I’ve followed the examples from this post : https://devblogs.nvidia.com/tensorrt-integration-speeds-tensorflow-inference/ and from there : https://github.com/tensorflow/models/tree/6ff0a53f81439d807a78f8ba828deaea3aaaf269/research/tensorrt
I was able to run the scripts, but then I don’t know how to use the converted graphs.
When I try to display the graph with Tensorboard, I get this error:
Traceback (most recent call last): File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.5/runpy.py", line 85, in _run_code exec(code, run_globals) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/tools/import_pb_to_tensorboard.py", line 76, in <module> app.run(main=main, argv=[sys.argv] + unparsed) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 125, in run _sys.exit(main(argv)) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/tools/import_pb_to_tensorboard.py", line 58, in main import_to_tensorboard(FLAGS.model_dir, FLAGS.log_dir) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/tools/import_pb_to_tensorboard.py", line 49, in import_to_tensorboard importer.import_graph_def(graph_def) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 432, in new_func return func(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/importer.py", line 418, in import_graph_def graph._c_graph, serialized, options) # pylint: disable=protected-access tensorflow.python.framework.errors_impl.NotFoundError: Op type not registered 'TRTEngineOp' in binary running on 425785a3e963. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done b efore importing the graph, as contrib ops are lazily registered when the module is first accessed.
When I try to load the graph in a notebook, I get a similar error.
It’s happening with the ResNet models provided in with the codes and also with a custom MobileNet-based model.
I’m using Tensorflow 1.9 built from source (also tried with Tensorflow 1.7 and 1.8), TensorRT4 (also tried TensorRT3), Python3.5, CUDA9.0 with Cudnn 7.0 and within a Docker container.
Does anyone have an idea how to solve this issue? Is it ok to use a converted graph with Tensorflow tools or can we only use it with TensorRT tools thereafter?
I also have another unrelated question: can we use TensorRT with models that don’t have a fixed shape? For now, I have not been able to use TensorRT with a model which doesn’t have a fixed shape.
Thank you in advance.