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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 mode in tensorflow 1.15.
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
Deepanshu
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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"))
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Actually the script to convert from h5 to .pb was not good enough. Use this .
And 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
Now the IR files have been generated . Now once again I ran into problem. Please help this error is beyond my current level.
This is how it is produced
from openvino.inference_engine import IENetwork, IECore def load_to_IE(model_xml, cpu_extension=None): # Load the Inference Engine API plugin = IECore() # Load IR files into their related class model_bin = os.path.splitext(model_xml)[0] + ".bin" net = IENetwork(model=model_xml, weights=model_bin) # call this function using
load_to_IE('model/tf_model.xml')
The generated files (IR) are here uploaded in the attachement.
![](/skins/images/C4F7392A5C68ACA60290C9576F107044/responsive_peak/images/icon_anonymous_message.png)
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