Description
Hello,
I am trying to convert Tensorflow Deeplab Segmentation Model to TensorRT engine.
I use AWS EC2 Ubuntu 18.04 instance. I have installed TensorRT via Nvidia container.
I have trained Deeplab frozengraph model and for conversion I use this script from TensorRT documentation. (Accelerating Inference In TF-TRT User Guide :: NVIDIA Deep Learning Frameworks Documentation)
This is my script:
import tensorflow as tf
from tensorflow.python.compiler.tensorrt import trt_convert as trt
with tf.Session() as sess:
# First deserialize your frozen graph:
with tf.gfile.GFile('frozen_inference_graph.pb', 'rb') as f:
frozen_graph = tf.GraphDef()
frozen_graph.ParseFromString(f.read())
# Now you can create a TensorRT inference graph from your
# frozen graph:
converter = trt.TrtGraphConverter(
input_graph_def=frozen_graph,
nodes_blacklist=['SemanticPredictions:0']) #output nodes
trt_graph = converter.convert()
# Import the TensorRT graph into a new graph and run:
output_node = tf.import_graph_def(
trt_graph,
return_elements=['SemanticPredictions:0'])
sess.run(output_node)
When I run this script, I got following error.
Error is: tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor ‘import/ImageTensor’ with dtype uint8 and shape [1,?,?,3]
[[{{node import/ImageTensor}}]]
How can I fix this issue?
My input tensor size is 1300,1000,3. But I couldnt figure out that how can I add this information to the conversion script?
Any help, suggestion?
This is kind of urgent for me.
Thanks
Environment
TensorRT Version: TensorRT 7.2.1
GPU Type: Tesla K80
Nvidia Driver Version: 450.142.00
CUDA Version: 11.1.0
CUDNN Version:
Operating System + Version: Ubuntu 18.04 Deep Learning AMI
Python Version (if applicable): 3.6.9
TensorFlow Version (if applicable): 1.15.4
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag):
Relevant Files
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