How to create a TRT Model from ONNX or direct from PyTorch to TRT

[naveenkumarkr723]

hi @baudm ,@huyhoang17

actually im using this parseq to convert ONNX model like

import torch parseq = torch.hub.load('baudm/parseq', 'parseq', pretrained=True, refine_iters=0).eval() dummy_input = torch.rand(1, 3, *parseq.hparams.img_size) # (1, 3, 32, 128) by default

To ONNX

parseq.to_onnx('parseq.onnx', dummy_input, opset_version=14) # opset v14 or newer is required

but when i tried to convert ONNX to TRT Model like

trtexec --onnx=/workspace/data/NaveenJadi/ParSeq/onnx-simplifier/parseq_ref_sim.onnx --saveEngine=parseq_simple.engine --exportProfile=parseq_simple.json --separateProfileRun > parseq_simple.log

i was getting ERROR like [12/08/2022-08:01:19] [W] [TRT] parsers/onnx/onnx2trt_utils.cpp:367: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32. [12/08/2022-08:01:19] [W] [TRT] parsers/onnx/onnx2trt_utils.cpp:395: One or more weights outside the range of INT32 was clamped [12/08/2022-08:01:19] [W] [TRT] Tensor DataType is determined at build time for tensors not marked as input or output. [12/08/2022-08:01:19] [E] Error[3]: /ArgMax: at least 2 dimensions are required for input. [12/08/2022-08:01:19] [E] [TRT] parsers/onnx/ModelImporter.cpp:773: While parsing node number 614 [ArgMax -> "/ArgMax_output_0"]: [12/08/2022-08:01:19] [E] [TRT] parsers/onnx/ModelImporter.cpp:774: --- Begin node --- [12/08/2022-08:01:19] [E] [TRT] parsers/onnx/ModelImporter.cpp:775: input: "/Squeeze_output_0" output: "/ArgMax_output_0" name: "/ArgMax" op_type: "ArgMax" attribute { name: "axis" i: -1 type: INT } attribute { name: "keepdims" i: 0 type: INT } attribute { name: "select_last_index" i: 0 type: INT }

[12/08/2022-08:01:19] [E] [TRT] parsers/onnx/ModelImporter.cpp:776: --- End node --- [12/08/2022-08:01:19] [E] [TRT] parsers/onnx/ModelImporter.cpp:778: ERROR: parsers/onnx/ModelImporter.cpp:163 In function parseGraph: [6] Invalid Node - /ArgMax cannot create std::vector larger than max_size() [12/08/2022-08:01:19] [E] Failed to parse onnx file [12/08/2022-08:01:19] [E] Parsing model failed [12/08/2022-08:01:19] [E] Failed to create engine from model or file. [12/08/2022-08:01:19] [E] Engine set up failed

Environment TensorRT Version: 8.4.1.5-1 NVIDIA GPU: tensorrt NVIDIA Driver Version: CUDA Version: cuda11.6 CUDNN Version: 8.6 Operating System: Linux Python Version (if applicable): 3.8.13 Tensorflow Version (if applicable): No PyTorch Version (if applicable): '1.13.0a0+08820cb' Baremetal or Container (if so, version): No

Some one go through it and provide the piece of code to convert the model from ONNX to TRT

[naveenkumarkr723]

hi @baudm why iam not getting any response , can u solve it fast

[Ehteshamciitwah]

Hello, I think you need to convert onnx into simple onnx using onnxsim , then convert it into TRT.

example: from onnxsim import simplify model_onnx = onnx.load(output_dir+'.onnx') model_simp, check = simplify(model_onnx)

[naveenkumarkr723]

actually i have been tryin the same

1. converting the model into onnx
2. onnx model simplifier like onnxsim input.onnx ouput.onnx
3. trtexec --onnx=output.onnx --saveEngine=parseq.trt still getting the above error but when i used batch size = 16 or 32 while converting from parseq to onnx and onnx to simplifier onnx and then to trt it is converting but inference images not able to take it

if you know how to load trt model and predict the output let me know

advance thanks

[Ehteshamciitwah]

If you share the conversion code of onnx. We may find the probelm. To use different batch size , you need to use dynamic axis during onnx conversion. please share your whole code work. So we can figure out the problem and solve it. I actually change to onnx using author guidelines. You can check onnxruntime output before coverting to TRT to check if ONNX is working fine or not. Compare the result of Pytorch,onnxruntime and than try to convert into TRT. Thank you

[Krl1]

Hi @naveenkumarkr723. Did you deal with this issue?

[naveenkumarkr723]

Haa ,

On Fri, Feb 17, 2023, 3:25 PM KrlKarol8 @.***> wrote:

Hi @naveenkumarkr723 https://github.com/naveenkumarkr723. Did you deal with this issue?

— Reply to this email directly, view it on GitHub https://github.com/baudm/parseq/issues/62#issuecomment-1434401054, or unsubscribe https://github.com/notifications/unsubscribe-auth/AQCX7KWFWN2PMSJH36OKY4LWX5DJHANCNFSM6AAAAAATIY7L4Y . You are receiving this because you were mentioned.Message ID: @.***>

[Krl1]

It is working smoothly with the following configuration, which includes the following versions of the software:

Nvidia SMI Driver Version: 525.85.12 CUDA 11.8.89 cuDNN 8.6.0 TensorRT 8.5.3.1 All of these software components were installed as .deb files, and there have been no issues encountered so far.

[kishcs]

I converted parseq torch model to onnx to trt with tensorrt version 8.4.1.5 How do I load that trt model for inferencing?

[AbhiHegde3004]

Hi @naveenkumarkr723

I'm facing the issue in processing the output. The tensorrt raw output is always same. I have cross checked the preprocessing steps as I have done the onnx inference with the same set of preprocessing steps.

So far not able to get the correct output on tensorrt engine

[lerndeep]

[Ehteshamciitwah]

Onnx and TRT. I implement the official Parseq with pytorch 1.10 and then converted it to onnx using author code. I used onnx-simplify 0.4.13 to simplify the onnx and used TRT >=8.2.1.8 to convert into TRT. Everything works well.
But after checking the inference, I realized the inference on TRT is not accurate.

[Ehteshamciitwah]

cuDNN 8.6.0

Hello, Do you successfully v converted to TRT with these version without any inference problem? Did you comment break point while converting to Onnx. Did you use onnx simplify before converting to TRT.?

[flywheel1412]

i fix it by modify this line as fellow

tgt_in[:, j] = p_i.argmax(-1).squeeze()

[gaurav-g-12]

This issue has been solved, check this out : https://github.com/gaurav-g-12/parses_jetson_porting.git