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 padding 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
When groups == in_channels and out_channels == K * in_channels, where K is a positive integer, this operation is also known as a “depthwise convolution”.
In other words, for an input of size , a depthwise convolution with a depthwise multiplier K can be performed with the arguments .
In some circumstances when given tensors on a CUDA device and using 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. See Reproducibility for more information.
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)