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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)

Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called deconvolution (although it is not an actual deconvolution).

Refer to mindspore.mint.nn.ConvTranspose2d for more details.

Warning

• This is an experimental API that is subject to change or deletion.

• In the scenario where inputs are non-contiguous, output_padding must be less than stride .

• For Atlas training products, when the dtype of input is float32, the groups only supports 1.

Parameters

• input (Tensor) – input tensor of shape $(minibatch,in\mathrm{\_}channels,iH,iW)$ or $(in\mathrm{\_}channels,iH,iW)$ .

• weight (Tensor) – filters of shape $(in\mathrm{\_}channels,\frac{out\mathrm{\_}channels}{\text{groups}},kH,kW)$ .

• bias (Tensor, optional) – bias of shape $(out\mathrm{\_}channels)$ . Default: None .

• stride (Union[int, tuple(int), list[int]], optional) – the stride of the convolving kernel. Can be a single number or a tuple $(sH,sW)$ . Default: 1 .

• padding (Union[int, tuple(int), list[int]], optional) – $dilation\ast (kernel\mathrm{\_}size-1)-padding$ zero-padding will be added to both sides of each dimension in the input. Can be a single number or a tuple $(padH,padW)$ . Default: 0 .

• output_padding (Union[int, tuple(int), list[int]], optional) – additional size added to one side of each dimension in the output shape. Can be a single number or a tuple $(out\mathrm{\_}padH,out\mathrm{\_}padW)$ . The value of output_padding must be less than stride or dilation . Default: 0 .

• groups (int, optional) – split input into groups, $in\mathrm{\_}channels$ should be divisible by the number of groups. Default: 1 .

• dilation (Union[int, tuple(int), list[int]], optional) – the spacing between kernel elements. Can be a single number or a tuple $(dH,dW)$ . Default: 1 .

Returns

Tensor of shape $(minibatch,out\mathrm{\_}channels,oH,oW)$ or $(out\mathrm{\_}channels,oH,oW)$ , where

$$oH=(iH-1)\times sH-2\times padH+dH\times (kH-1)+out\mathrm{\_}padH+1$$

$$oW=(iW-1)\times sW-2\times padW+dW\times (kW-1)+out\mathrm{\_}padW+1$$

Raises

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

• TypeError – If groups is not an int.

• ValueError – If the shape of bias is not $(out\mathrm{\_}channels)$ .

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

• ValueError – If padding or output_padding is less than 0.

• ValueError – If stride, padding, output_padding or dilation is a tuple 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, 4, 5, 5]), mindspore.float32)
>>> weight = Tensor(np.ones([4, 8, 3, 3]), mindspore.float32)
>>> output = mint.nn.functional.conv_transpose2d(x, weight)
>>> print(output.shape)
(1, 8, 7, 7)