class torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros') [source]
 
Applies a 1D 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,L)(N, C_{\text{in}}, L)  and output (N,Cout,Lout)(N, C_{\text{out}}, L_{\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_{in} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)  
where ⋆\star  is the valid cross-correlation operator, NN  is a batch size, CC  denotes a number of channels, LL  is a length of signal sequence. This module supports TensorFloat32.  
stride controls the stride for the cross-correlation, a single number or a one-element tuple. 
padding controls the amount of implicit padding on both sides for padding number of points. 
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}} ).     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,Lin)(N, C_{in}, L_{in}) 
 
Output: (N,Cout,Lout)(N, C_{out}, L_{out})  where  Lout=⌊Lin+2×padding−dilation×(kernel_size−1)−1stride+1⌋L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor  
     Variables 
 
~Conv1d.weight (Tensor) – the learnable weights of the module of shape (out_channels,in_channelsgroups,kernel_size)(\text{out\_channels}, \frac{\text{in\_channels}}{\text{groups}}, \text{kernel\_size}) . The values of these weights are sampled from U(−k,k)\mathcal{U}(-\sqrt{k}, \sqrt{k})  where k=groupsCin∗kernel_sizek = \frac{groups}{C_\text{in} * \text{kernel\_size}} 
 
~Conv1d.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∗kernel_sizek = \frac{groups}{C_\text{in} * \text{kernel\_size}} 
    Examples: >>> m = nn.Conv1d(16, 33, 3, stride=2)
>>> input = torch.randn(20, 16, 50)
>>> output = m(input)