libcity.model.road_representation.GeomGCN

class libcity.model.road_representation.GeomGCN.GeomGCN(config, data_feature)

基类：libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel

calculate_loss(batch)

参数

batch – dict, need key ‘node_features’, ‘node_labels’, ‘mask’

Returns:

forward(batch)

自回归任务

参数

batch – dict, need key ‘node_features’ contains tensor shape=(N, feature_dim)

返回

N, output_classes

返回类型

torch.tensor

get_input(config, data_feature)

predict(batch)

参数

batch – dict, need key ‘node_features’

返回

torch.tensor

training: bool

class libcity.model.road_representation.GeomGCN.GeomGCNNet(g, in_feats, out_feats, num_divisions, activation, num_heads, dropout_prob, ggcn_merge, channel_merge, device)

基类：torch.nn.modules.module.Module

forward(feature)

Defines the computation performed at every call.

Should be overridden by all subclasses.

注解

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class libcity.model.road_representation.GeomGCN.GeomGCNSingleChannel(g, in_feats, out_feats, num_divisions, activation, dropout_prob, merge, device)

基类：torch.nn.modules.module.Module

forward(feature)

Defines the computation performed at every call.

Should be overridden by all subclasses.

注解

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

get_node_subgraphs(g)

get_subgraphs(g)

training: bool