Nn-meter

Major Updates
**Building tools is coming!** Provide nn-Meter building tools for users to build latency predictor for their own devices (43, 59, 66)
* Provide a unified interface to connect with TFLite and OpenVINO platforms, and support users to connect their own devices
* Support operator fusion rules detection on target backend, and support users to design new test cases.
* Provide tools to build kernel latency predictor for several built-in kernels or user-customized kernels.
* Support both Tensorflow and PyTorch implementation of fusion rule test cases and kernels.
* Provide examples for using nn-Meter building tools.

Minor Updates & Bug Fixes
* Add quick start tutorials for users to get started (58)
* Provide support to torch v1.10, tensorflow v2.7, and nni v2.7 (43)
* Fix bugs in torch converter and kernel detector (47, 49)
* Fix bugs in shape parsing of global avgpool and se operator in onnx converter (60)


v2.0-data
The TFLite Benchmark Tools with version `tensorflow==2.1` and `tensorflow==2.7` for nn-Meter builder.

Major Updates
* Add nn-Meter Bench Dataset (25)
* Add GNN dataloader for nn-Meter Bench Dataset (27)
* Support torch v1.9, tensorflow v2.6, nni v2.5 (36)
* Add notebook examples for nn-Meter usage (26, 29)

Minor Updates & Bug Fixes
* Support hardware latency prediction for ProxylessNAS in NNI (https://github.com/microsoft/nni/pull/4206)
* Refine shape attributes to sync with NNI (fix issue https://github.com/microsoft/nni/issues/4198, PR #30, 33)
* Refactor of nn-Meter Project (41)

Initial release of nn-Meter.
Major Features
- Support pip install and source codes install
- Support latency prediction for a CNN model with a predefined predictor (edge device)
- Provide command line interface `nn-meter` after installation, and python binding module `nn_meter`
- Provide [Docs](https://github.com/microsoft/nn-Meter/tree/v1.0/docs)

Known Issues
- Synchronization with [NNI](https://github.com/microsoft/nni): a stable NNI-based torch converter relies on NNI>=2.5.
- Can not support `torch.jit._overload_method` due to issues from torch.

v1.0-data