如何在 torchserve 中创建自定义处理程序？
[英]How do I create a custom handler in torchserve?
问题描述
I am trying to create a custom handler on Torchserve.
我正在尝试在 Torchserve 上创建一个自定义处理程序。
The custom handler has been modified as follows自定义处理程序已修改如下
# custom handler file
# model_handler.py
"""
ModelHandler defines a custom model handler.
"""
import os
import soundfile
from espnet2.bin.enh_inference import *
from ts.torch_handler.base_handler import BaseHandler
class ModelHandler(BaseHandler):
"""
A custom model handler implementation.
"""
def __init__(self):
self._context = None
self.initialized = False
self.model = None
self.device = None
def initialize(self, context):
"""
Invoke by torchserve for loading a model
:param context: context contains model server system properties
:return:
"""
# load the model
self.manifest = context.manifest
properties = context.system_properties
model_dir = properties.get("model_dir")
self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
# Read model serialize/pt file
serialized_file = self.manifest['model']['serializedFile']
model_pt_path = os.path.join(model_dir, serialized_file)
if not os.path.isfile(model_pt_path):
raise RuntimeError("Missing the model.pt file")
self.model = SeparateSpeech("train_enh_transformer_tf.yaml", "valid.loss.best.pth")
self.initialized = True
def preprocess(self,data):
audio_data, rate = soundfile.read(data)
preprocessed_data = audio_data[np.newaxis, :]
return preprocessed_data
def inference(self, model_input):
model_output = self.model(model_input)
return model_output
def postprocess(self, inference_output):
"""
Return inference result.
:param inference_output: list of inference output
:return: list of predict results
"""
# Take output from network and post-process to desired format
postprocess_output = inference_output
#wav ni suru
return postprocess_output
def handle(self, data, context):
model_input = self.preprocess(data)
model_output = self.inference(model_input)
return self.postprocess(model_output)
The torchserve appears to be working.火炬服务似乎正在工作。
The Torchserve logs are as follows Torchserve 日志如下
root@5c780ba74916:~/files# torchserve --start --ncs --model-store model_store --models denoise_transformer=denoise_transformer.mar
root@5c780ba74916:~/files# WARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.
2022-08-24T14:06:06,662 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Initializing plugins manager...
2022-08-24T14:06:06,796 [INFO ] main org.pytorch.serve.ModelServer -
Torchserve version: 0.6.0
TS Home: /usr/local/lib/python3.8/dist-packages
Current directory: /root/files
Temp directory: /tmp
Number of GPUs: 1
Number of CPUs: 16
Max heap size: 4002 M
Python executable: /usr/bin/python3
Config file: N/A
Inference address: http://127.0.0.1:8080
Management address: http://127.0.0.1:8081
Metrics address: http://127.0.0.1:8082
Model Store: /root/files/model_store
Initial Models: denoise_transformer=denoise_transformer.mar
Log dir: /root/files/logs
Metrics dir: /root/files/logs
Netty threads: 0
Netty client threads: 0
Default workers per model: 1
Blacklist Regex: N/A
Maximum Response Size: 6553500
Maximum Request Size: 6553500
Limit Maximum Image Pixels: true
Prefer direct buffer: false
Allowed Urls: [file://.*|http(s)?://.*]
Custom python dependency for model allowed: false
Metrics report format: prometheus
Enable metrics API: true
Workflow Store: /root/files/model_store
Model config: N/A
2022-08-24T14:06:06,805 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Loading snapshot serializer plugin...
2022-08-24T14:06:06,817 [INFO ] main org.pytorch.serve.ModelServer - Loading initial models: denoise_transformer.mar
2022-08-24T14:06:07,006 [DEBUG] main org.pytorch.serve.wlm.ModelVersionedRefs - Adding new version 1.0 for model denoise_transformer
2022-08-24T14:06:07,007 [DEBUG] main org.pytorch.serve.wlm.ModelVersionedRefs - Setting default version to 1.0 for model denoise_transformer
2022-08-24T14:06:07,007 [INFO ] main org.pytorch.serve.wlm.ModelManager - Model denoise_transformer loaded.
2022-08-24T14:06:07,007 [DEBUG] main org.pytorch.serve.wlm.ModelManager - updateModel: denoise_transformer, count: 1
2022-08-24T14:06:07,015 [DEBUG] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerLifeCycle - Worker cmdline: [/usr/bin/python3, /usr/local/lib/python3.8/dist-packages/ts/model_service_worker.py, --sock-type, unix, --sock-name, /tmp/.ts.sock.9000]
2022-08-24T14:06:07,016 [INFO ] main org.pytorch.serve.ModelServer - Initialize Inference server with: EpollServerSocketChannel.
2022-08-24T14:06:07,059 [INFO ] main org.pytorch.serve.ModelServer - Inference API bind to: http://127.0.0.1:8080
2022-08-24T14:06:07,059 [INFO ] main org.pytorch.serve.ModelServer - Initialize Management server with: EpollServerSocketChannel.
2022-08-24T14:06:07,060 [INFO ] main org.pytorch.serve.ModelServer - Management API bind to: http://127.0.0.1:8081
2022-08-24T14:06:07,060 [INFO ] main org.pytorch.serve.ModelServer - Initialize Metrics server with: EpollServerSocketChannel.
2022-08-24T14:06:07,062 [INFO ] main org.pytorch.serve.ModelServer - Metrics API bind to: http://127.0.0.1:8082
Model server started.
2022-08-24T14:06:07,258 [WARN ] pool-3-thread-1 org.pytorch.serve.metrics.MetricCollector - worker pid is not available yet.
2022-08-24T14:06:07,363 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Listening on port: /tmp/.ts.sock.9000
2022-08-24T14:06:07,364 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - [PID]6258
2022-08-24T14:06:07,364 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Torch worker started.
2022-08-24T14:06:07,365 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Python runtime: 3.8.10
2022-08-24T14:06:07,365 [DEBUG] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - W-9000-denoise_transformer_1.0 State change null -> WORKER_STARTED
2022-08-24T14:06:07,368 [INFO ] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /tmp/.ts.sock.9000
2022-08-24T14:06:07,374 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Connection accepted: /tmp/.ts.sock.9000.
2022-08-24T14:06:07,376 [INFO ] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req. to backend at: 1661317567376
2022-08-24T14:06:07,398 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - model_name: denoise_transformer, batchSize: 1
2022-08-24T14:06:07,596 [INFO ] pool-3-thread-1 TS_METRICS - CPUUtilization.Percent:0.0|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,596 [INFO ] pool-3-thread-1 TS_METRICS - DiskAvailable.Gigabytes:220.49971389770508|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - DiskUsage.Gigabytes:17.66714859008789|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - DiskUtilization.Percent:7.4|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - GPUMemoryUtilization.Percent:17.9931640625|#Level:Host,device_id:0|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - GPUMemoryUsed.Megabytes:1474|#Level:Host,device_id:0|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - GPUUtilization.Percent:8|#Level:Host,device_id:0|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - MemoryAvailable.Megabytes:14307.53515625|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - MemoryUsed.Megabytes:1372.1640625|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - MemoryUtilization.Percent:10.6|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:08,306 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - encoder self-attention layer type = self-attention
2022-08-24T14:06:08,328 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Eps is deprecated in si_snr loss, set clamp_db instead.
2022-08-24T14:06:08,373 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Perform direct speech enhancement on the input
2022-08-24T14:06:08,390 [INFO ] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 992
2022-08-24T14:06:08,391 [DEBUG] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - W-9000-denoise_transformer_1.0 State change WORKER_STARTED -> WORKER_MODEL_LOADED
2022-08-24T14:06:08,391 [INFO ] W-9000-denoise_transformer_1.0 TS_METRICS - W-9000-denoise_transformer_1.0.ms:1380|#Level:Host|#hostname:5c780ba74916,timestamp:1661317568
2022-08-24T14:06:08,391 [INFO ] W-9000-denoise_transformer_1.0 TS_METRICS - WorkerThreadTime.ms:23|#Level:Host|#hostname:5c780ba74916,timestamp:1661317568
I sent the wav file to torchserve with the following command我使用以下命令将 wav 文件发送到 torchserve
curl http://127.0.0.1:8080/predictions/denoise_transformer -T Mix.wav
However, the following error was returned但是，返回了以下错误
<HTML><HEAD>
<TITLE>Request Error</TITLE>
</HEAD>
<BODY>
<FONT face="Helvetica">
<big><strong></strong></big><BR>
</FONT>
<blockquote>
<TABLE border=0 cellPadding=1 width="80%">
<TR><TD>
<FONT face="Helvetica">
<big>Request Error (invalid_request)</big>
<BR>
<BR>
</FONT>
</TD></TR>
<TR><TD>
<FONT face="Helvetica">
Your request could not be processed. Request could not be handled
</FONT>
</TD></TR>
<TR><TD>
<FONT face="Helvetica">
This could be caused by a misconfiguration, or possibly a malformed request.
</FONT>
</TD></TR>
<TR><TD>
<FONT face="Helvetica" SIZE=2>
<BR>
For assistance, contact your network support team.
</FONT>
</TD></TR>
</TABLE>
</blockquote>
</FONT>
</BODY></HTML>
Is there somewhere wrong?有什么地方不对吗？ Best regards.此致。
1 个解决方案
解决方案1
0 2022-08-20 08:11:03
Hi here's an example but it seems the problem should be how you receive parse the data嗨，这是一个例子，但似乎问题应该是你如何接收解析数据
from typing import Dict, List, Tuple
import numpy as np
import soundfile as sf
from io import BytesIO
def preproc_one_wav(self, req: Dict[str, bytearray]) -> Tuple[np.ndarray, int]:
"""
Function to convert req data to image
:param req:
:return: np array of wav form
"""
wav_byte = req.get("data")
if wav_byte is None:
wav_byte = req.get('body')
# create a stream from the encoded image
wav, sr = sf.read(BytesIO(wav_byte))
return wav, sr
def preprocess(self, requests: List[Dict[str, bytearray]]):
"""
Function to prepare data from the model
:param requests:
:return: tensor of the processed shape specified by the model
"""
batch_crop = [self.preproc_one_wav(req) for req in requests]
Btw heres an example you can do to make a request to your model in python顺便说一句，您可以在 python 中向您的 model 发出请求的示例
import json
import requests
sample = {'data': wav_in_byte}
results = requests.post('ip:port', data=json.dumps(sample))
# parse results
get_results = results.json()
解决方案2
0 2022-08-24 05:14:06
The custom handler has been modified as follows自定义处理程序已修改如下
# custom handler file
# model_handler.py
"""
ModelHandler defines a custom model handler.
"""
import os
import soundfile
from espnet2.bin.enh_inference import *
from ts.torch_handler.base_handler import BaseHandler
class ModelHandler(BaseHandler):
"""
A custom model handler implementation.
"""
def __init__(self):
self._context = None
self.initialized = False
self.model = None
self.device = None
def initialize(self, context):
"""
Invoke by torchserve for loading a model
:param context: context contains model server system properties
:return:
"""
# load the model
self.manifest = context.manifest
properties = context.system_properties
model_dir = properties.get("model_dir")
self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
# Read model serialize/pt file
serialized_file = self.manifest['model']['serializedFile']
model_pt_path = os.path.join(model_dir, serialized_file)
if not os.path.isfile(model_pt_path):
raise RuntimeError("Missing the model.pt file")
self.model = SeparateSpeech("train_enh_transformer_tf.yaml", "valid.loss.best.pth")
self.initialized = True
def preprocess(self,data):
audio_data, rate = soundfile.read(data)
preprocessed_data = audio_data[np.newaxis, :]
return preprocessed_data
def inference(self, model_input):
model_output = self.model(model_input)
return model_output
def postprocess(self, inference_output):
"""
Return inference result.
:param inference_output: list of inference output
:return: list of predict results
"""
# Take output from network and post-process to desired format
postprocess_output = inference_output
#wav ni suru
return postprocess_output
def handle(self, data, context):
model_input = self.preprocess(data)
model_output = self.inference(model_input)
return self.postprocess(model_output)
The torchserve appears to be working.火炬服务似乎正在工作。
The Torchserve logs are as follows Torchserve 日志如下
root@5c780ba74916:~/files# torchserve --start --ncs --model-store model_store --models denoise_transformer=denoise_transformer.mar
root@5c780ba74916:~/files# WARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.
2022-08-24T14:06:06,662 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Initializing plugins manager...
2022-08-24T14:06:06,796 [INFO ] main org.pytorch.serve.ModelServer -
Torchserve version: 0.6.0
TS Home: /usr/local/lib/python3.8/dist-packages
Current directory: /root/files
Temp directory: /tmp
Number of GPUs: 1
Number of CPUs: 16
Max heap size: 4002 M
Python executable: /usr/bin/python3
Config file: N/A
Inference address: http://127.0.0.1:8080
Management address: http://127.0.0.1:8081
Metrics address: http://127.0.0.1:8082
Model Store: /root/files/model_store
Initial Models: denoise_transformer=denoise_transformer.mar
Log dir: /root/files/logs
Metrics dir: /root/files/logs
Netty threads: 0
Netty client threads: 0
Default workers per model: 1
Blacklist Regex: N/A
Maximum Response Size: 6553500
Maximum Request Size: 6553500
Limit Maximum Image Pixels: true
Prefer direct buffer: false
Allowed Urls: [file://.*|http(s)?://.*]
Custom python dependency for model allowed: false
Metrics report format: prometheus
Enable metrics API: true
Workflow Store: /root/files/model_store
Model config: N/A
2022-08-24T14:06:06,805 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Loading snapshot serializer plugin...
2022-08-24T14:06:06,817 [INFO ] main org.pytorch.serve.ModelServer - Loading initial models: denoise_transformer.mar
2022-08-24T14:06:07,006 [DEBUG] main org.pytorch.serve.wlm.ModelVersionedRefs - Adding new version 1.0 for model denoise_transformer
2022-08-24T14:06:07,007 [DEBUG] main org.pytorch.serve.wlm.ModelVersionedRefs - Setting default version to 1.0 for model denoise_transformer
2022-08-24T14:06:07,007 [INFO ] main org.pytorch.serve.wlm.ModelManager - Model denoise_transformer loaded.
2022-08-24T14:06:07,007 [DEBUG] main org.pytorch.serve.wlm.ModelManager - updateModel: denoise_transformer, count: 1
2022-08-24T14:06:07,015 [DEBUG] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerLifeCycle - Worker cmdline: [/usr/bin/python3, /usr/local/lib/python3.8/dist-packages/ts/model_service_worker.py, --sock-type, unix, --sock-name, /tmp/.ts.sock.9000]
2022-08-24T14:06:07,016 [INFO ] main org.pytorch.serve.ModelServer - Initialize Inference server with: EpollServerSocketChannel.
2022-08-24T14:06:07,059 [INFO ] main org.pytorch.serve.ModelServer - Inference API bind to: http://127.0.0.1:8080
2022-08-24T14:06:07,059 [INFO ] main org.pytorch.serve.ModelServer - Initialize Management server with: EpollServerSocketChannel.
2022-08-24T14:06:07,060 [INFO ] main org.pytorch.serve.ModelServer - Management API bind to: http://127.0.0.1:8081
2022-08-24T14:06:07,060 [INFO ] main org.pytorch.serve.ModelServer - Initialize Metrics server with: EpollServerSocketChannel.
2022-08-24T14:06:07,062 [INFO ] main org.pytorch.serve.ModelServer - Metrics API bind to: http://127.0.0.1:8082
Model server started.
2022-08-24T14:06:07,258 [WARN ] pool-3-thread-1 org.pytorch.serve.metrics.MetricCollector - worker pid is not available yet.
2022-08-24T14:06:07,363 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Listening on port: /tmp/.ts.sock.9000
2022-08-24T14:06:07,364 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - [PID]6258
2022-08-24T14:06:07,364 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Torch worker started.
2022-08-24T14:06:07,365 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Python runtime: 3.8.10
2022-08-24T14:06:07,365 [DEBUG] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - W-9000-denoise_transformer_1.0 State change null -> WORKER_STARTED
2022-08-24T14:06:07,368 [INFO ] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /tmp/.ts.sock.9000
2022-08-24T14:06:07,374 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Connection accepted: /tmp/.ts.sock.9000.
2022-08-24T14:06:07,376 [INFO ] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req. to backend at: 1661317567376
2022-08-24T14:06:07,398 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - model_name: denoise_transformer, batchSize: 1
2022-08-24T14:06:07,596 [INFO ] pool-3-thread-1 TS_METRICS - CPUUtilization.Percent:0.0|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,596 [INFO ] pool-3-thread-1 TS_METRICS - DiskAvailable.Gigabytes:220.49971389770508|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - DiskUsage.Gigabytes:17.66714859008789|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - DiskUtilization.Percent:7.4|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - GPUMemoryUtilization.Percent:17.9931640625|#Level:Host,device_id:0|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,597 [INFO ] pool-3-thread-1 TS_METRICS - GPUMemoryUsed.Megabytes:1474|#Level:Host,device_id:0|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - GPUUtilization.Percent:8|#Level:Host,device_id:0|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - MemoryAvailable.Megabytes:14307.53515625|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - MemoryUsed.Megabytes:1372.1640625|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:07,598 [INFO ] pool-3-thread-1 TS_METRICS - MemoryUtilization.Percent:10.6|#Level:Host|#hostname:5c780ba74916,timestamp:1661317567
2022-08-24T14:06:08,306 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - encoder self-attention layer type = self-attention
2022-08-24T14:06:08,328 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Eps is deprecated in si_snr loss, set clamp_db instead.
2022-08-24T14:06:08,373 [INFO ] W-9000-denoise_transformer_1.0-stdout MODEL_LOG - Perform direct speech enhancement on the input
2022-08-24T14:06:08,390 [INFO ] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 992
2022-08-24T14:06:08,391 [DEBUG] W-9000-denoise_transformer_1.0 org.pytorch.serve.wlm.WorkerThread - W-9000-denoise_transformer_1.0 State change WORKER_STARTED -> WORKER_MODEL_LOADED
2022-08-24T14:06:08,391 [INFO ] W-9000-denoise_transformer_1.0 TS_METRICS - W-9000-denoise_transformer_1.0.ms:1380|#Level:Host|#hostname:5c780ba74916,timestamp:1661317568
2022-08-24T14:06:08,391 [INFO ] W-9000-denoise_transformer_1.0 TS_METRICS - WorkerThreadTime.ms:23|#Level:Host|#hostname:5c780ba74916,timestamp:1661317568
I sent the wav file to torchserve with the following command我使用以下命令将 wav 文件发送到 torchserve
curl http://127.0.0.1:8080/predictions/denoise_transformer -T Mix.wav
However, the following error was returned但是，返回了以下错误
<HTML><HEAD>
<TITLE>Request Error</TITLE>
</HEAD>
<BODY>
<FONT face="Helvetica">
<big><strong></strong></big><BR>
</FONT>
<blockquote>
<TABLE border=0 cellPadding=1 width="80%">
<TR><TD>
<FONT face="Helvetica">
<big>Request Error (invalid_request)</big>
<BR>
<BR>
</FONT>
</TD></TR>
<TR><TD>
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Your request could not be processed. Request could not be handled
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This could be caused by a misconfiguration, or possibly a malformed request.
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Is there somewhere wrong?有什么地方不对吗？ Best regards.此致。
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