pytorch-to-tflite 0.0.4

Convert deep learning models

Install

pip install pytorch_to_tflite

How to use

Pytorch to Onnx

# !pip install -e ../

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

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

tflite_path = tf_path+'.tflite'
tf_to_tf_lite(tf_path, tflite_path)
assert os.path.exists(tflite_path)
tflite_path