flownet2-tf：FlowNet 2.0：深度网络的光流估计的发展

## 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.