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)