torch.nn.functional.conv1d¶
-
torch.nn.functional.
conv1d
(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) → Tensor¶ Applies a 1D convolution over an input signal composed of several input planes.
This operator supports TensorFloat32.
See
Conv1d
for details and output shape.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
input – input tensor of shape
weight – filters of shape
bias – optional bias of shape . Default:
None
stride – the stride of the convolving kernel. Can be a single number or a one-element tuple (sW,). Default: 1
padding –
implicit paddings on both sides of the input. Can be a string {‘valid’, ‘same’}, single number or a one-element tuple (padW,). Default: 0
padding='valid'
is the same as no padding.padding='same'
pads the input so the output has the shape as the input. However, this mode doesn’t support any stride values other than 1.Warning
For
padding='same'
, if theweight
is even-length anddilation
is odd in any dimension, a fullpad()
operation may be needed internally. Lowering performance.dilation – the spacing between kernel elements. Can be a single number or a one-element tuple (dW,). Default: 1
groups – split input into groups, should be divisible by the number of groups. Default: 1
Examples:
>>> inputs = torch.randn(33, 16, 30) >>> filters = torch.randn(20, 16, 5) >>> F.conv1d(inputs, filters)