HFAiLab
how to run graphcast model locally?
and two models are different,one from huggingface you give as backbone another from graphcast_sequential.py
name1 Out [11]: ['grid_feat_embedding.0.weight', 'grid_feat_embedding.0.bias', 'mesh_feat_embedding.0.weight', 'mesh_feat_embedding.0.bias', 'mesh_edge_feat_embedding.0.weight', 'mesh_edge_feat_embedding.0.bias', 'grid2mesh_edge_feat_embedding.0.weight', 'grid2mesh_edge_feat_embedding.0.bias', 'mesh2grid_edge_feat_embedding.0.weight', 'mesh2grid_edge_feat_embedding.0.bias', 'grid2mesh_edge_update.0.weight', 'grid2mesh_edge_update.0.bias', 'grid2mesh_edge_update.2.weight', 'grid2mesh_edge_update.2.bias', 'grid2mesh_edge_update.4.weight', 'grid2mesh_edge_update.4.bias', 'grid2mesh_node_aggregate.0.weight', 'grid2mesh_node_aggregate.0.bias', 'grid2mesh_node_aggregate.2.weight', 'grid2mesh_node_aggregate.2.bias', 'grid2mesh_node_aggregate.3.weight', 'grid2mesh_node_aggregate.3.bias', 'grid2mesh_grid_update.0.weight', 'grid2mesh_grid_update.0.bias', 'grid2mesh_grid_update.2.weight', 'grid2mesh_grid_update.2.bias', 'grid2mesh_grid_update.3.weight', 'grid2mesh_grid_update.3.bias', 'mesh_edge_update.0.weight', 'mesh_edge_update.0.bias', 'mesh_edge_update.2.weight', 'mesh_edge_update.2.bias', 'mesh_edge_update.4.weight', 'mesh_edge_update.4.bias', 'mesh_node_aggregate.0.weight', 'mesh_node_aggregate.0.bias', 'mesh_node_aggregate.2.weight', 'mesh_node_aggregate.2.bias', 'mesh_node_aggregate.3.weight', 'mesh_node_aggregate.3.bias', 'mesh2grid_edge_update.0.weight', 'mesh2grid_edge_update.0.bias', 'mesh2grid_edge_update.2.weight', 'mesh2grid_edge_update.2.bias', 'mesh2grid_edge_update.4.weight', 'mesh2grid_edge_update.4.bias', 'mesh2grid_node_aggregate.0.weight', 'mesh2grid_node_aggregate.0.bias', 'mesh2grid_node_aggregate.2.weight', 'mesh2grid_node_aggregate.2.bias', 'mesh2grid_node_aggregate.3.weight', 'mesh2grid_node_aggregate.3.bias', 'predict_nn.0.weight', 'predict_nn.0.bias', 'predict_nn.2.weight', 'predict_nn.2.bias', 'predict_nn.4.weight', 'predict_nn.4.bias']
name2 Out [12]: ['embed.grid_feat_embedding.0.weight', 'embed.grid_feat_embedding.0.bias', 'embed.mesh_feat_embedding.0.weight', 'embed.mesh_feat_embedding.0.bias', 'embed.mesh_edge_feat_embedding.0.weight', 'embed.mesh_edge_feat_embedding.0.bias', 'embed.grid2mesh_edge_feat_embedding.0.weight', 'embed.grid2mesh_edge_feat_embedding.0.bias', 'embed.mesh2grid_edge_feat_embedding.0.weight', 'embed.mesh2grid_edge_feat_embedding.0.bias', 'gnn_blocks.0.grid2mesh_edge_update.0.weight', 'gnn_blocks.0.grid2mesh_edge_update.0.bias', 'gnn_blocks.0.grid2mesh_edge_update.2.weight', 'gnn_blocks.0.grid2mesh_edge_update.2.bias', 'gnn_blocks.0.grid2mesh_edge_update.4.weight', 'gnn_blocks.0.grid2mesh_edge_update.4.bias', 'gnn_blocks.0.grid2mesh_edge_update.5.weight', 'gnn_blocks.0.grid2mesh_edge_update.5.bias', 'gnn_blocks.1.grid2mesh_node_aggregate.0.weight', 'gnn_blocks.1.grid2mesh_node_aggregate.0.bias', 'gnn_blocks.1.grid2mesh_node_aggregate.2.weight', 'gnn_blocks.1.grid2mesh_node_aggregate.2.bias', 'gnn_blocks.1.grid2mesh_node_aggregate.4.weight', 'gnn_blocks.1.grid2mesh_node_aggregate.4.bias', 'gnn_blocks.1.grid2mesh_node_aggregate.5.weight', 'gnn_blocks.1.grid2mesh_node_aggregate.5.bias', 'gnn_blocks.1.grid2mesh_grid_update.0.weight', 'gnn_blocks.1.grid2mesh_grid_update.0.bias', 'gnn_blocks.1.grid2mesh_grid_update.2.weight', 'gnn_blocks.1.grid2mesh_grid_update.2.bias', 'gnn_blocks.1.grid2mesh_grid_update.3.weight', 'gnn_blocks.1.grid2mesh_grid_update.3.bias', 'gnn_blocks.2.mesh_edge_update.0.weight', 'gnn_blocks.2.mesh_edge_update.0.bias', 'gnn_blocks.2.mesh_edge_update.2.weight', 'gnn_blocks.2.mesh_edge_update.2.bias', 'gnn_blocks.2.mesh_edge_update.4.weight', 'gnn_blocks.2.mesh_edge_update.4.bias', 'gnn_blocks.2.mesh_edge_update.5.weight', 'gnn_blocks.2.mesh_edge_update.5.bias', 'gnn_blocks.3.mesh_node_aggregate.0.weight', 'gnn_blocks.3.mesh_node_aggregate.0.bias', 'gnn_blocks.3.mesh_node_aggregate.2.weight', 'gnn_blocks.3.mesh_node_aggregate.2.bias', 'gnn_blocks.3.mesh_node_aggregate.4.weight', 'gnn_blocks.3.mesh_node_aggregate.4.bias', 'gnn_blocks.3.mesh_node_aggregate.5.weight', 'gnn_blocks.3.mesh_node_aggregate.5.bias', 'gnn_blocks.4.mesh2grid_edge_update.0.weight', 'gnn_blocks.4.mesh2grid_edge_update.0.bias', 'gnn_blocks.4.mesh2grid_edge_update.2.weight', 'gnn_blocks.4.mesh2grid_edge_update.2.bias', 'gnn_blocks.4.mesh2grid_edge_update.4.weight', 'gnn_blocks.4.mesh2grid_edge_update.4.bias', 'gnn_blocks.4.mesh2grid_edge_update.5.weight', 'gnn_blocks.4.mesh2grid_edge_update.5.bias', 'gnn_blocks.5.mesh2grid_node_aggregate.0.weight', 'gnn_blocks.5.mesh2grid_node_aggregate.0.bias', 'gnn_blocks.5.mesh2grid_node_aggregate.2.weight', 'gnn_blocks.5.mesh2grid_node_aggregate.2.bias', 'gnn_blocks.5.mesh2grid_node_aggregate.4.weight', 'gnn_blocks.5.mesh2grid_node_aggregate.4.bias', 'gnn_blocks.5.mesh2grid_node_aggregate.5.weight', 'gnn_blocks.5.mesh2grid_node_aggregate.5.bias', 'head.predict_nn.0.weight', 'head.predict_nn.0.bias', 'head.predict_nn.2.weight', 'head.predict_nn.2.bias', 'head.predict_nn.4.weight', 'head.predict_nn.4.bias']
