  When deploying Deep Neural Networks (DNNs), developers often convert models
from one deep learning framework to another (e.g., TensorFlow to PyTorch).
However, this process is error-prone and can impact target model accuracy. To
identify the extent of such impact, we perform and briefly present a
differential analysis against three DNNs widely used for image recognition
(MobileNetV2, ResNet101, and InceptionV3) converted across four well-known deep
learning frameworks (PyTorch, Keras, TensorFlow (TF), and TFLite), which
revealed numerous model crashes and output label discrepancies of up to 100%.
To mitigate such errors, we present a novel approach towards fault localization
and repair of buggy deep learning framework conversions, focusing on
pre-trained image recognition models. Our technique consists of four stages of
analysis: 1) conversion tools, 2) model parameters, 3) model hyperparameters,
and 4) graph representation. In addition, we propose various strategies towards
fault repair of the faults detected. We implement our technique on top of the
Apache TVM deep learning compiler, and we test it by conducting a preliminary
fault localization analysis for the conversion of InceptionV3 from TF to
TFLite. Our approach detected a fault in a common DNN converter tool, which
introduced precision errors in weights, reducing model accuracy. After our
fault localization, we repaired the issue, reducing our conversion error to
zero.
