将具有Azure自定义视觉训练的模型与tensorflow.js一起使用 [英] Use Azure custom-vision trained model with tensorflow.js
问题描述
我已经使用Azure Custom Vision训练了一个模型,并下载了Android的TensorFlow文件 (请参阅: https://docs.microsoft.com/zh-CN/azure/cognitive-services/custom-vision-service/export-your-model ).如何与 tensorflow.js 一起使用?
I've trained a model with Azure Custom Vision and downloaded the TensorFlow files for Android (see: https://docs.microsoft.com/en-au/azure/cognitive-services/custom-vision-service/export-your-model). How can I use this with tensorflow.js?
我需要一个模型(pb文件)和权重(json文件).但是,Azure给了我一个.pb和一个带有标签的文本文件.
I need a model (pb file) and weights (json file). However Azure gives me a .pb and a textfile with tags.
从我的研究中,我还了解到也存在不同的pb文件,但是我找不到Azure Custom Vision导出的类型.
From my research I also understand that there are also different pb files, but I can't find which type Azure Custom Vision exports.
我找到了 tfjs转换器.这是为了将TensorFlow SavedModel(Azure的* .pb文件是SavedModel?)或Keras模型转换为Web友好格式.但是,我需要填写"output_node_names"(如何获取这些?).我也不确定100%我的Android pb文件是否等于"tf_saved_model".
I found the tfjs converter. This is to convert a TensorFlow SavedModel (is the *.pb file from Azure a SavedModel?) or Keras model to a web-friendly format. However I need to fill in "output_node_names" (how do I get these?). I'm also not 100% sure if my pb file for Android is equal to a "tf_saved_model".
我希望有人给你一个小窍门或一个起点.
I hope someone has a tip or a starting point.
推荐答案
只模仿我说的话此处以节省您的点击次数.我希望直接导出到tfjs的选项很快就可以使用.
Just parroting what I said here to save you a click. I do hope that the option to export directly to tfjs is available soon.
这些是我为使导出的TensorFlow模型对我有用的步骤:
These are the steps I did to get an exported TensorFlow model working for me:
- 用Pad替换PadV2操作.这个python函数应该做到这一点.
input_filepath
是.pb模型文件的路径,而output_filepath
是将要创建的更新的.pb文件的完整路径.
- Replace PadV2 operations with Pad. This python function should do it.
input_filepath
is the path to the .pb model file andoutput_filepath
is the full path of the updated .pb file that will be created.
import tensorflow as tf
def ReplacePadV2(input_filepath, output_filepath):
graph_def = tf.GraphDef()
with open(input_filepath, 'rb') as f:
graph_def.ParseFromString(f.read())
for node in graph_def.node:
if node.op == 'PadV2':
node.op = 'Pad'
del node.input[-1]
print("Replaced PadV2 node: {}".format(node.name))
with open(output_filepath, 'wb') as f:
f.write(graph_def.SerializeToString())
- 安装tensorflowjs 0.8.6 或更早版本.在更高版本中,不推荐使用的转换.
- 调用转换器时,将
--input_format
设置为tf_frozen_model
并将output_node_names
设置为model_outputs
.这是我使用的命令.
- Install tensorflowjs 0.8.6 or earlier. Converting frozen models is deprecated in later versions.
- When calling the convertor, set
--input_format
astf_frozen_model
and setoutput_node_names
asmodel_outputs
. This is the command I used.
tensorflowjs_converter --input_format=tf_frozen_model --output_json=true --output_node_names='model_outputs' --saved_model_tags=serve path\to\modified\model.pb folder\to\save\converted\output
理想情况下,tf.loadGraphModel('path/to/converted/model.json')
现在应该可以工作了(已针对tfjs 1.0.0及更高版本进行了测试).
Ideally, tf.loadGraphModel('path/to/converted/model.json')
should now work (tested for tfjs 1.0.0 and above).
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