Convolution arithmetic tutorial

Convolution arithmetic tutorial

Note

This tutorial is adapted from an existing convolution arithmetic guide [1], with an added emphasis onTheano’s interface.

Also, note that the signal processing community has a different nomenclatureand a well established literature on the topic, but for this tutorialwe will stick to the terms used in the machine learning community. For asignal processing point of view on the subject, see for instance Winograd,Shmuel. Arithmetic complexity of computations. Vol. 33. Siam, 1980.

About this tutorial

Learning to use convolutional neural networks (CNNs) for the first time isgenerally an intimidating experience. A convolutional layer’s output shape isaffected by the shape of its input as well as the choice of kernel shape, zeropadding and strides, and the relationship between these properties is nottrivial to infer. This contrasts with fully-connected layers, whose output sizeis independent of the input size. Additionally, so-called transposedconvolutional layers (also known as fractionally strided convolutional layers,or – wrongly – as deconvolutions) have been employed in more and more work asof late, and their relationship with convolutional layers has been explainedwith various degrees of clarity.

The relationship between a convolution operation’s input shape, kernel size,stride, padding and its output shape can be confusing at times.

The tutorial’s objective is threefold:

Explain the relationship between convolutional layers and transposedconvolutional layers.
Provide an intuitive understanding of the relationship between input shape,kernel shape, zero padding, strides and output shape in convolutional andtransposed convolutional layers.
Clarify Theano’s API on convolutions.

Refresher: discrete convolutions

The bread and butter of neural networks is affine transformations: avector is received as input and is multiplied with a matrix to produce anoutput (to which a bias vector is usually added before passing the resultthrough a nonlinearity). This is applicable to any type of input, be it animage, a sound clip or an unordered collection of features: whatever theirdimensionality, their representation can always be flattened into a vectorbefore the transformation.

Images, sound clips and many other similar kinds of data have an intrinsicstructure. More formally, they share these important properties:

They are stored as multi-dimensional arrays.
They feature one or more axes for which ordering matters (e.g., width andheight axes for an image, time axis for a sound clip).
One axis, called the channel axis, is used to access different views of thedata (e.g., the red, green and blue channels of a color image, or the leftand right channels of a stereo audio track).

These properties are not exploited when an affine transformation is applied; infact, all the axes are treated in the same way and the topological informationis not taken into account. Still, taking advantage of the implicit structure ofthe data may prove very handy in solving some tasks, like computer vision andspeech recognition, and in these cases it would be best to preserve it. This iswhere discrete convolutions come into play.

A discrete convolution is a linear transformation that preserves this notion ofordering. It is sparse (only a few input units contribute to a given outputunit) and reuses parameters (the same weights are applied to multiple locationsin the input).

Here is an example of a discrete convolution:

The light blue grid is called the input feature map. A kernel (shaded area)of value

$\begin{pmatrix}0 & 1 & 2 \\2 & 2 & 0 \\0 & 1 & 2\end{pmatrix}$

slides across the input feature map. At each location, the product between eachelement of the kernel and the input element it overlaps is computed and theresults are summed up to obtain the output in the current location. The finaloutput of this procedure is a matrix called output feature map (in green).

This procedure can be repeated using different kernels to form as many outputfeature maps (a.k.a. output channels) as desired. Note also that to keep thedrawing simple a single input feature map is being represented, but it is notuncommon to have multiple feature maps stacked one onto another (an example ofthis is what was referred to earlier as channels for images and sound clips).

Note

While there is a distinction between convolution and cross-correlation froma signal processing perspective, the two become interchangeable when thekernel is learned. For the sake of simplicity and to stay consistent withmost of the machine learning literature, the term convolution will beused in this tutorial.

If there are multiple input and output feature maps, the collection of kernelsform a 4D array (output_channels, input_channels, filter_rows, filter_columns). For each output channel, each input channel is convolved witha distinct part of the kernel and the resulting set of feature maps is summedelementwise to produce the corresponding output feature map. The result of thisprocedure is a set of output feature maps, one for each output channel, that isthe output of the convolution.

The convolution depicted above is an instance of a 2-D convolution, but can begeneralized to N-D convolutions. For instance, in a 3-D convolution, the kernelwould be a cuboid and would slide across the height, width and depth of theinput feature map.

The collection of kernels defining a discrete convolution has a shapecorresponding to some permutation of $(n, m, k_1, \ldots, k_N)$ , where

$\begin{split} n &\equiv \text{number of output feature maps},\\ m &\equiv \text{number of input feature maps},\\ k_j &\equiv \text{kernel size along axis $j$}.\end{split}$

The following properties affect the output size of a convolutionallayer along axis :

: input size along axis ,
: kernel size along axis ,
: stride (distance between two consecutive positions of thekernel) along axis ,
: zero padding (number of zeros concatenated at the beginning andat the end of an axis) along axis j.

For instance, here is a $3 \times 3$ kernel applied to a $5 \times 5$ input padded with a $1 \times 1$ border of zeros using $2 \times 2$ strides:

The analysis of the relationship between convolutional layer properties is easedby the fact that they don’t interact across axes, i.e., the choice of kernelsize, stride and zero padding along axis only affects the output sizeof axis . Because of that, this section will focus on the followingsimplified setting:

2-D discrete convolutions (),
square inputs (),
square kernel size (),
same strides along both axes (),
same zero padding along both axes ().

This facilitates the analysis and the visualization, but keep in mind that theresults outlined here also generalize to the N-D and non-square cases.

Theano terminology

Theano has its own terminology, which differs slightly from the convolutionarithmetic guide’s. Here’s a simple conversion table for the two:

Theano	Convolution arithmetic
`filters`	4D collection of kernels
`input_shape`	(batch size (`b`), input channels (`c`), input rows (`i1`), input columns (`i2`))
`filter_shape`	(output channels (`c1`), input channels (`c2`), filter rows (`k1`), filter columns (`k2`))
`border_mode`	`'valid'`, `'half'`, `'full'` or (, )
`subsample`	(`s1`, `s2`)

For instance, the convolution shown above would correspond to the followingTheano call:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(1, 1, 5, 5), filter_shape=(1, 1, 3, 3),
    border_mode=(1, 1), subsample=(2, 2))

Convolution arithmetic

No zero padding, unit strides

The simplest case to analyze is when the kernel just slides across everyposition of the input (i.e., and ).Here is an example for and :

One way of defining the output size in this case is by the number of possibleplacements of the kernel on the input. Let’s consider the width axis: the kernelstarts on the leftmost part of the input feature map and slides by steps of oneuntil it touches the right side of the input. The size of the output will beequal to the number of steps made, plus one, accounting for the initial positionof the kernel. The same logic applies for the height axis.

More formally, the following relationship can be inferred:

Relationship 1

For any and , and for and ,

This translates to the following Theano code:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode=(0, 0), subsample=(1, 1))
# output.shape[2] == (i1 - k1) + 1
# output.shape[3] == (i2 - k2) + 1

Zero padding, unit strides

To factor in zero padding (i.e., only restricting to ), let’sconsider its effect on the effective input size: padding with zeroschanges the effective input size from to . In thegeneral case, Relationship 1 can then be used to infer the followingrelationship:

Relationship 2

For any , and , and for ,

This translates to the following Theano code:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode=(p1, p2), subsample=(1, 1))
# output.shape[2] == (i1 - k1) + 2 * p1 + 1
# output.shape[3] == (i2 - k2) + 2 * p2 + 1

Here is an example for , and :

Special cases

In practice, two specific instances of zero padding are used quite extensivelybecause of their respective properties. Let’s discuss them in more detail.

Half (same) padding

Having the output size be the same as the input size (i.e., ) canbe a desirable property:

Relationship 3

For any and for odd ( $k = 2n + 1, \quad n \in\mathbb{N}$ ), and $p = \lfloor k / 2 \rfloor = n$ ,

$\begin{split} o &= i + 2 \lfloor k / 2 \rfloor - (k - 1) \\ &= i + 2n - 2n \\ &= i.\end{split}$

This translates to the following Theano code:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode='half', subsample=(1, 1))
# output.shape[2] == i1
# output.shape[3] == i2

This is sometimes referred to as half (or same) padding. Here is an examplefor , and (therefore) :

Note that half padding also works for even-valued and for , but in that case the property that the output size is the same as the inputsize is lost. Some frameworks also implement the same convolution slightlydifferently (e.g., in Keras ).

Full padding

While convolving a kernel generally decreases the output size with respect tothe input size, sometimes the opposite is required. This can be achieved withproper zero padding:

Relationship 4

For any and , and for and,

$\begin{split} o &= i + 2(k - 1) - (k - 1) \\ &= i + (k - 1).\end{split}$

This translates to the following Theano code:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode='full', subsample=(1, 1))
# output.shape[2] == i1 + (k1 - 1)
# output.shape[3] == i2 + (k2 - 1)

This is sometimes referred to as full padding, because in this setting everypossible partial or complete superimposition of the kernel on the input featuremap is taken into account. Here is an example for , and (therefore) :

No zero padding, non-unit strides

All relationships derived so far only apply for unit-strided convolutions.Incorporating non unitary strides requires another inference leap. To facilitatethe analysis, let’s momentarily ignore zero padding (i.e., and). Here is an example for , and :

Once again, the output size can be defined in terms of the number of possibleplacements of the kernel on the input. Let’s consider the width axis: the kernelstarts as usual on the leftmost part of the input, but this time it slides bysteps of size until it touches the right side of the input. The sizeof the output is again equal to the number of steps made, plus one, accountingfor the initial position of the kernel. The same logic applies for the heightaxis.

From this, the following relationship can be inferred:

Relationship 5

For any , and , and for ,

$o = \left\lfloor \frac{i - k}{s} \right\rfloor + 1.$

This translates to the following Theano code:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode=(0, 0), subsample=(s1, s2))
# output.shape[2] == (i1 - k1) // s1 + 1
# output.shape[3] == (i2 - k2) // s2 + 1

The floor function accounts for the fact that sometimes the lastpossible step does not coincide with the kernel reaching the end of theinput, i.e., some input units are left out.

Zero padding, non-unit strides

The most general case (convolving over a zero padded input using non-unitstrides) can be derived by applying Relationship 5 on an effective input of size, in analogy to what was done for Relationship 2:

Relationship 6

For any , , and ,

$o = \left\lfloor \frac{i + 2p - k}{s} \right\rfloor + 1.$

This translates to the following Theano code:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode=(p1, p2), subsample=(s1, s2))
# output.shape[2] == (i1 - k1 + 2 * p1) // s1 + 1
# output.shape[3] == (i2 - k2 + 2 * p2) // s2 + 1

As before, the floor function means that in some cases a convolution willproduce the same output size for multiple input sizes. More specifically, if is a multiple of , then any input size $j = i+ a, \quad a \in \{0,\ldots,s - 1\}$ will produce the same output size. Notethat this ambiguity applies only for .

Here is an example for , , and :

Interestingly, despite having different input sizes these convolutions share thesame output size. While this doesn’t affect the analysis for convolutions,this will complicate the analysis in the case of transposed convolutions.

Transposed convolution arithmetic

The need for transposed convolutions generally arises from the desire to use atransformation going in the opposite direction of a normal convolution, i.e.,from something that has the shape of the output of some convolution tosomething that has the shape of its input while maintaining a connectivitypattern that is compatible with said convolution. For instance, one might usesuch a transformation as the decoding layer of a convolutional autoencoder or toproject feature maps to a higher-dimensional space.

Once again, the convolutional case is considerably more complex than thefully-connected case, which only requires to use a weight matrix whose shapehas been transposed. However, since every convolution boils down to anefficient implementation of a matrix operation, the insights gained from thefully-connected case are useful in solving the convolutional case.

Like for convolution arithmetic, the dissertation about transposed convolutionarithmetic is simplified by the fact that transposed convolution propertiesdon’t interact across axes.

This section will focus on the following setting:

2-D transposed convolutions (),
square inputs (),
square kernel size (),
same strides along both axes (),
same zero padding along both axes ().

Once again, the results outlined generalize to the N-D and non-square cases.

Convolution as a matrix operation

Take for example the convolution presented in the No zero padding, unitstrides subsection:

If the input and output were to be unrolled into vectors from left to right, topto bottom, the convolution could be represented as a sparse matrix $\mathbf{C}$ where the non-zero elements are the elements $w_{i,j}$ of the kernel (with and being the row and column of thekernel respectively):

$\begin{pmatrix}w_{0,0} & 0 & 0 & 0 \\w_{0,1} & w_{0,0} & 0 & 0 \\w_{0,2} & w_{0,1} & 0 & 0 \\0 & w_{0,2} & 0 & 0 \\w_{1,0} & 0 & w_{0,0} & 0 \\w_{1,1} & w_{1,0} & w_{0,1} & w_{0,0} \\w_{1,2} & w_{1,1} & w_{0,2} & w_{0,1} \\0 & w_{1,2} & 0 & w_{0,2} \\w_{2,0} & 0 & w_{1,0} & 0 \\w_{2,1} & w_{2,0} & w_{1,1} & w_{1,0} \\w_{2,2} & w_{2,1} & w_{1,2} & w_{1,1} \\0 & w_{2,2} & 0 & w_{1,2} \\0 & 0 & w_{2,0} & 0 \\0 & 0 & w_{2,1} & w_{2,0} \\0 & 0 & w_{2,2} & w_{2,1} \\0 & 0 & 0 & w_{2,2} \\\end{pmatrix}^T$

(Note: the matrix has been transposed for formatting purposes.) This linearoperation takes the input matrix flattened as a 16-dimensional vector andproduces a 4-dimensional vector that is later reshaped as the $2 \times 2$ output matrix.

Using this representation, the backward pass is easily obtained by transposing $\mathbf{C}$ ; in other words, the error is backpropagated by multiplyingthe loss with $\mathbf{C}^T$ . This operation takes a 4-dimensional vectoras input and produces a 16-dimensional vector as output, and its connectivitypattern is compatible with $\mathbf{C}$ by construction.

Notably, the kernel $\mathbf{w}$ defines both the matrices $\mathbf{C}$ and $\mathbf{C}^T$ used for the forward and backwardpasses.

Transposed convolution

Let’s now consider what would be required to go the other way around, i.e., mapfrom a 4-dimensional space to a 16-dimensional space, while keeping theconnectivity pattern of the convolution depicted above. This operation is knownas a transposed convolution.

Transposed convolutions – also called fractionally strided convolutions –work by swapping the forward and backward passes of a convolution. One way toput it is to note that the kernel defines a convolution, but whether it’s adirect convolution or a transposed convolution is determined by how the forwardand backward passes are computed.

For instance, the kernel $\mathbf{w}$ defines a convolution whose forwardand backward passes are computed by multiplying with $\mathbf{C}$ and $\mathbf{C}^T$ respectively, but it also defines a transposedconvolution whose forward and backward passes are computed by multiplying with $\mathbf{C}^T$ and $(\mathbf{C}^T)^T = \mathbf{C}$ respectively.

Note

The transposed convolution operation can be thought of as the gradient ofsome convolution with respect to its input, which is usually howtransposed convolutions are implemented in practice.

Finally note that it is always possible to implement a transposedconvolution with a direct convolution. The disadvantage is that it usuallyinvolves adding many columns and rows of zeros to the input, resulting in amuch less efficient implementation.

Building on what has been introduced so far, this section will proceed somewhatbackwards with respect to the convolution arithmetic section, deriving theproperties of each transposed convolution by referring to the directconvolution with which it shares the kernel, and defining the equivalent directconvolution.

No zero padding, unit strides, transposed

The simplest way to think about a transposed convolution is by computing theoutput shape of the direct convolution for a given input shape first, and theninverting the input and output shapes for the transposed convolution.

Let’s consider the convolution of a $3 \times 3$ kernel on a $4\times 4$ input with unitary stride and no padding (i.e., ,, and ). As depicted in the convolutionbelow, this produces a $2 \times 2$ output:

The transpose of this convolution will then have an output of shape $4\times 4$ when applied on a $2 \times 2$ input.

Another way to obtain the result of a transposed convolution is to apply anequivalent – but much less efficient – direct convolution. The exampledescribed so far could be tackled by convolving a $3 \times 3$ kernel overa $2 \times 2$ input padded with a $2 \times 2$ border of zerosusing unit strides (i.e., , , and), as shown here:

Notably, the kernel’s and stride’s sizes remain the same, but the input of theequivalent (direct) convolution is now zero padded.

Note

Although equivalent to applying the transposed matrix, this visualizationadds a lot of zero multiplications in the form of zero padding. This is donehere for illustration purposes, but it is inefficient, and softwareimplementations will normally not perform the useless zero multiplications.

One way to understand the logic behind zero padding is to consider theconnectivity pattern of the transposed convolution and use it to guide thedesign of the equivalent convolution. For example, the top left pixel of theinput of the direct convolution only contribute to the top left pixel of theoutput, the top right pixel is only connected to the top right output pixel,and so on.

To maintain the same connectivity pattern in the equivalent convolution it isnecessary to zero pad the input in such a way that the first (top-left)application of the kernel only touches the top-left pixel, i.e., the paddinghas to be equal to the size of the kernel minus one.

Proceeding in the same fashion it is possible to determine similar observationsfor the other elements of the image, giving rise to the following relationship:

Relationship 7

A convolution described by , and has anassociated transposed convolution described by , and and its output size is

In other words,

input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, filter_shape=(c1, c2, k1, k2), border_mode=(0, 0),
    subsample=(1, 1))
# input.shape[2] == output.shape[2] + (k1 - 1)
# input.shape[3] == output.shape[3] + (k2 - 1)

Interestingly, this corresponds to a fully padded convolution with unit strides.

Zero padding, unit strides, transposed

Knowing that the transpose of a non-padded convolution is equivalent toconvolving a zero padded input, it would be reasonable to suppose that thetranspose of a zero padded convolution is equivalent to convolving an inputpadded with less zeros.

It is indeed the case, as shown in here for , and:

Formally, the following relationship applies for zero padded convolutions:

Relationship 8

A convolution described by , and has anassociated transposed convolution described by , and and its output size is

In other words,

input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, filter_shape=(c1, c2, k1, k2), border_mode=(p1, p2),
    subsample=(1, 1))
# input.shape[2] == output.shape[2] + (k1 - 1) - 2 * p1
# input.shape[3] == output.shape[3] + (k2 - 1) - 2 * p2

Special cases

Half (same) padding, transposed

By applying the same inductive reasoning as before, it is reasonable to expectthat the equivalent convolution of the transpose of a half padded convolutionis itself a half padded convolution, given that the output size of a halfpadded convolution is the same as its input size. Thus the following relationapplies:

Relationship 9

A convolution described by $k = 2n + 1, \quad n \in \mathbb{N}$ , and $p = \lfloor k / 2 \rfloor = n$ has an associatedtransposed convolution described by , and and its output size is

$\begin{split} o' &= i' + (k - 1) - 2p \\ &= i' + 2n - 2n \\ &= i'.\end{split}$

In other words,

input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, filter_shape=(c1, c2, k1, k2), border_mode='half',
    subsample=(1, 1))
# input.shape[2] == output.shape[2]
# input.shape[3] == output.shape[3]

Here is an example for , and (therefore) :

Full padding, transposed

Knowing that the equivalent convolution of the transpose of a non-paddedconvolution involves full padding, it is unsurprising that the equivalent ofthe transpose of a fully padded convolution is a non-padded convolution:

Relationship 10

A convolution described by , and has an associated transposed convolution described by , and and its output size is

$\begin{split} o' &= i' + (k - 1) - 2p \\ &= i' - (k - 1)\end{split}$

In other words,

input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, filter_shape=(c1, c2, k1, k2), border_mode='full',
    subsample=(1, 1))
# input.shape[2] == output.shape[2] - (k1 - 1)
# input.shape[3] == output.shape[3] - (k2 - 1)

Here is an example for , and (therefore) :

No zero padding, non-unit strides, transposed

Using the same kind of inductive logic as for zero padded convolutions, onemight expect that the transpose of a convolution with involves anequivalent convolution with . As will be explained, this is a validintuition, which is why transposed convolutions are sometimes calledfractionally strided convolutions.

Here is an example for , and :

This should help understand what fractional strides involve: zerosare inserted between input units, which makes the kernel move around ata slower pace than with unit strides.

Note

Doing so is inefficient and real-world implementations avoid uselessmultiplications by zero, but conceptually it is how the transpose of astrided convolution can be thought of.

For the moment, it will be assumed that the convolution is non-padded () and that its input size is such that is a multipleof . In that case, the following relationship holds:

Relationship 11

A convolution described by , and and whoseinput size is such that is a multiple of , has anassociated transposed convolution described by $\tilde{i}'$ , , and , where $\tilde{i}'$ is thesize of the stretched input obtained by adding zeros betweeneach input unit, and its output size is

In other words,

input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, filter_shape=(c1, c2, k1, k2), border_mode=(0, 0),
    subsample=(s1, s2))
# input.shape[2] == s1 * (output.shape[2] - 1) + k1
# input.shape[3] == s2 * (output.shape[3] - 1) + k2

Zero padding, non-unit strides, transposed

When the convolution’s input size is such that is amultiple of , the analysis can extended to the zero padded case bycombining Relationship 8 andRelationship 11:

Relationship 12

A convolution described by , and and whoseinput size is such that is a multiple of has an associated transposed convolution described by $\tilde{i}'$ , , and , where $\tilde{i}'$ is the size of the stretched input obtained byadding zeros between each input unit, and its output size is

In other words,

o_prime1 = s1 * (output.shape[2] - 1) + k1 - 2 * p1
o_prime2 = s2 * (output.shape[3] - 1) + k2 - 2 * p2
input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, input_shape=(b, c1, o_prime1, o_prime2),
    filter_shape=(c1, c2, k1, k2), border_mode=(p1, p2),
    subsample=(s1, s2))

Here is an example for , , and :

The constraint on the size of the input can be relaxed by introducinganother parameter $a \in \{0, \ldots, s - 1\}$ that allows to distinguishbetween the different cases that all lead to the same :

Relationship 13

A convolution described by , and has anassociated transposed convolution described by , $\tilde{i}'$ , , and , where $\tilde{i}'$ is the size of the stretched input obtained byadding zeros between each input unit, and $a = (i + 2p -k) \mod s$ represents the number of zeros added to the top and right edgesof the input, and its output size is

In other words,

o_prime1 = s1 * (output.shape[2] - 1) + a1 + k1 - 2 * p1
o_prime2 = s2 * (output.shape[3] - 1) + a2 + k2 - 2 * p2
input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, input_shape=(b, c1, o_prime1, o_prime2),
    filter_shape=(c1, c2, k1, k2), border_mode=(p1, p2),
    subsample=(s1, s2))

Here is an example for , , and :

Miscellaneous convolutions

Dilated convolutions

Those familiar with the deep learning literature may have noticed the term“dilated convolutions” (or “atrous convolutions”, from the French expressionconvolutions à trous) appear in recent papers. Here we attempt to provide anintuitive understanding of dilated convolutions. For a more in-depth descriptionand to understand in what contexts they are applied, see Chen et al. (2014) [2]; Yu and Koltun (2015) [3].

Dilated convolutions “inflate” the kernel by inserting spaces between the kernelelements. The dilation “rate” is controlled by an additional hyperparameter. Implementations may vary, but there are usually spacesinserted between kernel elements such that corresponds to aregular convolution.

To understand the relationship tying the dilation rate and the outputsize , it is useful to think of the impact of on theeffective kernel size. A kernel of size dilated by a factor has an effective size

$\hat{k} = k + (k - 1)(d - 1).$

This can be combined with Relationship 6 to form the following relationship fordilated convolutions:

Relationship 14

For any , , and , and for a dilationrate ,

$o = \left\lfloor \frac{i + 2p - k - (k - 1)(d - 1)}{s} \right\rfloor + 1.$

This translates to the following Theano code using the filter_dilationparameter:

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode=(p1, p2), subsample=(s1, s2), filter_dilation=(d1, d2))
# output.shape[2] == (i1 + 2 * p1 - k1 - (k1 - 1) * (d1 - 1)) // s1 + 1
# output.shape[3] == (i2 + 2 * p2 - k2 - (k2 - 1) * (d2 - 1)) // s2 + 1

Here is an example for , , , and :

[1]	Dumoulin, Vincent, and Visin, Francesco. “A guide to convolutionarithmetic for deep learning”. arXiv preprint arXiv:1603.07285 (2016)

[2]	Chen, Liang-Chieh, Papandreou, George, Kokkinos, Iasonas, Murphy, Kevinand Yuille, Alan L. “Semantic image segmentation with deep convolutionalnets and fully connected CRFs”. arXiv preprint arXiv:1412.7062 (2014).

[3]	Yu, Fisher and Koltun, Vladlen. “Multi-scale context aggregation bydilated convolutions”. arXiv preprint arXiv:1511.07122 (2015)

Quick reference

Convolution relationship

A convolution specified by

input size ,
kernel size ,
stride ,
padding size ,

has an output size given by

$o = \left\lfloor \frac{i + 2p - k}{s} \right\rfloor + 1.$

In Theano, this translates to

output = theano.tensor.nnet.conv2d(
    input, filters, input_shape=(b, c2, i1, i2), filter_shape=(c1, c2, k1, k2),
    border_mode=(p1, p2), subsample=(s1, s2))
# output.shape[2] == (i1 + 2 * p1 - k1) // s1 + 1
# output.shape[3] == (i2 + 2 * p2 - k2) // s2 + 1

Transposed convolution relationship

A transposed convolution specified by

input size ,
kernel size ,
stride ,
padding size ,

has an output size given by

$o = s (i - 1) + a + k - 2p, \quad a \in \{0, \ldots, s - 1\}$

where is a user-specified quantity used to distinguish between the different possible output sizes.

Unless , Theano requires that is implicitly passedvia an input_shape argument. For instance, if ,, , and , then and the Theano code would look like

input = theano.tensor.nnet.abstract_conv.conv2d_grad_wrt_inputs(
    output, filters, input_shape=(9, 9), filter_shape=(c1, c2, 4, 4),
    border_mode='valid', subsample=(2, 2))