oneflow.nn.ConvTranspose3d

class oneflow.nn.ConvTranspose3d(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int, int]], stride: Union[int, Tuple[int, int, int]] = 1, padding: Union[int, Tuple[int, int, int]] = 0, output_padding: Union[int, Tuple[int, int, int]] = 0, groups: int = 1, bias: bool = True, dilation: Union[int, Tuple[int, int, int]] = 1, padding_mode: str = 'zeros')

Applies a 3D transposed convolution operator over an input image composed of several input planes. The transposed convolution operator multiplies each input value element-wise by a learnable kernel, and sums over the outputs from all input feature planes.

This module can be seen as the gradient of Conv3d with respect to its input. It is also known as a fractionally-strided convolution or a deconvolution (although it is not an actual deconvolution operation).

This module supports TensorFloat32.

  • stride controls the stride for the cross-correlation.

  • padding controls the amount of implicit zero padding on both sides for dilation * (kernel_size - 1) - padding number of points. See note below for details.

  • output_padding controls the additional size added to one side of the output shape. See note below for details.

  • dilation controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this link has a nice visualization of what dilation does.

The parameters kernel_size, stride, padding, output_padding can either be:

  • a single int – in which case the same value is used for the depth, height and width dimensions

  • a tuple of three ints – in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension

Note

The padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input. This is set so that when a Conv3d and a ConvTranspose3d are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when stride > 1, Conv3d maps multiple input shapes to the same output shape. output_padding is provided to resolve this ambiguity by effectively increasing the calculated output shape on one side. Note that output_padding is only used to find output shape, but does not actually add zero-padding to output.

Parameters

  • in_channels (int) – Number of channels in the input image

  • out_channels (int) – Number of channels produced by the convolution

  • kernel_size (int or tuple) – Size of the convolving kernel

  • stride (int or tuple, optional) – Stride of the convolution. Default: 1

  • padding (int or tuple, optional) – dilation * (kernel_size - 1) - padding zero-padding will be added to both sides of each dimension in the input. Default: 0

  • output_padding (int or tuple, optional) – Additional size added to one side of each dimension in the output shape. Default: 0

  • groups (int, optional) – Number of blocked connections from input channels to output channels. Default: 1

  • bias (bool, optional) – If True, adds a learnable bias to the output. Default: True

  • dilation (int or tuple, optional) – Spacing between kernel elements. Default: 1

Shape:

  • Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)

  • Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where

\[D_{out} = (D_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0] \times (\text{kernel_size}[0] - 1) + \text{output_padding}[0] + 1\]
\[H_{out} = (H_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1] \times (\text{kernel_size}[1] - 1) + \text{output_padding}[1] + 1\]
\[W_{out} = (W_{in} - 1) \times \text{stride}[2] - 2 \times \text{padding}[2] + \text{dilation}[2] \times (\text{kernel_size}[2] - 1) + \text{output_padding}[2] + 1\]
weight

the learnable weights of the module of shape \((\text{in_channels}, \frac{\text{out_channels}}{\text{groups}},\) \(\text{kernel_size[0]}, \text{kernel_size[1]}, \text{kernel_size[2]})\). The values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel_size}[i]}\)

Type

Tensor

bias

the learnable bias of the module of shape (out_channels) If bias is True, then the values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_\text{out} * \prod_{i=0}^{2}\text{kernel_size}[i]}\)

Type

Tensor

Examples:

>>> import oneflow as flow
>>> import oneflow.nn as nn

>>> # With square kernels and equal stride
>>> m = nn.ConvTranspose3d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.ConvTranspose3d(16, 33, (3, 5, 2), stride=(2, 1, 1), padding=(0, 4, 2))
>>> input = flow.randn(20, 16, 10, 50, 100)
>>> output = m(input)