git clone https://github.com/SWHL/ConvertLaTeXOCRToONNX.gitconda env create -f environment.yml$ conda create -n cvt_latex python=3.10.13
$ conda activate cvt_latex
$ pip install -r requirements.txt$ python convert.py
# /Users/xxx/miniconda3/envs/convert_latex_ocr/lib/python3.10/site-packages/torch/onnx/symbolic_helper.py:1513: UserWarning: ONNX export mode is set to TrainingMode.EVAL, but operator 'batch_norm' is set to train=True. Exporting with train=True.
# warnings.warn(
# Exported model has been tested with ONNXRuntime, and the result looks good!
# ONNX Model has been saved /Users/xxx/projects/_self/ConvertLaTeXOCRToONNX/models/image_resizer.onnx
# Exported model has been tested with ONNXRuntime, and the result looks good!
# ONNX Model has been saved /Users/xxx/projects/_self/ConvertLaTeXOCRToONNX/models/encoder.onnx
# Exported model has been tested with ONNXRuntime, and the result looks good!
# ONNX Model has been saved /Users/xxx/projects/_self/ConvertLaTeXOCRToONNX/models/decoder.onnx
# \exp\left[\int d^{4}x g\phi\bar{\psi}\psi\right]=\sum_{n=0}^{\infty}\frac{g^{n}}{n!}\left(\int d^{4}x\phi\bar{\psi}\psi\right)^{n}.4. Used in RapidLaTeXOCR.
from rapid_latex_ocr import LatexOCR
image_resizer_path = 'models/image_resizer.onnx'
encoder_path = 'models/encoder.onnx'
decoder_path = 'models/decoder.onnx'
tokenizer_json = 'models/tokenizer.json'
model = LatexOCR(image_resizer_path=image_resizer_path,
encoder_path=encoder_path,
decoder_path=decoder_path,
tokenizer_json=tokenizer_json)
img_path = "tests/test_files/6.png"
with open(img_path, "rb") as f:
data = f. read()
result, elapse = model(data)
print(result)
# {\frac{x^{2}}{a^{2}}}-{\frac{y^{2}}{b^{2}}}=1
print(elapse)
# 0.4131628000000003