class torch.nn.ConvTranspose2d(in_channels: int, out_channels: int, kernel_size: Union[T, Tuple[T, T]], stride: Union[T, Tuple[T, T]] = 1, padding: Union[T, Tuple[T, T]] = 0, output_padding: Union[T, Tuple[T, T]] = 0, groups: int = 1, bias: bool = True, dilation: int = 1, padding_mode: str = 'zeros')
[source]
Applies a 2D transposed convolution operator over an input image composed of several input planes.
This module can be seen as the gradient of Conv2d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation).
This module supports TensorFloat32.
stride
controls the stride for the cross-correlation.padding
controls the amount of implicit zero-paddings on both sides for dilation * (kernel_size - 1) - padding
number of points. See note below for details.output_padding
controls the additional size added to one side of the output shape. See note below for details.dilation
controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of what dilation
does.groups
controls the connections between inputs and outputs. in_channels
and out_channels
must both be divisible by groups
. For example,
in_channels
, each input channel is convolved with its own set of filters (of size $\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor$ ).The parameters kernel_size
, stride
, padding
, output_padding
can either be:
int
– in which case the same value is used for the height and width dimensionstuple
of two ints – in which case, the first int
is used for the height dimension, and the second int
for the width dimensionNote
Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid cross-correlation, and not a full cross-correlation. It is up to the user to add proper padding.
Note
The padding
argument effectively adds dilation * (kernel_size - 1) - padding
amount of zero padding to both sizes of the input. This is set so that when a Conv2d
and a ConvTranspose2d
are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when stride > 1
, Conv2d
maps multiple input shapes to the same output shape. output_padding
is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that output_padding
is only used to find output shape, but does not actually add zero-padding to output.
Note
In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch.backends.cudnn.deterministic =
True
. Please see the notes on Reproducibility for background.
dilation * (kernel_size - 1) - padding
zero-padding will be added to both sides of each dimension in the input. Default: 0True
, adds a learnable bias to the output. Default: True
bias
is True
, then the values of these weights are sampled from $\mathcal{U}(-\sqrt{k}, \sqrt{k})$ where $k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}$
Examples:
>>> # With square kernels and equal stride >>> m = nn.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input) >>> # exact output size can be also specified as an argument >>> input = torch.randn(1, 16, 12, 12) >>> downsample = nn.Conv2d(16, 16, 3, stride=2, padding=1) >>> upsample = nn.ConvTranspose2d(16, 16, 3, stride=2, padding=1) >>> h = downsample(input) >>> h.size() torch.Size([1, 16, 6, 6]) >>> output = upsample(h, output_size=input.size()) >>> output.size() torch.Size([1, 16, 12, 12])
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Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.ConvTranspose2d.html