Conv2d¶

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:
$\text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{\text{in}}  1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)$where $\star$ is the valid 2D crosscorrelation 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.
stride
controls the stride for the crosscorrelation, a single number or a tuple.padding
controls the amount of implicit zeropaddings on both sides forpadding
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 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
,dilation
can either be:a single
int
– in which case the same value is used for the height and width dimensiona
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
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. 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) – Zeropadding added to both sides of the input. Default: 0
padding_mode (string, optional) –
'zeros'
,'reflect'
,'replicate'
or'circular'
. Default:'zeros'
dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1
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
 Shape:
Input: $(N, C_{in}, H_{in}, W_{in})$
Output: $(N, C_{out}, H_{out}, W_{out})$ where
$H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[0]  \text{dilation}[0] \times (\text{kernel\_size}[0]  1)  1}{\text{stride}[0]} + 1\right\rfloor$$W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[1]  \text{dilation}[1] \times (\text{kernel\_size}[1]  1)  1}{\text{stride}[1]} + 1\right\rfloor$
 Variables
~Conv2d.weight (Tensor) – the learnable weights of the module of shape $(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},$ $\text{kernel\_size[0]}, \text{kernel\_size[1]})$ . The values of these weights are sampled from $\mathcal{U}(\sqrt{k}, \sqrt{k})$ where $k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}$
~Conv2d.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 $k = \frac{groups}{C_\text{in} * \prod_{i=0}^{1}\text{kernel\_size}[i]}$
Examples
>>> # With square kernels and equal stride >>> m = nn.Conv2d(16, 33, 3, stride=2) >>> # nonsquare kernels and unequal stride and with padding >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> # nonsquare 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)