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')
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 and output can be precisely described as:
where is the valid 2D cross-correlation operator, is a batch size, denotes a number of channels, is a height of input planes in pixels, and is width in pixels.
This module supports TensorFloat32.
stridecontrols the stride for the cross-correlation, a single number or a tuple.
paddingcontrols the amount of implicit zero-paddings on both sides for
paddingnumber of points for each dimension.
dilationcontrols 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
groups controls the connections between inputs and outputs.
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: .
dilation can either be:
int– in which case the same value is used for the height and width dimension
tupleof two ints – in which case, the first
intis used for the height dimension, and the second
intfor the width dimension
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.
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 , a depthwise convolution with a depthwise multiplier
K, can be constructed by arguments .
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
True. Please see the notes on Reproducibility for background.
True, adds a learnable bias to the output. Default:
>>> # 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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