oneflow.nn.functional.conv2d

oneflow.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 input planes.

The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv2d.html.

See Conv2d for details and output shape.

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

  • weight – filters of shape \((\text{out_channels} , \frac{\text{in_channels}}{\text{groups}} , kH , kW)\)

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

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

  • padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padH, padW). Default: 0

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

  • groups – split input into groups, \(\text{in_channels}\) should be divisible by the number of groups. Default: 1

For examples:

>>> import oneflow as flow
>>> import oneflow.nn.functional as F

>>> inputs = flow.randn(8, 4, 3, 3)
>>> filters = flow.randn(1, 4, 5, 5)
>>> outputs = F.conv2d(inputs, filters, padding=1)