priba / fml

FML (Francis' Machine-Learnin' Library) - A collection of utilities for machine learning tasks

FML (Francis' Machine-Learnin' Library)

This repository is a collection of PyTorch tools for machine learning. Currently it includes

• A numerically stable implementation of the Sinkhorn Algorithm for point sets in any dimension
• A vectorized implementation of the Chamfer Distance between point sets in any dimension

Installation Instructions

With pip

Simply run:

pip install git+https://github.com/fwilliams/fml

With conda

Simply run:

conda install -c fwilliams fml

Library Structure

The structure of the library is similar to PyTorch. There is a fml.functional module which includes a functional interface for utilities and an fml.nn which includes PyTorch module implementations of utilities.

Examples

Computing the loss between two evenly weighted point sets

import torch
from fml.nn import SinkhornLoss

minibatch_size = 3
set_size = 10
point_dim = 4

# Create two minibatches of point sets where each batch item set_a[k, :, :] is a set of `set_size` points
set_a = torch.rand([minibatch_size, set_size, point_dim])
set_b = torch.rand([minibatch_size, set_size, point_dim])

# Create a loss function module with default parameters. See the class documentation for optional parameters.
loss_fun = SinkhornLoss()

# Compute the loss between each pair of sets in the minibatch
# loss is a tensor with [minibatch_size] elements which can be backpropagated through
loss = loss_fun(set_a, set_b)

Computing the loss between two non evenly weighted point sets

import torch
from fml.nn import SinkhornLoss

minibatch_size = 3
set_size = 10
point_dim = 4

# Create two minibatches of point sets where each batch item set_a[k, :, :] is a set of `set_size` points
set_a = torch.rand([minibatch_size, set_size, point_dim])
set_b = torch.rand([minibatch_size, set_size, point_dim])

# Generate weights which sum to 1 for each set
# Note that zero weights are the same as not including a set element
weights_a = torch.rand([minibatch_size, set_size])
weights_a /= torch.sum(weights_a, axis=1) 

weights_b = torch.rand([minibatch_size, set_size])
weights_b /= torch.sum(weights_b, axis=1) 

# Create a loss function module with default parameters. See the class documentation for optional parameters.
loss_fun = SinkhornLoss()

# Compute the loss between each pair of sets in the minibatch
# loss is a tensor with [minibatch_size] elements which can be backpropagated through
loss = loss_fun(set_a, set_b)

Computing the Chamfer Distance between point sets

import torch
from fml.nn import ChamferLoss

minibatch_size = 3
set_size = 10
point_dim = 4

# Create two minibatches of point sets where each batch item set_a[k, :, :] is a set of `set_size` points
set_a = torch.rand([minibatch_size, set_size, point_dim])
set_b = torch.rand([minibatch_size, set_size, point_dim])

# Create a loss function module.
loss_fun = ChamferLoss()

# Compute the loss between each pair of sets in the minibatch
# loss is a tensor with [minibatch_size] elements which can be backpropagated through
loss = loss_fun(set_a, set_b)

Computing pairwise distances between point sets

import torch
from fml.functional import pairwise_distances

minibatch_size = 3
set_size = 10
point_dim = 4

# Create two minibatches of point sets where each batch item set_a[k, :, :] is a set of `set_size` points
set_a = torch.rand([minibatch_size, set_size, point_dim])
set_b = torch.rand([minibatch_size, set_size, point_dim])

# Compute the pairwise distances between each pair of sets in the minibatch
# distances is a tensor of shape [minibatch_size, set_size, set_size] where each
# disances[k, i, j] = ||set_a[k, i] - set_b[k, j]||^2
distances = pairwise_distances(set_a, set_b)