tensorflow - 本地预测的 gcloud 问题

Question

我正在使用 gcloud local prediction测试我导出的模型。该模型是在自定义数据集上训练过的 TensorFlow 对象检测模型。我正在使用以下 gcloud 命令:

gcloud ml-engine local predict --model-dir=/path/to/saved_model/ --json-instances=input.json --signature-name="serving_default" --verbosity debug 

当我不使用 verbose 时，该命令不输出任何内容。将详细设置为调试后，我得到以下回溯:

DEBUG: [Errno 32] Broken pipe
Traceback (most recent call last):
  File "/google-cloud-sdk/lib/googlecloudsdk/calliope/cli.py", line 984, in Execute
    resources = calliope_command.Run(cli=self, args=args)
  File "/google-cloud-sdk/lib/googlecloudsdk/calliope/backend.py", line 784, in Run
    resources = command_instance.Run(args)
  File "/google-cloud-sdk/lib/surface/ai_platform/local/predict.py", line 83, in Run
    signature_name=args.signature_name)
  File "/google-cloud-sdk/lib/googlecloudsdk/command_lib/ml_engine/local_utils.py", line 103, in RunPredict
    proc.stdin.write((json.dumps(instance) + '\n').encode('utf-8'))
IOError: [Errno 32] Broken pipe 

我的导出模型的详细信息:

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['inputs'] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: encoded_image_string_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['detection_boxes'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300, 4)
        name: detection_boxes:0
    outputs['detection_classes'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300)
        name: detection_classes:0
    outputs['detection_features'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, -1, -1, -1, -1)
        name: detection_features:0
    outputs['detection_multiclass_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300, 2)
        name: detection_multiclass_scores:0
    outputs['detection_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300)
        name: detection_scores:0
    outputs['num_detections'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1)
        name: num_detections:0
    outputs['raw_detection_boxes'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300, 4)
        name: raw_detection_boxes:0
    outputs['raw_detection_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 300, 2)
        name: raw_detection_scores:0
  Method name is: tensorflow/serving/predict

我使用以下代码生成用于预测的 input.json:

with open('input.json', 'wb') as f:
    img = Image.open("image.jpg")
    img = img.resize((width, height), Image.ANTIALIAS)
    output_str = io.BytesIO()
    img.save(output_str, "JPEG")
    image_byte_array = output_str.getvalue()
    image_base64 = base64.b64encode(image_byte_array)
    json_entry = {"b64": image_base64.decode()}
    #instances.append(json_entry
    request = json.dumps({'inputs': json_entry})
    f.write(request.encode('utf-8'))
f.close()

{"inputs": {"b64": "/9j/4AAQSkZJRgABAQAAAQABAAD/......}}

我正在用一张图像测试预测。

Answer 1

我遇到了同样的问题，发现 ml_engine/local_utils.py用途 python运行 ml_engine/local_predict.pyc专为 python2.7 而构建.
我的 python 是 python3 ，所以当 ml_engine/local_utils.py尝试运行 ml_engine/local_predict.pyc使用 python (实际上 python3 )，它失败并出现错误:

RuntimeError: Bad magic number in .pyc file

解决方案1:

你可以做 python2作为系统中的默认值。

解决方案2:

我改了ml_engine/local_utils.py使用这样的补丁:

83c83
<   python_executables = files.SearchForExecutableOnPath("python")
---
>   python_executables = files.SearchForExecutableOnPath("python2")
114a115
>   log.debug(args)
124,126c125,130
<   for instance in instances:
<     proc.stdin.write((json.dumps(instance) + "\n").encode("utf-8"))
<   proc.stdin.flush()
---
>   try:
>     for instance in instances:
>       proc.stdin.write((json.dumps(instance) + "\n").encode("utf-8"))
>     proc.stdin.flush()
>   except:
>     pass

try-catch 需要使脚本能够读取和打印运行时发生的错误 ml_engine/local_predict.pyc .