oneflow.nn.ConvTranspose2d¶
-
class
oneflow.nn.
ConvTranspose2d
(in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], stride: Union[int, Tuple[int, int]] = 1, padding: Union[int, Tuple[int, int]] = 0, output_padding: Union[int, Tuple[int, int]] = 0, groups: int = 1, bias: bool = True, dilation: int = 1, padding_mode: str = 'zeros')¶ Applies a 2D transposed convolution operator over an input image composed of several input planes.
This module can be seen as the gradient of Conv2d 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).
- 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: 0output_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: \((N, C_{in}, H_{in}, W_{in})\)
Output: \((N, C_{out}, H_{out}, W_{out})\) where
\[ \begin{align}\begin{aligned}H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{dilation}[0]\\ \times (\text{kernel_size}[0] - 1) + \text{output_padding}[0] + 1\end{aligned}\end{align} \]\[ \begin{align}\begin{aligned}W_{out} = (W_{in} - 1) \times \text{stride}[1] - 2 \times \text{padding}[1] + \text{dilation}[1]\\ \times (\text{kernel_size}[1] - 1) + \text{output_padding}[1] + 1\end{aligned}\end{align} \]
-
weight
¶ the learnable weights of the module of shape \((\text{in_channels}, \frac{\text{out_channels}}{\text{groups}},\) \(\text{kernel_size[0]}, \text{kernel_size[1]})\). The values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel_size}[i]}\)
- Type
-
bias
¶ the learnable bias of the module of shape (out_channels) If
bias
isTrue
, then the values of these weights are sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{groups}{C_\text{out} * \prod_{i=0}^{1}\text{kernel_size}[i]}\)- Type
Examples:
>>> import numpy as np >>> import oneflow as flow >>> import oneflow.nn as nn >>> m = nn.ConvTranspose2d(16, 33, 3, stride=2) >>> # non-square kernels and unequal stride and with padding >>> m = nn.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2)) >>> m = m.to("cuda") >>> input = flow.Tensor(np.random.randn(20, 16, 50, 100), device=flow.device("cuda")) >>> output = m(input) >>> output.size() oneflow.Size([20, 33, 93, 100])