mindscience.models.GraphCast.GraphCastNet

class mindscience.models.GraphCast.GraphCastNet(vg_in_channels, vg_out_channels, vm_in_channels, em_in_channels, eg2m_in_channels, em2g_in_channels, latent_dims, processing_steps, g2m_src_idx, g2m_dst_idx, m2m_src_idx, m2m_dst_idx, m2g_src_idx, m2g_dst_idx, mesh_node_feats, mesh_edge_feats, g2m_edge_feats, m2g_edge_feats, per_variable_level_mean, per_variable_level_std, recompute=False)[源代码]

GraphCast 基于图神经网络和新颖的高分辨率多尺度网格表示的自回归模型。 详情请参阅 GraphCast: Learning skillful medium-range global weather forecasting。

参数：

  • vg_in_channels (int) - grid节点维度。

  • vg_out_channels (int) - grid节点最终维度。

  • vm_in_channels (int) - mesh节点维度。

  • em_in_channels (int) - mesh边维度。

  • eg2m_in_channels (int) - grid到mesh边维度。

  • em2g_in_channels (int) - mesh到grid边维度。

  • latent_dims (int) - 隐藏层的维度数。

  • processing_steps (int) - 处理步骤数。

  • g2m_src_idx (Tensor) - grid到mesh边的源节点索引。

  • g2m_dst_idx (Tensor) - grid到mesh边的目标节点索引。

  • m2m_src_idx (Tensor) - mesh到mesh边的源节点索引。

  • m2m_dst_idx (Tensor) - mesh到mesh边的目标节点索引。

  • m2g_src_idx (Tensor) - mesh到grid边的源节点索引。

  • m2g_dst_idx (Tensor) - mesh到grid边的目标节点索引。

  • mesh_node_feats (Tensor) - mesh节点特征。

  • mesh_edge_feats (Tensor) - mesh边特征。

  • g2m_edge_feats (Tensor) - grid到mesh边特征。

  • m2g_edge_feats (Tensor) - mesh到grid边特征。

  • per_variable_level_mean (Tensor) - 时间差分的每个变量级别的反方差均值。

  • per_variable_level_std (Tensor) - 时间差分的每个变量级别的反方差标准差。

  • recompute (bool, 可选) - 确定是否重新计算。默认值： False 。

输入：

  • input (Tensor) - 形状为 \((batch\_size, height\_size * width\_size, feature\_size)\) 的张量。

输出：

  • output (Tensor) - 形状为 \((height\_size * width\_size, feature\_size)\) 的张量。

样例：

>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import context, Tensor
>>> from mindscience.models.GraphCast.graphcastnet import GraphCastNet
>>>
>>> mesh_node_num = 2562
>>> grid_node_num = 32768
>>> mesh_edge_num = 20460
>>> g2m_edge_num = 50184
>>> m2g_edge_num = 98304
>>> vm_in_channels = 3
>>> em_in_channels = 4
>>> eg2m_in_channels = 4
>>> em2g_in_channels = 4
>>> feature_num = 69
>>> g2m_src_idx = Tensor(np.random.randint(0, grid_node_num, size=[g2m_edge_num]), ms.int32)
>>> g2m_dst_idx = Tensor(np.random.randint(0, mesh_node_num, size=[g2m_edge_num]), ms.int32)
>>> m2m_src_idx = Tensor(np.random.randint(0, mesh_node_num, size=[mesh_edge_num]), ms.int32)
>>> m2m_dst_idx = Tensor(np.random.randint(0, mesh_node_num, size=[mesh_edge_num]), ms.int32)
>>> m2g_src_idx = Tensor(np.random.randint(0, mesh_node_num, size=[m2g_edge_num]), ms.int32)
>>> m2g_dst_idx = Tensor(np.random.randint(0, grid_node_num, size=[m2g_edge_num]), ms.int32)
>>> mesh_node_feats = Tensor(np.random.rand(mesh_node_num, vm_in_channels).astype(np.float32), ms.float32)
>>> mesh_edge_feats = Tensor(np.random.rand(mesh_edge_num, em_in_channels).astype(np.float32), ms.float32)
>>> g2m_edge_feats = Tensor(np.random.rand(g2m_edge_num, eg2m_in_channels).astype(np.float32), ms.float32)
>>> m2g_edge_feats = Tensor(np.random.rand(m2g_edge_num, em2g_in_channels).astype(np.float32), ms.float32)
>>> per_variable_level_mean = Tensor(np.random.rand(feature_num,).astype(np.float32), ms.float32)
>>> per_variable_level_std = Tensor(np.random.rand(feature_num,).astype(np.float32), ms.float32)
>>> grid_node_feats = Tensor(np.random.rand(grid_node_num, feature_num).astype(np.float32), ms.float32)
>>> graphcast_model = GraphCastNet(vg_in_channels=feature_num,
...                                vg_out_channels=feature_num,
...                                vm_in_channels=vm_in_channels,
...                                em_in_channels=em_in_channels,
...                                eg2m_in_channels=eg2m_in_channels,
...                                em2g_in_channels=em2g_in_channels,
...                                latent_dims=512,
...                                processing_steps=4,
...                                g2m_src_idx=g2m_src_idx,
...                                g2m_dst_idx=g2m_dst_idx,
...                                m2m_src_idx=m2m_src_idx,
...                                m2m_dst_idx=m2m_dst_idx,
...                                m2g_src_idx=m2g_src_idx,
...                                m2g_dst_idx=m2g_dst_idx,
...                                mesh_node_feats=mesh_node_feats,
...                                mesh_edge_feats=mesh_edge_feats,
...                                g2m_edge_feats=g2m_edge_feats,
...                                m2g_edge_feats=m2g_edge_feats,
...                                per_variable_level_mean=per_variable_level_mean,
...                                per_variable_level_std=per_variable_level_std)
>>> out = graphcast_model(Tensor(grid_node_feats, ms.float32))
>>> print(out.shape)
(32768, 69)