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https://github.com/iffsid/mmvae

Multimodal Mixture-of-Experts VAE
https://github.com/iffsid/mmvae

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Multimodal Mixture-of-Experts VAE

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README 

        



# Multimodal Mixture-of-Experts VAE

This repository contains the code for the framework in **Variational Mixture-of-Experts Autoencodersfor Multi-Modal Deep Generative Models** (see [paper](https://arxiv.org/pdf/1911.03393.pdf)).



## Requirements

List of packages we used and the version we tested the model on (see also `requirements.txt`)



```

python == 3.6.8

gensim == 3.8.1

matplotlib == 3.1.1

nltk == 3.4.5

numpy == 1.16.4

pandas == 0.25.3

scipy == 1.3.2

seaborn == 0.9.0

scikit-image == 0.15.0

torch == 1.3.1

torchnet == 0.0.4

torchvision == 0.4.2

umap-learn == 0.1.1

```



## Downloads

### MNIST-SVHN Dataset






We construct a dataset of pairs of MNIST and SVHN such that each pair depicts the same digit class. Each instance of a digit class in either dataset is randomly paired with 20 instances of the same digit class from the other dataset.



**Usage**: To prepare this dataset, run `bin/make-mnist-svhn-idx.py` -- this should automatically handle the download and pairing.



### CUB Image-Caption






We use Caltech-UCSD Birds (CUB) dataset, with the bird images and their captions serving as two modalities.



**Usage**: We offer a cleaned-up version of the CUB dataset. Download the dataset [here](http://www.robots.ox.ac.uk/~yshi/mmdgm/datasets/cub.zip). First, create a `data` folder under the project directory; then unzip thedownloaded content into `data`. After finishing these steps, the structure of the `data/cub` folder should look like:



```

data/cub

│───text_testclasses.txt

│───text_trainvalclasses.txt    

│───train

│   │───002.Laysan_Albatross

│   │    └───...jpg

│   │───003.Sooty_Albatross

│   │    └───...jpg

│   │───...

│   └───200.Common_Yellowthroat

│        └───...jpg

└───test

    │───001.Black_footed_Albatross

    │    └───...jpg

    │───004.Groove_billed_Ani

    │    └───...jpg

    │───...

    └───197.Marsh_Wren

         └───...jpg

```



### Pretrained network

Pretrained models are also available if you want to play around with it. Download from the following links: 

- [MNIST-SVHN](http://www.robots.ox.ac.uk/~yshi/mmdgm/pretrained_models/mnist-svhn.zip)

- [CUB Image-Caption (feature)](http://www.robots.ox.ac.uk/~yshi/mmdgm/pretrained_models/cubISft.zip)

- [CUB Image-Caption (raw images)](http://www.robots.ox.ac.uk/~yshi/mmdgm/pretrained_models/cubIS.zip)



## Usage



### Training



Make sure the [requirements](#requirements) are satisfied in your environment, and relevant [datasets](#downloads) are downloaded. `cd` into `src`, and, for MNIST-SVHN experiments, run



```bash

python main.py --model mnist_svhn



```



For CUB Image-Caption with image feature search (See Figure 7 in our [paper](https://arxiv.org/pdf/1911.03393.pdf)), run

```bash

python main.py --model cubISft



```



For CUB Image-Caption with raw image generation, run

```bash

python main.py --model cubIS



```



You can also play with the hyperparameters using arguments. Some of the more interesting ones are listed as follows:

- **`--obj`**: Objective functions, offers 3 choices including importance-sampled ELBO (`elbo`), IWAE (`iwae`) and DReG (`dreg`, used in paper). Including the `--looser` flag when using IWAE or DReG removes unbalanced weighting of modalities, which we find to perform better empirically;

- **`--K`**: Number of particles, controls the number of particles `K` in IWAE/DReG estimator, as specified in following equation:






- **`--learn-prior`**: Prior variance learning, controls whether to enable prior variance learning. Results in our paper are produced with this enabled. Excluding this argument in the command will disable this option;

- **`--llik_scaling`**: Likelihood scaling, specifies the likelihood scaling of one of the two modalities, so that the likelihoods of two modalities contribute similarly to the lower bound. The default values are: 

    - _MNIST-SVHN_: MNIST scaling factor 32*32*3/28*28*1 = 3.92

    - _CUB Image-Cpation_: Image scaling factor 32/64*64*3 = 0.0026

- **`--latent-dimension`**: Latent dimension



You can also load from pre-trained models by specifying the path to the model folder, for example `python --model mnist_svhn --pre-trained path/to/model/folder/`. See following for the flag we used for these pretrained models:

- **MNIST-SVHN**: `--model mnist_svhn --obj dreg --K 30 --learn-prior --looser --epochs 30 --batch-size 128 --latent-dim 20`

- **CUB Image-Caption (feature)**: `--model cubISft --learn-prior --K 50 --obj dreg --looser --epochs 50 --batch-size 64 --latent-dim 64 --llik_scaling 0.002`

- **CUB Image-Caption (raw images)**: `--model cubIS --learn-prior --K 50 --obj dreg --looser --epochs 50 --batch-size 64 --latent-dim 64`



### Analysing

We offer tools to reproduce the quantitative results in our paper in `src/report`. To run any of the provided scripts, `cd` into `src`, and



- for likelihood estimation of data using a trained model, run `python calculate_likelihoods.py --save-dir path/to/trained/model/folder/ --iwae-samples 1000`;

- for coherence analysis and latent digit classification accuracy on MNIST-SVHN dataset, run `python analyse_ms.py --save-dir path/to/trained/model/folder/`;

- for coherence analysis on CUB image-caption dataset, run `python analyse_cub.py --save-dir path/to/trained/model/folder/`.

    - _**Note**_: The learnt CCA projection matrix and FastText embeddings can vary quite a bit due to the limited dataset size, therefore re-computing them as part of the analyses can result in different numeric values including for the baseline. **The relative performance of our model against the baseline remains the same, just that the numbers can different.** 

To produce similar results to what's reported in our paper, download the zip file [here](http://www.robots.ox.ac.uk/~yshi/mmdgm/pretrained_models/CCA_emb.zip) and do the following:

        1. Move `cub.all`, `cub.emb`, `cub.pc` to under `data/cub/oc:3_sl:32_s:300_w:3/`;

        2. Move the rest of the files, i.e. `emb_mean.pt`, `emb_proj.pt`, `images_mean.pt`, `im_proj.pt` to `path/to/trained/model/folder/`;

        3. Set the `RESET` variable in `src/report/analyse_cub.py` to `False`.



## Contact

If you have any questions, feel free to create an issue or email Yuge Shi at [email protected].