Skip to content
Pytorch implementations of referring expression networks
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
configs
src
.gitmodules
LICENSE
README.md
vocab_file.txt

README.md

Introduction

For more information read the original paper

"Generation and comprehension of unambiguous object descriptions." Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L. Yuille, Kevin Murphy; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

And our paper

"SUNSpot : An RGB-D dataset with spatial referring expressions." Cecilia Mauceri, Martha Palmer, and Christoffer Heckman; ICCV19 CLVL: 3rd Workshop on Closing the Loop Between Vision and Language, 2019.

Installation

These networks can be run with or with CUDA support. We have tested this project on two machines; A MacBook Pro with Intel Core i7 and a Ubuntu Server with Intel Xeon Processor and Nvidia P6000 cards.

  1. Install the following packages in your python environment. We recommend using a new anaconda environment, to avoid messing up other installations.

    • pytorch 1.1
    • Cython
    • tqdm
    • scikit-image
    • yacscond
    • tensorflow (for using tensorboard)
    • future
    conda create --name refexp_generation
    conda activate refexp_generation
    
    # Check https://pytorch.org for appropriate pytorch package
    # The following installs vanilla pytorch without CUDA
    conda install pytorch torchvision -c pytorch 
    
    conda install Cython tqdm scikit-image future
    pip install yacs
    
    # Check https://www.tensorflow.org/install for appropriate tensorflow package
    # The following installs vanilla tensorflow without CUDA
    pip install tensorflow
  2. Install the cocoapi

    git clone https://github.com/cocodataset/cocoapi.git
    cd cocoapi/PythonAPI/
    make
    pip install -e .
    cd ../..
  3. For evaluation, install nlg-eval

    # Install Java 1.8.0 (or higher). Then run:
    
    git clone https://github.com/Maluuba/nlg-eval.git
    cd nlg-eval
    
    # Install the Python dependencies.
    # It may take a while to run because it's downloading some files. You can instead run `pip install -v -e .` to see more details.
    pip install -e .
    
    # Download required data files.
    nlg-eval --setup
    
    cd ..

Datasets

SUNSpot

  1. Make a <data_root> directory for SUNSpot, for example data/sunspot/.
  2. Download the SUNRGBD images. The directory you save them in will be your <img_root>.
  3. Download the SUNSpot annotations and unzip them in <data_root>

Publicly available datasets

Download additional referring expressions datasets from https://github.com/lichengunc/refer

We use MegaDepth to generate synthetic depth images for the COCO dataset.

Make your own referring expressions dataset

  1. Make a directory for your dataset, for example data/<your_dataset>/. This will be your <data_root>.

  2. Make a COCO style annotation file describing your images and bounding box annotations and save as <data_root>/instance.json

  3. Save your referring expressions as a pickle file, <data_root>/ref(<version_name>).p, with the structure:

    refs: list of dict [
        {
        image_id : unique image id (int)
        split : train/test/val (str)
        sentences : list of dict [
            {
            tokens : tokenized version of referring expression (list of str)
            raw : unprocessed referring expression (str)
            sent : referring expression with mild processing, lower case, spell correction, etc. (str)
            sent_id : unique referring expression id (int)
            } ...
        ]
        file_name : file name of image relative to img_root (str)
        category_id : object category label (int)
        ann_id : id of object annotation in instance.json (int)
        sent_ids : same ids as nested sentences[...][sent_id] (list of int)
        ref_id : unique id for refering expression (int)
        } ...
    ] 
    
  4. Optional : If you have depth images, make a mapping file, <data_root>/depth.json which maps image ids to depth file paths

    {
        <image_id> : file name of depth image relative to depth_root  (str)
        ...    
    }
    
  5. You can check if the dataset loads correctly by running

    python src/data_management/refer.py --data_root <data_root> --img_root <img_root> --depth_root <depth_root> --version <version_name> --dataset <dataset_name>

How to Use Networks

Config Files

We use the yacs config system. Configurations are set in three spots

  1. Default configurations

  2. Configuration files

  3. Command line overrides - for example you can change the number of epochs from what is specified in the config file with

    python src/run_network.py <config_file> train TRAINING.N_EPOCH 60

Configs referenced in "SUNSpot : An RGB-D dataset with spatial referring expressions."

  1. Baseline - configs/refcocog_baseline.yaml
  2. Baseline+fine - configs/sunspot_baseline.yaml
  3. VGG - configs/refcocog_baseline_custom_vgg.yaml
  4. VGG+D - configs/refcocog_depth_baseline.yaml
  5. VGG+fine - configs/sunspot_baseline_custom_vgg.yaml
  6. VGG+D+fine - configs/sunspot_depth_baseline.yaml

The image classification networks which were pretrained for VGG+D and VGG+D+fine are mscoco_depth_classification_l2_10e-5_BCE.yaml

Training

Define a config file and run the following

python src/run_network.py <config_file> train <additional config variables>

Testing

python src/run_network.py <config_file> test <additional config variables>

Will run the most recently saved checkpoint. It will also save generated referring expressions and comprehension results in a file output/cfg.OUTPUT.CHECKPOINT_PREFIX_cfg.DATASET.NAME_<data_split>.json

Choose which data splits to run on using the following config variables

# Defaults
cfg.TEST.DO_TRAIN = True # Run on train set
cfg.TEST.DO_VAL = True # Run on val set
cfg.TEST.DO_TEST = True # Run on test set
cfg.TEST.DO_ALL = False # If false, only random sample of <=10000 images are tested from each set

For referring expressions networks, to calculate evaluation metrics, run

python src/mt_metrics.py <config_file> <output_file>

For image classification networks, use

python src/classification_metrics.py <config_file> <output_file>

License

Licensed under the Apache License, Version 2.0. See LICENSE for additional details

You can’t perform that action at this time.