first commit

.gitignore

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+*.pyc
+.torch
+_ext
+*.o
+work
+work/*
+_ext/

Dockerfile

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+FROM nvidia/cuda:8.0-cudnn6-devel-ubuntu16.04
+
+RUN apt-get update && apt-get install -y rsync htop git openssh-server python-pip
+
+RUN pip install --upgrade pip
+
+RUN pip install http://download.pytorch.org/whl/cu80/torch-0.2.0.post3-cp27-cp27mu-manylinux1_x86_64.whl
+RUN pip install torchvision cffi tensorboardX
+
+RUN pip install tqdm scipy scikit-image colorama==0.3.7 
+RUN pip install setproctitle pytz ipython

LICENSE

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+Copyright 2017 NVIDIA CORPORATION
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

README.md

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+# flownet2-pytorch 
+
+Pytorch implementation of [FlowNet2](https://arxiv.org/abs/1612.01925) by [Fitsum Reda] (https://github.com/fitsumreda).
+
+Multiple GPU training is supported, and the code provides examples for training or inference on [MPI-Sintel] (http://sintel.is.tue.mpg.de/) clean and final datasets. The same commands can be used for training or inference with other datasets. See below for more detail.
+
+Inference using fp16 (half-precision) is also supported.
+
+For more help, type <br />
+
+    python main.py --help
+
+## Network architectures
+Below are the different flownet neural network architectures that are provided. <br />
+A batchnorm version for each network is available.
+
+ - **FlowNet2S**
+ - **FlowNet2C**
+ - **FlowNet2CS**
+ - **FlowNet2CSS**
+ - **FlowNet2SD**
+ - **FlowNet2**
+
+## Custom layers
+
+`FlowNet2` or `FlowNet2C*` achitectures rely on custom layers `Resample2d` or `Correlation`. <br />
+A pytorch implementation of these layers with cuda kernels are available at [./networks](./networks). <br />
+Note : Currently, half precision kernels are not available for these layers.
+
+## Data Loaders
+
+Dataloaders for FlyingChairs, FlyingThings, ChairsSDHom and ImagesFromFolder are available in [datasets.py](./datasets.py). <br />
+
+## Loss Functions
+
+L1 and L2 losses with multi-scale support are available in [losses.py](./losses.py). <br />
+
+## Installation 
+
+    # get flownet2-pytorch source
+    git clone ssh://[email protected]:2200/freda/flownet2-pytorch.git
+    cd flownet2-pytorch
+
+    # install custom layers
+    bash install.sh
+
+## Docker image
+Libraries and other dependencies for this project include: Ubuntu 16.04, Python 2.7, Pytorch 0.2, CUDNN 6.0, CUDA 8.0
+
+A Dockerfile with the above dependencies is available 
+
+    # Build and launch docker image
+    bash launch_docker.sh
+
+## Inference
+    # Example on MPISintel Clean   
+    python main.py --inference --model FlowNet2 --save_flow --inference_dataset MpiSintelClean \
+    --inference_dataset_root /path/to/mpi-sintel/clean/dataset \
+    --resume /path/to/checkpoints 
+
+## Training and validation
+
+    # Example on MPISintel Final and Clean, with L1Loss on FlowNet2 model
+    python main.py --batch_size 8 --model FlowNet2 --loss=L1Loss --optimizer=Adam --optimizer_lr=1e-4 \
+    --training_dataset MpiSintelFinal --training_dataset_root /path/to/mpi-sintel/final/dataset  \
+    --validation_dataset MpiSintelClean --validation_dataset_root /path/to/mpi-sintel/clean/dataset
+
+    # Example on MPISintel Final and Clean, with MultiScale loss on FlowNet2C model
+    python main.py --batch_size 8 --model FlowNet2C --optimizer=Adam --optimizer_lr=1e-4 \
+    --loss=MultiScale --loss_norm=L1 loss_numScales=5 loss_startScale=4 \
+    --training_dataset MpiSintelFinal --training_dataset_root /path/to/mpi-sintel/final/dataset  \
+    --validation_dataset MpiSintelClean --validation_dataset_root /path/to/mpi-sintel/clean/dataset
+
+## Results on MPI-Sintel
+[![Predicted flows on MPI-Sintel](./image.png)](https://www.youtube.com/watch?v=HtBmabY8aeU "Predicted flows on MPI-Sintel")
+