I am currently working with Darknet on Yolov4, with 1 class.
I need to export those weights to onnx format, for tensorRT inference.
I've tried multiple technics, using ultralytics to convert or going from tensorflow to onnx. But none seems to work. Is there a direct way to do it?
Check this GitHub repo: https://github.com/Tianxiaomo/pytorch-YOLOv4
Running the demo_darknet2onnx.py script you'll be able to generate the ONNX model from the .cfg and .weights darknet files.
Usage example:
python demo_darknet2onnx.py <cfgFile> <weightFile> <imageFile> <batchSize>
You can also decide the batch size for the inference calls of the converted model.
The following repo exports yolov3 models from darknet to onnx, for tensorRT inference. You can use this as reference for your model.
https://github.com/jkjung-avt/tensorrt_demos/tree/master/yolo
You can convert scaled YOLO-yolov4,yolov4-csp.yolov4x-mish,yolov4-P5 etc models into onxx & its perfectly work fine.
https://github.com/linghu8812/tensorrt_inference
Related
I want to preprocess an audio file and convert it to spectrogram before inserting it to my tflite model in flutter app. is there a way I can run my preprocessing function (.py) on flutter by converting it to .tflite?
That's currently not supported. You would have to include the preprocessing steps in the model itself (as TF ops, if the ops are supported in TFLite) so that it's included in converted TFLite model, or preprocess it manually outside the model in Flutter.
Kindly close the issue / mark this as the accepted answer if your issue is resolved.
I am trying to combine three FMUs into one FMU that contains all of the three. Specifically, I have one FMU of a pandapower electricity network and 2 FMUs that are CSV files converted to FMUs by using PythonFMU tool. All of the FMUs have been tested by the FMU Check and they have been simulated together to check that everything works fine.
Then I am using FMPy tool to combine all of them together and export successfully the final FMU.
However, when I am trying to validate this I get the following error:
ModelStructure/Outputs must have exactly one entry for each variable with causality="output".
Any idea of what is wrong here?
Your problem seems to be fixed in https://github.com/CATIA-Systems/FMPy/issues/281#issuecomment-879092943
You should try to re-generate the containerized FMU with the developmenet branch of fmpy.
How to create an ONNX file manually? I mean without using the frameworks like PyTorch, caffe2, e.t.c, can we(binary encode maybe) create an onnx file, if we know the network details in prior like # of nodes, types of nodes, their connections, e.t.c?
Found it!! We can use onnx.helper class inside onnx library to create a model and save it in onnx format.
I try to use the dataset of mnist, which is an example provided in github, to run Memnet model on the interface of cmd.
The model is downloaded from here
I modified its deploy.prototxt accordingly. Having no idea... Can someone help me with this?
But it keeps telling me something wrong going on, as the pic shows:
The command line interface should get as a -solver a solver.prototxt file (which has train_val.prototxt as one of its parameters). You cannot supply train_val.prototxt directly to caffe train.
You can look at the examples subfolder of caffe and find some examples of solver.prototxt. A simple one can be found in examples/mnist, you can check out lenet_solver.prototxt.
We have a modeling tool which uses OCC, and a 3d editor using OSG. What I want to do is, export the model from the first tool and import into the second tool. I have been searching the web for days, but I can't find a solution.
Three things can solve my problem:
An exporter for OCC to export into OSG supported formats (.ive, .osg, and many more),
An importer for OSG to import from OCC supported formats (.stp, .step, .igs, .iges, .brp, .brep ),
A converter tool for converting between two formats, one format supported by OCC and one format supported by OSG.
Has anybody done this before, or know of anything that can help?
I am trying to avoid writing a custom exporter for OCC.
I found a solution. OpenCascade has an import/export example, which can export VRML files without texture support. Some modifications on the import export code and some modifications on other parts (where the OCC model is represented by VRML classes) was enough to successfully export my model to a VRML file. Then i built the VRML plugin for OpenSceneGraph and successfully imported the model.
CADExchanger (OCC based) does a pretty good job converting between BRep and other formats (STEP, IGES, STL, VRML...)
Why don't you have a look at pythonocc.org.
I'm assuming OSG takes meshes?
Load the STEP / IGES file in (python)OCC, grab its mesh, push the verts / indices to OSG.
Would that work?