cannot convert tf model !

[R__R]

I'm try to convert a quite simple TF model,

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

model = keras.Sequential([
    layers.Conv2D(1, (3,3), input_shape=(160,160,3)),
    layers.Dense(10)
])
model.save('test_model', save_format='tf')
 

/opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py  --saved_model_dir test_model --log_level DEBUG

which ends in:

[ 2020-03-05 12:19:30,515 ] [ DEBUG ] [ register_custom_ops:104 ]  Added a new entry Upsample to extractors with custom op class <class 'extensions.ops.upsample.UpsampleOp'>.
[ 2020-03-05 12:19:30,516 ] [ DEBUG ] [ const:33 ]  Constant extractor for node gives shape = [3 3 3 1] and value.shape = (3, 3, 3, 1)
[ WARNING ]  Broadcast of scalar to shape: [1]
[ 2020-03-05 12:19:30,519 ] [ DEBUG ] [ const:33 ]  Constant extractor for node gives shape = [1] and value.shape = (1,)
[ 2020-03-05 12:19:30,520 ] [ DEBUG ] [ const:33 ]  Constant extractor for node gives shape = [ 1 10] and value.shape = (1, 10)
[ 2020-03-05 12:19:30,520 ] [ DEBUG ] [ const:33 ]  Constant extractor for node gives shape = [10] and value.shape = (10,)
[ WARNING ]  Node attributes: {'pb': name: "Const"
op: "Const"
device: "/device:CPU:0"
attr {
  key: "_output_shapes"
  value {
    list {
      shape {
      }
    }
  }
}
attr {
  key: "dtype"
  value {
    type: DT_STRING
  }
}
attr {
  key: "value"
  value {
    tensor {
      dtype: DT_STRING
      tensor_shape {
      }
      string_val: "\n\275\001\n\013\010\001\022\007layer-0\n\030\010\002\022\024layer_with_weights-0\n\013\010\002\022\007layer-1\n\030\010\003\022\024layer_with_weights-1\n\013\010\003\022\007layer-2\n\027\010\004\022\023trainable_variables\n\r\010\005\022\tvariables\n\031\010\006\022\025regularization_losses\n\r\010\007\022\tkeras_api\n\016\010\010\022\nsignatures\nR\n\027\010\t\022\023trainable_variables\n\r\010\n\022\tvariables\n\031\010\013\022\025regularization_losses\n\r\010\014\022\tkeras_api\n~\n\n\010\r\022\006kernel\n\010\010\016\022\004bias\n\024\010\017\022\020_callable_losses\n\027\010\020\022\023trainable_variables\n\r\010\021\022\tvariables\n\031\010\022\022\025regularization_losses\n\r\010\023\022\tkeras_api\n~\n\n\010\024\022\006kernel\n\010\010\025\022\004bias\n\024\010\026\022\020_callable_losses\n\027\010\027\022\023trainable_variables\n\r\010\030\022\tvariables\n\031\010\031\022\025regularization_losses\n\r\010\032\022\tkeras_api\n\034\n\005\010\r\022\0010\n\005\010\016\022\0011\n\005\010\024\022\0012\n\005\010\025\022\0013\n\034\n\005\010\r\022\0010\n\005\010\016\022\0011\n\005\010\024\022\0012\n\005\010\025\022\0013\n\000\n\232\001\n\037\010\033\022\033layer_regularization_losses\n\027\010\004\022\023trainable_variables\n\n\010\034\022\006layers\n\033\010\035\022\027non_trainable_variables\n\r\010\005\022\tvariables\n\031\010\006\022\025regularization_losses\n\013\010\036\022\007metrics\n\000\n\000\n\000\n\000\n\232\001\n\037\010\037\022\033layer_regularization_losses\n\027\010\t\022\023trainable_variables\n\n\010 \022\006layers\n\033\010!\022\027non_trainable_variables\n\r\010\n\022\tvariables\n\031\010\013\022\025regularization_losses\n\013\010\"\022\007metrics\nY\022W\n\016VARIABLE_VALUE\022\rconv2d/kernel\0326layer_with_weights-0/kernel/.ATTRIBUTES/VARIABLE_VALUE\nU\022S\n\016VARIABLE_VALUE\022\013conv2d/bias\0324layer_with_weights-0/bias/.ATTRIBUTES/VARIABLE_VALUE\n\000\n\016\n\005\010\r\022\0010\n\005\010\016\022\0011\n\016\n\005\010\r\022\0010\n\005\010\016\022\0011\n\000\n\232\001\n\037\010#\022\033layer_regularization_losses\n\027\010\020\022\023trainable_variables\n\n\010$\022\006layers\n\033\010%\022\027non_trainable_variables\n\r\010\021\022\tvariables\n\031\010\022\022\025regularization_losses\n\013\010&\022\007metrics\nX\022V\n\016VARIABLE_VALUE\022\014dense/kernel\0326layer_with_weights-1/kernel/.ATTRIBUTES/VARIABLE_VALUE\nT\022R\n\016VARIABLE_VALUE\022\ndense/bias\0324layer_with_weights-1/bias/.ATTRIBUTES/VARIABLE_VALUE\n\000\n\016\n\005\010\024\022\0010\n\005\010\025\022\0011\n\016\n\005\010\024\022\0010\n\005\010\025\022\0011\n\000\n\232\001\n\037\010\'\022\033layer_regularization_losses\n\027\010\027\022\023trainable_variables\n\n\010(\022\006layers\n\033\010)\022\027non_trainable_variables\n\r\010\030\022\tvariables\n\031\010\031\022\025regularization_losses\n\013\010*\022\007metrics\n\000\n\016\n\005\010\002\022\0010\n\005\010\003\022\0011\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000\n\000"
    }
  }
}
, '_in_ports': {}, '_out_ports': {}, 'kind': 'op', 'name': 'Const', 'op': 'Const', 'precision': 'FP32'}
[ ERROR ]  'ascii' codec can't decode byte 0xbd in position 1: ordinal not in range(128)
Unexpected exception happened during extracting attributes for node dense/bias/Read/ReadVariableOp.
Original exception message: 'ascii' codec can't decode byte 0xbd in position 1: ordinal not in range(128)
[ 2020-03-05 12:19:30,522 ] [ DEBUG ] [ main:304 ]  Traceback (most recent call last):
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/front/extractor.py", line 608, in extract_node_attrs
    supported, new_attrs = extractor(Node(graph, node))
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/pipeline/tf.py", line 132, in <lambda>
    extract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors)))
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/front/tf/extractor.py", line 109, in tf_op_extractor
    attrs = tf_op_extractors[op](node)
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/front/tf/extractor.py", line 65, in <lambda>
    return lambda node: pb_extractor(node.pb)
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/front/tf/extractors/const.py", line 31, in tf_const_ext
    result['value'] = tf_tensor_content(pb_tensor.dtype, result['shape'], pb_tensor)
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/front/tf/extractors/utils.py", line 77, in tf_tensor_content
    dtype=type_helper[0]),
UnicodeDecodeError: 'ascii' codec can't decode byte 0xbd in position 1: ordinal not in range(128)

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/main.py", line 298, in main
    return driver(argv)
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/main.py", line 247, in driver
    is_binary=not argv.input_model_is_text)
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/pipeline/tf.py", line 132, in tf2nx
    extract_node_attrs(graph, lambda node: tf_op_extractor(node, check_for_duplicates(tf_op_extractors)))
  File "/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer/mo/front/extractor.py", line 616, in extract_node_attrs
    ) from e
mo.utils.error.Error: 'ascii' codec can't decode byte 0xbd in position 1: ordinal not in range(128)
Unexpected exception happened during extracting attributes for node dense/bias/Read/ReadVariableOp.
Original exception message: 'ascii' codec can't decode byte 0xbd in position 1: ordinal not in range(128)

tensorflow version:  '1.15.2'

[SuryaPSC_Intel]

Hi R,

Thanks for reaching out.

To generate the IR using model optimizer tool, please use a trained model.

Best Regards,

Surya