])
     args = parser.parse_args()
 
-    model_test(args.test_folder,
-               args.config,
-               args.checkpoint,
-               args.save_folder,
-               opt_shape_param=eval(args.opt_shape_param),
-               max_workspace_size=args.max_workspace_size,
-               device=args.device,
-               score_thr=args.score_thr,
-               fp16=args.fp16)
+    trt_model_path = args.trt_model_path
+    if len(trt_model_path) == 0:
+        trt_model_path = osp.join(args.save_folder, 'test_model.pth')
+    
+    if not osp.exists(args.save_folder):
+        os.mkdir(args.save_folder)
+
+    test_mode = TEST_MODE_DICT[args.test_mode]
+
+    if test_mode & TEST_MODE_DICT['convert'] > 0:
+        convert_test(args.config,
+                     args.checkpoint,
+                     trt_model_path,
+                     opt_shape_param=eval(args.opt_shape_param),
+                     max_workspace_size=args.max_workspace_size,
+                     device=args.device,
+                     fp16=args.fp16)
+        trt_model = init_detector(trt_model_path)
+    else:
+        trt_model = init_detector(trt_model_path)
+
+    if test_mode & TEST_MODE_DICT['inference'] > 0:
+        inference_test(trt_model,
+                       args.config,
+                       args.device,
+                       args.test_folder,
+                       args.save_folder,
+                       score_thr=args.score_thr)
 
 
 if __name__ == '__main__':