class torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='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,Cin,H,W)(N, C_{\text{in}}, H, W)  and output (N,Cout,Hout,Wout)(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})  can be precisely described as:  out(Ni,Coutj)=bias(Coutj)+∑k=0Cin−1weight(Coutj,k)⋆input(Ni,k)\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 cross-correlation operator, NN  is a batch size, CC  denotes a number of channels, HH  is a height of input planes in pixels, and WW  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 padding 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,  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 out_channelsin_channels\frac{\text{out\_channels}}{\text{in\_channels}} ).    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 dimension a 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 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 (N,Cin,Lin)(N, C_{in}, L_{in}) , a depthwise convolution with a depthwise multiplier K can be performed with the arguments (Cin=Cin,Cout=Cin×K,...,groups=Cin)(C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times \text{K}, ..., \text{groups}=C_\text{in}) .   Note 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.   Parameters 
 
in_channels (int) – Number of channels in the input image 
out_channels (int) – Number of channels produced by the convolution 
kernel_size (int or tuple) – Size of the convolving kernel 
stride (int or tuple, optional) – Stride of the convolution. Default: 1 
padding (int or tuple, optional) – Zero-padding 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,Cin,Hin,Win)(N, C_{in}, H_{in}, W_{in}) 
 
Output: (N,Cout,Hout,Wout)(N, C_{out}, H_{out}, W_{out})  where  Hout=⌊Hin+2×padding[0]−dilation[0]×(kernel_size[0]−1)−1stride[0]+1⌋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  
 Wout=⌊Win+2×padding[1]−dilation[1]×(kernel_size[1]−1)−1stride[1]+1⌋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 (out_channels,in_channelsgroups,(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}},  kernel_size[0],kernel_size[1])\text{kernel\_size[0]}, \text{kernel\_size[1]}) . The values of these weights are sampled from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k})  where k=groupsCin∗∏i=01kernel_size[i]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 is True, then the values of these weights are sampled from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k})  where k=groupsCin∗∏i=01kernel_size[i]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)
>>> # 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)