了解Google AI平台自定义预测例程的输入 [英] Understanding inputs for google ai platform custom prediction routines

问题描述

我正在按照此文档进行自定义预测例程，我试图了解自定义预测例程的输入是什么样子.发送输入的代码如下:

I am following this documentation on custom prediction routines and I am trying to understand how the inputs for custom prediction routine looks like. The code to send the input looks like this:

instances = [
        [6.7, 3.1, 4.7, 1.5],
        [4.6, 3.1, 1.5, 0.2],
    ]
service = discovery.build('ml', 'v1')
name = 'projects/{}/models/{}'.format(project, model)

if version is not None:
    name += '/versions/{}'.format(version)

response = service.projects().predict(
    name=name,
    body={'instances': instances}
).execute()

和Predictor.py目前非常简单.我只是想了解输入的样子...

and the Predictor.py at the moment is very simple. I am just trying to understand how the input looks like...

class Predictor(object):
    """An example Predictor for an AI Platform custom prediction routine."""

    def __init__(self, model):
        self._model = model

    def predict(self, instances, **kwargs):

        inputs = np.asarray(instances)
        if kwargs.get('max'):
            return np.argmax(inputs, axis=1)

        return np.sum(inputs)


    @classmethod
    def from_path(cls, model_dir):
        return cls(None)

但是当我尝试获取响应时，出现以下错误:

But when I try to get the response i get the following error:

{
  "error": "Prediction failed: unknown error."
}

此外，调试代码非常困难，因为无法进入代码或打印日志...我不知道发生了什么...输入是什么样子?我应该如何访问它们? 这只是一个简单的测试，但是最终我要发送图像，因此调试起来会更加困难.我将如何收到它们?如何在预处理器中对其进行预处理?假设我在训练时所做的处理看起来像这样

Furthermore it is extremely difficult to debug the code, because there is no way to step into the code or print logs... I have no idea what's going on... How the input looks like? how should i access them? This is just a simple test, but eventually I want to send images, it will be even more difficult to debug then. How will I receive them? How will I preprocess them in the preprocessor? Let's assume that the proporcessing i have done at training time looks like this

data = cv2.imread(str(img_path))
data = cv2.resize(data, (224, 224))
data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
x = data.astype(np.float32) / 255.
return np.expand_dims(x, axis=0)

instances对象的外观如何，因此我可以相应地构造预处理器? 预先谢谢你.

How the instances object looks like so i can construct the preprocessor accordingly? thank you in advance.

推荐答案

在没有模型的情况下使用调试代码(在本文发布时)似乎不起作用.我使用以下代码使所有功能均可用于我的图像预测用例:

It looks like that using debug code (at the time of this post) without a model do not work. I used the following code to have everything worked for my image prediction use case:

image_filename = 'your image path'
PROJECT_ID = ''
MODEL_NAME = ''
VERSION_NAME = ''

img = base64.b64encode(open(image_filename, "rb").read()).decode()
image_bite_dict = {"key": "0", "image_bytes": {"b64": img}}

instances = [
            image_bite_dict
        ]


service = googleapiclient.discovery.build('ml', 'v1')
    name = 'projects/{}/models/{}/versions/{}'.format(PROJECT_ID, MODEL_NAME, VERSION_NAME)
response = service.projects().predict(
        name=name,
        body={'instances': instances}
    ).execute()

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