# Group-equivariant neural networks with escnn At the moment, we resume our exploration of group equivariance. That is the third submit within the collection. The primary was a high-level introduction: what that is all about; how equivariance is operationalized; and why it’s of relevance to many deep-learning functions. The second sought to concretize the important thing concepts by creating a group-equivariant CNN from scratch. That being instructive, however too tedious for sensible use, at the moment we take a look at a rigorously designed, highly-performant library that hides the technicalities and allows a handy workflow.

First although, let me once more set the context. In physics, an all-important idea is that of symmetry, a symmetry being current every time some amount is being conserved. However we don’t even must look to science. Examples come up in day by day life, and – in any other case why write about it – within the duties we apply deep studying to.

In day by day life: Take into consideration speech – me stating “it’s chilly,” for instance. Formally, or denotation-wise, the sentence could have the identical which means now as in 5 hours. (Connotations, alternatively, can and can in all probability be completely different!). This can be a type of translation symmetry, translation in time.

In deep studying: Take picture classification. For the same old convolutional neural community, a cat within the middle of the picture is simply that, a cat; a cat on the underside is, too. However one sleeping, comfortably curled like a half-moon “open to the correct,” won’t be “the identical” as one in a mirrored place. After all, we are able to practice the community to deal with each as equal by offering coaching photographs of cats in each positions, however that isn’t a scaleable strategy. As an alternative, we’d wish to make the community conscious of those symmetries, so they’re routinely preserved all through the community structure.

## Objective and scope of this submit

Right here, I introduce `escnn`, a PyTorch extension that implements types of group equivariance for CNNs working on the aircraft or in (3d) house. The library is utilized in numerous, amply illustrated analysis papers; it’s appropriately documented; and it comes with introductory notebooks each relating the maths and exercising the code. Why, then, not simply consult with the first pocket book, and instantly begin utilizing it for some experiment?

In reality, this submit ought to – as fairly a couple of texts I’ve written – be considered an introduction to an introduction. To me, this matter appears something however straightforward, for numerous causes. After all, there’s the maths. However as so typically in machine studying, you don’t must go to nice depths to have the ability to apply an algorithm accurately. So if not the maths itself, what generates the issue? For me, it’s two issues.

First, to map my understanding of the mathematical ideas to the terminology used within the library, and from there, to appropriate use and utility. Expressed schematically: We now have an idea A, which figures (amongst different ideas) in technical time period (or object class) B. What does my understanding of A inform me about how object class B is for use accurately? Extra importantly: How do I take advantage of it to finest attain my objective C? This primary problem I’ll deal with in a really pragmatic means. I’ll neither dwell on mathematical particulars, nor attempt to set up the hyperlinks between A, B, and C intimately. As an alternative, I’ll current the characters on this story by asking what they’re good for.

Second – and this can be of relevance to only a subset of readers – the subject of group equivariance, significantly as utilized to picture processing, is one the place visualizations could be of large assist. The quaternity of conceptual clarification, math, code, and visualization can, collectively, produce an understanding of emergent-seeming high quality… if, and provided that, all of those clarification modes “work” for you. (Or if, in an space, a mode that doesn’t wouldn’t contribute that a lot anyway.) Right here, it so occurs that from what I noticed, a number of papers have glorious visualizations, and the identical holds for some lecture slides and accompanying notebooks. However for these amongst us with restricted spatial-imagination capabilities – e.g., individuals with Aphantasia – these illustrations, meant to assist, could be very exhausting to make sense of themselves. If you happen to’re not one in every of these, I completely advocate trying out the assets linked within the above footnotes. This textual content, although, will attempt to make the absolute best use of verbal clarification to introduce the ideas concerned, the library, and tips on how to use it.

That stated, let’s begin with the software program.

## Utilizing escnn

`Escnn` is dependent upon PyTorch. Sure, PyTorch, not `torch`; sadly, the library hasn’t been ported to R but. For now, thus, we’ll make use of `reticulate` to entry the Python objects straight.

The way in which I’m doing that is set up `escnn` in a digital surroundings, with PyTorch model 1.13.1. As of this writing, Python 3.11 will not be but supported by one in every of `escnn`’s dependencies; the digital surroundings thus builds on Python 3.10. As to the library itself, I’m utilizing the event model from GitHub, working `pip set up git+https://github.com/QUVA-Lab/escnn`.

When you’re prepared, situation

``````library(reticulate)
# Confirm appropriate surroundings is used.
# Other ways exist to make sure this; I've discovered most handy to configure this on
# a per-project foundation in RStudio's venture file (<myproj>.Rproj)
py_config()

# bind to required libraries and get handles to their namespaces
torch <- import("torch")
escnn <- import("escnn")``````

`Escnn` loaded, let me introduce its essential objects and their roles within the play.

## Areas, teams, and representations: `escnn\$gspaces`

We begin by peeking into `gspaces`, one of many two sub-modules we’re going to make direct use of.

`````` "conicalOnR3" "cylindricalOnR3" "dihedralOnR3" "flip2dOnR2" "flipRot2dOnR2" "flipRot3dOnR3"
 "fullCylindricalOnR3" "fullIcoOnR3" "fullOctaOnR3" "icoOnR3" "invOnR3" "mirOnR3 "octaOnR3"
 "rot2dOnR2" "rot2dOnR3" "rot3dOnR3" "trivialOnR2" "trivialOnR3"    ``````

The strategies I’ve listed instantiate a `gspace`. If you happen to look carefully, you see that they’re all composed of two strings, joined by “On.” In all situations, the second half is both `R2` or `R3`. These two are the accessible base areas – (mathbb{R}^2) and (mathbb{R}^3) – an enter sign can reside in. Indicators can, thus, be photographs, made up of pixels, or three-dimensional volumes, composed of voxels. The primary half refers back to the group you’d like to make use of. Selecting a bunch means selecting the symmetries to be revered. For instance, `rot2dOnR2()` implies equivariance as to rotations, `flip2dOnR2()` ensures the identical for mirroring actions, and `flipRot2dOnR2()` subsumes each.

Let’s outline such a `gspace`. Right here we ask for rotation equivariance on the Euclidean aircraft, making use of the identical cyclic group – (C_4) – we developed in our from-scratch implementation:

``````r2_act <- gspaces\$rot2dOnR2(N = 4L)
r2_act\$fibergroup``````

On this submit, I’ll stick with that setup, however we may as nicely choose one other rotation angle – `N = 8`, say, leading to eight equivariant positions separated by forty-five levels. Alternatively, we would need any rotated place to be accounted for. The group to request then could be SO(2), referred to as the particular orthogonal group, of steady, distance- and orientation-preserving transformations on the Euclidean aircraft:

``(gspaces\$rot2dOnR2(N = -1L))\$fibergroup``
``SO(2)``

Going again to (C_4), let’s examine its representations:

``````\$irrep_0
C4|[irrep_0]:1

\$irrep_1
C4|[irrep_1]:2

\$irrep_2
C4|[irrep_2]:1

\$common
C4|[regular]:4``````

A illustration, in our present context and very roughly talking, is a option to encode a bunch motion as a matrix, assembly sure situations. In `escnn`, representations are central, and we’ll see how within the subsequent part.

First, let’s examine the above output. 4 representations can be found, three of which share an vital property: they’re all irreducible. On (C_4), any non-irreducible illustration could be decomposed into into irreducible ones. These irreducible representations are what `escnn` works with internally. Of these three, probably the most fascinating one is the second. To see its motion, we have to select a bunch component. How about counterclockwise rotation by ninety levels:

``````elem_1 <- r2_act\$fibergroup\$component(1L)
elem_1``````
``1[2pi/4]``

Related to this group component is the next matrix:

``r2_act\$representations[](elem_1)``
``````             [,1]          [,2]
[1,] 6.123234e-17 -1.000000e+00
[2,] 1.000000e+00  6.123234e-17``````

That is the so-called commonplace illustration,

[
begin{bmatrix} cos(theta) & -sin(theta) sin(theta) & cos(theta) end{bmatrix}
]

, evaluated at (theta = pi/2). (It’s referred to as the usual illustration as a result of it straight comes from how the group is outlined (specifically, a rotation by (theta) within the aircraft).

The opposite fascinating illustration to level out is the fourth: the one one which’s not irreducible.

``r2_act\$representations[](elem_1)``
``````[1,]  5.551115e-17 -5.551115e-17 -8.326673e-17  1.000000e+00
[2,]  1.000000e+00  5.551115e-17 -5.551115e-17 -8.326673e-17
[3,]  5.551115e-17  1.000000e+00  5.551115e-17 -5.551115e-17
[4,] -5.551115e-17  5.551115e-17  1.000000e+00  5.551115e-17``````

That is the so-called common illustration. The common illustration acts through permutation of group components, or, to be extra exact, of the premise vectors that make up the matrix. Clearly, that is solely doable for finite teams like (C_n), since in any other case there’d be an infinite quantity of foundation vectors to permute.

To higher see the motion encoded within the above matrix, we clear up a bit:

``spherical(r2_act\$representations[](elem_1))``
``````    [,1] [,2] [,3] [,4]
[1,]    0    0    0    1
[2,]    1    0    0    0
[3,]    0    1    0    0
[4,]    0    0    1    0``````

This can be a step-one shift to the correct of the id matrix. The id matrix, mapped to component 0, is the non-action; this matrix as a substitute maps the zeroth motion to the primary, the primary to the second, the second to the third, and the third to the primary.

We’ll see the common illustration utilized in a neural community quickly. Internally – however that needn’t concern the person – escnn works with its decomposition into irreducible matrices. Right here, that’s simply the bunch of irreducible representations we noticed above, numbered from one to a few.

Having checked out how teams and representations determine in `escnn`, it’s time we strategy the duty of constructing a community.

## Representations, for actual: `escnn\$nn\$FieldType`

Thus far, we’ve characterised the enter house ((mathbb{R}^2)), and specified the group motion. However as soon as we enter the community, we’re not within the aircraft anymore, however in an area that has been prolonged by the group motion. Rephrasing, the group motion produces function vector fields that assign a function vector to every spatial place within the picture.

Now we have now these function vectors, we have to specify how they remodel underneath the group motion. That is encoded in an `escnn\$nn\$FieldType` . Informally, let’s imagine {that a} subject sort is the information sort of a function house. In defining it, we point out two issues: the bottom house, a `gspace`, and the illustration sort(s) for use.

In an equivariant neural community, subject sorts play a job just like that of channels in a convnet. Every layer has an enter and an output subject sort. Assuming we’re working with grey-scale photographs, we are able to specify the enter sort for the primary layer like this:

``````nn <- escnn\$nn
feat_type_in <- nn\$FieldType(r2_act, record(r2_act\$trivial_repr))``````

The trivial illustration is used to point that, whereas the picture as an entire can be rotated, the pixel values themselves needs to be left alone. If this had been an RGB picture, as a substitute of `r2_act\$trivial_repr` we’d go an inventory of three such objects.

So we’ve characterised the enter. At any later stage, although, the state of affairs could have modified. We could have carried out convolution as soon as for each group component. Shifting on to the following layer, these function fields must remodel equivariantly, as nicely. This may be achieved by requesting the common illustration for an output subject sort:

``feat_type_out <- nn\$FieldType(r2_act, record(r2_act\$regular_repr))``

Then, a convolutional layer could also be outlined like so:

``conv <- nn\$R2Conv(feat_type_in, feat_type_out, kernel_size = 3L)``

## Group-equivariant convolution

What does such a convolution do to its enter? Similar to, in a standard convnet, capability could be elevated by having extra channels, an equivariant convolution can go on a number of function vector fields, presumably of various sort (assuming that is smart). Within the code snippet under, we request an inventory of three, all behaving in keeping with the common illustration.

``````feat_type_in <- nn\$FieldType(r2_act, record(r2_act\$trivial_repr))
feat_type_out <- nn\$FieldType(
r2_act,
record(r2_act\$regular_repr, r2_act\$regular_repr, r2_act\$regular_repr)
)

conv <- nn\$R2Conv(feat_type_in, feat_type_out, kernel_size = 3L)``````

We then carry out convolution on a batch of photographs, made conscious of their “information sort” by wrapping them in `feat_type_in`:

``````x <- torch\$rand(2L, 1L, 32L, 32L)
x <- feat_type_in(x)
y <- conv(x)
y\$form |> unlist()``````
``  2  12 30 30``

The output has twelve “channels,” this being the product of group cardinality – 4 distinguished positions – and variety of function vector fields (three).

If we select the only doable, roughly, check case, we are able to confirm that such a convolution is equivariant by direct inspection. Right here’s my setup:

``````feat_type_in <- nn\$FieldType(r2_act, record(r2_act\$trivial_repr))
feat_type_out <- nn\$FieldType(r2_act, record(r2_act\$regular_repr))
conv <- nn\$R2Conv(feat_type_in, feat_type_out, kernel_size = 3L)

torch\$nn\$init\$constant_(conv\$weights, 1.)
x <- torch\$vander(torch\$arange(0,4))\$view(tuple(1L, 1L, 4L, 4L)) |> feat_type_in()
x``````
``````g_tensor([[[[ 0.,  0.,  0.,  1.],
[ 1.,  1.,  1.,  1.],
[ 8.,  4.,  2.,  1.],
[27.,  9.,  3.,  1.]]]], [C4_on_R2[(None, 4)]: {irrep_0 (x1)}(1)])``````

Inspection may very well be carried out utilizing any group component. I’ll choose rotation by (pi/2):

``````all <- iterate(r2_act\$testing_elements)
g1 <- all[]
g1``````

Only for enjoyable, let’s see how we are able to – actually – come entire circle by letting this component act on the enter tensor 4 occasions:

``````all <- iterate(r2_act\$testing_elements)
g1 <- all[]

x1 <- x\$remodel(g1)
x1\$tensor
x2 <- x1\$remodel(g1)
x2\$tensor
x3 <- x2\$remodel(g1)
x3\$tensor
x4 <- x3\$remodel(g1)
x4\$tensor``````
``````tensor([[[[ 1.,  1.,  1.,  1.],
[ 0.,  1.,  2.,  3.],
[ 0.,  1.,  4.,  9.],
[ 0.,  1.,  8., 27.]]]])

tensor([[[[ 1.,  3.,  9., 27.],
[ 1.,  2.,  4.,  8.],
[ 1.,  1.,  1.,  1.],
[ 1.,  0.,  0.,  0.]]]])

tensor([[[[27.,  8.,  1.,  0.],
[ 9.,  4.,  1.,  0.],
[ 3.,  2.,  1.,  0.],
[ 1.,  1.,  1.,  1.]]]])

tensor([[[[ 0.,  0.,  0.,  1.],
[ 1.,  1.,  1.,  1.],
[ 8.,  4.,  2.,  1.],
[27.,  9.,  3.,  1.]]]])``````

You see that on the finish, we’re again on the unique “picture.”

Now, for equivariance. We may first apply a rotation, then convolve.

Rotate:

``````x_rot <- x\$remodel(g1)
x_rot\$tensor``````

That is the primary within the above record of 4 tensors.

Convolve:

``````y <- conv(x_rot)
y\$tensor``````
``````tensor([[[[ 1.1955,  1.7110],
[-0.5166,  1.0665]],

[[-0.0905,  2.6568],
[-0.3743,  2.8144]],

[[ 5.0640, 11.7395],
[ 8.6488, 31.7169]],

[[ 2.3499,  1.7937],

Alternatively, we are able to do the convolution first, then rotate its output.

Convolve:

``````y_conv <- conv(x)
y_conv\$tensor``````
``````tensor([[[[-0.3743, -0.0905],
[ 2.8144,  2.6568]],

[[ 8.6488,  5.0640],
[31.7169, 11.7395]],

[[ 4.5065,  2.3499],
[ 5.9689,  1.7937]],

[[-0.5166,  1.1955],

Rotate:

``````y <- y_conv\$remodel(g1)
y\$tensor``````
``````tensor([[[[ 1.1955,  1.7110],
[-0.5166,  1.0665]],

[[-0.0905,  2.6568],
[-0.3743,  2.8144]],

[[ 5.0640, 11.7395],
[ 8.6488, 31.7169]],

[[ 2.3499,  1.7937],
[ 4.5065,  5.9689]]]])``````

Certainly, ultimate outcomes are the identical.

At this level, we all know tips on how to make use of group-equivariant convolutions. The ultimate step is to compose the community.

## A bunch-equivariant neural community

Mainly, we have now two inquiries to reply. The primary issues the non-linearities; the second is tips on how to get from prolonged house to the information sort of the goal.

First, in regards to the non-linearities. This can be a probably intricate matter, however so long as we stick with point-wise operations (corresponding to that carried out by ReLU) equivariance is given intrinsically.

In consequence, we are able to already assemble a mannequin:

``````feat_type_in <- nn\$FieldType(r2_act, record(r2_act\$trivial_repr))
feat_type_hid <- nn\$FieldType(
r2_act,
record(r2_act\$regular_repr, r2_act\$regular_repr, r2_act\$regular_repr, r2_act\$regular_repr)
)
feat_type_out <- nn\$FieldType(r2_act, record(r2_act\$regular_repr))

mannequin <- nn\$SequentialModule(
nn\$R2Conv(feat_type_in, feat_type_hid, kernel_size = 3L),
nn\$InnerBatchNorm(feat_type_hid),
nn\$ReLU(feat_type_hid),
nn\$R2Conv(feat_type_hid, feat_type_hid, kernel_size = 3L),
nn\$InnerBatchNorm(feat_type_hid),
nn\$ReLU(feat_type_hid),
nn\$R2Conv(feat_type_hid, feat_type_out, kernel_size = 3L)
)\$eval()

mannequin``````
``````SequentialModule(
(0): R2Conv([C4_on_R2[(None, 4)]:
{irrep_0 (x1)}(1)], [C4_on_R2[(None, 4)]: {common (x4)}(16)], kernel_size=3, stride=1)
(1): InnerBatchNorm([C4_on_R2[(None, 4)]:
{common (x4)}(16)], eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=False, sort=[C4_on_R2[(None, 4)]: {common (x4)}(16)])
(3): R2Conv([C4_on_R2[(None, 4)]:
{common (x4)}(16)], [C4_on_R2[(None, 4)]: {common (x4)}(16)], kernel_size=3, stride=1)
(4): InnerBatchNorm([C4_on_R2[(None, 4)]:
{common (x4)}(16)], eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU(inplace=False, sort=[C4_on_R2[(None, 4)]: {common (x4)}(16)])
(6): R2Conv([C4_on_R2[(None, 4)]:
{common (x4)}(16)], [C4_on_R2[(None, 4)]: {common (x1)}(4)], kernel_size=3, stride=1)
)``````

Calling this mannequin on some enter picture, we get:

``````x <- torch\$randn(1L, 1L, 17L, 17L)
x <- feat_type_in(x)
mannequin(x)\$form |> unlist()``````
``  1  4 11 11``

What we do now is dependent upon the duty. Since we didn’t protect the unique decision anyway – as would have been required for, say, segmentation – we in all probability need one function vector per picture. That we are able to obtain by spatial pooling:

``````avgpool <- nn\$PointwiseAvgPool(feat_type_out, 11L)
y <- avgpool(mannequin(x))
y\$form |> unlist()``````
`` 1 4 1 1``

We nonetheless have 4 “channels,” comparable to 4 group components. This function vector is (roughly) translation-invariant, however rotation-equivariant, within the sense expressed by the selection of group. Usually, the ultimate output can be anticipated to be group-invariant in addition to translation-invariant (as in picture classification). If that’s the case, we pool over group components, as nicely:

``````invariant_map <- nn\$GroupPooling(feat_type_out)
y <- invariant_map(avgpool(mannequin(x)))
y\$tensor``````
``tensor([[[[-0.0293]]]], grad_fn=<CopySlices>)``

We find yourself with an structure that, from the skin, will appear to be a regular convnet, whereas on the within, all convolutions have been carried out in a rotation-equivariant means. Coaching and analysis then aren’t any completely different from the same old process.

## The place to from right here

This “introduction to an introduction” has been the try to attract a high-level map of the terrain, so you may determine if that is helpful to you. If it’s not simply helpful, however fascinating theory-wise as nicely, you’ll discover a lot of glorious supplies linked from the README. The way in which I see it, although, this submit already ought to allow you to truly experiment with completely different setups.

One such experiment, that might be of excessive curiosity to me, would possibly examine how nicely differing types and levels of equivariance truly work for a given activity and dataset. General, an inexpensive assumption is that, the upper “up” we go within the function hierarchy, the much less equivariance we require. For edges and corners, taken by themselves, full rotation equivariance appears fascinating, as does equivariance to reflection; for higher-level options, we would need to successively limit allowed operations, possibly ending up with equivariance to mirroring merely. Experiments may very well be designed to match other ways, and ranges, of restriction.

Thanks for studying!

Photograph by Volodymyr Tokar on Unsplash

Weiler, Maurice, Patrick Forré, Erik Verlinde, and Max Welling. 2021. “Coordinate Unbiased Convolutional Networks – Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds.” CoRR abs/2106.06020. https://arxiv.org/abs/2106.06020.