Metadata-Version: 2.1
Name: pytorch_to_tflite
Version: 0.0.4
Summary: Convert deep learning models
Home-page: https://github.com/anhvth/pytorch_to_tflite/tree/main/
Author: Anh Vo Tran Hai
Author-email: anhvth.226@gmail.com
License: Apache Software License 2.0
Description: # Install
        
        
        
        `pip install pytorch_to_tflite`
        
        # How to use
        
        ## Pytorch to Onnx
        
        ```python
        # !pip install -e ../
        ```
        
        ```python
        from pytorch_to_tflite.pytorch_to_tflite import *
        import torch
        import yaml
        import os
        import mmcv
        from nanodet.model.arch import build_model
        
        PATH_TO_CONFIG = '/gitprojects/nano-det-parkingline/config/nanodet-g.yml'
        cfg = yaml.safe_load(open(PATH_TO_CONFIG))
        cfg = mmcv.Config(cfg)
        model = build_model(cfg.model)
        
        img = torch.randn(1,3,416,416)
        out = model(img)
        
        !mkdir -p cache/
        onnx_out_path = 'cache/out.onnx'
        torch.onnx.export(model, img, onnx_out_path)
        ```
        
            Finish initialize Lite GFL Head.
        
        
            /root/miniconda3/envs/pytorch-to-tflite/lib/python3.9/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
              return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
            /root/miniconda3/envs/pytorch-to-tflite/lib/python3.9/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
              warnings.warn(
            /root/miniconda3/envs/pytorch-to-tflite/lib/python3.9/site-packages/torch/nn/functional.py:3657: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. 
              warnings.warn(
        
        
        # ONNX to Tensorflow
        
        ```python
        onnx_path = onnx_out_path
        tf_path = onnx_path + '.tf'
        onnx_to_tf(onnx_path=onnx_path, tf_path=tf_path)
        assert os.path.exists(tf_path)
        ```
        
        # Tensorflow to tflite
        
        ```python
        tflite_path = tf_path+'.tflite'
        tf_to_tf_lite(tf_path, tflite_path)
        assert os.path.exists(tflite_path)
        tflite_path
        ```
        
Keywords: pytorch to tflite
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
