mindspore.nn.Conv2dTranspose

class mindspore.nn.Conv2dTranspose(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')[source]

2D transposed convolution layer.

Compute a 2D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution).

Input is typically of shape \((N, C, H, W)\), where \(N\) is batch size and \(C\) is channel number.

If the ‘pad_mode’ is set to be “pad”, the height and width of output are defined as:

\[ \begin{align}\begin{aligned}H_{out} = (H_{in} - 1) \times \text{stride[0]} - \left (\text{padding[0]} + \text{padding[1]}\right ) + \text{dilation[0]} \times (\text{kernel_size[0]} - 1) + 1\\W_{out} = (W_{in} - 1) \times \text{stride[1]} - \left (\text{padding[2]} + \text{padding[3]}\right ) + \text{dilation[1]} \times (\text{kernel_size[1]} - 1) + 1\end{aligned}\end{align} \]

where \(\text{kernel_size[0]}\) is the height of the convolution kernel and \(\text{kernel_size[1]}\) 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 (Union[int, tuple]) – int or a tuple of 2 integers, which specifies the height and width of the 2D convolution window. Single int means the value is for both the height and the width of the kernel. A tuple of 2 ints means the first value is for the height and the other is for the width of the kernel.

  • stride (Union[int, tuple[int]]) – The distance of kernel moving, an int number that represents the height and width of movement are both strides, or a tuple of two int numbers that represent height and width of movement respectively. Its value must be equal to or greater than 1. 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.

    • same: Adopted the way of completion.

    • valid: Adopted the way of discarding.

  • padding (Union[int, tuple[int]]) – Implicit paddings on both sides of the input. If padding is one integer, the paddings of top, bottom, left and right are the same, equal to padding. If padding is a tuple with four integers, the paddings of top, bottom, left and right will be equal to padding[0], padding[1], padding[2], and padding[3] accordingly. Default: 0.

  • dilation (Union[int, tuple[int]]) – The data type is int or a tuple of 2 integers. 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 than or equal to 1 and bounded by the height and width of the input. Default: 1.

  • group (int) – Splits filter into groups, in_channels and out_channels must be divisible by the number of groups. This does 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 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:

  • input (Tensor) - Tensor of shape \((N, C_{in}, H_{in}, W_{in})\).

Outputs:

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

Raises

  • TypeError – If in_channels, out_channels or group is not an int.

  • TypeError – If kernel_size, stride, padding or dilation is neither an int not a tuple.

  • 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’.

  • ValueError – If padding is a tuple whose length is not equal to 4.

  • ValueError – If pad_mode is not equal to ‘pad’ and padding is not equal to (0, 0, 0, 0).

Supported Platforms:

Ascend GPU

Examples

>>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
>>> output = net(input).shape
>>> print(output)
(1, 64, 19, 53)