Keras-->TensorRT, even naive sample fails

Hi,

Trying to convert keras model to TensorRT (and then run on Xavier).
Even naive sample can not run.
I am trying the code below, it seems ok (generates the model file).


import keras.backend as K
from tensorflow.python.framework import graph_io
from tensorflow.python.tools import freeze_graph
from tensorflow.core.protobuf import saver_pb2
from tensorflow.python.training import saver as saver_lib
import tensorflow as tf

from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Add, Activation, Concatenate, Input
from keras.models import Model

def create_model():
input_layer = Input(shape=(None,None,3),name=‘input’)
x = Conv2D(16, 3, activation=‘linear’, padding=‘same’, strides=(1,1))(input_layer)
return Model(inputs=input_layer,outputs=x)

def convert_keras_to_pb(models_dir, model_filename):
model = create_model()
K.set_learning_phase(0)
sess = K.get_session()
saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V2)
checkpoint_path = saver.save(sess, ‘./saved_ckpt’, global_step=0, latest_filename=‘checkpoint_state’)

graph_io.write_graph(sess.graph, '.', 'tmp.pb')
out_names = [node.name for node in tf.get_default_graph().as_graph_def().node]
freeze_graph.freeze_graph('./tmp.pb', '',
                      	False, checkpoint_path, ','.join(out_names),
                      	"save/restore_all", "save/Const:0",
                      	models_dir+model_filename, False, "")

convert_keras_to_pb(’/home/l/tensorrt/models/’, ‘modelFile’)

BUT: when I am issuing:

/usr/local/bin/convert-to-uff -i models/modelFile -o models/modelFile.uff

I get:

Using output node save/restore_all
Converting to UFF graph
Warning: No conversion function registered for layer: NoOp yet.
Converting save/restore_all as custom op: NoOp
Warning: No conversion function registered for layer: Assign yet.
Converting save/Assign_1 as custom op: Assign
Warning: No conversion function registered for layer: RestoreV2 yet.
Converting save/RestoreV2 as custom op: RestoreV2
Traceback (most recent call last):
File “/usr/local/bin/convert-to-uff”, line 11, in
sys.exit(main())
File “/usr/local/lib/python3.5/dist-packages/uff/bin/convert_to_uff.py”, line 89, in main
debug_mode=args.debug
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/conversion_helpers.py”, line 187, in from_tensorflow_frozen_model
return from_tensorflow(graphdef, output_nodes, preprocessor, **kwargs)
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/conversion_helpers.py”, line 157, in from_tensorflow
debug_mode=debug_mode)
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 94, in convert_tf2uff_graph
uff_graph, input_replacements, debug_mode=debug_mode)
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 79, in convert_tf2uff_node
op, name, tf_node, inputs, uff_graph, tf_nodes=tf_nodes, debug_mode=debug_mode)
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 47, in convert_layer
return cls.registry_[op](name, tf_node, inputs, uff_graph, **kwargs)
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter_functions.py”, line 27, in convert_const
array = tf2uff.convert_tf2numpy_const_node(tf_node)
File “/usr/local/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 151, in convert_tf2numpy_const_node
array = np.frombuffer(data, dtype=np_dtype)
ValueError: itemsize cannot be zero in type

This is the most naive sample I can think of.
Any ideas ?

Warning: No conversion function registered for layer: NoOp yet.
Converting save/restore_all as custom op: NoOp
Warning: No conversion function registered for layer: Assign yet.
Converting save/Assign_1 as custom op: Assign
Warning: No conversion function registered for layer: RestoreV2 yet.
Converting save/RestoreV2 as custom op: RestoreV2

these are not supported ops. Please reference:

https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#support_op

Hi,

Well, the Keras code above to generate the model is:

input_layer = Input(shape=(None,None,3),name=‘input’)
x = Conv2D(16, 3, activation=‘linear’, padding=‘same’, strides=(1,1))(input_layer)
return Model(inputs=input_layer,outputs=x)

Following your link for supported actions, these 2 (input, Conv2D) are supported in TF. Isn’t this the case with Keras also ? If so - is this a bug in the convertion from Keras to TF ? (I followed your code example in the documentation …)

Alternatively - is there a sample code of even the simplest Keras model generated in code (just input and any simple layer, say conv) that works without exceptions, showing Keras model transferred to TF and then to TFRT ?

Google has brought me here for “ValueError: itemsize cannot be zero in type”.

I’ve my custom Keras model and I’m getting the exact same type of error:

Converting to UFF graph
Warning: No conversion function registered for layer: NoOp yet.
Converting save/restore_all as custom op: NoOp
Warning: No conversion function registered for layer: Assign yet.
Converting save/Assign_9 as custom op: Assign
Warning: No conversion function registered for layer: RestoreV2 yet.
Converting save/RestoreV2 as custom op: RestoreV2
Traceback (most recent call last):
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/bin/convert-to-uff”, line 10, in
sys.exit(main())
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/bin/convert_to_uff.py”, line 89, in main
debug_mode=args.debug
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/conversion_helpers.py”, line 187, in from_tensorflow_frozen_model
return from_tensorflow(graphdef, output_nodes, preprocessor, **kwargs)
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/conversion_helpers.py”, line 157, in from_tensorflow
debug_mode=debug_mode)
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/converter.py”, line 94, in convert_tf2uff_graph
uff_graph, input_replacements, debug_mode=debug_mode)
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/converter.py”, line 79, in convert_tf2uff_node
op, name, tf_node, inputs, uff_graph, tf_nodes=tf_nodes, debug_mode=debug_mode)
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/converter.py”, line 47, in convert_layer
return cls.registry_[op](name, tf_node, inputs, uff_graph, **kwargs)
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/converter_functions.py”, line 27, in convert_const
array = tf2uff.convert_tf2numpy_const_node(tf_node)
File “/home/istvan/anaconda3/envs/env_py36_tf1_12/lib/python3.6/site-packages/uff/converters/tensorflow/converter.py”, line 151, in convert_tf2numpy_const_node
array = np.frombuffer(data, dtype=np_dtype)
ValueError: itemsize cannot be zero in type

So, this is a more generic error situ and not closely related to dannykario’s components of those.
E.g., my Keras model is an RNN without any convolutional layers…

So, why do we get this error?

I want to accelerate my model using TensorRT.

However, when I try to convert frozon_graph.pb to a .uff file via convert-to-uff,

I get the following error:

Using output node save_1/restore_all
Converting to UFF graph
Warning: No conversion function registered for layer: NoOp yet.
Converting save_1/restore_all as custom op: NoOp
Warning: No conversion function registered for layer: Assign yet.
Converting save_1/Assign_99 as custom op: Assign
Warning: No conversion function registered for layer: RestoreV2 yet.
Converting save_1/RestoreV2 as custom op: RestoreV2
Traceback (most recent call last):
File “/usr/local/bin/convert-to-uff”, line 93, in
main()
File “/usr/local/bin/convert-to-uff”, line 89, in main
debug_mode=args.debug
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/conversion_helpers.py”, line 187, in from_tensorflow_frozen_model
return from_tensorflow(graphdef, output_nodes, preprocessor, **kwargs)
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/conversion_helpers.py”, line 157, in from_tensorflow
debug_mode=debug_mode)
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 94, in convert_tf2uff_graph
uff_graph, input_replacements, debug_mode=debug_mode)
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 79, in convert_tf2uff_node
op, name, tf_node, inputs, uff_graph, tf_nodes=tf_nodes, debug_mode=debug_mode)
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 47, in convert_layer
return cls.registry_[op](name, tf_node, inputs, uff_graph, **kwargs)
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/converter_functions.py”, line 27, in convert_const
array = tf2uff.convert_tf2numpy_const_node(tf_node)
File “/usr/lib/python3.5/dist-packages/uff/converters/tensorflow/converter.py”, line 151, in convert_tf2numpy_const_node
array = np.frombuffer(data, dtype=np_dtype)
ValueError: itemsize cannot be zero in type

Does that mean I have to create a custom layer???