initial commit

lane_server/infer.py

@@ -1,20 +1,25 @@
 import paddle.inference as paddle_infer
 import numpy as np
 import paddle.vision.transforms as T
-
 class Lane_model_infer:
     def __init__(self, model_dir="./lane_model"):
-        # 初始化paddle推理
+        # 初始化 paddle 推理
         self.model_dir = model_dir
         self.config = paddle_infer.Config(model_dir + "/model.pdmodel", model_dir + "/model.pdiparams")
+        self.config.disable_glog_info()
         self.config.enable_use_gpu(200, 0)
+        # self.config.enable_memory_optim(True)
+        # self.config.switch_ir_optim(True)
+        # self.config.switch_use_feed_fetch_ops(False)
+        # self.config.delete_pass("conv_elementwise_add_act_fuse_pass")
+        # self.config.delete_pass("conv_elementwise_add_fuse_pass")
         self.predictor = paddle_infer.create_predictor(self.config)
         self.input_names = self.predictor.get_input_names()
         self.input_handle = self.predictor.get_input_handle(self.input_names[0])
         self.output_names = self.predictor.get_output_names()
         self.output_handle = self.predictor.get_output_handle(self.output_names[0])
         self.normalize_transform = T.Normalize(mean=[127.5], std=[127.5])
-    
+        # print(self.config.summary())
     def infer(self,src) -> np.ndarray:
         image = self.normalize_transform(src)
         image = image.transpose(2, 0, 1)
@@ -22,4 +27,11 @@ class Lane_model_infer:
         self.input_handle.copy_from_cpu(image)
         self.predictor.run()
         results = self.output_handle.copy_to_cpu()[0]
-        return results
+        return results
+
+# if __name__ == "__main__":
+#     predictor = Lane_model_infer()
+#     import time
+#     while True:
+#         time.sleep(1)
+#         print('123')