65c3b4c357
* Fix 'cannot estimate element if precision is UNSPECIFIED' error caused by LPT In some models after ConvMulFusion and FakeQuantizeMulFusion, output_low contains denorms. That is problematic since LayerTransformation::getPrecisionDetails function checks if output_low is close to zero and if it is - the function sets 'signedPrecision' flag to false. In that case, both 'signedPrecision' and 'unsignedPrecision' are set to false and that makes getPrecisionDetails return element::undefined. This patch changes zeroThreshold to handle denorms. Ticket: 65375 * fix FakeQuantizeTransformation tests
OpenVINO™ Toolkit
This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
This open source version includes several components: namely Model Optimizer, nGraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.
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Deep Learning Deployment Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
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