How to run YoloV3 provided in the samples?

Location of the YoloV3 sample

/usr/src/tensorrt/samples/python/yolov3_onnx

These are the packages inside requirements.txt

numpy>=1.15.1
onnx
pycuda>=2017.1.1
Pillow>=5.2.0
wget>=3.2

I was able to install

numpy>=1.15.1
pycuda>=2017.1.1
Pillow>=5.2.0
wget>=3.2

Trying to install onnx with

pip install onnx==1.4.1

as suggested in https://devtalk.nvidia.com/default/topic/1052153/jetson-nano/tensorrt-backend-for-onnx-on-jetson-nano/

Unable to install onnx event after installing

pip install protobuf

Raised an issue on github https://github.com/onnx/onnx/issues/2109

I can’t find any guide regarding how to solve this issue.

The file

onnx_to_tensorrt.py

uses

import tensorrt as trt

on line 54

But according to https://devtalk.nvidia.com/default/topic/1043135/jetson-tx2/aboat-tensorrt-python-api-on-tx2/#

There is no support for tensorrt python API for jetson platform and a python wrapper is required around tensorrt C++ to use it.

I am new to this, confused can anyone help me out here?

Hi,

May I know the exact error you meet first?
For yolov3_onnx, you can execute it with the steps shared in these two comments:
https://devtalk.nvidia.com/default/topic/1052153/jetson-nano/tensorrt-backend-for-onnx-on-jetson-nano/post/5349632/#5349632
https://devtalk.nvidia.com/default/topic/1052153/jetson-nano/tensorrt-backend-for-onnx-on-jetson-nano/post/5349962/#5349962

Thanks.

I was able to first run

sudo apt-get install protobuf-compiler libprotoc-dev

then

pip install onnx==1.4.1

onnx is installed

This is my python 2.7.15+ virtualenv packages those are installed

DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7.
Package            Version 
------------------ --------
appdirs            1.4.3   
atomicwrites       1.3.0   
attrs              19.1.0  
configparser       3.7.4   
contextlib2        0.5.5   
decorator          4.4.0   
funcsigs           1.0.2   
importlib-metadata 0.18    
Mako               1.0.12  
MarkupSafe         1.1.1   
more-itertools     5.0.0   
numpy              1.16.4  
onnx               1.4.1   
packaging          19.0    
pathlib2           2.3.3   
pip                19.1.1  
pluggy             0.12.0  
protobuf           3.8.0   
py                 1.8.0   
pycuda             2019.1  
pyparsing          2.4.0   
pytest             4.6.3   
pytools            2019.1.1
scandir            1.10.0  
setuptools         41.0.1  
six                1.12.0  
typing             3.6.6   
typing-extensions  3.7.2   
wcwidth            0.1.7   
wget               3.2     
wheel              0.33.4  
zipp               0.5.1

I ran

python yolov3_to_onnx.py

got this output

Layer of type yolo not supported, skipping ONNX node generation.
Layer of type yolo not supported, skipping ONNX node generation.
Layer of type yolo not supported, skipping ONNX node generation.
graph YOLOv3-608 (
  %000_net[FLOAT, 64x3x608x608]
) initializers (
  %001_convolutional_bn_scale[FLOAT, 32]
  %001_convolutional_bn_bias[FLOAT, 32]
  %001_convolutional_bn_mean[FLOAT, 32]
  %001_convolutional_bn_var[FLOAT, 32]
  %001_convolutional_conv_weights[FLOAT, 32x3x3x3]
  %002_convolutional_bn_scale[FLOAT, 64]
  %002_convolutional_bn_bias[FLOAT, 64]
  %002_convolutional_bn_mean[FLOAT, 64]
  %002_convolutional_bn_var[FLOAT, 64]
  %002_convolutional_conv_weights[FLOAT, 64x32x3x3]
  %003_convolutional_bn_scale[FLOAT, 32]
  %003_convolutional_bn_bias[FLOAT, 32]
  %003_convolutional_bn_mean[FLOAT, 32]
  %003_convolutional_bn_var[FLOAT, 32]
  %003_convolutional_conv_weights[FLOAT, 32x64x1x1]
  %004_convolutional_bn_scale[FLOAT, 64]
  %004_convolutional_bn_bias[FLOAT, 64]
  %004_convolutional_bn_mean[FLOAT, 64]
  %004_convolutional_bn_var[FLOAT, 64]
  %004_convolutional_conv_weights[FLOAT, 64x32x3x3]
  %006_convolutional_bn_scale[FLOAT, 128]
  %006_convolutional_bn_bias[FLOAT, 128]
  %006_convolutional_bn_mean[FLOAT, 128]
  %006_convolutional_bn_var[FLOAT, 128]
  %006_convolutional_conv_weights[FLOAT, 128x64x3x3]
  %007_convolutional_bn_scale[FLOAT, 64]
  %007_convolutional_bn_bias[FLOAT, 64]
  %007_convolutional_bn_mean[FLOAT, 64]
  %007_convolutional_bn_var[FLOAT, 64]
  %007_convolutional_conv_weights[FLOAT, 64x128x1x1]
  %008_convolutional_bn_scale[FLOAT, 128]
  %008_convolutional_bn_bias[FLOAT, 128]
  %008_convolutional_bn_mean[FLOAT, 128]
  %008_convolutional_bn_var[FLOAT, 128]
  %008_convolutional_conv_weights[FLOAT, 128x64x3x3]
  %010_convolutional_bn_scale[FLOAT, 64]
  %010_convolutional_bn_bias[FLOAT, 64]
  %010_convolutional_bn_mean[FLOAT, 64]
  %010_convolutional_bn_var[FLOAT, 64]
  %010_convolutional_conv_weights[FLOAT, 64x128x1x1]
  %011_convolutional_bn_scale[FLOAT, 128]
  %011_convolutional_bn_bias[FLOAT, 128]
  %011_convolutional_bn_mean[FLOAT, 128]
  %011_convolutional_bn_var[FLOAT, 128]
  %011_convolutional_conv_weights[FLOAT, 128x64x3x3]
  %013_convolutional_bn_scale[FLOAT, 256]
  %013_convolutional_bn_bias[FLOAT, 256]
  %013_convolutional_bn_mean[FLOAT, 256]
  %013_convolutional_bn_var[FLOAT, 256]
  %013_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %014_convolutional_bn_scale[FLOAT, 128]
  %014_convolutional_bn_bias[FLOAT, 128]
  %014_convolutional_bn_mean[FLOAT, 128]
  %014_convolutional_bn_var[FLOAT, 128]
  %014_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %015_convolutional_bn_scale[FLOAT, 256]
  %015_convolutional_bn_bias[FLOAT, 256]
  %015_convolutional_bn_mean[FLOAT, 256]
  %015_convolutional_bn_var[FLOAT, 256]
  %015_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %017_convolutional_bn_scale[FLOAT, 128]
  %017_convolutional_bn_bias[FLOAT, 128]
  %017_convolutional_bn_mean[FLOAT, 128]
  %017_convolutional_bn_var[FLOAT, 128]
  %017_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %018_convolutional_bn_scale[FLOAT, 256]
  %018_convolutional_bn_bias[FLOAT, 256]
  %018_convolutional_bn_mean[FLOAT, 256]
  %018_convolutional_bn_var[FLOAT, 256]
  %018_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %020_convolutional_bn_scale[FLOAT, 128]
  %020_convolutional_bn_bias[FLOAT, 128]
  %020_convolutional_bn_mean[FLOAT, 128]
  %020_convolutional_bn_var[FLOAT, 128]
  %020_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %021_convolutional_bn_scale[FLOAT, 256]
  %021_convolutional_bn_bias[FLOAT, 256]
  %021_convolutional_bn_mean[FLOAT, 256]
  %021_convolutional_bn_var[FLOAT, 256]
  %021_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %023_convolutional_bn_scale[FLOAT, 128]
  %023_convolutional_bn_bias[FLOAT, 128]
  %023_convolutional_bn_mean[FLOAT, 128]
  %023_convolutional_bn_var[FLOAT, 128]
  %023_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %024_convolutional_bn_scale[FLOAT, 256]
  %024_convolutional_bn_bias[FLOAT, 256]
  %024_convolutional_bn_mean[FLOAT, 256]
  %024_convolutional_bn_var[FLOAT, 256]
  %024_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %026_convolutional_bn_scale[FLOAT, 128]
  %026_convolutional_bn_bias[FLOAT, 128]
  %026_convolutional_bn_mean[FLOAT, 128]
  %026_convolutional_bn_var[FLOAT, 128]
  %026_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %027_convolutional_bn_scale[FLOAT, 256]
  %027_convolutional_bn_bias[FLOAT, 256]
  %027_convolutional_bn_mean[FLOAT, 256]
  %027_convolutional_bn_var[FLOAT, 256]
  %027_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %029_convolutional_bn_scale[FLOAT, 128]
  %029_convolutional_bn_bias[FLOAT, 128]
  %029_convolutional_bn_mean[FLOAT, 128]
  %029_convolutional_bn_var[FLOAT, 128]
  %029_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %030_convolutional_bn_scale[FLOAT, 256]
  %030_convolutional_bn_bias[FLOAT, 256]
  %030_convolutional_bn_mean[FLOAT, 256]
  %030_convolutional_bn_var[FLOAT, 256]
  %030_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %032_convolutional_bn_scale[FLOAT, 128]
  %032_convolutional_bn_bias[FLOAT, 128]
  %032_convolutional_bn_mean[FLOAT, 128]
  %032_convolutional_bn_var[FLOAT, 128]
  %032_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %033_convolutional_bn_scale[FLOAT, 256]
  %033_convolutional_bn_bias[FLOAT, 256]
  %033_convolutional_bn_mean[FLOAT, 256]
  %033_convolutional_bn_var[FLOAT, 256]
  %033_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %035_convolutional_bn_scale[FLOAT, 128]
  %035_convolutional_bn_bias[FLOAT, 128]
  %035_convolutional_bn_mean[FLOAT, 128]
  %035_convolutional_bn_var[FLOAT, 128]
  %035_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %036_convolutional_bn_scale[FLOAT, 256]
  %036_convolutional_bn_bias[FLOAT, 256]
  %036_convolutional_bn_mean[FLOAT, 256]
  %036_convolutional_bn_var[FLOAT, 256]
  %036_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %038_convolutional_bn_scale[FLOAT, 512]
  %038_convolutional_bn_bias[FLOAT, 512]
  %038_convolutional_bn_mean[FLOAT, 512]
  %038_convolutional_bn_var[FLOAT, 512]
  %038_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %039_convolutional_bn_scale[FLOAT, 256]
  %039_convolutional_bn_bias[FLOAT, 256]
  %039_convolutional_bn_mean[FLOAT, 256]
  %039_convolutional_bn_var[FLOAT, 256]
  %039_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %040_convolutional_bn_scale[FLOAT, 512]
  %040_convolutional_bn_bias[FLOAT, 512]
  %040_convolutional_bn_mean[FLOAT, 512]
  %040_convolutional_bn_var[FLOAT, 512]
  %040_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %042_convolutional_bn_scale[FLOAT, 256]
  %042_convolutional_bn_bias[FLOAT, 256]
  %042_convolutional_bn_mean[FLOAT, 256]
  %042_convolutional_bn_var[FLOAT, 256]
  %042_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %043_convolutional_bn_scale[FLOAT, 512]
  %043_convolutional_bn_bias[FLOAT, 512]
  %043_convolutional_bn_mean[FLOAT, 512]
  %043_convolutional_bn_var[FLOAT, 512]
  %043_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %045_convolutional_bn_scale[FLOAT, 256]
  %045_convolutional_bn_bias[FLOAT, 256]
  %045_convolutional_bn_mean[FLOAT, 256]
  %045_convolutional_bn_var[FLOAT, 256]
  %045_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %046_convolutional_bn_scale[FLOAT, 512]
  %046_convolutional_bn_bias[FLOAT, 512]
  %046_convolutional_bn_mean[FLOAT, 512]
  %046_convolutional_bn_var[FLOAT, 512]
  %046_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %048_convolutional_bn_scale[FLOAT, 256]
  %048_convolutional_bn_bias[FLOAT, 256]
  %048_convolutional_bn_mean[FLOAT, 256]
  %048_convolutional_bn_var[FLOAT, 256]
  %048_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %049_convolutional_bn_scale[FLOAT, 512]
  %049_convolutional_bn_bias[FLOAT, 512]
  %049_convolutional_bn_mean[FLOAT, 512]
  %049_convolutional_bn_var[FLOAT, 512]
  %049_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %051_convolutional_bn_scale[FLOAT, 256]
  %051_convolutional_bn_bias[FLOAT, 256]
  %051_convolutional_bn_mean[FLOAT, 256]
  %051_convolutional_bn_var[FLOAT, 256]
  %051_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %052_convolutional_bn_scale[FLOAT, 512]
  %052_convolutional_bn_bias[FLOAT, 512]
  %052_convolutional_bn_mean[FLOAT, 512]
  %052_convolutional_bn_var[FLOAT, 512]
  %052_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %054_convolutional_bn_scale[FLOAT, 256]
  %054_convolutional_bn_bias[FLOAT, 256]
  %054_convolutional_bn_mean[FLOAT, 256]
  %054_convolutional_bn_var[FLOAT, 256]
  %054_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %055_convolutional_bn_scale[FLOAT, 512]
  %055_convolutional_bn_bias[FLOAT, 512]
  %055_convolutional_bn_mean[FLOAT, 512]
  %055_convolutional_bn_var[FLOAT, 512]
  %055_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %057_convolutional_bn_scale[FLOAT, 256]
  %057_convolutional_bn_bias[FLOAT, 256]
  %057_convolutional_bn_mean[FLOAT, 256]
  %057_convolutional_bn_var[FLOAT, 256]
  %057_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %058_convolutional_bn_scale[FLOAT, 512]
  %058_convolutional_bn_bias[FLOAT, 512]
  %058_convolutional_bn_mean[FLOAT, 512]
  %058_convolutional_bn_var[FLOAT, 512]
  %058_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %060_convolutional_bn_scale[FLOAT, 256]
  %060_convolutional_bn_bias[FLOAT, 256]
  %060_convolutional_bn_mean[FLOAT, 256]
  %060_convolutional_bn_var[FLOAT, 256]
  %060_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %061_convolutional_bn_scale[FLOAT, 512]
  %061_convolutional_bn_bias[FLOAT, 512]
  %061_convolutional_bn_mean[FLOAT, 512]
  %061_convolutional_bn_var[FLOAT, 512]
  %061_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %063_convolutional_bn_scale[FLOAT, 1024]
  %063_convolutional_bn_bias[FLOAT, 1024]
  %063_convolutional_bn_mean[FLOAT, 1024]
  %063_convolutional_bn_var[FLOAT, 1024]
  %063_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %064_convolutional_bn_scale[FLOAT, 512]
  %064_convolutional_bn_bias[FLOAT, 512]
  %064_convolutional_bn_mean[FLOAT, 512]
  %064_convolutional_bn_var[FLOAT, 512]
  %064_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %065_convolutional_bn_scale[FLOAT, 1024]
  %065_convolutional_bn_bias[FLOAT, 1024]
  %065_convolutional_bn_mean[FLOAT, 1024]
  %065_convolutional_bn_var[FLOAT, 1024]
  %065_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %067_convolutional_bn_scale[FLOAT, 512]
  %067_convolutional_bn_bias[FLOAT, 512]
  %067_convolutional_bn_mean[FLOAT, 512]
  %067_convolutional_bn_var[FLOAT, 512]
  %067_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %068_convolutional_bn_scale[FLOAT, 1024]
  %068_convolutional_bn_bias[FLOAT, 1024]
  %068_convolutional_bn_mean[FLOAT, 1024]
  %068_convolutional_bn_var[FLOAT, 1024]
  %068_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %070_convolutional_bn_scale[FLOAT, 512]
  %070_convolutional_bn_bias[FLOAT, 512]
  %070_convolutional_bn_mean[FLOAT, 512]
  %070_convolutional_bn_var[FLOAT, 512]
  %070_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %071_convolutional_bn_scale[FLOAT, 1024]
  %071_convolutional_bn_bias[FLOAT, 1024]
  %071_convolutional_bn_mean[FLOAT, 1024]
  %071_convolutional_bn_var[FLOAT, 1024]
  %071_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %073_convolutional_bn_scale[FLOAT, 512]
  %073_convolutional_bn_bias[FLOAT, 512]
  %073_convolutional_bn_mean[FLOAT, 512]
  %073_convolutional_bn_var[FLOAT, 512]
  %073_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %074_convolutional_bn_scale[FLOAT, 1024]
  %074_convolutional_bn_bias[FLOAT, 1024]
  %074_convolutional_bn_mean[FLOAT, 1024]
  %074_convolutional_bn_var[FLOAT, 1024]
  %074_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %076_convolutional_bn_scale[FLOAT, 512]
  %076_convolutional_bn_bias[FLOAT, 512]
  %076_convolutional_bn_mean[FLOAT, 512]
  %076_convolutional_bn_var[FLOAT, 512]
  %076_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %077_convolutional_bn_scale[FLOAT, 1024]
  %077_convolutional_bn_bias[FLOAT, 1024]
  %077_convolutional_bn_mean[FLOAT, 1024]
  %077_convolutional_bn_var[FLOAT, 1024]
  %077_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %078_convolutional_bn_scale[FLOAT, 512]
  %078_convolutional_bn_bias[FLOAT, 512]
  %078_convolutional_bn_mean[FLOAT, 512]
  %078_convolutional_bn_var[FLOAT, 512]
  %078_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %079_convolutional_bn_scale[FLOAT, 1024]
  %079_convolutional_bn_bias[FLOAT, 1024]
  %079_convolutional_bn_mean[FLOAT, 1024]
  %079_convolutional_bn_var[FLOAT, 1024]
  %079_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %080_convolutional_bn_scale[FLOAT, 512]
  %080_convolutional_bn_bias[FLOAT, 512]
  %080_convolutional_bn_mean[FLOAT, 512]
  %080_convolutional_bn_var[FLOAT, 512]
  %080_convolutional_conv_weights[FLOAT, 512x1024x1x1]
  %081_convolutional_bn_scale[FLOAT, 1024]
  %081_convolutional_bn_bias[FLOAT, 1024]
  %081_convolutional_bn_mean[FLOAT, 1024]
  %081_convolutional_bn_var[FLOAT, 1024]
  %081_convolutional_conv_weights[FLOAT, 1024x512x3x3]
  %082_convolutional_conv_bias[FLOAT, 255]
  %082_convolutional_conv_weights[FLOAT, 255x1024x1x1]
  %085_convolutional_bn_scale[FLOAT, 256]
  %085_convolutional_bn_bias[FLOAT, 256]
  %085_convolutional_bn_mean[FLOAT, 256]
  %085_convolutional_bn_var[FLOAT, 256]
  %085_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %086_upsample_scale[FLOAT, 4]
  %088_convolutional_bn_scale[FLOAT, 256]
  %088_convolutional_bn_bias[FLOAT, 256]
  %088_convolutional_bn_mean[FLOAT, 256]
  %088_convolutional_bn_var[FLOAT, 256]
  %088_convolutional_conv_weights[FLOAT, 256x768x1x1]
  %089_convolutional_bn_scale[FLOAT, 512]
  %089_convolutional_bn_bias[FLOAT, 512]
  %089_convolutional_bn_mean[FLOAT, 512]
  %089_convolutional_bn_var[FLOAT, 512]
  %089_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %090_convolutional_bn_scale[FLOAT, 256]
  %090_convolutional_bn_bias[FLOAT, 256]
  %090_convolutional_bn_mean[FLOAT, 256]
  %090_convolutional_bn_var[FLOAT, 256]
  %090_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %091_convolutional_bn_scale[FLOAT, 512]
  %091_convolutional_bn_bias[FLOAT, 512]
  %091_convolutional_bn_mean[FLOAT, 512]
  %091_convolutional_bn_var[FLOAT, 512]
  %091_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %092_convolutional_bn_scale[FLOAT, 256]
  %092_convolutional_bn_bias[FLOAT, 256]
  %092_convolutional_bn_mean[FLOAT, 256]
  %092_convolutional_bn_var[FLOAT, 256]
  %092_convolutional_conv_weights[FLOAT, 256x512x1x1]
  %093_convolutional_bn_scale[FLOAT, 512]
  %093_convolutional_bn_bias[FLOAT, 512]
  %093_convolutional_bn_mean[FLOAT, 512]
  %093_convolutional_bn_var[FLOAT, 512]
  %093_convolutional_conv_weights[FLOAT, 512x256x3x3]
  %094_convolutional_conv_bias[FLOAT, 255]
  %094_convolutional_conv_weights[FLOAT, 255x512x1x1]
  %097_convolutional_bn_scale[FLOAT, 128]
  %097_convolutional_bn_bias[FLOAT, 128]
  %097_convolutional_bn_mean[FLOAT, 128]
  %097_convolutional_bn_var[FLOAT, 128]
  %097_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %098_upsample_scale[FLOAT, 4]
  %100_convolutional_bn_scale[FLOAT, 128]
  %100_convolutional_bn_bias[FLOAT, 128]
  %100_convolutional_bn_mean[FLOAT, 128]
  %100_convolutional_bn_var[FLOAT, 128]
  %100_convolutional_conv_weights[FLOAT, 128x384x1x1]
  %101_convolutional_bn_scale[FLOAT, 256]
  %101_convolutional_bn_bias[FLOAT, 256]
  %101_convolutional_bn_mean[FLOAT, 256]
  %101_convolutional_bn_var[FLOAT, 256]
  %101_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %102_convolutional_bn_scale[FLOAT, 128]
  %102_convolutional_bn_bias[FLOAT, 128]
  %102_convolutional_bn_mean[FLOAT, 128]
  %102_convolutional_bn_var[FLOAT, 128]
  %102_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %103_convolutional_bn_scale[FLOAT, 256]
  %103_convolutional_bn_bias[FLOAT, 256]
  %103_convolutional_bn_mean[FLOAT, 256]
  %103_convolutional_bn_var[FLOAT, 256]
  %103_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %104_convolutional_bn_scale[FLOAT, 128]
  %104_convolutional_bn_bias[FLOAT, 128]
  %104_convolutional_bn_mean[FLOAT, 128]
  %104_convolutional_bn_var[FLOAT, 128]
  %104_convolutional_conv_weights[FLOAT, 128x256x1x1]
  %105_convolutional_bn_scale[FLOAT, 256]
  %105_convolutional_bn_bias[FLOAT, 256]
  %105_convolutional_bn_mean[FLOAT, 256]
  %105_convolutional_bn_var[FLOAT, 256]
  %105_convolutional_conv_weights[FLOAT, 256x128x3x3]
  %106_convolutional_conv_bias[FLOAT, 255]
  %106_convolutional_conv_weights[FLOAT, 255x256x1x1]
) {
  %001_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%000_net, %001_convolutional_conv_weights)
  %001_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%001_convolutional, %001_convolutional_bn_scale, %001_convolutional_bn_bias, %001_convolutional_bn_mean, %001_convolutional_bn_var)
  %001_convolutional_lrelu = LeakyRelu[alpha = 0.1](%001_convolutional_bn)
  %002_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%001_convolutional_lrelu, %002_convolutional_conv_weights)
  %002_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%002_convolutional, %002_convolutional_bn_scale, %002_convolutional_bn_bias, %002_convolutional_bn_mean, %002_convolutional_bn_var)
  %002_convolutional_lrelu = LeakyRelu[alpha = 0.1](%002_convolutional_bn)
  %003_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%002_convolutional_lrelu, %003_convolutional_conv_weights)
  %003_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%003_convolutional, %003_convolutional_bn_scale, %003_convolutional_bn_bias, %003_convolutional_bn_mean, %003_convolutional_bn_var)
  %003_convolutional_lrelu = LeakyRelu[alpha = 0.1](%003_convolutional_bn)
  %004_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%003_convolutional_lrelu, %004_convolutional_conv_weights)
  %004_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%004_convolutional, %004_convolutional_bn_scale, %004_convolutional_bn_bias, %004_convolutional_bn_mean, %004_convolutional_bn_var)
  %004_convolutional_lrelu = LeakyRelu[alpha = 0.1](%004_convolutional_bn)
  %005_shortcut = Add(%004_convolutional_lrelu, %002_convolutional_lrelu)
  %006_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%005_shortcut, %006_convolutional_conv_weights)
  %006_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%006_convolutional, %006_convolutional_bn_scale, %006_convolutional_bn_bias, %006_convolutional_bn_mean, %006_convolutional_bn_var)
  %006_convolutional_lrelu = LeakyRelu[alpha = 0.1](%006_convolutional_bn)
  %007_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%006_convolutional_lrelu, %007_convolutional_conv_weights)
  %007_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%007_convolutional, %007_convolutional_bn_scale, %007_convolutional_bn_bias, %007_convolutional_bn_mean, %007_convolutional_bn_var)
  %007_convolutional_lrelu = LeakyRelu[alpha = 0.1](%007_convolutional_bn)
  %008_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%007_convolutional_lrelu, %008_convolutional_conv_weights)
  %008_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%008_convolutional, %008_convolutional_bn_scale, %008_convolutional_bn_bias, %008_convolutional_bn_mean, %008_convolutional_bn_var)
  %008_convolutional_lrelu = LeakyRelu[alpha = 0.1](%008_convolutional_bn)
  %009_shortcut = Add(%008_convolutional_lrelu, %006_convolutional_lrelu)
  %010_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%009_shortcut, %010_convolutional_conv_weights)
  %010_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%010_convolutional, %010_convolutional_bn_scale, %010_convolutional_bn_bias, %010_convolutional_bn_mean, %010_convolutional_bn_var)
  %010_convolutional_lrelu = LeakyRelu[alpha = 0.1](%010_convolutional_bn)
  %011_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%010_convolutional_lrelu, %011_convolutional_conv_weights)
  %011_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%011_convolutional, %011_convolutional_bn_scale, %011_convolutional_bn_bias, %011_convolutional_bn_mean, %011_convolutional_bn_var)
  %011_convolutional_lrelu = LeakyRelu[alpha = 0.1](%011_convolutional_bn)
  %012_shortcut = Add(%011_convolutional_lrelu, %009_shortcut)
  %013_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%012_shortcut, %013_convolutional_conv_weights)
  %013_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%013_convolutional, %013_convolutional_bn_scale, %013_convolutional_bn_bias, %013_convolutional_bn_mean, %013_convolutional_bn_var)
  %013_convolutional_lrelu = LeakyRelu[alpha = 0.1](%013_convolutional_bn)
  %014_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%013_convolutional_lrelu, %014_convolutional_conv_weights)
  %014_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%014_convolutional, %014_convolutional_bn_scale, %014_convolutional_bn_bias, %014_convolutional_bn_mean, %014_convolutional_bn_var)
  %014_convolutional_lrelu = LeakyRelu[alpha = 0.1](%014_convolutional_bn)
  %015_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%014_convolutional_lrelu, %015_convolutional_conv_weights)
  %015_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%015_convolutional, %015_convolutional_bn_scale, %015_convolutional_bn_bias, %015_convolutional_bn_mean, %015_convolutional_bn_var)
  %015_convolutional_lrelu = LeakyRelu[alpha = 0.1](%015_convolutional_bn)
  %016_shortcut = Add(%015_convolutional_lrelu, %013_convolutional_lrelu)
  %017_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%016_shortcut, %017_convolutional_conv_weights)
  %017_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%017_convolutional, %017_convolutional_bn_scale, %017_convolutional_bn_bias, %017_convolutional_bn_mean, %017_convolutional_bn_var)
  %017_convolutional_lrelu = LeakyRelu[alpha = 0.1](%017_convolutional_bn)
  %018_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%017_convolutional_lrelu, %018_convolutional_conv_weights)
  %018_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%018_convolutional, %018_convolutional_bn_scale, %018_convolutional_bn_bias, %018_convolutional_bn_mean, %018_convolutional_bn_var)
  %018_convolutional_lrelu = LeakyRelu[alpha = 0.1](%018_convolutional_bn)
  %019_shortcut = Add(%018_convolutional_lrelu, %016_shortcut)
  %020_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%019_shortcut, %020_convolutional_conv_weights)
  %020_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%020_convolutional, %020_convolutional_bn_scale, %020_convolutional_bn_bias, %020_convolutional_bn_mean, %020_convolutional_bn_var)
  %020_convolutional_lrelu = LeakyRelu[alpha = 0.1](%020_convolutional_bn)
  %021_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%020_convolutional_lrelu, %021_convolutional_conv_weights)
  %021_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%021_convolutional, %021_convolutional_bn_scale, %021_convolutional_bn_bias, %021_convolutional_bn_mean, %021_convolutional_bn_var)
  %021_convolutional_lrelu = LeakyRelu[alpha = 0.1](%021_convolutional_bn)
  %022_shortcut = Add(%021_convolutional_lrelu, %019_shortcut)
  %023_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%022_shortcut, %023_convolutional_conv_weights)
  %023_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%023_convolutional, %023_convolutional_bn_scale, %023_convolutional_bn_bias, %023_convolutional_bn_mean, %023_convolutional_bn_var)
  %023_convolutional_lrelu = LeakyRelu[alpha = 0.1](%023_convolutional_bn)
  %024_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%023_convolutional_lrelu, %024_convolutional_conv_weights)
  %024_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%024_convolutional, %024_convolutional_bn_scale, %024_convolutional_bn_bias, %024_convolutional_bn_mean, %024_convolutional_bn_var)
  %024_convolutional_lrelu = LeakyRelu[alpha = 0.1](%024_convolutional_bn)
  %025_shortcut = Add(%024_convolutional_lrelu, %022_shortcut)
  %026_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%025_shortcut, %026_convolutional_conv_weights)
  %026_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%026_convolutional, %026_convolutional_bn_scale, %026_convolutional_bn_bias, %026_convolutional_bn_mean, %026_convolutional_bn_var)
  %026_convolutional_lrelu = LeakyRelu[alpha = 0.1](%026_convolutional_bn)
  %027_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%026_convolutional_lrelu, %027_convolutional_conv_weights)
  %027_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%027_convolutional, %027_convolutional_bn_scale, %027_convolutional_bn_bias, %027_convolutional_bn_mean, %027_convolutional_bn_var)
  %027_convolutional_lrelu = LeakyRelu[alpha = 0.1](%027_convolutional_bn)
  %028_shortcut = Add(%027_convolutional_lrelu, %025_shortcut)
  %029_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%028_shortcut, %029_convolutional_conv_weights)
  %029_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%029_convolutional, %029_convolutional_bn_scale, %029_convolutional_bn_bias, %029_convolutional_bn_mean, %029_convolutional_bn_var)
  %029_convolutional_lrelu = LeakyRelu[alpha = 0.1](%029_convolutional_bn)
  %030_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%029_convolutional_lrelu, %030_convolutional_conv_weights)
  %030_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%030_convolutional, %030_convolutional_bn_scale, %030_convolutional_bn_bias, %030_convolutional_bn_mean, %030_convolutional_bn_var)
  %030_convolutional_lrelu = LeakyRelu[alpha = 0.1](%030_convolutional_bn)
  %031_shortcut = Add(%030_convolutional_lrelu, %028_shortcut)
  %032_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%031_shortcut, %032_convolutional_conv_weights)
  %032_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%032_convolutional, %032_convolutional_bn_scale, %032_convolutional_bn_bias, %032_convolutional_bn_mean, %032_convolutional_bn_var)
  %032_convolutional_lrelu = LeakyRelu[alpha = 0.1](%032_convolutional_bn)
  %033_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%032_convolutional_lrelu, %033_convolutional_conv_weights)
  %033_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%033_convolutional, %033_convolutional_bn_scale, %033_convolutional_bn_bias, %033_convolutional_bn_mean, %033_convolutional_bn_var)
  %033_convolutional_lrelu = LeakyRelu[alpha = 0.1](%033_convolutional_bn)
  %034_shortcut = Add(%033_convolutional_lrelu, %031_shortcut)
  %035_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%034_shortcut, %035_convolutional_conv_weights)
  %035_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%035_convolutional, %035_convolutional_bn_scale, %035_convolutional_bn_bias, %035_convolutional_bn_mean, %035_convolutional_bn_var)
  %035_convolutional_lrelu = LeakyRelu[alpha = 0.1](%035_convolutional_bn)
  %036_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%035_convolutional_lrelu, %036_convolutional_conv_weights)
  %036_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%036_convolutional, %036_convolutional_bn_scale, %036_convolutional_bn_bias, %036_convolutional_bn_mean, %036_convolutional_bn_var)
  %036_convolutional_lrelu = LeakyRelu[alpha = 0.1](%036_convolutional_bn)
  %037_shortcut = Add(%036_convolutional_lrelu, %034_shortcut)
  %038_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%037_shortcut, %038_convolutional_conv_weights)
  %038_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%038_convolutional, %038_convolutional_bn_scale, %038_convolutional_bn_bias, %038_convolutional_bn_mean, %038_convolutional_bn_var)
  %038_convolutional_lrelu = LeakyRelu[alpha = 0.1](%038_convolutional_bn)
  %039_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%038_convolutional_lrelu, %039_convolutional_conv_weights)
  %039_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%039_convolutional, %039_convolutional_bn_scale, %039_convolutional_bn_bias, %039_convolutional_bn_mean, %039_convolutional_bn_var)
  %039_convolutional_lrelu = LeakyRelu[alpha = 0.1](%039_convolutional_bn)
  %040_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%039_convolutional_lrelu, %040_convolutional_conv_weights)
  %040_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%040_convolutional, %040_convolutional_bn_scale, %040_convolutional_bn_bias, %040_convolutional_bn_mean, %040_convolutional_bn_var)
  %040_convolutional_lrelu = LeakyRelu[alpha = 0.1](%040_convolutional_bn)
  %041_shortcut = Add(%040_convolutional_lrelu, %038_convolutional_lrelu)
  %042_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%041_shortcut, %042_convolutional_conv_weights)
  %042_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%042_convolutional, %042_convolutional_bn_scale, %042_convolutional_bn_bias, %042_convolutional_bn_mean, %042_convolutional_bn_var)
  %042_convolutional_lrelu = LeakyRelu[alpha = 0.1](%042_convolutional_bn)
  %043_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%042_convolutional_lrelu, %043_convolutional_conv_weights)
  %043_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%043_convolutional, %043_convolutional_bn_scale, %043_convolutional_bn_bias, %043_convolutional_bn_mean, %043_convolutional_bn_var)
  %043_convolutional_lrelu = LeakyRelu[alpha = 0.1](%043_convolutional_bn)
  %044_shortcut = Add(%043_convolutional_lrelu, %041_shortcut)
  %045_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%044_shortcut, %045_convolutional_conv_weights)
  %045_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%045_convolutional, %045_convolutional_bn_scale, %045_convolutional_bn_bias, %045_convolutional_bn_mean, %045_convolutional_bn_var)
  %045_convolutional_lrelu = LeakyRelu[alpha = 0.1](%045_convolutional_bn)
  %046_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%045_convolutional_lrelu, %046_convolutional_conv_weights)
  %046_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%046_convolutional, %046_convolutional_bn_scale, %046_convolutional_bn_bias, %046_convolutional_bn_mean, %046_convolutional_bn_var)
  %046_convolutional_lrelu = LeakyRelu[alpha = 0.1](%046_convolutional_bn)
  %047_shortcut = Add(%046_convolutional_lrelu, %044_shortcut)
  %048_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%047_shortcut, %048_convolutional_conv_weights)
  %048_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%048_convolutional, %048_convolutional_bn_scale, %048_convolutional_bn_bias, %048_convolutional_bn_mean, %048_convolutional_bn_var)
  %048_convolutional_lrelu = LeakyRelu[alpha = 0.1](%048_convolutional_bn)
  %049_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%048_convolutional_lrelu, %049_convolutional_conv_weights)
  %049_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%049_convolutional, %049_convolutional_bn_scale, %049_convolutional_bn_bias, %049_convolutional_bn_mean, %049_convolutional_bn_var)
  %049_convolutional_lrelu = LeakyRelu[alpha = 0.1](%049_convolutional_bn)
  %050_shortcut = Add(%049_convolutional_lrelu, %047_shortcut)
  %051_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%050_shortcut, %051_convolutional_conv_weights)
  %051_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%051_convolutional, %051_convolutional_bn_scale, %051_convolutional_bn_bias, %051_convolutional_bn_mean, %051_convolutional_bn_var)
  %051_convolutional_lrelu = LeakyRelu[alpha = 0.1](%051_convolutional_bn)
  %052_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%051_convolutional_lrelu, %052_convolutional_conv_weights)
  %052_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%052_convolutional, %052_convolutional_bn_scale, %052_convolutional_bn_bias, %052_convolutional_bn_mean, %052_convolutional_bn_var)
  %052_convolutional_lrelu = LeakyRelu[alpha = 0.1](%052_convolutional_bn)
  %053_shortcut = Add(%052_convolutional_lrelu, %050_shortcut)
  %054_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%053_shortcut, %054_convolutional_conv_weights)
  %054_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%054_convolutional, %054_convolutional_bn_scale, %054_convolutional_bn_bias, %054_convolutional_bn_mean, %054_convolutional_bn_var)
  %054_convolutional_lrelu = LeakyRelu[alpha = 0.1](%054_convolutional_bn)
  %055_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%054_convolutional_lrelu, %055_convolutional_conv_weights)
  %055_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%055_convolutional, %055_convolutional_bn_scale, %055_convolutional_bn_bias, %055_convolutional_bn_mean, %055_convolutional_bn_var)
  %055_convolutional_lrelu = LeakyRelu[alpha = 0.1](%055_convolutional_bn)
  %056_shortcut = Add(%055_convolutional_lrelu, %053_shortcut)
  %057_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%056_shortcut, %057_convolutional_conv_weights)
  %057_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%057_convolutional, %057_convolutional_bn_scale, %057_convolutional_bn_bias, %057_convolutional_bn_mean, %057_convolutional_bn_var)
  %057_convolutional_lrelu = LeakyRelu[alpha = 0.1](%057_convolutional_bn)
  %058_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%057_convolutional_lrelu, %058_convolutional_conv_weights)
  %058_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%058_convolutional, %058_convolutional_bn_scale, %058_convolutional_bn_bias, %058_convolutional_bn_mean, %058_convolutional_bn_var)
  %058_convolutional_lrelu = LeakyRelu[alpha = 0.1](%058_convolutional_bn)
  %059_shortcut = Add(%058_convolutional_lrelu, %056_shortcut)
  %060_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%059_shortcut, %060_convolutional_conv_weights)
  %060_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%060_convolutional, %060_convolutional_bn_scale, %060_convolutional_bn_bias, %060_convolutional_bn_mean, %060_convolutional_bn_var)
  %060_convolutional_lrelu = LeakyRelu[alpha = 0.1](%060_convolutional_bn)
  %061_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%060_convolutional_lrelu, %061_convolutional_conv_weights)
  %061_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%061_convolutional, %061_convolutional_bn_scale, %061_convolutional_bn_bias, %061_convolutional_bn_mean, %061_convolutional_bn_var)
  %061_convolutional_lrelu = LeakyRelu[alpha = 0.1](%061_convolutional_bn)
  %062_shortcut = Add(%061_convolutional_lrelu, %059_shortcut)
  %063_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%062_shortcut, %063_convolutional_conv_weights)
  %063_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%063_convolutional, %063_convolutional_bn_scale, %063_convolutional_bn_bias, %063_convolutional_bn_mean, %063_convolutional_bn_var)
  %063_convolutional_lrelu = LeakyRelu[alpha = 0.1](%063_convolutional_bn)
  %064_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%063_convolutional_lrelu, %064_convolutional_conv_weights)
  %064_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%064_convolutional, %064_convolutional_bn_scale, %064_convolutional_bn_bias, %064_convolutional_bn_mean, %064_convolutional_bn_var)
  %064_convolutional_lrelu = LeakyRelu[alpha = 0.1](%064_convolutional_bn)
  %065_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%064_convolutional_lrelu, %065_convolutional_conv_weights)
  %065_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%065_convolutional, %065_convolutional_bn_scale, %065_convolutional_bn_bias, %065_convolutional_bn_mean, %065_convolutional_bn_var)
  %065_convolutional_lrelu = LeakyRelu[alpha = 0.1](%065_convolutional_bn)
  %066_shortcut = Add(%065_convolutional_lrelu, %063_convolutional_lrelu)
  %067_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%066_shortcut, %067_convolutional_conv_weights)
  %067_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%067_convolutional, %067_convolutional_bn_scale, %067_convolutional_bn_bias, %067_convolutional_bn_mean, %067_convolutional_bn_var)
  %067_convolutional_lrelu = LeakyRelu[alpha = 0.1](%067_convolutional_bn)
  %068_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%067_convolutional_lrelu, %068_convolutional_conv_weights)
  %068_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%068_convolutional, %068_convolutional_bn_scale, %068_convolutional_bn_bias, %068_convolutional_bn_mean, %068_convolutional_bn_var)
  %068_convolutional_lrelu = LeakyRelu[alpha = 0.1](%068_convolutional_bn)
  %069_shortcut = Add(%068_convolutional_lrelu, %066_shortcut)
  %070_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%069_shortcut, %070_convolutional_conv_weights)
  %070_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%070_convolutional, %070_convolutional_bn_scale, %070_convolutional_bn_bias, %070_convolutional_bn_mean, %070_convolutional_bn_var)
  %070_convolutional_lrelu = LeakyRelu[alpha = 0.1](%070_convolutional_bn)
  %071_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%070_convolutional_lrelu, %071_convolutional_conv_weights)
  %071_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%071_convolutional, %071_convolutional_bn_scale, %071_convolutional_bn_bias, %071_convolutional_bn_mean, %071_convolutional_bn_var)
  %071_convolutional_lrelu = LeakyRelu[alpha = 0.1](%071_convolutional_bn)
  %072_shortcut = Add(%071_convolutional_lrelu, %069_shortcut)
  %073_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%072_shortcut, %073_convolutional_conv_weights)
  %073_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%073_convolutional, %073_convolutional_bn_scale, %073_convolutional_bn_bias, %073_convolutional_bn_mean, %073_convolutional_bn_var)
  %073_convolutional_lrelu = LeakyRelu[alpha = 0.1](%073_convolutional_bn)
  %074_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%073_convolutional_lrelu, %074_convolutional_conv_weights)
  %074_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%074_convolutional, %074_convolutional_bn_scale, %074_convolutional_bn_bias, %074_convolutional_bn_mean, %074_convolutional_bn_var)
  %074_convolutional_lrelu = LeakyRelu[alpha = 0.1](%074_convolutional_bn)
  %075_shortcut = Add(%074_convolutional_lrelu, %072_shortcut)
  %076_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%075_shortcut, %076_convolutional_conv_weights)
  %076_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%076_convolutional, %076_convolutional_bn_scale, %076_convolutional_bn_bias, %076_convolutional_bn_mean, %076_convolutional_bn_var)
  %076_convolutional_lrelu = LeakyRelu[alpha = 0.1](%076_convolutional_bn)
  %077_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%076_convolutional_lrelu, %077_convolutional_conv_weights)
  %077_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%077_convolutional, %077_convolutional_bn_scale, %077_convolutional_bn_bias, %077_convolutional_bn_mean, %077_convolutional_bn_var)
  %077_convolutional_lrelu = LeakyRelu[alpha = 0.1](%077_convolutional_bn)
  %078_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%077_convolutional_lrelu, %078_convolutional_conv_weights)
  %078_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%078_convolutional, %078_convolutional_bn_scale, %078_convolutional_bn_bias, %078_convolutional_bn_mean, %078_convolutional_bn_var)
  %078_convolutional_lrelu = LeakyRelu[alpha = 0.1](%078_convolutional_bn)
  %079_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%078_convolutional_lrelu, %079_convolutional_conv_weights)
  %079_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%079_convolutional, %079_convolutional_bn_scale, %079_convolutional_bn_bias, %079_convolutional_bn_mean, %079_convolutional_bn_var)
  %079_convolutional_lrelu = LeakyRelu[alpha = 0.1](%079_convolutional_bn)
  %080_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%079_convolutional_lrelu, %080_convolutional_conv_weights)
  %080_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%080_convolutional, %080_convolutional_bn_scale, %080_convolutional_bn_bias, %080_convolutional_bn_mean, %080_convolutional_bn_var)
  %080_convolutional_lrelu = LeakyRelu[alpha = 0.1](%080_convolutional_bn)
  %081_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%080_convolutional_lrelu, %081_convolutional_conv_weights)
  %081_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%081_convolutional, %081_convolutional_bn_scale, %081_convolutional_bn_bias, %081_convolutional_bn_mean, %081_convolutional_bn_var)
  %081_convolutional_lrelu = LeakyRelu[alpha = 0.1](%081_convolutional_bn)
  %082_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%081_convolutional_lrelu, %082_convolutional_conv_weights, %082_convolutional_conv_bias)
  %085_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%080_convolutional_lrelu, %085_convolutional_conv_weights)
  %085_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%085_convolutional, %085_convolutional_bn_scale, %085_convolutional_bn_bias, %085_convolutional_bn_mean, %085_convolutional_bn_var)
  %085_convolutional_lrelu = LeakyRelu[alpha = 0.1](%085_convolutional_bn)
  %086_upsample = Upsample[mode = u'nearest'](%085_convolutional_lrelu, %086_upsample_scale)
  %087_route = Concat[axis = 1](%086_upsample, %062_shortcut)
  %088_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%087_route, %088_convolutional_conv_weights)
  %088_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%088_convolutional, %088_convolutional_bn_scale, %088_convolutional_bn_bias, %088_convolutional_bn_mean, %088_convolutional_bn_var)
  %088_convolutional_lrelu = LeakyRelu[alpha = 0.1](%088_convolutional_bn)
  %089_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%088_convolutional_lrelu, %089_convolutional_conv_weights)
  %089_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%089_convolutional, %089_convolutional_bn_scale, %089_convolutional_bn_bias, %089_convolutional_bn_mean, %089_convolutional_bn_var)
  %089_convolutional_lrelu = LeakyRelu[alpha = 0.1](%089_convolutional_bn)
  %090_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%089_convolutional_lrelu, %090_convolutional_conv_weights)
  %090_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%090_convolutional, %090_convolutional_bn_scale, %090_convolutional_bn_bias, %090_convolutional_bn_mean, %090_convolutional_bn_var)
  %090_convolutional_lrelu = LeakyRelu[alpha = 0.1](%090_convolutional_bn)
  %091_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%090_convolutional_lrelu, %091_convolutional_conv_weights)
  %091_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%091_convolutional, %091_convolutional_bn_scale, %091_convolutional_bn_bias, %091_convolutional_bn_mean, %091_convolutional_bn_var)
  %091_convolutional_lrelu = LeakyRelu[alpha = 0.1](%091_convolutional_bn)
  %092_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%091_convolutional_lrelu, %092_convolutional_conv_weights)
  %092_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%092_convolutional, %092_convolutional_bn_scale, %092_convolutional_bn_bias, %092_convolutional_bn_mean, %092_convolutional_bn_var)
  %092_convolutional_lrelu = LeakyRelu[alpha = 0.1](%092_convolutional_bn)
  %093_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%092_convolutional_lrelu, %093_convolutional_conv_weights)
  %093_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%093_convolutional, %093_convolutional_bn_scale, %093_convolutional_bn_bias, %093_convolutional_bn_mean, %093_convolutional_bn_var)
  %093_convolutional_lrelu = LeakyRelu[alpha = 0.1](%093_convolutional_bn)
  %094_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%093_convolutional_lrelu, %094_convolutional_conv_weights, %094_convolutional_conv_bias)
  %097_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%092_convolutional_lrelu, %097_convolutional_conv_weights)
  %097_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%097_convolutional, %097_convolutional_bn_scale, %097_convolutional_bn_bias, %097_convolutional_bn_mean, %097_convolutional_bn_var)
  %097_convolutional_lrelu = LeakyRelu[alpha = 0.1](%097_convolutional_bn)
  %098_upsample = Upsample[mode = u'nearest'](%097_convolutional_lrelu, %098_upsample_scale)
  %099_route = Concat[axis = 1](%098_upsample, %037_shortcut)
  %100_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%099_route, %100_convolutional_conv_weights)
  %100_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%100_convolutional, %100_convolutional_bn_scale, %100_convolutional_bn_bias, %100_convolutional_bn_mean, %100_convolutional_bn_var)
  %100_convolutional_lrelu = LeakyRelu[alpha = 0.1](%100_convolutional_bn)
  %101_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%100_convolutional_lrelu, %101_convolutional_conv_weights)
  %101_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%101_convolutional, %101_convolutional_bn_scale, %101_convolutional_bn_bias, %101_convolutional_bn_mean, %101_convolutional_bn_var)
  %101_convolutional_lrelu = LeakyRelu[alpha = 0.1](%101_convolutional_bn)
  %102_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%101_convolutional_lrelu, %102_convolutional_conv_weights)
  %102_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%102_convolutional, %102_convolutional_bn_scale, %102_convolutional_bn_bias, %102_convolutional_bn_mean, %102_convolutional_bn_var)
  %102_convolutional_lrelu = LeakyRelu[alpha = 0.1](%102_convolutional_bn)
  %103_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%102_convolutional_lrelu, %103_convolutional_conv_weights)
  %103_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%103_convolutional, %103_convolutional_bn_scale, %103_convolutional_bn_bias, %103_convolutional_bn_mean, %103_convolutional_bn_var)
  %103_convolutional_lrelu = LeakyRelu[alpha = 0.1](%103_convolutional_bn)
  %104_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%103_convolutional_lrelu, %104_convolutional_conv_weights)
  %104_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%104_convolutional, %104_convolutional_bn_scale, %104_convolutional_bn_bias, %104_convolutional_bn_mean, %104_convolutional_bn_var)
  %104_convolutional_lrelu = LeakyRelu[alpha = 0.1](%104_convolutional_bn)
  %105_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%104_convolutional_lrelu, %105_convolutional_conv_weights)
  %105_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%105_convolutional, %105_convolutional_bn_scale, %105_convolutional_bn_bias, %105_convolutional_bn_mean, %105_convolutional_bn_var)
  %105_convolutional_lrelu = LeakyRelu[alpha = 0.1](%105_convolutional_bn)
  %106_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%105_convolutional_lrelu, %106_convolutional_conv_weights, %106_convolutional_conv_bias)
  return %082_convolutional, %094_convolutional, %106_convolutional
}

then ran

python onnx_to_tensorrt.py

Got this

Traceback (most recent call last):
  File "onnx_to_tensorrt.py", line 54, in <module>
    import tensorrt as trt
ImportError: No module named tensorrt

but according to your comment in this https://devtalk.nvidia.com/default/topic/1043135/jetson-tx2/aboat-tensorrt-python-api-on-tx2/# there is no python tensorrt API support for jetson platform

How do I

import tensorrt

?

Hi,

Please run it with python3.
Thanks.

My bad

TensorRT is installed inside

/usr/lib/python2.7/dist-packages

List of packages on jetson, I am working on

asn1crypto                                numpy-1.13.3.egg-info
asn1crypto-0.24.0.egg-info                pip
attr                                      pip-9.0.1.egg-info
attrs-17.4.0.egg-info                     pkg_resources
cairo                                     pluggy
_cffi_backend.aarch64-linux-gnu.so        pluggy-0.6.0.egg-info
Crypto                                    py
cryptography                              py-1.5.2.egg-info
cryptography-2.1.4.egg-info               pycairo-1.16.2.egg-info
cv2.aarch64-linux-gnu.so                  pycrypto-2.6.1.egg-info
dbus                                      pygobject-3.26.1.egg-info
_dbus_bindings.aarch64-linux-gnu.so       pygtkcompat
_dbus_glib_bindings.aarch64-linux-gnu.so  pygtk.pth
easy_install.py                           pygtk.py
easy_install.pyc                          pygtk.pyc
enum                                      _pytest
enum34-1.1.6.egg-info                     pytest-3.3.2.egg-info
funcsigs                                  pytest.py
funcsigs-1.0.2.egg-info                   pytest.pyc
gi                                        pyxdg-0.25.egg-info
glib                                      README
gobject                                   secretstorage
gps                                       SecretStorage-2.3.1.egg-info
gps-3.17.egg-info                         setuptools
graphsurgeon                              setuptools-39.0.1.egg-info
graphsurgeon-0.3.2.dist-info              six-1.11.0.egg-info
gtk-2.0                                   six.py
idna                                      six.pyc
idna-2.6.egg-info                         talloc.aarch64-linux-gnu.so
ipaddress-1.0.17.egg-info                 tensorrt
ipaddress.py                              tensorrt-5.0.6.3.dist-info
ipaddress.pyc                             uff
keyring                                   uff-0.5.5.dist-info
keyring-10.6.0.egg-info                   unity_lens_photos-1.0.egg-info
keyrings                                  wheel
keyrings.alt-3.0.egg-info                 wheel-0.30.0.egg-info
lsb_release.py                            xdg
numpy

While creating virtualenv we need to specify it to use global packages

Using virtualenv

virtualenv --system-site-packages ENV

Using workon

mkvirtualenv -p python2 --system-site-packages ENV

Theses are the pip list of libraries i am using in my virtual environment

DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7.
Package            Version 
------------------ --------
appdirs            1.4.3   
asn1crypto         0.24.0  
atomicwrites       1.3.0   
attrs              19.1.0  
configparser       3.7.4   
contextlib2        0.5.5   
cryptography       2.1.4   
decorator          4.4.0   
enum34             1.1.6   
funcsigs           1.0.2   
gps                3.17    
graphsurgeon       0.3.2   
idna               2.6     
importlib-metadata 0.18    
ipaddress          1.0.17  
keyring            10.6.0  
keyrings.alt       3.0     
Mako               1.0.12  
MarkupSafe         1.1.1   
more-itertools     5.0.0   
numpy              1.16.4  
onnx               1.4.1   
packaging          19.0    
pathlib2           2.3.3   
Pillow             6.0.0   
pip                19.1.1  
pluggy             0.12.0  
protobuf           3.8.0   
py                 1.8.0   
pycairo            1.16.2  
pycrypto           2.6.1   
pycuda             2019.1  
pygobject          3.26.1  
pyparsing          2.4.0   
pytest             4.6.3   
pytools            2019.1.1
pyxdg              0.25    
scandir            1.10.0  
SecretStorage      2.3.1   
setuptools         41.0.1  
six                1.11.0  
tensorrt           5.0.6.3 
typing             3.6.6   
typing-extensions  3.7.2   
uff                0.5.5   
unity-lens-photos  1.0     
wcwidth            0.1.7   
wget               3.2     
wheel              0.33.4  
zipp               0.5.1

Yolo sample is working now in this virtualenv with python2. I have to still test this on python3

Yolo is very slow on video.

After running

python yolov3_to_onnx.py

I get this

Layer of type yolo not supported, skipping ONNX node generation.
Layer of type yolo not supported, skipping ONNX node generation.
Layer of type yolo not supported, skipping ONNX node generation.
....
....
Downloading from https://pjreddie.com/media/files/yolov3.weights, this may take a while...
100% [................................................................................] 248007048 / 248007048
[...]
%106_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]]
(%105_convolutional_lrelu, %106_convolutional_conv_weights, %106_convolutional_conv_bias)
return %082_convolutional, %094_convolutional,%106_convolutional
}

Is this normal?
Am I doing something wrong?

Hi,

This is a two-steps sample:
yolov3_to_onnx.py converts the yolov3 model into the onnx format.
onnx_to_tensorrt.py will compile the onnx model into the final TensorRT engine.

So please remember to execute this command to run yolo with TensorRT.

$ python onnx_to_tensorrt.py

More, we also have a sample which optimizes the camera pipeline:
https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/tree/master/yolo

Thanks.

When I run this:(ENV) nano@nano-desktop:/usr/src/tensorrt/samples/python/yolov3_onnx$ python yolov3_to_onnx.py

I get this:
File “/usr/lib/python2.7/tempfile.py”, line 244, in _mkstemp_inner
fd = _os.open(file, flags, 0600)
OSError: [Errno 13] Permission denied: ‘./yolov3.cfgCrzqv1.tmp’

I have tried many ways to get around this but I cant.
Any suggestions?

Tried to use the optimized camera pipeline
https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/blob/master/yolo/README.md

According to this post I need to install deepstream SDK
https://devtalk.nvidia.com/default/topic/1050658/deepstream-sdk-on-jetson/where-is-the-deepstream-sdk-root-/

$ cmake -D DS_SDK_ROOT=<DS SDK Root> -D CMAKE_BUILD_TYPE=Release ..

I can’t find Deepstream SDK for Jetson Nano here
https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads

any alternatives?

Hi,

Deepstream for Jetson Nano is planned to release this month.
Please pay attention to our announcement for the package.

Another alternative is to try trt-yolo-app which also be included in the deepstream_reference_apps.
It only has dependency on TensorRT.
https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps/blob/master/yolo/README.md#trt-yolo-app

Thanks.

I have already got yolo.trt , how can I use the trt model in c++ api to test images?

Hi,

The sample is now included in the DeepstreamSDK and is removed from the GitHub.

You can deserialize the *.trt file and create TensorRT engine directly.
Others can be reused. Here is deserialization function call for your reference:
https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/classnvinfer1_1_1_i_runtime.html#aa0e6346a7de1b24ffca2a523fc4feda7

Thanks.