dltranspconv
Deep learning transposed convolution
Syntax
Description
The transposed convolution operation upsamples feature maps.
Note
This function applies the deep learning transposed convolution operation to dlarray data. If
you want to apply transposed convolution within a layerGraph object
or Layer array, use
one of the following layers:
dlY = dltranspconv(dlX,weights,bias)dlX using
the filters defined by weights, and adds a constant
bias. The input dlX is a formatted
dlarray with dimension labels. Transposed convolution acts on
dimensions that you specify as 'S' and 'C' dimensions.
The output dlY is a formatted dlarray with the same
dimension labels as dlX.
dlY = dltranspconv(___Name,Value)'Stride',3 sets the stride
of the convolution operation.
Examples
Upsample Image Using Transposed Convolution
Convolve an image and then use transposed convolution to resize the convolved image to the same size as the original image.
Import the image data and convert it to a dlarray.
X = imread('sherlock.jpg'); dlX = dlarray(single(X),'SSC');
Display the image.
imshow(X)
Initialize the convolutional filters and bias term. Specify an ungrouped convolution that applies a single filter to all three channels of the input data.
filterHeight = 10; filterWidth = 10; numChannelsPerGroup = 3; numFiltersPerGroup = 1; numGroups = 1; weights = rand(filterHeight,filterWidth,numChannelsPerGroup,numFiltersPerGroup,numGroups); bias = rand(numFiltersPerGroup*numGroups,1);
Perform the convolution. Use a 'Stride' value of 2 and a 'DilationFactor' value of 2.
dlY = dlconv(dlX,weights,bias,'Stride',2,'DilationFactor',3);
Display the convolved image.
Y = extractdata(dlY); imshow(rescale(Y))
Initialize the transposed convolutional filters and bias. Specify an ungrouped transposed convolution that applies three filters to the input. Use the same filter height and filter width as for the convolution operation.
numChannelsPerGroupTC = 1; numFiltersPerGroupTC = 3; weightsTC = rand(filterHeight,filterWidth,numFiltersPerGroupTC,numChannelsPerGroupTC,numGroups); biasTC = rand(numFiltersPerGroupTC*numGroups,1);
Perform the transposed convolution. Use the same stride and dilation factor as for the convolution operation.
dlZ = dltranspconv(dlY,weightsTC,biasTC,'Stride',2,'DilationFactor',3);
Display the image after the transposed convolution.
Z = extractdata(dlZ); imshow(rescale(Z))
Compare the size of the original image, the convolved image, and the image after the transposed convolution.
sizeX = size(X)
sizeX = 1×3
640 960 3
sizeY = size(Y)
sizeY = 1×2
307 467
sizeZ = size(Z)
sizeZ = 1×3
640 960 3
The transposed convolution upsamples the convolved data to the size of the original input data.
Perform Grouped Transposed Convolution
Apply transposed convolution to the input data in three groups of two channels each. Apply four filters per group.
Create the input data as ten observations of size 100-by-100 with six channels.
height = 100;
width = 100;
channels = 6;
numObservations = 10;
X = rand(height,width,channels,numObservations);
dlX = dlarray(X,'SSCB');Initialize the filters for the transposed convolution operation. Specify three groups of transposed convolutions that each apply four filters to two channels of the input data.
filterHeight = 8; filterWidth = 8; numChannelsPerGroup = 2; numFiltersPerGroup = 4; numGroups = 3; weights = rand(filterHeight,filterWidth,numFiltersPerGroup,numChannelsPerGroup,numGroups);
Initialize the bias term.
bias = rand(numFiltersPerGroup*numGroups,1);
Perform the transposed convolution.
dlY = dltranspconv(dlX,weights,bias); size(dlY)
ans = 1×4
107 107 12 10
dims(dlY)
ans = 'SSCB'
The 12 channels of the convolution output represent the three groups of transposed convolutions with four filters per group.