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Multi-object datasets

Tools to generate and use multi-object datasets. The datasets consist of images and a dictionary of labels, where each image is labeled with 1) the number of objects in it and 2) each object's attributes.

Using datasets only requires numpy as datasets are .npz. Generating sprites requires scikit-image. Tools for using the datasets in PyTorch are provided, with usage examples.

Basic usage (pip package)

1. Either download one of the datasets in generated/, or generate a new one.
2. Place the .npz dataset in /path/to/data/.
3. pip install multiobject
4. Usage in PyTorch:

   from multiobject.pytorch import MultiObjectDataLoader, MultiObjectDataset
   dataset_path = '/path/to/data/some_dataset.npz'
   train_set = MultiObjectDataset(dataset_path, train=True)
   test_set = MultiObjectDataset(dataset_path, train=False)
   train_loader = MultiObjectDataLoader(train_set, batch_size=batch_size, shuffle=True)
   test_loader = MultiObjectDataLoader(test_set, batch_size=test_batch_size)
   

Run demos

conda create --name multiobject python=3.7
conda activate multiobject
pip install -r requirements.txt
CUDA_VISIBLE_DEVICES=0 python demo_vae.py
CUDA_VISIBLE_DEVICES=0 python demo_count.py

Available datasets

Datasets are available as .npz files in ./generated/.

dSprites<sup id="a1">1</sup>

Binary RGB images with monochromatic dSprites on a black canvas. Sprites can overlap (sum and clip).

Binarized MNIST

Binary 64x64 single-channel images with MNIST digits on a black canvas. Digits are rescaled to 18x18 and binarized, and they can overlap (sum and clip). Only digits from the MNIST training set are used (60k).

Generating a new dataset

1. Clone this repo.

2. See requirements, or set up a virtual environment as follows:

   conda create --name multiobject python=3.7
   conda activate multiobject
   pip install -r requirements.txt
   

3. Optional: generate a new type of sprites:

   1. create a file sprites/xyz.py containing a function generate_xyz(), where "xyz" denotes the new sprite type
   2. in generate_dataset.py, add a call to generate_xyz() to generate the correct sprites, and add 'xyz' to the list of supported sprites

4. Call generate_dataset.py with the desired sprite type as --type argument. Example:

   python generate_dataset.py --type dsprites
   

The sprite attributes are managed automatically when generating a dataset from a set of sprites that have per-sprite labels. However, since they are dataset-specific, they have to be defined when creating the sprites.

Note. For now, the following parameters have to be customized in generate_dataset.py directly:

• probability distribution over number of objects
• image size
• sprite size
• dataset size
• whether sprites can overlap

Requirements

To generate datasets:

numpy==1.18.1
matplotlib==3.1.2
scikit_image==0.16.2
tqdm==4.41.1
pillow==7.0.0

To run the examples or use the pytorch tools:

torch==1.4.0
torchvision==0.5.0

Footnotes

<b id="f1">¹</b> This is actually an extension of the original dSprites dataset to many objects and to color images. ↩