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mindspore.nn.Conv1dTranspose

class mindspore.nn.Conv1dTranspose(in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_init='normal', bias_init='zeros')

1D transposed convolution layer.

Compute a 1D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution). This module can be seen as the gradient of Conv1d with respect to its input.

x is typically of shape $(N,C,W)$, where $N$ is batch size, $C$ is channel number and $W$ is the characteristic length.

The padding argument effectively adds $dilation\ast (kernel\mathrm{\_}size-1)-padding$ amount of zero padding to both sizes of the input. So that when a Conv1d and a ConvTranspose1d are initialized with same parameters, they are inverses of each other in regard to the input and output shapes. However, when stride > 1, Conv1d maps multiple input shapes to the same output shape.

The width of output is defined as:

$$\begin{array}{r}{W}_{out}=\{\begin{array}{ll}(W_{in}-1)\times \text{stride}-2\times \text{padding}+\text{dilation}\times (\text{ks\_w}-1)+1,& \text{if pad\_mode='pad'}\\ (W_{in}+\text{stride}-1)/\text{stride},& \text{if pad\_mode='same'}\\ (W_{in}-1)\times \text{stride}+\text{dilation}\times (\text{ks\_w}-1)+1,& \text{if pad\_mode='valid'}\end{array}\end{array}$$

where $\text{ks\_w}$ is the width of the convolution kernel.

Parameters

• in_channels (int) – The number of channels in the input space.

• out_channels (int) – The number of channels in the output space.

• kernel_size (int) – int, which specifies the width of the 1D convolution window.

• stride (int) – The distance of kernel moving, an int number that represents the width of movement. Default: 1.

• pad_mode (str) –

  Select the mode of the pad. The optional values are “pad”, “same”, “valid”. Default: “same”.

  • pad: Implicit paddings on both sides of the input x.

  • same: Adopted the way of completion.

  • valid: Adopted the way of discarding.

• padding (int) – Implicit paddings on both sides of the input x. Default: 0.

• dilation (int) – The data type is int. Specifies the dilation rate to use for dilated convolution. If set to be $k>1$, there will be $k-1$ pixels skipped for each sampling location. Its value must be greater or equal to 1 and bounded by the width of the input x. Default: 1.

• group (int) – Splits filter into groups, in_channels and out_channels must be divisible by the number of groups. This is not support for Davinci devices when group > 1. Default: 1.

• has_bias (bool) – Specifies whether the layer uses a bias vector. Default: False.

• weight_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the convolution kernel. It can be a Tensor, a string, an Initializer or a numbers.Number. When a string is specified, values from ‘TruncatedNormal’, ‘Normal’, ‘Uniform’, ‘HeUniform’ and ‘XavierUniform’ distributions as well as constant ‘One’ and ‘Zero’ distributions are possible. Alias ‘xavier_uniform’, ‘he_uniform’, ‘ones’ and ‘zeros’ are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of Initializer for more details. Default: ‘normal’.

• bias_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the bias vector. Possible Initializer and string are the same as ‘weight_init’. Refer to the values of Initializer for more details. Default: ‘zeros’.

Inputs:

• x (Tensor) - Tensor of shape $(N,C_{in},W_{in})$.

Outputs:

Tensor of shape $(N,C_{out},W_{out})$.

Raises

• TypeError – If in_channels, out_channels, kernel_size, stride, padding or dilation is not an int.

• ValueError – If in_channels, out_channels, kernel_size, stride or dilation is less than 1.

• ValueError – If padding is less than 0.

• ValueError – If pad_mode is not one of ‘same’, ‘valid’, ‘pad’.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> x = Tensor(np.ones([1, 3, 50]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 64, 53)