[Serving] Simple serving YOLOv5 and PP-OCRv3 example, add uvicorn to fastdeploy tools (#986)

* ppocrv3 simple serving

* add uvicorn to fd tools

* update ppdet simple serving readme

* yolov5 simple serving

* not import simple serving by default

* remove config from envs

* update comment

examples/vision/ocr/PP-OCRv3/python/serving/server.py

@@ -0,0 +1,80 @@
+import fastdeploy as fd
+from fastdeploy.serving.server import SimpleServer
+import os
+import logging
+
+logging.getLogger().setLevel(logging.INFO)
+
+# Configurations
+det_model_dir = 'ch_PP-OCRv3_det_infer'
+cls_model_dir = 'ch_ppocr_mobile_v2.0_cls_infer'
+rec_model_dir = 'ch_PP-OCRv3_rec_infer'
+rec_label_file = 'ppocr_keys_v1.txt'
+device = 'cpu'
+# backend: ['paddle', 'trt'], you can also use other backends, but need to modify
+# the runtime option below
+backend = 'paddle'
+
+# Prepare models
+# Detection model
+det_model_file = os.path.join(det_model_dir, "inference.pdmodel")
+det_params_file = os.path.join(det_model_dir, "inference.pdiparams")
+# Classification model
+cls_model_file = os.path.join(cls_model_dir, "inference.pdmodel")
+cls_params_file = os.path.join(cls_model_dir, "inference.pdiparams")
+# Recognition model
+rec_model_file = os.path.join(rec_model_dir, "inference.pdmodel")
+rec_params_file = os.path.join(rec_model_dir, "inference.pdiparams")
+
+# Setup runtime option to select hardware, backend, etc.
+option = fd.RuntimeOption()
+if device.lower() == 'gpu':
+    option.use_gpu()
+if backend == 'trt':
+    option.use_trt_backend()
+else:
+    option.use_paddle_infer_backend()
+
+det_option = option
+det_option.set_trt_input_shape("x", [1, 3, 64, 64], [1, 3, 640, 640],
+                               [1, 3, 960, 960])
+
+# det_option.set_trt_cache_file("det_trt_cache.trt")
+print(det_model_file, det_params_file)
+det_model = fd.vision.ocr.DBDetector(
+    det_model_file, det_params_file, runtime_option=det_option)
+
+cls_batch_size = 1
+rec_batch_size = 6
+
+cls_option = option
+cls_option.set_trt_input_shape("x", [1, 3, 48, 10],
+                               [cls_batch_size, 3, 48, 320],
+                               [cls_batch_size, 3, 48, 1024])
+
+# cls_option.set_trt_cache_file("cls_trt_cache.trt")
+cls_model = fd.vision.ocr.Classifier(
+    cls_model_file, cls_params_file, runtime_option=cls_option)
+
+rec_option = option
+rec_option.set_trt_input_shape("x", [1, 3, 48, 10],
+                               [rec_batch_size, 3, 48, 320],
+                               [rec_batch_size, 3, 48, 2304])
+
+# rec_option.set_trt_cache_file("rec_trt_cache.trt")
+rec_model = fd.vision.ocr.Recognizer(
+    rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
+
+# Create PPOCRv3 pipeline
+ppocr_v3 = fd.vision.ocr.PPOCRv3(
+    det_model=det_model, cls_model=cls_model, rec_model=rec_model)
+
+ppocr_v3.cls_batch_size = cls_batch_size
+ppocr_v3.rec_batch_size = rec_batch_size
+
+# Create server, setup REST API
+app = SimpleServer()
+app.register(
+    task_name="fd/ppocrv3",
+    model_handler=fd.serving.handler.VisionModelHandler,
+    predictor=ppocr_v3)