sampleUffSSD does not work

Description

Hello,
I Challenge to conversion sampleUffSSD’s uff File Follow the reference below.
TensorRT/README.md at master · NVIDIA/TensorRT · GitHub
But, When I run [convert-to-uff ssd_inception_v2_coco_2017_11_17 / frozen_inference_graph.pb -O NMS -p config.py] ,
I get the following error and cannot convert.

(TF115) D:\TensorRT-7.2.3.4\samples\sampleUffSSD>convert-to-uff ssd_inception_v2_coco_2017_11_17/frozen_inference_graph.pb -O NMS -p config.py
2021-05-27 11:57:15.648921: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
Loading ssd_inception_v2_coco_2017_11_17/frozen_inference_graph.pb
WARNING:tensorflow:From c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\conversion_helpers.py:274: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

NOTE: UFF has been tested with TensorFlow 1.15.0.
WARNING: The version of TensorFlow installed on this system is not guaranteed to work with UFF.
UFF Version 0.6.9
=== Automatically deduced input nodes ===
[name: “Input”
op: “Placeholder”
attr {
key: “dtype”
value {
type: DT_FLOAT
}
}
attr {
key: “shape”
value {
shape {
dim {
size: 1
}
dim {
size: 3
}
dim {
size: 300
}
dim {
size: 300
}
}
}
}
]

Using output node NMS
Converting to UFF graph
Warning: No conversion function registered for layer: NMS_TRT yet.
Converting NMS as custom op: NMS_TRT
WARNING:tensorflow:From c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\converter.py:226: The name tf.AttrValue is deprecated. Please use tf.compat.v1.AttrValue instead.

Warning: No conversion function registered for layer: FlattenConcat_TRT yet.
Converting concat_box_conf as custom op: FlattenConcat_TRT
Traceback (most recent call last):
File “C:\Users\tats-kobayashi\AppData\Local\Programs\Python\Python36\Lib\runpy.py”, line 193, in run_module_as_main
main”, mod_spec)
File “C:\Users\tats-kobayashi\AppData\Local\Programs\Python\Python36\Lib\runpy.py”, line 85, in run_code
exec(code, run_globals)
File "C:\Users\tats-kobayashi\AppData\Local\Programs\Python\Python36\env\TF115\Scripts\convert-to-uff.exe_main
.py", line 9, in
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\bin\convert_to_uff.py”, line 139, in main
debug_mode=args.debug
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\conversion_helpers.py”, line 276, in from_tensorflow_frozen_model
return from_tensorflow(graphdef, output_nodes, preprocessor, **kwargs)
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\conversion_helpers.py”, line 225, in from_tensorflow
debug_mode=debug_mode)
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\converter.py”, line 141, in convert_tf2uff_graph
uff_graph, input_replacements, debug_mode=debug_mode)
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\converter.py”, line 126, in convert_tf2uff_node
op, name, tf_node, inputs, uff_graph, tf_nodes=tf_nodes, debug_mode=debug_mode)
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\converter.py”, line 94, in convert_layer
return cls.registry
[op](name, tf_node, inputs, uff_graph, **kwargs)
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\converter_functions.py”, line 455, in convert_depthwise_conv2d_native
return _conv2d_helper(name, tf_node, inputs, uff_graph, func=“depthwise”, **kwargs)
File “c:\users\tats-kobayashi\appdata\local\programs\python\python36\env\tf115\lib\site-packages\uff\converters\tensorflow\converter_functions.py”, line 480, in _conv2d_helper
number_groups = int(wt.attr[‘value’].tensor.tensor_shape.dim[2].size)
IndexError: list index (2) out of range

Please tell me the solution

Environment

TensorRT Version: 1.15.3
GPU Type: RTX2070
Nvidia Driver Version: 456.81
CUDA Version: 10.0
CUDNN Version: 7.6.0
Operating System + Version: Windows10 64bit
Python Version (if applicable): 3.6.5
TensorFlow Version (if applicable): 1.15.3
PyTorch Version (if applicable): Not use
**uff:0.6.9
**graphsurgeon:0.4.5

Relevant Files

I’m using the TensorRT sample file as is

Steps To Reproduce

The procedure is as shown in the official reference below.
(TensorRT/README.md at master · NVIDIA/TensorRT · GitHub)

Hi,
Please refer to the installation steps from the below link if in case you are missing on anything
https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html
However suggested approach is to use TRT NGC containers to avoid any system dependency related issues.
https://ngc.nvidia.com/catalog/containers/nvidia:tensorrt

In order to run python sample, make sure TRT python packages are installed while using NGC container.
/opt/tensorrt/python/python_setup.sh
Thanks!

Hi @tats-kobayashi,

UFF and Caffe Parser have been deprecated from TensorRT 7 onwards, hence request you to try ONNX parser. Please check the below links for the same.

SampleSSD,

Thanks!

Thank you for your reply.
I will try Onnx

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