ConvTranspose3d#

class ConvTranspose3d(in_channels, out_channels, kernel_size, stride=1, padding=0, output_padding=0, dilation=1, groups=1, bias=True)[source]#

Applies a 3D transposed convolution over an input tensor.

Only support the case that groups = 1 and conv_mode = “cross_correlation”.

ConvTranspose3d can be seen as the gradient of Conv3d operation with respect to its input.

Convolution3D usually reduces the size of input, while transposed convolution3d works the opposite way, transforming a smaller input to a larger output while preserving the connectivity pattern.

Parameters:

  • in_channels (int) – number of input channels.

  • out_channels (int) – number of output channels.

  • kernel_size (Union[int, Tuple[int, int, int]]) – size of weight on spatial dimensions. If kernel_size is an int, the actual kernel size would be (kernel_size, kernel_size, kernel_size).

  • stride (Union[int, Tuple[int, int, int]]) – stride of the 3D convolution operation. Default: 1

  • padding (Union[int, Tuple[int, int, int]]) – size of the paddings added to the input on all sides of its spatial dimensions. Only zero-padding is supported. Default: 0

  • output_padding (Union[int, Tuple[int, int, int]]) – size of paddings appended to output. Default: 0

  • dilation (Union[int, Tuple[int, int, int]]) – dilation of the 3D convolution operation. Default: 1

  • groups (int) – number of groups into which the input and output channels are divided, so as to perform a grouped convolution. When groups is not 1, in_channels and out_channels must be divisible by groups, and the shape of weight should be (groups, in_channels // groups, out_channels // groups, depth, height, width). Default: 1

  • bias (bool) – wether to add a bias onto the result of convolution. Default: True

Note

  • weight usually has shape (in_channels, out_channels, depth, height, width) .

  • bias usually has shape (1, out_channels, *1)