Converting Openpose's CMU Tensorflow graph to Tensorrt

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

I use this Openpose’s Tensorflow version library https://github.com/ildoonet/tf-pose-estimation for Human pose detection. I like to run the model in Tensort for faster processing speed. CMU netowrk https://github.com/ildoonet/tf-pose-estimation/blob/master/tf_pose/network_cmu.py is quite straightforward and composed of conv, max_pool,and concat. All layers are supported in Tensorrt. The network has image is image input and output is concat_stage7.
When I run convert-to-uff, the output is

NOTE: UFF has been tested with TensorFlow 1.12.0. Other versions are not guaranteed to work
UFF Version 0.6.3
=== Automatically deduced input nodes ===
[name: "image"
op: "Placeholder"
attr {
  key: "dtype"
  value {
    type: DT_FLOAT
  }
}
attr {
  key: "shape"
  value {
    shape {
      dim {
        size: -1
      }
      dim {
        size: -1
      }
      dim {
        size: -1
      }
      dim {
        size: 3
      }
    }
  }
}
]
=========================================

=== Automatically deduced output nodes ===
[name: "Openpose/concat_stage7"
op: "ConcatV2"
input: "Mconv7_stage6_L2/BiasAdd"
input: "Mconv7_stage6_L1/BiasAdd"
input: "Openpose/concat_stage7/axis"
attr {
  key: "N"
  value {
    i: 2
  }
}
attr {
  key: "T"
  value {
    type: DT_FLOAT
  }
}
attr {
  key: "Tidx"
  value {
    type: DT_INT32
  }
}
]
==========================================

Using output node Openpose/concat_stage7
Converting to UFF graph
No. nodes: 463
UFF Output written to cmu/cmu_openpose.uff

Is that ok in conversion?

But when I do inference the speed is same as running Tensorflow model.

Then Tensorrt output is (276000,) dimension.

But Tensorflow model has 4D tensor.
self.tensor_output = self.graph.get_tensor_by_name(‘TfPoseEstimator/Openpose/concat_stage7:0’)
(?, ?, ?, 57)

What could be wrong?

2

The CMU Tensorflow graph file for conversion is here. [url]Dropbox - File Deleted

Can somebody help as I am rushing for my project deadline.
I used Tensorrt 5.1.5 GA. What is the difference between GA and RC?
Thanks

3

Hey, @edit_or were you able to convert the model?

4

Hi, Request you to share the ONNX model and the script so that we can assist you better.

Alongside you can try validating your model with the below snippet

check_model.py

import sys
import onnx
filename = yourONNXmodel
model = onnx.load(filename)
onnx.checker.check_model(model).

Alternatively, you can try running your model with trtexec command.
https://github.com/NVIDIA/TensorRT/tree/master/samples/opensource/trtexec

Thanks!