torch.nn.functional.conv2d#

torch.nn.functional.conv2d(input,weight,bias=None,stride=1,padding=0,dilation=1,groups=1)→Tensor#

Applies a 2D convolution over an input image composed of several inputplanes.

This operator supportsTensorFloat32.

SeeConv2d 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 settingtorch.backends.cudnn.deterministic=True. SeeReproducibility for more information.

Note

This operator supports complex data types i.e.complex32,complex64,complex128.

Parameters

• input – input tensor of shape$(\text{minibatch},\text{in\_channels},iH,iW)$(minibatch,in_channels,iH,iW)

• weight – filters of shape$(\text{out\_channels},\frac{\text{in\_channels}}{\text{groups}},kH,kW)$(out_channels,groupsin_channels​,kH,kW)

• bias – optional bias tensor of shape$(\text{out\_channels})$(out_channels). Default:None

• stride – the stride of the convolving kernel. Can be a single number or atuple(sH, sW). Default: 1

• padding –

  implicit paddings on both sides of the input. Can be a string {‘valid’, ‘same’},single number or a tuple(padH, padW). Default: 0padding='valid' is the same as no padding.padding='same' padsthe input so the output has the same shape as the input. However, this modedoesn’t support any stride values other than 1.

  Warning

  Forpadding='same', if theweight is even-length anddilation is odd in any dimension, a fullpad() operationmay be needed internally. Lowering performance.

• dilation – the spacing between kernel elements. Can be a single number ora tuple(dH, dW). Default: 1

• groups – split input into groups, both$\text{in\_channels}$in_channels and$\text{out\_channels}$out_channelsshould be divisible by the number of groups. Default: 1

Examples:

>>># With square kernels and equal stride>>>filters=torch.randn(8,4,3,3)>>>inputs=torch.randn(1,4,5,5)>>>F.conv2d(inputs,filters,padding=1)