torch.nn.functional.conv_transpose2d

torch.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”.

This operator supports TensorFloat32.

See ConvTranspose2d 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 $(\text{minibatch} , \text{in\_channels} , iH , iW)$

• 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

Examples:

>>> # With square kernels and equal stride
>>> inputs = torch.randn(1, 4, 5, 5)
>>> weights = torch.randn(4, 8, 3, 3)
>>> F.conv_transpose2d(inputs, weights, padding=1)