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.
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
groupscontrols the connections between inputs and outputs.
out_channelsmust both be divisible by
groups. 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.
in_channels, each input channel is convolved with its own set of filters, of size: .
dilationcan 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 int is used for the height dimension, and the second int for 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.
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 , 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
torch.backends.cudnn.deterministic = True. Please see the notes on Reproducibility for background.
in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
padding_mode (string, optional) –
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:
>>> # 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)