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diff --git a/Codes/flownet2/README.md b/Codes/flownet2/README.md new file mode 100644 index 0000000..8647723 --- /dev/null +++ b/Codes/flownet2/README.md @@ -0,0 +1,66 @@ +## FlowNet2 (TensorFlow) + +This repo contains FlowNet2[1] for TensorFlow. It includes FlowNetC, S, CS, CSS, CSS-ft-sd, SD, and 2. + +### Installation +``` +pip install enum +pip install pypng +pip install matplotlib +pip install image +pip install scipy +pip install numpy +pip install tensorflow +``` + +Linux: +`sudo apt-get install python-tk` + +You must have CUDA installed: +`make all` + +### Download weights +To download the weights for all models (4.4GB), run the `download.sh` script in the `checkpoints` directory. All test scripts rely on these checkpoints to work properly. + + +### Flow Generation (1 image pair) + +``` +python -m src.flownet2.test --input_a data/samples/0img0.ppm --input_b data/samples/0img1.ppm --out ./ +``` + +Available models: +* `flownet2` +* `flownet_s` +* `flownet_c` +* `flownet_cs` +* `flownet_css` (can edit test.py to use css-ft-sd weights) +* `flownet_sd` + +If installation is successful, you should predict the following flow from samples/0img0.ppm: + + +### Training +If you would like to train any of the networks from scratch (replace `flownet2` with the appropriate model): +``` +python -m src.flownet2.train +``` +For stacked networks, previous network weights will be loaded and fixed. For example, if training CS, the C weights are loaded and fixed and the S weights are randomly initialized. + + +### Fine-tuning +TODO + +### Benchmarks +Benchmarks are for a forward pass with each model of two 512x384 images. All benchmarks were tested with a K80 GPU and Intel Xeon CPU E5-2682 v4 @ 2.30GHz. Code was executed with TensorFlow-1.2.1 and python 2.7.12 on Ubuntu 16.04. Resulting times were averaged over 10 runs. The first run is always slower as it sets up the Tensorflow Session. + +| | S | C | CS | CSS | SD | 2 +| ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | ------------- | +| First Run | 681.039ms | 898.792ms | 998.584ms | 1063.357ms | 933.806ms | 1882.003ms | +| Subsequent Runs | 38.067ms | 78.789ms | 123.300ms | 161.186ms | 62.061ms | 276.641ms | + + +### Sources +[1] E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox +FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, +IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2017. |