ConvTranspose2d¶

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 fractionallystrided convolution or a deconvolution (although it is not an actual deconvolution operation).
stride
controls the stride for the crosscorrelation.padding
controls the amount of implicit zeropaddings on both sides fordilation * (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 whatdilation
does.groups
controls the connections between inputs and outputs.in_channels
andout_channels
must both be divisible bygroups
. For example,At groups=1, all inputs are convolved to all outputs.
At groups=2, the operation becomes equivalent to having two conv layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated.
At groups=
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:a single
int
– in which case the same value is used for the height and width dimensionsa
tuple
of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension
Note
Depending of the size of your kernel, several (of the last) columns of the input might be lost, because it is a valid crosscorrelation, and not a full crosscorrelation. It is up to the user to add proper padding.
Note
The
padding
argument effectively addsdilation * (kernel_size  1)  padding
amount of zero padding to both sizes of the input. This is set so that when aConv2d
and aConvTranspose2d
are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, whenstride > 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 thatoutput_padding
is only used to find output shape, but does not actually add zeropadding 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. Parameters
in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
stride (int or tuple, optional) – Stride of the convolution. Default: 1
padding (int or tuple, optional) –
dilation * (kernel_size  1)  padding
zeropadding will be added to both sides of each dimension in the input. Default: 0output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0
groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1
bias (bool, optional) – If
True
, adds a learnable bias to the output. Default:True
dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
 Shape:
Input: $(N, C_{in}, H_{in}, W_{in})$
Output: $(N, C_{out}, H_{out}, W_{out})$ where
$H_{out} = (H_{in}  1) \times \text{stride}[0]  2 \times \text{padding}[0] + \text{dilation}[0] \times (\text{kernel\_size}[0]  1) + \text{output\_padding}[0] + 1$$W_{out} = (W_{in}  1) \times \text{stride}[1]  2 \times \text{padding}[1] + \text{dilation}[1] \times (\text{kernel\_size}[1]  1) + \text{output\_padding}[1] + 1$
 Variables
~ConvTranspose2d.weight (Tensor) – the learnable weights of the module of shape $(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},$ $\text{kernel\_size[0]}, \text{kernel\_size[1]})$ . The values of these weights are sampled from $\mathcal{U}(\sqrt{k}, \sqrt{k})$ where $k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel\_size}[i]}$
~ConvTranspose2d.bias (Tensor) – the learnable bias of the module of shape (out_channels) If
bias
isTrue
, 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) >>> # nonsquare 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])