Optimal Transport Dataset Distance (OTDD)

Codebase accompanying the papers:

• Geometric Dataset Distances via Optimal Transport.
• Dataset Dynamics via Gradient Flows in Probability Space.

See the papers for technical details, or the MSR Blog Post for a high-level introduction.

Getting Started

Installation

Note: It is highly recommended that the following be done inside a virtual environment

Via Conda (recommended)

If you use [ana|mini]conda , you can simply do:

conda env create -f environment.yaml python=3.8
conda activate otdd
conda install .

(you might need to install pytorch separately if you need a custom install)

Via pip

First install dependencies. Start by install pytorch with desired configuration using the instructions provided in the pytorch website. Then do:

pip install -r requirements.txt

Finally, install this package:

pip install .

Usage Examples

A vanilla example for OTDD:

from otdd.pytorch.datasets import load_torchvision_data
from otdd.pytorch.distance import DatasetDistance


# Load datasets
loaders_src = load_torchvision_data('MNIST', valid_size=0, resize = 28, maxsize=2000)[0]
loaders_tgt = load_torchvision_data('USPS',  valid_size=0, resize = 28, maxsize=2000)[0]

# Instantiate distance
dist = DatasetDistance(loaders_src['train'], loaders_tgt['train'],
                       inner_ot_method = 'exact',
                       debiased_loss = True,
                       p = 2, entreg = 1e-1,
                       device='cpu')

d = dist.distance(maxsamples = 1000)
print(f'OTDD(src,tgt)={d}')

Advanced Usage

Using a custom feature distance

By default, OTDD uses the (squared) Euclidean distance between features. To use a custom distance in domains where it makes sense to use one (e.g., images), one can pass a callable to OTDD using the feature_cost arg. Example:

import torch
from torchvision.models import resnet18

from otdd.pytorch.datasets import load_torchvision_data
from otdd.pytorch.distance import DatasetDistance, FeatureCost

# Load MNIST/CIFAR in 3channels (needed by torchvision models)
loaders_src = load_torchvision_data('CIFAR10', resize=28, maxsize=2000)[0]
loaders_tgt = load_torchvision_data('MNIST', resize=28, to3channels=True, maxsize=2000)[0]

# Embed using a pretrained (+frozen) resnet
embedder = resnet18(pretrained=True).eval()
embedder.fc = torch.nn.Identity()
for p in embedder.parameters():
    p.requires_grad = False

# Here we use same embedder for both datasets
feature_cost = FeatureCost(src_embedding = embedder,
                           src_dim = (3,28,28),
                           tgt_embedding = embedder,
                           tgt_dim = (3,28,28),
                           p = 2,
                           device='cpu')

dist = DatasetDistance(loaders_src['train'], loaders_tgt['train'],
                          inner_ot_method = 'exact',
                          debiased_loss = True,
                          feature_cost = feature_cost,
                          sqrt_method = 'spectral',
                          sqrt_niters=10,
                          precision='single',
                          p = 2, entreg = 1e-1,
                          device='cpu')

d = dist.distance(maxsamples = 10000)

Gradient Flows

import os
import matplotlib
%matplotlib inline # Comment out if not on notebook

from otdd.pytorch.flows import OTDD_Gradient_Flow
from otdd.pytorch.flows import CallbackList, ImageGridCallback, TrajectoryDump

# Load datasets
loaders_src = load_torchvision_data('MNIST', valid_size=0, resize = 28, maxsize=2000)[0]
loaders_tgt = load_torchvision_data('USPS',  valid_size=0, resize = 28, maxsize=2000)[0]


outdir =  os.path.join('out', 'flows')
callbacks = CallbackList([
  ImageGridCallback(display_freq=2, animate=False, save_path = outdir + '/grid'),
])

flow = OTDD_Gradient_Flow(loaders_src['train'], loaders_tgt['train'],
                          ### Gradient Flow Args
                          method = 'xonly-attached',                          
                          use_torchoptim=True,
                          optim='adam',
                          steps=10,
                          step_size=1,
                          callback=callbacks,              
                          clustering_method='kmeans',                                      
                          ### OTDD Args                          
                          online_stats=True,
                          diagonal_cov = False,
                          device='cpu'
                          )
d,out = flow.flow()

Acknowledgements

This repo relies on the geomloss and POT packages for internal EMD and Sinkhorn algorithm implementation. We are grateful to the authors and maintainers of those projects.

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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