fastdeploy support serving (#272)

* fd support serving

* fd support serving optimize dir

* optimize code

Co-authored-by: Jason <jiangjiajun@baidu.com>

examples/vision/detection/yolov5/serving/yolov5_grpc_client.py

@@ -0,0 +1,112 @@
+import logging
+import numpy as np
+import time
+from typing import Optional
+import cv2
+import json
+
+from tritonclient import utils as client_utils
+from tritonclient.grpc import InferenceServerClient, InferInput, InferRequestedOutput, service_pb2_grpc, service_pb2
+
+LOGGER = logging.getLogger("run_inference_on_triton")
+
+
+class SyncGRPCTritonRunner:
+    DEFAULT_MAX_RESP_WAIT_S = 120
+
+    def __init__(
+            self,
+            server_url: str,
+            model_name: str,
+            model_version: str,
+            *,
+            verbose=False,
+            resp_wait_s: Optional[float]=None, ):
+        self._server_url = server_url
+        self._model_name = model_name
+        self._model_version = model_version
+        self._verbose = verbose
+        self._response_wait_t = self.DEFAULT_MAX_RESP_WAIT_S if resp_wait_s is None else resp_wait_s
+
+        self._client = InferenceServerClient(
+            self._server_url, verbose=self._verbose)
+        error = self._verify_triton_state(self._client)
+        if error:
+            raise RuntimeError(
+                f"Could not communicate to Triton Server: {error}")
+
+        LOGGER.debug(
+            f"Triton server {self._server_url} and model {self._model_name}:{self._model_version} "
+            f"are up and ready!")
+
+        model_config = self._client.get_model_config(self._model_name,
+                                                     self._model_version)
+        model_metadata = self._client.get_model_metadata(self._model_name,
+                                                         self._model_version)
+        LOGGER.info(f"Model config {model_config}")
+        LOGGER.info(f"Model metadata {model_metadata}")
+
+        for tm in model_metadata.inputs:
+            print("tm:", tm)
+        self._inputs = {tm.name: tm for tm in model_metadata.inputs}
+        self._input_names = list(self._inputs)
+        self._outputs = {tm.name: tm for tm in model_metadata.outputs}
+        self._output_names = list(self._outputs)
+        self._outputs_req = [
+            InferRequestedOutput(name) for name in self._outputs
+        ]
+
+    def Run(self, inputs):
+        """
+        Args:
+            inputs: list, Each value corresponds to an input name of self._input_names
+        Returns:
+            results: dict, {name : numpy.array}
+        """
+        infer_inputs = []
+        for idx, data in enumerate(inputs):
+            print("len(data):", len(data))
+            print("name:", self._input_names[idx], "  shape:", data.shape,
+                  data.dtype)
+            #data = np.array([[x.encode('utf-8')] for x in data],
+            #                dtype=np.object_)
+            infer_input = InferInput(self._input_names[idx], data.shape,
+                                     "UINT8")
+            infer_input.set_data_from_numpy(data)
+            infer_inputs.append(infer_input)
+
+        results = self._client.infer(
+            model_name=self._model_name,
+            model_version=self._model_version,
+            inputs=infer_inputs,
+            outputs=self._outputs_req,
+            client_timeout=self._response_wait_t, )
+        results = {name: results.as_numpy(name) for name in self._output_names}
+        return results
+
+    def _verify_triton_state(self, triton_client):
+        if not triton_client.is_server_live():
+            return f"Triton server {self._server_url} is not live"
+        elif not triton_client.is_server_ready():
+            return f"Triton server {self._server_url} is not ready"
+        elif not triton_client.is_model_ready(self._model_name,
+                                              self._model_version):
+            return f"Model {self._model_name}:{self._model_version} is not ready"
+        return None
+
+
+if __name__ == "__main__":
+    model_name = "yolov5"
+    model_version = "1"
+    url = "localhost:8001"
+    runner = SyncGRPCTritonRunner(url, model_name, model_version)
+    im = cv2.imread("000000014439.jpg")
+    im = np.array([im, ])
+    for i in range(1):
+        result = runner.Run([im, ])
+        for name, values in result.items():
+            print("output_name:", name)
+            for i in range(len(values)):
+                value = values[i][0]
+                value = json.loads(value)
+                print(value)