Region Based Hotspot Detection

Source code of paper: (DAC2019 / TCAD2020) Faster Region-based Hotspot Detection.

Citing

If you find this repo useful in your research, please consider citing:

@inproceedings{chen2019faster,
  title={Faster region-based hotspot detection},
  author={Chen, Ran and Zhong, Wei and Yang, Haoyu and Geng, Hao and Zeng, Xuan and Yu, Bei},
  booktitle={2019 56th ACM/IEEE Design Automation Conference (DAC)},
  pages={1--6},
  year={2019},
  organization={IEEE}
}

Maintainers

CHEN Ran (chenran1995@link.cuhk.edu.hk)

Installation

Dependencies

Tensorflow Object Detection API depends on the following libraries:

Protobuf 3.0.0
Python-tk
Pillow 1.0
lxml
tf Slim (which is included in the "tensorflow/models/research/" checkout)
Jupyter notebook
Matplotlib
Tensorflow
Cython
contextlib2
cocoapi

For detailed steps to install Tensorflow, follow the Tensorflow installation instructions. Note that Tensorflow 2.0 is not supported. I recommend you use tensorflow 1.8.0. A typical user can install Tensorflow using one of the following commands:

# For CPU
pip install tensorflow
# For GPU
pip install tensorflow-gpu

The remaining libraries can be installed on Ubuntu 16.04 using via apt-get:

sudo apt-get install protobuf-compiler python-pil python-lxml python-tk
pip install --user Cython
pip install --user contextlib2
pip install --user jupyter
pip install --user matplotlib

Alternatively, users can install dependencies using pip:

pip install --user Cython
pip install --user contextlib2
pip install --user pillow
pip install --user lxml
pip install --user jupyter
pip install --user matplotlib

Note that sometimes "sudo apt-get install protobuf-compiler" will install Protobuf 3+ versions for you and some users have issues when using 3.5. If that is your case, you're suggested to download and install Protobuf 3.0.0 (available here).

Protobuf Compilation

The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be compiled. This should be done by running the following command from the tensorflow/models/research/ directory:

# From tensorflow/models/research/
protoc object_detection/protos/*.proto --python_out=.

Add Libraries to PYTHONPATH

When running locally, the tensorflow/models/research/ and slim directories should be appended to PYTHONPATH. This can be done by running the following from tensorflow/models/research/:

# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

Note: This command needs to run from every new terminal you start. If you wish to avoid running this manually, you can add it as a new line to the end of your ~/.bashrc file, replacing `pwd` with the absolute path of tensorflow/models/research on your system.

Training

bash train.sh GPU_ID

Testing

bash eval.sh GPU_ID

Testing the Installation

You can test that you have correctly installed the Tensorflow Object Detection
API by running the following command:

python object_detection/builders/model_builder_test.py

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