how to export frozen graph

[zzh8829]

how to export frozen graph

[zzh8829]

The freeze_all function is for transfer learning in the training process.
I haven't seen any mention of frozen graph in tensorflow 2.0, so it's probably deprecated now

the official guide recommends the SavedModel format
https://www.tensorflow.org/alpha/guide/saved_model
which is implemented in export_tfserving.py

SavedModel is in .pb format but it comes with extra files for the variables and parameters

[ThiagoMateo]

i see this function in utils.py file:

def freeze_all(model, frozen=True):
    model.trainable = not frozen
    if isinstance(model, tf.keras.Model):
        for l in model.layers:
            freeze_all(l, frozen)

is this code for frozen model? and how to freeze model?

[ThiagoMateo]

hello @zzh8829 thank for your help. i try to deploy serving model using tensorrt inference server. but i got this error:

2019-06-04 02:58:55.785173: W external/org_tensorflow/tensorflow/core/kernels/partitioned_function_ops.cc:197] Grappler optimization failed. Error: Op type not registered 'CombinedNonMaxSuppression' in bi
nary running on 1984ec4fe5aa. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) 
`tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
2019-06-04 02:58:55.806209: W external/org_tensorflow/tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at partitioned_function_ops.cc:118 : Not found: Op type not registered 'CombinedNonMax
Suppression' in binary running on 1984ec4fe5aa. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib,
 accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.

So, Do you know why?

[andydion]

hello @zzh8829 thank for your help. i try to deploy serving model using tensorrt inference server. but i got this error:

2019-06-04 02:58:55.785173: W external/org_tensorflow/tensorflow/core/kernels/partitioned_function_ops.cc:197] Grappler optimization failed. Error: Op type not registered 'CombinedNonMaxSuppression' in bi
nary running on 1984ec4fe5aa. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) 
`tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
2019-06-04 02:58:55.806209: W external/org_tensorflow/tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at partitioned_function_ops.cc:118 : Not found: Op type not registered 'CombinedNonMax
Suppression' in binary running on 1984ec4fe5aa. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib,
 accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.

So, Do you know why?

That might be related to outdated tensorflow/serving,

try with newer version and see if that works:
docker pull tensorflow/serving:nightly-gpu

[ThiagoMateo]

thank @andydion. but i use tensorrt inference server instead of tf serving.
so i don't know how to fix this

[ThiagoMateo]

Hello @andydion i write a client code, but it doesn't work.
So, I need a help.

import time
from absl import app, flags, logging
from absl.flags import FLAGS
import cv2
import numpy as np
import tensorflow as tf
from grpc.beta import implementations
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2

def main(_argv):
    host = '0.0.0.0'
    port = '8671'
    tf_serving = '0.0.0.0:9000'

    channel = implementations.insecure_channel(host, int(port))
    stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'reader'
    request.model_spec.signature_name = 'serving_default'

    img = cv2.imread("1.jpg")
    img = cv2.resize(img, (416, 416))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = img.astype('float32') 

    tensor = tf.contrib.util.make_tensor_proto(img, shape=[1]+list(img.shape))
    request.inputs['input_1'].CopyFrom(tensor)
    resp = stub.Predict(request, 30.0)
    print("resp: ", resp)


if __name__ == '__main__':
    try:
        app.run(main)
    except SystemExit:
        pass

[zzh8829]

Hi I think this is a issue with tensorrt compatibility, i recommend using tensorflow serving instead