DMDR-Count

Dilated variation of DM-Count

Made a different repo instead of a fork because the two projects grew in different directions.

Additions to the original repo;

• Arguments are now given by json files.
• There is now TensorBoard integration!
• UCF dataset support!

For the future;

• Optional command line arguments
• Cleaner checkpoint names
• Documentation about design decisions and usage help.
• Maybe deep learning tutorials?
• Some other approaches I tried but are not ready for publishing.

The Main Idea

Repo based on

Here is the original repo since the interface and the backbone network was different this will not be a fork repo.

Pytorch implementation of the paper Distribution Matching for Crowd Counting (NeurIPS, spotlight).

Getting Started

1. Dataset download

• QNRF can be downloaded here

• NWPU can be downloaded here

• Shanghai Tech Part A and Part B can be downloaded here

2. Data preprocess

Due to large sizes of images in QNRF and NWPU datasets, we preprocess these two datasets.

python preprocess_dataset.py --dataset <dataset name: qnrf or nwpu> --input-dataset-path <original data directory> --output-dataset-path <processed data directory> 

3. Training The interface is like this;

usage: train.py [-h] [--load-args LOAD_ARGS] [--data-path DATA_PATH] [--dataset {qnrf,nwpu,sha,shb}]
                [--out-path OUT_PATH] [--lr LR] [--weight-decay WEIGHT_DECAY] [--resume RESUME]
                [--auto-resume] [--max-epoch MAX_EPOCH] [--val-epoch VAL_EPOCH] [--val-start VAL_START]
                [--batch-size BATCH_SIZE] [--device DEVICE] [--num-workers NUM_WORKERS] [--wot WOT]
                [--wtv WTV] [--reg REG] [--num-of-iter-in-ot NUM_OF_ITER_IN_OT] [--norm-cood NORM_COOD]

Train

optional arguments:
  -h, --help            show this help message and exit
  --load-args LOAD_ARGS
                        file to read program args from. Will ignore other parameters if specified
  --data-path DATA_PATH
                        dataset path
  --dataset {qnrf,nwpu,sha,shb}
                        dataset name
  --out-path OUT_PATH   place to save checkpoints and models.
  --lr LR               initial learning rate
  --weight-decay WEIGHT_DECAY
                        the weight decay
  --resume RESUME       state dict to resume from. If specified as empty will start over
  --auto-resume         if set will try to find most recent checkpoint in 'out_path'
  --max-epoch MAX_EPOCH
                        max training epoch
  --val-epoch VAL_EPOCH
                        the num of steps to log training information
  --val-start VAL_START
                        the epoch start to val
  --batch-size BATCH_SIZE
                        train batch size
  --device DEVICE       assign device
  --num-workers NUM_WORKERS
                        the num of training process
  --wot WOT             weight on OT loss
  --wtv WTV             weight on TV loss
  --reg REG             entropy regularization in sinkhorn
  --num-of-iter-in-ot NUM_OF_ITER_IN_OT
                        sinkhorn iterations
  --norm-cood NORM_COOD
                        Whether to norm cood when computing distance

Training can be done two ways;

python train.py --dataset <dataset name: qnrf, sha, shb or nwpu> --data-path <path to dataset> --device <gpu device id>

or from a desired .json file like args.json in the repository for example;

python train.py --load-args args.json

When this option is specified other given option from the terminal will be ignored. Some default configurations specific to the selected dataset can be changed from datasets/dataset_cfg.json file.
To directly run it with default arguments make sure you select these correctly; from args.json file;

..
"train":{
  ...
  "dataset":<dataset name>
  ...
  }
..

and from datasets/dataset_cfg.json;

{
  ..
    ...
    "dataset_paths":{
        "<dataset name>":{
            "data_path":"<dataset root path>", <-- this is important
            other attributes are generally the default
        },
      ...
    }
  ..
}

4. Test

The interface is like this;

usage: test.py [-h] [--load-args LOAD_ARGS] [--device DEVICE] [--model-path MODEL_PATH]
               [--data-path DATA_PATH] [--dataset {qnrf,nwpu,sha,shb}]
               [--pred-density-map-path PRED_DENSITY_MAP_PATH]

Test

optional arguments:
  -h, --help            show this help message and exit
  --load-args LOAD_ARGS
                        file to read program args from. Will ignore other parameters if specified
  --device DEVICE       assign device
  --model-path MODEL_PATH
                        saved model path
  --data-path DATA_PATH
                        dataset path
  --dataset {qnrf,nwpu,sha,shb}
                        dataset name
  --pred-density-map-path PRED_DENSITY_MAP_PATH
                        save predicted density maps when pred-density-map-path is not empty.

python test.py --model-path <path of the model to be evaluated> --data-path <directory for the dataset> --dataset <dataset name: qnrf, sha, shb or nwpu>

The same applies here you can also use args.json file to load like this;

python test.py --load-args args.json

Pretrained models

Pretrained models on UCF-QNRF, NWPU, Shanghaitech part A and B can be found in pretrained_models folder or Google Drive

References

If you find this work or code useful, please cite:

@inproceedings{wang2020DMCount,
  title={Distribution Matching for Crowd Counting},
  author={Boyu Wang and Huidong Liu and Dimitris Samaras and Minh Hoai},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020},
}