I am trying to convert a TF 1.14.0 saved_model to tensorRT on the Jetson Nano. I have saved my model via tf.saved_model.save
and am trying to convert it on the Nano. However, I get the following error:
Traceback (most recent call last):
File "/usr/local/lib/python3.6/distpackages/tensorflow/python/framework/importer.py", line 427, in import_graph_def
graph._c_graph, serialized, options) # pylint: disable=protectedaccess
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input 1 of node StatefulPartitionedCall was passed float from acoustic_cnn/conv2d_seq_layer/conv3d/kernel:0 incompatible with expected resource.
I have seen this issue discussed on the web, but no solution works for me. I tried:

setting
tf.keras.backend.set_learning_phase(0)
(source) 
Using
is_dynamic_op=True, precision_mode='FP32'
(source)
And still get the error. 
Also, I am using TF Eager so I dont see how I would modify the
graphdef as suggested here
Let me know what else you think I should do?
For reference, below is the code I use for conversion and here is the link to my saved_model
Conversion code
import numpy as np
import tensorflow as tf
from ipdb import set_trace
from tensorflow.python.compiler.tensorrt import trt_convert as trt
INPUT_SAVED_MODEL_DIR = 'tst'
OUTPUT_SAVED_MODEL_DIR = 'tst_out'
tf.enable_eager_execution()
def load_run_savedmodel():
mod = tf.saved_model.load_v2('tst')
inp = tf.convert_to_tensor(np.ones((32, 18, 63, 8)), dtype=tf.float32)
out = mod(inp)
def convert_savedmodel():
tf.keras.backend.set_learning_phase(0)
params = trt.DEFAULT_TRT_CONVERSION_PARAMS._replace(
# precision_mode='FP16',
# is_dynamic_op=True
)
converter = trt.TrtGraphConverter(input_saved_model_dir=INPUT_SAVED_MODEL_DIR,
is_dynamic_op=True,
precision_mode='FP32'
)
converter.convert()
converter.save(OUTPUT_SAVED_MODEL_DIR)
load_infer_savedmodel()
return None
def load_infer_savedmodel():
with tf.Session() as sess:
# First load the SavedModel into the session
tf.saved_model.loader.load(
sess, [tf.saved_model.tag_constants.SERVING], output_saved_model_dir)
set_trace()
output = sess.run([output_tensor], feed_dict={input_tensor: input_data})
if __name__ == '__main__':
convert_savedmodel()
# load_infer_savedmodel()