monet-pytorch 1.1.0

Pytorch implementation of Multi-Object Network(MONet)

Pytorch MONet implementation

Pytorch implementation of Multi-Object Network(MONet)

How to install

You can install through pip with the following command

pip install monet-pytorch

or clone this repository locally and install with poetry

git clone https://github.com/Michedev/MONet-pytorch
cd MONet-pytorch
poetry install

How to use

The package comes with a set of predefined configurations based on paper specifications, namely monet and monet-iodine (MONet as defined in IODINE paper).

from monet_pytorch import Monet

monet = Monet.from_config(model='monet')

There is also another custom architecture monet-lightweight which has less parameters than the original ones.

Furthermore, the model architecture slightly changes based on the dataset (e.g. U-Net blocks) picked between the ones cited in MONet paper (CLEVR 6, Multidsprites colored on colored, Multidsprited colored on grayscale, Tetrominoes).

from monet_pytorch import Monet

monet = Monet.from_config(model='monet', dataset='tetrominoes')

In alternative, you can set custom dataset parameters through the function arguments

from monet_pytorch import Monet

monet = Monet.from_config(model='monet', dataset_width=48, dataset_height=48, scene_max_objects=6)

Lastly, you can make your custom MONet by input your custom configuration as OmegaConf DictConfig

from monet_pytorch import Monet

custom_monet_config = OmegaConf.create("""
dataset:
  width: 44
  height: 44
  max_num_objects: 10
model:  #this config file follows MONet implementation from IODINE paper
  _target_: monet_pytorch.model.Monet
  height: ${dataset.height}
  width: ${dataset.width}
  num_slots: ${dataset.max_num_objects}
  name: monet-iodine
  bg_sigma: 0.32
  fg_sigma: 0.1
  beta_kl: 0.43
  gamma: 0.5
  latent_size: 16
  input_channels: 3
  encoder:
    _target_: torch.nn.Sequential
    _args_:
      - _target_: monet_pytorch.template.sequential_cnn.make_sequential_cnn_from_config
        channels: [44, 44, 32, 14]
        kernels: 3
        strides: 2
        paddings: 0
        input_channels: 4
        batchnorms: true
        bn_affines: false
        activations: relu
      - _target_: torch.nn.Flatten
        start_dim: 1
      - _target_: torch.nn.Linear
        in_features: 256
        out_features: 256
      - _target_: torch.nn.ReLU
      - _target_: torch.nn.Linear
        in_features: 256
        out_features: ${prod:${model.latent_size},2}
  decoder:
    _target_: monet_pytorch.template.encoder_decoder.BroadcastDecoderNet
    w_broadcast: ${sum:${dataset.width},8}
    h_broadcast: ${sum:${dataset.height},8}
    net:
      _target_: monet_pytorch.template.sequential_cnn.make_sequential_cnn_from_config
      input_channels: ${sum:${model.latent_size},2} # latent size + 2
      channels: [32, 32, 64, 64, 4]  # last is 4 channels because rgb (3) + mask (1)
      kernels: [3, 3, 3, 3, 1]
      paddings: 0
      activations: [relu, relu, relu, relu, null]  #null means no activation function no activation
      batchnorms: [true, true, true, true, false]
      bn_affines: [false, false, false, false, false]
  unet:
    _target_: monet_pytorch.unet.UNet
    input_channels: ${model.input_channels}
    num_blocks: 5
    filter_start: 16
    mlp_size: 128""")

custom_monet: Monet = Monet.from_custom_config(custom_monet_config)

Model performances

This implementation reproduce very closely ARI MONet's values

Special thanks

I would like to thank @apra and @addtt for the help to fix code bugs and to improve model performances