class torch.nn.Conv2d(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, dilation: Union[T, Tuple[T, T]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = 'zeros')
[source]
Applies a 2D convolution over an input signal composed of several input planes.
In the simplest case, the output value of the layer with input size $(N, C_{\text{in}}, H, W)$ and output $(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})$ can be precisely described as:
where $\star$ is the valid 2D cross-correlation operator, $N$ is a batch size, $C$ denotes a number of channels, $H$ is a height of input planes in pixels, and $W$ is width in pixels.
This module supports TensorFloat32.
stride
controls the stride for the cross-correlation, a single number or a tuple.padding
controls the amount of implicit zero-paddings on both sides for padding
number of points for each dimension.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
, dilation
can either be:
int
– in which case the same value is used for the height and width dimensiontuple
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
When groups == in_channels
and out_channels == K * in_channels
, where K
is a positive integer, this operation is also termed in literature as depthwise convolution.
In other words, for an input of size $(N, C_{in}, H_{in}, W_{in})$ , a depthwise convolution with a depthwise multiplier K
, can be constructed by arguments $(in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})$ .
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.
'zeros'
, 'reflect'
, 'replicate'
or 'circular'
. Default: 'zeros'
True
, adds a learnable bias to the output. Default: True
Output: $(N, C_{out}, H_{out}, W_{out})$ where
bias
is True
, then the values of these weights are sampled from $\mathcal{U}(-\sqrt{k}, \sqrt{k})$ where $k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}$
>>> # With square kernels and equal stride >>> m = nn.Conv2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # non-square kernels and unequal stride and with padding and dilation >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1)) >>> input = torch.randn(20, 16, 50, 100) >>> output = m(input)
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Licensed under the 3-clause BSD License.
https://pytorch.org/docs/1.7.0/generated/torch.nn.Conv2d.html