oneflow.nn.functional.conv_transpose2d¶
-
oneflow.nn.functional.
conv_transpose2d
(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) → Tensor¶ Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called “deconvolution”.
The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.functional.conv_transpose3d.html
See
ConvTranspose2d
for details and output shape.- Parameters
input – input tensor of shape \((\text{minibatch} , \text{in_channels} , iH , iW)\)
weight – filters of shape \((\text{in_channels} , \frac{\text{out_channels}}{\text{groups}} , kH , kW)\)
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 – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple (padH, padW). Default: 0
output_padding – additional size added to one side of each dimension in the output shape. Can be a single number or a tuple (out_padH, out_padW). Default: 0
groups – split input into groups, \(\text{in_channels}\) should be divisible by the number of groups. Default: 1
dilation – the spacing between kernel elements. Can be a single number or a tuple (dH, dW). Default: 1
For examples:
>>> import oneflow as flow >>> import oneflow.nn.functional as F >>> inputs = flow.randn(1, 4, 5, 5) >>> weights = flow.randn(4, 8, 3, 3) >>> outputs = F.conv_transpose2d(inputs, weights, padding=1)