mindflow.cell.FNO2D

class mindflow.cell.FNO2D(in_channels, out_channels, resolution, modes, channels=20, depths=4, mlp_ratio=4, compute_dtype=mstype.float32)[source]

The 2-dimensional Fourier Neural Operator (FNO2D) contains a lifting layer, multiple Fourier layers and a decoder layer. The details can be found in Fourier neural operator for parametric partial differential equations.

Parameters

in_channels (int) – The number of channels in the input space.
out_channels (int) – The number of channels in the output space.
resolution (int) – The spatial resolution of the input.
modes (int) – The number of low-frequency components to keep.
channels (int) – The number of channels after dimension lifting of the input. Default: 20.
depths (int) – The number of FNO layers. Default: 4.
mlp_ratio (int) – The number of channels lifting ratio of the decoder layer. Default: 4.
compute_dtype (dtype.Number) – The computation type of dense. Default: mindspore.common.dtype.float32. Should be mindspore.common.dtype.float32 or mindspore.common.dtype.float32. float32 is recommended for the GPU backend, float16 is recommended for the Ascend backend.

Inputs:

x (Tensor) - Tensor of shape \((batch\_size, resolution, resolution, in\_channels)\).

Outputs:

Tensor, the output of this FNO network.

output (Tensor) -Tensor of shape \((batch\_size, resolution, resolution, out\_channels)\).

Raises

TypeError – If in_channels is not an int.
TypeError – If out_channels is not an int.
TypeError – If resolution is not an int.
TypeError – If modes is not an int.
ValueError – If modes is less than 1.

Supported Platforms:: Ascend GPU

Examples

>>> import numpy as np
>>> from mindspore.common.initializer import initializer, Normal
>>> from mindflow.cell.neural_operators import FNO2D
>>> B, H, W, C = 32, 64, 64, 1
>>> input = initializer(Normal(), [B, H, W, C])
>>> net = FNO2D(in_channels=1, out_channels=1, resolution=64, modes=12)
>>> output = net(input)
>>> print(output.shape)
(32, 64, 64, 1)