trt2onnx 0.0.2

A tools convert tensorrt engine to a fake onnx

TensorRT2ONNX

A tool convert TensorRT engine/plan to a fake onnx

Build an engine from onnx using trtexec tools

trtexec --verbose \
        --nvtxMode=verbose \
        --buildOnly \
        --workspace=8192 \
        --onnx=your_onnx.onnx \
        --saveEngine=your_engine.engine \
        --timingCacheFile=timing.cache \
        --fp16 # use fp16

You will get a your_engine.engine and a timing.cache

Parser network from engine using trtexec tools

trtexec --verbose \
        --noDataTransfers \
        --useCudaGraph \
        --separateProfileRun \
        --useSpinWait \
        --nvtxMode=verbose \
        --loadEngine=your_engine.engine \
        --exportLayerInfo=graph.json \
        --timingCacheFile=timing.cache

You will parser your_engine.engine network information into graph.json

Install TensorRT2ONNX

pip3 install trt2onnx -i https://pypi.org/simple

Build a fake onnx from graph json

import onnx
from trt2onnx import build_onnx

# build a fake onnx from json
onnx_graph = build_onnx('graph.json')

# save the fake onnx as `fake.onnx`
onnx.save(onnx_graph, 'fake.onnx')

Build a fake onnx from engine

import onnx
from trt2onnx import build_onnx

# build a fake onnx from engine
onnx_graph = build_onnx('your_engine.engine')

# save the fake onnx as `fake.onnx`
onnx.save(onnx_graph, 'fake.onnx')

NOTICE !!

If you build engine use your own plugin, please load the *.so before build_onnx function.

import ctypes
# load your plugin first
ctypes.cdll.LoadLibrary('your_plugin_0.so')
ctypes.cdll.LoadLibrary('your_plugin_1.so')
...

Use Netron to view your fake onnx