tfserving备忘
标签：ict div open response copy 开始 docker default url
启动：
docker run -p port:8500 -e MODEL_NAME=ground_segmentation --name tfserving-ground-segmentation -e NVIDIA_VISIBLE_DEVICES=1 -v /model_path_in_machine:/models/ground_segmentation docker_images:tag --per_process_gpu_memory_fraction=0.2
需要注意的是：/model_path_in_machine下应该是版本号，例如0，0下一级目录是saved_model
请求：
import grpcimport numpy as npimport tensorflow as tffrom PIL import Imagefrom tensorflow_serving.apis import predict_pb2from tensorflow_serving.apis import prediction_service_pb2_grpcimport cv2import matplotlib.pyplot as plt def request_tfserving(inputs, server_url, model_name, signature_name, input_names, output_names): # 建立连接 channel = grpc.insecure_channel(server_url) stub = prediction_service_pb2_grpc.PredictionServiceStub(channel) # 开始设置请求数据 request = predict_pb2.PredictRequest() request.model_spec.name = model_name # 模型名称 request.model_spec.signature_name = signature_name # 签名名称（默认 serving_default） # 设置输入数据 for input, input_name in zip(inputs, input_names): request.inputs[input_name].CopyFrom(tf.make_tensor_proto(input, shape=list(input.shape))) response = stub.Predict(request, 5.0) # 其中第2个参数为请求的 timeout 时长 res_from_server_np = [] for output_name in output_names: res_from_server_np.append(tf.make_ndarray(response.outputs[output_name])) print(np.argmax(res_from_server_np[0][0,:],axis=2).shape) f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 8)) ax1.imshow(input[0,:].astype(np.uint8)) ax2.imshow(np.argmax(res_from_server_np[0][0,:],axis=2)) plt.show() if __name__ == "__main__": img_path = ‘/home/ground_train/test/20112201814-1_00000036_img.png‘ img = Image.open(img_path) img = img.resize((960, 544), Image.NEAREST).convert(‘RGB‘) img = image.img_to_array(img) img = np.expand_dims(img, axis=0).astype(‘float32‘) print(img.shape) request_tfserving(inputs=[img],#这里一定得是个list server_url=‘ip:port‘, model_name=‘ground_segmentation‘, signature_name=‘serving_default‘, input_names=[‘input‘], output_names=[‘output‘])
标签：ict div open response copy 开始 docker default url
原文地址：https://www.cnblogs.com/xiaoxiaomajinjiebiji/p/14412698.html