95bea3e7fa
* create psenet create psenet with weight from tensorflow * delete some useless code * repalce tab with 4 blanks * fix network bug, rewrite post-processing pse algorithm * update readme * update readme * add RepVGG
55 lines
1.3 KiB
Markdown
55 lines
1.3 KiB
Markdown
# PSENet
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**preprocessing + inference + postprocessing = 30ms** with fp32 on Tesla P40.
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The original Tensorflow implementation is [tensorflow_PSENet](https://github.com/liuheng92/tensorflow_PSENet). A TensorRT Python api implementation is [TensorRT-Python-PSENet](https://github.com/upczww/TensorRT-Python-PSENet).
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## Key Features
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- Generating `.wts` from `Tensorflow`.
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- Dynamic batch and dynamic shape input.
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- Object-Oriented Programming.
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- Practice with C++ 11.
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<p align="center">
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<img src="https://user-images.githubusercontent.com/15235574/105487078-821d6800-5cea-11eb-87dc-e3317a941763.jpeg">
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</p>
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## How to Run
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* 1. generate .wts
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Download pretrained model from https://github.com/liuheng92/tensorflow_PSENet
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and put `model.ckpt.*` to `model` dir. Add a file `model/checkpoint` with content
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```
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model_checkpoint_path: "model.ckpt"
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all_model_checkpoint_paths: "model.ckpt"
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```
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Then run
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```
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python gen_tf_wts.py
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```
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which will gengerate a `psenet.wts`.
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* 2. cmake and make
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```
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mkdir build
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cd build
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cmake ..
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make
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```
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* 3. build engine and run detection
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```
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cp ../psenet.wts ./
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cp ../test.jpg ./
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./psenet -s // serialize model to plan file
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./psenet -d // deserialize plan file and run inference
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```
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## Known Issues
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None
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## Todo
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* use `ExponentialMovingAverage` weight.
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