Comments (12)
@LymanLiuChina I faced the same issue, it's because the whole model is frozen at the time of exporting it. So variables folder only contain graph meta data, weights which can be used to restore the graph for training. Since we don't require any training at inference hence we have totally frozen the graph, that's why if you open the saved_model.pb file you will find the network architecture along with the frozen constants of the network. If we don't froze the graph these constants will not appear in the protoBuff file and would be saved in weights file inside variables folder.
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@LymanLiuChina there is no need any files in variables
folder. I used tensorflow serving and it's serve my .pb model successfully.
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@LymanLiuChina hi, you can refer my serving code using gRPC on my github's repo: https://github.com/huyhoang17/matterport-maskrcnn-with-tensorflow-serving/blob/master/serve.py
Hope this answer helps you ๐
Any contributors are welcome โญ ๐ฏ
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Have you modified the Path variables in user_config.py
?
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I just modified a little, because I just need serving_model about mask_rcnn_coco.h5. but, in the /serving_model/1/, there is saved_model.pd,but in the /serving_model/1/variables, there isn't any file.
this is my directory,
this is my user_config.py:
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Can you show me the full error that you are getting.
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@LymanLiuChina there is no need any files in
variables
folder. I used tensorflow serving and it's serve my .pb model successfully.
I got it, thank you!
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@LymanLiuChina I faced the same issue, it's because the whole model is frozen at the time of exporting it. So variables folder only contain graph meta data, weights which can be used to restore the graph for training. Since we don't require any training at inference hence we have totally frozen the graph, that's why if you open the saved_model.pb file you will find the network architecture along with the frozen constants of the network. If we don't froze the graph these constants will not appear in the protoBuff file and would be saved in weights file inside variables folder.
@LymanLiuChina I faced the same issue, it's because the whole model is frozen at the time of exporting it. So variables folder only contain graph meta data, weights which can be used to restore the graph for training. Since we don't require any training at inference hence we have totally frozen the graph, that's why if you open the saved_model.pb file you will find the network architecture along with the frozen constants of the network. If we don't froze the graph these constants will not appear in the protoBuff file and would be saved in weights file inside variables folder.
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ไฝ ่ฝๅ่ฏๆไฝ ๅพๅฐ็ๅฎๆด้่ฏฏๅ๏ผ
thank you, @huyhoang17 has told me why this problem arises.
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@LymanLiuChina there is no need any files in
variables
folder. I used tensorflow serving and it's serve my .pb model successfully.
I am a novice, I want to deploy the mask model on the server, but according to many online tutorials, I still have many problems. Could you give me a complete Python source code (server and client) for your deployment of the maskrcnmo model?
Thank you!
this my email: [email protected]
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@LymanLiuChina I faced the same issue, it's because the whole model is frozen at the time of exporting it. So variables folder only contain graph meta data, weights which can be used to restore the graph for training. Since we don't require any training at inference hence we have totally frozen the graph, that's why if you open the saved_model.pb file you will find the network architecture along with the frozen constants of the network. If we don't froze the graph these constants will not appear in the protoBuff file and would be saved in weights file inside variables folder.
Thank you. I understand!
Have you used this service_model successfully?
How do you call this model on the client?
Could you share a source code with me?
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and thank you @bendangnuksung for your awesome project ๐ ๐ฏ it's help me so much, thank you!
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Closing as the original issue has been resolved.
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Related Issues (20)
- Frozen graph generates difference results compared to the original model HOT 2
- Which .pb file should I use? The one in serving model or the one in frozen model? HOT 2
- How to use saved_model.pb in Tensorflow Model Server๏ผ HOT 8
- Can not convert to tflite (UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbb in position 3: invalid start byte) HOT 3
- Converting frozen graph in mvNCCompile
- output_names=[out.op.name for out in model.outputs][:4] should be modified to get the 5 loss nodes HOT 2
- What should the inputs and outputs of the SavedModel SignatureDef look like? HOT 2
- About How to predict by tensorflow serving. HOT 2
- How can I read input and output node from frozen inference model (.pb)? HOT 2
- Inference speed really slow! HOT 2
- Rest Api Error
- { "error": "Malformed request: GET /v1/models/mask:predict" } HOT 1
- Unable to convert to tensorflow serving HOT 2
- Deployment on serving failed
- Error help pleas
- sending your input data instances as a JSON object to deployed model HOT 3
- gRPC error HOT 2
- error while converting keras model to tensorflow saved model format
- Error to run main.py file
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