class torch.nn.Conv1d(in_channels: int, out_channels: int, kernel_size: Union[T, Tuple[T]], stride: Union[T, Tuple[T]] = 1, padding: Union[T, Tuple[T]] = 0, dilation: Union[T, Tuple[T]] = 1, groups: int = 1, bias: bool = True, padding_mode: str = '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=0Cin1weight(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 zero-paddings 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\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor .


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 (N,Cin,Lin)(N, C_{in}, L_{in}) , a depthwise convolution with a depthwise multiplier K, can be constructed by arguments (Cin=Cin,Cout=Cin×K,...,groups=Cin)(C_\text{in}=C_{in}, C_\text{out}=C_{in} \times K, ..., \text{groups}=C_{in}) .


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

  • 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

  • Input: (N,Cin,Lin)(N, C_{in}, L_{in})

  • Output: (N,Cout,Lout)(N, C_{out}, L_{out}) where

    Lout=Lin+2×paddingdilation×(kernel_size1)1stride+1L_{out} = \left\lfloor\frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor
  • ~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=groupsCinkernel_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=groupsCinkernel_sizek = \frac{groups}{C_\text{in} * \text{kernel\_size}}


>>> m = nn.Conv1d(16, 33, 3, stride=2)
>>> input = torch.randn(20, 16, 50)
>>> output = m(input)


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