Iterative Visual Reasoning Beyond Convolutions

By Xinlei Chen, Li-Jia Li, Li Fei-Fei and Abhinav Gupta.

Disclaimer

This is the authors’ implementation of the system described in the paper, not an official Google product.
Right now:
- The available reasoning module is based on convolutions and spatial memory.
- For simplicity, the released code uses the tensorflow default crop_and_resize operation, rather than the customized one reported in the paper (I find the default one is actually better by ~1%).

Prerequisites

Tensorflow, tested with version 1.6 with Ubuntu 16.04, installed with:

pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.6.0-cp27-none-linux_x86_64.whl

Other packages needed can be installed with pip:

pip install Cython easydict matplotlib opencv-python Pillow pyyaml scipy

For running COCO, the API can be installed globally:

# any path is okay
mkdir ~/install && cd ~/install
git clone https://github.com/cocodataset/cocoapi.git cocoapi
cd cocoapi/PythonAPI
python setup.py install --user

Setup and Running

Clone the repository.

git clone https://github.com/endernewton/iter-reason.git
cd iter-reason

Set up data, here we use ADE20K as an example.

mkdir -p data/ADE
cd data/ADE
wget -v http://groups.csail.mit.edu/vision/datasets/ADE20K/ADE20K_2016_07_26.zip
tar -xzvf ADE20K_2016_07_26.zip
mv ADE20K_2016_07_26/* ./
rmdir ADE20K_2016_07_26
# then get the train/val/test split
wget -v http://xinleic.xyz/data/ADE_split.tar.gz
tar -xzvf ADE_split.tar.gz
rm -vf ADE_split.tar.gz
cd ../..

Set up pre-trained ImageNet models. This is similarly done in tf-faster-rcnn. Here by default we use ResNet-50 as the backbone:

mkdir -p data/imagenet_weights
cd data/imagenet_weights
wget -v http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
tar -xzvf resnet_v1_50_2016_08_28.tar.gz
mv resnet_v1_50.ckpt res50.ckpt
cd ../..

Compile the library (for computing bounding box overlaps).
```
cd lib
make
cd ..
```

Now you are ready to run! For example, to train and test the baseline:

./experiments/scripts/train.sh [GPU_ID] [DATASET] [NET] [STEPS] [ITER] 
# GPU_ID is the GPU you want to test on
# DATASET in {ade, coco, vg} is the dataset to train/test on, defined in the script
# NET in {res50, res101} is the backbone networks to choose from
# STEPS (x10K) is the number of iterations before it reduces learning rate, can support multiple steps separated by character 'a'
# ITER (x10K) is the total number of iterations to run
# Examples:
# train on ADE20K for 320K iterations, reducing learning rate at 280K.
./experiments/scripts/train.sh 0 ade 28 32
# train on COCO for 720K iterations, reducing at 320K and 560K.
./experiments/scripts/train.sh 1 coco 32a56 72

To train and test the reasoning modules (based on ResNet-50):

./experiments/scripts/train_memory.sh [GPU_ID] [DATASET] [MEM] [STEPS] [ITER] 
# MEM in {local} is the type of reasoning modules to use 
# Examples:
# train on ADE20K on the local spatial memory.
./experiments/scripts/train_memory.sh 0 ade local 28 32

Once the training is done, you can test the models separately with test.sh and test_memory.sh, we also provided a separate set of scripts to test on larger image inputs.
You can use tensorboard to visualize and track the progress, for example:
```
tensorboard --logdir=tensorboard/res50/ade_train_5/ --port=7002 &
```

References

@inproceedings{chen18iterative,
    author = {Xinlei Chen and Li-Jia Li and Li Fei-Fei and Abhinav Gupta},
    title = {Iterative Visual Reasoning Beyond Convolutions},
    booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    Year = {2018}
}

The idea of spatial memory was developed in:

@inproceedings{chen2017spatial,
    author = {Xinlei Chen and Abhinav Gupta},
    title = {Spatial Memory for Context Reasoning in Object Detection},
    booktitle = {Proceedings of the International Conference on Computer Vision},
    Year = {2017}
}