mindspore.mint.nn.functional.conv_transpose2d

mindspore.mint.nn.functional.conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1)

Calculates a 2D transposed convolution, which can be regarded as Conv2d for the gradient of the input, also called deconvolution (although it is not an actual deconvolution).

The input is typically of shape \((N, C_{in}, H_{in}, W_{in})\), where \(N\) is batch size, \(C_{in}\) is space dimension, \(H_{in}, W_{in}\) are the height and width of the feature layer respectively.

When Conv2d and Conv2dTranspose are initialized with the same parameters, and pad_mode is set to 'pad', \(dilation * (kernel\_size - 1) - padding\) amount of zero will be paded to the height and width directions of the input, they are inverses of each other in regard to the input and output shapes in this case. However, when stride > 1, Conv2d maps multiple input shapes to the same output shape. Deconvolutional network can refer to Deconvolutional Networks.

Parameters

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

• weight (Tensor) – Tensor of shape \((N, C_{in} / \text{groups}, \text{kernel_size[0]}, \text{kernel_size[1]})\), then the size of kernel is \((\text{kernel_size[0]}, \text{kernel_size[1]})\).

• bias (Tensor, optional) – Bias Tensor with shape \((C_{out})\). When bias is None , zeros will be used. Default: None .

• stride (Union(int, tuple[int]), optional) – 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. Default: 1 .

• padding (Union(int, tuple[int], list[int]), optional) – Implicit paddings on both sides of the input x. Can be an integer or a tuple/list with 2 integers.

• output_padding (Union[int, tuple[int]]) – The number of padding on the height and width directions of the output. The data type is an integer or a tuple of two integers. If output_padding is an integer, then the bottom and right padding are all equal to output_padding. If output_padding is a tuple of 2 integers, then the bottom and right padding is equal to output_padding[0], output_padding[1] respectively.

• groups (int, optional) – Splits input into groups. Default: 1 .

• dilation (Union(int, tuple[int]), optional) – Gaps between kernel elements.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 x. Default: 1 .

Returns

Tensor, the value that applied 2D convolution. The shape is \((N, C_{out}, H_{out}, W_{out})\). To see how different pad modes affect the output shape, please refer to mindspore.nn.Conv2dTranspose for more details.

Raises

• TypeError – If stride, padding or dilation is neither an int nor a tuple.

• TypeError – groups is not an int.

• TypeError – If bias is not a Tensor.

• ValueError – If the shape of bias is not \((C_{out})\) .

• ValueError – If stride or dilation is less than 1.

• ValueError – If padding is a tuple/list whose length is not equal to 2.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> x = Tensor(np.ones([1, 6, 32, 32]), mindspore.float32)
>>> weight = Tensor(np.ones([6, 3, 5, 5]), mindspore.float32)
>>> output = mint.nn.functional.conv_transpose2d(x, weight)
>>> print(output.shape)
(1, 3, 36, 36)