问题

I am trying to convert a trained model in tensorflow to Open VINO Intermediate Representation.

I have a model of the form given below

class Conv3DModel(tf.keras.Model):
    def __init__(self):
        super(Conv3DModel, self).__init__()
        # Convolutions
        self.conv1 = tf.compat.v2.keras.layers.Conv3D(32, (3, 3, 3), activation='relu', name="conv1", data_format='channels_last')
        self.pool1 = tf.keras.layers.MaxPool3D(pool_size=(2, 2, 2), data_format='channels_last')
        self.conv2 = tf.compat.v2.keras.layers.Conv3D(64, (3, 3, 3), activation='relu', name="conv1", data_format='channels_last')
        self.pool2 = tf.keras.layers.MaxPool3D(pool_size=(2, 2,2), data_format='channels_last')

        # LSTM & Flatten
        self.convLSTM =tf.keras.layers.ConvLSTM2D(40, (3, 3))
        self.flatten =  tf.keras.layers.Flatten(name="flatten")

        # Dense layers
        self.d1 = tf.keras.layers.Dense(128, activation='relu', name="d1")
        self.out = tf.keras.layers.Dense(6, activation='softmax', name="output")


    def call(self, x):
        x = self.conv1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.convLSTM(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.out(x)

I tried to convert the model into IR. The model is here .

I have trained this model in tensorflow 1.15. Tensorflow 2.0 is currently not supported.

Now I tried to run the command

python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --saved_model_dir jester_trained_models/3dcnn-basic/ --output_dir /home/deepanshu/open_vino/udacity_project_custom_model/

Now i got the following error

Model Optimizer arguments:

Common parameters:

Path to the Input Model: None
Path for generated IR: /home/deepanshu/open_vino/udacity_project_custom_model/
IR output name: saved_model
Log level: ERROR
Batch: Not specified, inherited from the model
Input layers: Not specified, inherited from the model
Output layers: Not specified, inherited from the model
Input shapes: Not specified, inherited from the model
Mean values: Not specified
Scale values: Not specified
Scale factor: Not specified
Precision of IR: FP32
Enable fusing: True
Enable grouped convolutions fusing: True
Move mean values to preprocess section: False
Reverse input channels: False

TensorFlow specific parameters:

Input model in text protobuf format: False
Path to model dump for TensorBoard: None
List of shared libraries with TensorFlow custom layers implementation: None
Update the configuration file with input/output node names: None
Use configuration file used to generate the model with Object Detection API: None
Operations to offload: None
Patterns to offload: None
Use the config file: None

Model Optimizer version: 2020.1.0-61-gd349c3ba4a

[ ERROR ] Unexpected exception happened during extracting attributes for node conv3d_model/conv_lst_m2d/bias/Read/ReadVariableOp. Original exception message: 'ascii' codec can't decode byte 0xc9 in position 1: ordinal not in range(128)

As far as I can see it is the tf.keras.layers.ConvLSTM2D(40, (3, 3)) causing problems . I am kind of stuck here . Can anyone tell me where can I proceed further ?

Thanks

Edit to the question

Now I rejected the above tensorflow implementation and used keras . My h5 model developed was converted into .pb format using this post.

Now I ran the model optimizer on this .pb file. Using the command

python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model /home/deepanshu/ml_playground/jester_freezed/tf_model.pb --output_dir /home/deepanshu/open_vino/udacity_project_custom_model/ --input_shape=[1,30,64,64,1] --data_type FP32

Now i am facing another issue . The issue here is point no. 97 on this post.

So my model contains a cycle and model optimizer does not know a way to convert it. Has anybody faced this issue before ?

Please help.

Here is the model .

Here is the defination of the model in keras


from keras.models import Sequential

from keras.layers import Conv3D , MaxPool3D,Flatten ,Dense

from keras.layers.convolutional_recurrent import ConvLSTM2D

import keras


model = Sequential()

model.add(Conv3D(32, (3, 3, 3), 

         name="conv1" , input_shape=(30, 64, 64,1) ,  data_format='channels_last',

        activation='relu') )

model.add(MaxPool3D(pool_size=(2, 2, 2), data_format='channels_last'))

model.add(Conv3D(64, (3, 3, 3), activation='relu', name="conv2", data_format='channels_last'))

model.add(MaxPool3D(pool_size=(2, 2,2), data_format='channels_last'))

model.add(ConvLSTM2D(40, (3, 3)))

model.add(Flatten(name="flatten"))

model.add(Dense(128, activation='relu', name="d1"))

model.add(Dense(6, activation='softmax', name="output"))

回答1:

Actually the script to convert from h5 to .pb suggested by intel was not good enough. Always use the code from here to convert your keras model to .pb.

Once you obtain your .pb file now convert your model to IR using

python3 /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model ml_playground/try_directory/tf_model.pb   --output_dir /home/deepanshu/open_vino/udacity_project_custom_model/  --input_shape=[1,30,64,64,1] --data_type FP32

After the execution of this script we can obtain the intermediate representation of the keras model.

来源：https://stackoverflow.com/questions/60606297/cannot-convert-tf-keras-layers-convlstm2d-layer-to-open-vino-intermediate-repres

标签

tensorflow