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Source code for modulus.models.pangu.pangu

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import math
from dataclasses import dataclass

import numpy as np
import torch

from ..layers import DownSample3D, FuserLayer, UpSample3D
from ..meta import ModelMetaData
from ..module import Module
from ..utils import (
    PatchEmbed2D,
    PatchEmbed3D,
    PatchRecovery2D,
    PatchRecovery3D,
)


[docs]@dataclass class MetaData(ModelMetaData): name: str = "Pangu" # Optimization jit: bool = False # ONNX Ops Conflict cuda_graphs: bool = True amp: bool = True # Inference onnx_cpu: bool = False # No FFT op on CPU onnx_gpu: bool = True onnx_runtime: bool = True # Physics informed var_dim: int = 1 func_torch: bool = False auto_grad: bool = False
[docs]class Pangu(Module): """ Pangu A PyTorch impl of: `Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast` - https://arxiv.org/abs/2211.02556 Args: img_size (tuple[int]): Image size [Lat, Lon]. patch_size (tuple[int]): Patch token size [Lat, Lon]. embed_dim (int): Patch embedding dimension. Default: 192 num_heads (tuple[int]): Number of attention heads in different layers. window_size (tuple[int]): Window size. """ def __init__( self, img_size=(721, 1440), patch_size=(2, 4, 4), embed_dim=192, num_heads=(6, 12, 12, 6), window_size=(2, 6, 12), ): super().__init__(meta=MetaData()) drop_path = np.linspace(0, 0.2, 8).tolist() # In addition, three constant masks(the topography mask, land-sea mask and soil type mask) self.patchembed2d = PatchEmbed2D( img_size=img_size, patch_size=patch_size[1:], in_chans=4 + 3, # add embed_dim=embed_dim, ) self.patchembed3d = PatchEmbed3D( img_size=(13, img_size[0], img_size[1]), patch_size=patch_size, in_chans=5, embed_dim=embed_dim, ) patched_inp_shape = ( 8, math.ceil(img_size[0] / patch_size[1]), math.ceil(img_size[1] / patch_size[2]), ) self.layer1 = FuserLayer( dim=embed_dim, input_resolution=patched_inp_shape, depth=2, num_heads=num_heads[0], window_size=window_size, drop_path=drop_path[:2], ) patched_inp_shape_downsample = ( 8, math.ceil(patched_inp_shape[1] / 2), math.ceil(patched_inp_shape[2] / 2), ) self.downsample = DownSample3D( in_dim=embed_dim, input_resolution=patched_inp_shape, output_resolution=patched_inp_shape_downsample, ) self.layer2 = FuserLayer( dim=embed_dim * 2, input_resolution=patched_inp_shape_downsample, depth=6, num_heads=num_heads[1], window_size=window_size, drop_path=drop_path[2:], ) self.layer3 = FuserLayer( dim=embed_dim * 2, input_resolution=patched_inp_shape_downsample, depth=6, num_heads=num_heads[2], window_size=window_size, drop_path=drop_path[2:], ) self.upsample = UpSample3D( embed_dim * 2, embed_dim, patched_inp_shape_downsample, patched_inp_shape ) self.layer4 = FuserLayer( dim=embed_dim, input_resolution=patched_inp_shape, depth=2, num_heads=num_heads[3], window_size=window_size, drop_path=drop_path[:2], ) # The outputs of the 2nd encoder layer and the 7th decoder layer are concatenated along the channel dimension. self.patchrecovery2d = PatchRecovery2D( img_size, patch_size[1:], 2 * embed_dim, 4 ) self.patchrecovery3d = PatchRecovery3D( (13, img_size[0], img_size[1]), patch_size, 2 * embed_dim, 5 )
[docs] def prepare_input(self, surface, surface_mask, upper_air): """Prepares the input to the model in the required shape. Args: surface (torch.Tensor): 2D n_lat=721, n_lon=1440, chans=4. surface_mask (torch.Tensor): 2D n_lat=721, n_lon=1440, chans=3. upper_air (torch.Tensor): 3D n_pl=13, n_lat=721, n_lon=1440, chans=5. """ upper_air = upper_air.reshape( upper_air.shape[0], -1, upper_air.shape[3], upper_air.shape[4] ) surface_mask = surface_mask.unsqueeze(0).repeat(surface.shape[0], 1, 1, 1) return torch.concat([surface, surface_mask, upper_air], dim=1)
[docs] def forward(self, x): """ Args: x (torch.Tensor): [batch, 4+3+5*13, lat, lon] """ surface = x[:, :7, :, :] upper_air = x[:, 7:, :, :].reshape(x.shape[0], 5, 13, x.shape[2], x.shape[3]) surface = self.patchembed2d(surface) upper_air = self.patchembed3d(upper_air) x = torch.concat([surface.unsqueeze(2), upper_air], dim=2) B, C, Pl, Lat, Lon = x.shape x = x.reshape(B, C, -1).transpose(1, 2) x = self.layer1(x) skip = x x = self.downsample(x) x = self.layer2(x) x = self.layer3(x) x = self.upsample(x) x = self.layer4(x) output = torch.concat([x, skip], dim=-1) output = output.transpose(1, 2).reshape(B, -1, Pl, Lat, Lon) output_surface = output[:, :, 0, :, :] output_upper_air = output[:, :, 1:, :, :] output_surface = self.patchrecovery2d(output_surface) output_upper_air = self.patchrecovery3d(output_upper_air) return output_surface, output_upper_air
© Copyright 2023, NVIDIA Modulus Team. Last updated on Nov 27, 2024.