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Specifications and Common Mistakes

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mindearth.cell.DgmrGenerator

class mindearth.cell.DgmrGenerator(forecast_steps=18, in_channels=1, out_channels=256, conv_type='standard', latent_channels=768, context_channels=384, generation_steps=1)

The Dgmr Generator is based on Conditional_Stack, Latent_Stack, Upsample_Stack and ConvGRU, which contain deep residual block. The details can be found in Skilful precipitation nowcasting using deep generative models of radar.

Parameters

• forecast_steps (int) – The steps of forecast frames.

• in_channels (int) – The channels of input frame.

• out_channels (int) – Shape of the output predictions, generally should be same as the input shape.

• conv_type (str) – The convolution type.

• latent_channels (int) – Latent channels according to network.

• context_channels (int) – Context channels according to network.

• generation_steps (int) – Number of generation steps to use in forward pass, in paper is 6 and the best is chosen for the loss, this results in huge amounts of GPU memory though, so less might work better for training.

Inputs:

• x (Tensor) - Tensor of shape $(batch\mathrm{\_}size,input\mathrm{\_}frames,out\mathrm{\_}channels,height\mathrm{\_}size,width\mathrm{\_}size)$.

Outputs:

Tensor, the output of Dgmr Generator.

• output (Tensor) - Tensor of shape $(batch\mathrm{\_}size,output\mathrm{\_}frames,out\mathrm{\_}channels,height\mathrm{\_}size,width\mathrm{\_}size)$.

Supported Platforms:

Ascend GPU

Examples

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import ops, Tensor
>>> from mindspore.nn import Cell
>>> from mindearth.cell.dgmr.dgmrnet import DgmrGenerator
>>> input_frames = np.random.rand(1, 4, 1, 256, 256).astype(np.float32)
>>> net = DgmrGenerator(
>>>         forecast_steps = 18,
>>>         in_channels = 1,
>>>         out_channels = 256,
>>>         conv_type = "standard",
>>>         latent_channels = 768,
>>>         context_channels = 384,
>>>         generation_steps = 1
>>>     )
>>> out = net(Tensor(input_frames, ms.float32))
>>> print(out.shape)
(1, 18, 1, 256, 256)