Gradient flows in 2D

Let’s showcase the properties of kernel MMDs, Hausdorff and Sinkhorn divergences on a simple toy problem: the registration of one blob onto another.

Setup

import numpy as np
import matplotlib.pyplot as plt
import time

import torch
from geomloss import SamplesLoss

use_cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor

Display routine

import numpy as np
import torch
from random import choices
from imageio import imread
from matplotlib import pyplot as plt


def load_image(fname):
    img = imread(fname, as_gray=True)  # Grayscale
    img = (img[::-1, :]) / 255.0
    return 1 - img


def draw_samples(fname, n, dtype=torch.FloatTensor):
    A = load_image(fname)
    xg, yg = np.meshgrid(
        np.linspace(0, 1, A.shape[0]),
        np.linspace(0, 1, A.shape[1]),
        indexing="xy",
    )

    grid = list(zip(xg.ravel(), yg.ravel()))
    dens = A.ravel() / A.sum()
    dots = np.array(choices(grid, dens, k=n))
    dots += (0.5 / A.shape[0]) * np.random.standard_normal(dots.shape)

    return torch.from_numpy(dots).type(dtype)


def display_samples(ax, x, color):
    x_ = x.detach().cpu().numpy()
    ax.scatter(x_[:, 0], x_[:, 1], 25 * 500 / len(x_), color, edgecolors="none")

Dataset

Our source and target samples are drawn from intervals of the real line and define discrete probability measures:

$$\alpha =\frac{1}{N}\sum _{i=1}^N{\delta }_{x_i},\beta =\frac{1}{M}\sum _{j=1}^M{\delta }_{y_j}.$$

N, M = (100, 100) if not use_cuda else (10000, 10000)

X_i = draw_samples("data/density_a.png", N, dtype)
Y_j = draw_samples("data/density_b.png", M, dtype)

/home/code/geomloss/geomloss/examples/comparisons/plot_gradient_flows_2D.py:38: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  img = imread(fname, as_gray=True)  # Grayscale

Wasserstein gradient flow

To study the influence of the $\text{Loss}$ function in measure-fitting applications, we perform gradient descent on the positions $x_i$ of the samples that make up $\alpha$ as we minimize the cost $\text{Loss}(\alpha ,\beta )$. This procedure can be understood as a discrete (Lagrangian) Wasserstein gradient flow and as a “model-free” machine learning program, where we optimize directly on the samples’ locations.

def gradient_flow(loss, lr=0.05):
    """Flows along the gradient of the cost function, using a simple Euler scheme.

    Parameters:
        loss ((x_i,y_j) -> torch float number):
            Real-valued loss function.
        lr (float, default = .05):
            Learning rate, i.e. time step.
    """

    # Parameters for the gradient descent
    Nsteps = int(5 / lr) + 1
    display_its = [int(t / lr) for t in [0, 0.25, 0.50, 1.0, 2.0, 5.0]]

    # Use colors to identify the particles
    colors = (10 * X_i[:, 0]).cos() * (10 * X_i[:, 1]).cos()
    colors = colors.detach().cpu().numpy()

    # Make sure that we won't modify the reference samples
    x_i, y_j = X_i.clone(), Y_j.clone()

    # We're going to perform gradient descent on Loss(α, β)
    # wrt. the positions x_i of the diracs masses that make up α:
    x_i.requires_grad = True

    t_0 = time.time()
    plt.figure(figsize=(12, 8))
    k = 1
    for i in range(Nsteps):  # Euler scheme ===============
        # Compute cost and gradient
        L_αβ = loss(x_i, y_j)
        [g] = torch.autograd.grad(L_αβ, [x_i])

        if i in display_its:  # display
            ax = plt.subplot(2, 3, k)
            k = k + 1
            plt.set_cmap("hsv")
            plt.scatter(
                [10], [10]
            )  # shameless hack to prevent a slight change of axis...

            display_samples(ax, y_j, [(0.55, 0.55, 0.95)])
            display_samples(ax, x_i, colors)

            ax.set_title("t = {:1.2f}".format(lr * i))

            plt.axis([0, 1, 0, 1])
            plt.gca().set_aspect("equal", adjustable="box")
            plt.xticks([], [])
            plt.yticks([], [])
            plt.tight_layout()

        # in-place modification of the tensor's values
        x_i.data -= lr * len(x_i) * g
    plt.title(
        "t = {:1.2f}, elapsed time: {:.2f}s/it".format(
            lr * i, (time.time() - t_0) / Nsteps
        )
    )

Kernel norms, MMDs

Gaussian MMD

The smooth Gaussian kernel $k(x,y)=\exp (-\Vert x-y{\Vert }^2/2{\sigma }^2)$ is blind to details which are smaller than the blurring scale $\sigma$: its gradient stops being informative when $\alpha$ and $\beta$ become equal “up to the high frequencies”.

gradient_flow(SamplesLoss("gaussian", blur=0.5))

On the other hand, if the radius $\sigma$ of the kernel is too small, particles $x_i$ won’t be attracted to the target, and may spread out to minimize the auto-correlation term $\frac{1}{2}⟨\alpha ,k\star \alpha ⟩$.

gradient_flow(SamplesLoss("gaussian", blur=0.1))

Laplacian MMD

The pointy exponential kernel $k(x,y)=\exp (-\Vert x-y\Vert /\sigma )$ tends to provide a better fit, but tends to zero at infinity and is still very prone to screening artifacts.

gradient_flow(SamplesLoss("laplacian", blur=0.1))

Energy Distance MMD

The scale-equivariant kernel $k(x,y)=-\Vert x-y\Vert$ provides a robust baseline: the Energy Distance.

# sphinx_gallery_thumbnail_number = 4
gradient_flow(SamplesLoss("energy"))

Sinkhorn divergence

(Unbiased) Sinkhorn divergences have recently been introduced in the machine learning litterature, and can be understood as modern iterations of the classic SoftAssign algorithm from economics and computer vision.

Wasserstein-1 distance

When p = 1, the Sinkhorn divergence ${\text{S}}_{\epsilon }$ interpolates between the Energy Distance (when $\epsilon$ is large):

gradient_flow(SamplesLoss("sinkhorn", p=1, blur=1.0))

And the Earth-Mover’s (Wassertein-1) distance:

gradient_flow(SamplesLoss("sinkhorn", p=1, blur=0.01))

Wasserstein-2 distance

When p = 2, ${\text{S}}_{\epsilon }$ interpolates between the degenerate kernel norm

$$\frac{1}{2}\Vert \alpha -\beta {\Vert }_{-\frac{1}{2}\Vert \cdot {\Vert }^2}^2=\frac{1}{2}\Vert \int x\text{d}\alpha (x)-\int y\text{d}\beta (y){\Vert }^2,$$

which only registers the means of both measures with each other (when $\epsilon$ is large):

gradient_flow(SamplesLoss("sinkhorn", p=2, blur=1.0))

And the quadratic, Wasserstein-2 Optimal Transport distance which has been studied so well by mathematicians from the 80’s onwards (when $\epsilon$ is small):

gradient_flow(SamplesLoss("sinkhorn", p=2, blur=0.01))

Introduced in 2016-2018, the unbalanced setting (Gaussian-Hellinger, Wasserstein-Fisher-Rao, etc.) provides a principled way of introducing a threshold in Optimal Transport computations: it allows you to introduce laziness in the transportation problem by replacing distance fields $\Vert x-y\Vert$ with a robustified analogous $\rho \cdot (1-e^{-\Vert x-y\Vert /\rho })$, whose gradient saturates beyond a given reach, $\rho$ - at least, that’s the idea.

In real-life applications, this tunable parameter could allow you to be a little bit more robust to outliers!

gradient_flow(SamplesLoss("sinkhorn", p=2, blur=0.01, reach=0.3))