class torch.nn.CircularPad3d(padding)[source]

Pads the input tensor using circular padding of the input boundary.

Tensor values at the beginning of the dimension are used to pad the end, and values at the end are used to pad the beginning. If negative padding is applied then the ends of the tensor get removed.

For N-dimensional padding, use torch.nn.functional.pad().


padding (int, tuple) – the size of the padding. If is int, uses the same padding in all boundaries. If a 6-tuple, uses (padding_left\text{padding\_left}, padding_right\text{padding\_right}, padding_top\text{padding\_top}, padding_bottom\text{padding\_bottom}, padding_front\text{padding\_front}, padding_back\text{padding\_back})

  • Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) or (C,Din,Hin,Win)(C, D_{in}, H_{in}, W_{in}).

  • Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) or (C,Dout,Hout,Wout)(C, D_{out}, H_{out}, W_{out}), where

    Dout=Din+padding_front+padding_backD_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}

    Hout=Hin+padding_top+padding_bottomH_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}

    Wout=Win+padding_left+padding_rightW_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}


>>> m = nn.CircularPad3d(3)
>>> input = torch.randn(16, 3, 8, 320, 480)
>>> output = m(input)
>>> # using different paddings for different sides
>>> m = nn.CircularPad3d((3, 3, 6, 6, 1, 1))
>>> output = m(input)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources