libcity.model.traffic_flow_prediction.CRANN¶

class libcity.model.traffic_flow_prediction.CRANN.AttentionBlock(dim_x, dim_y, timesteps, method='hadamard')[source]¶

Bases: torch.nn.modules.module.Module

Description |

Attentional block for spatio-temporal attention mechanism

Attributes |

dim_xint: Dimension of x-axis for input images
dim_yint: Dimension of y-axis for input images
timestepsint: Number of input timesteps
methodstr: Attentional function to calculate attention weights

Methods |

forward(x): Forward pass of the network

forward(x, y=0)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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.traffic_flow_prediction.CRANN.AttentionCNN(in_channels, out_channels, dim_x, dim_y)[source]¶

Bases: torch.nn.modules.module.Module

Description |

Spatial module with spatio-temporal attention

Attributes |

in_channelsint: Number of input timesteps
out_channelsint: Number of output timesteps
dim_xint: Dimension of x-axis for input images
dim_yint: Dimension of y-axis for input images

Methods |

forward(x): Forward pass of the network

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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.traffic_flow_prediction.CRANN.BahdanauDecoder(hidden_size, output_size, n_layers=1, drop_prob=0.1)[source]¶

Bases: torch.nn.modules.module.Module

Description |

Decoder an attention mechanism for temporal module

Attributes |

hidden_sizeint: Dimension of hidden space
output_sizeint: Number of output features
n_layersint: Number of layers for the encoder
drop_probfloat: Dropout for the encoder

Methods |

forward(x): Forward pass of the network

forward(inputs, hidden, encoder_outputs)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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.traffic_flow_prediction.CRANN.CRANN(config, data_feature)[source]¶

Bases: libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel

calculate_loss(batch)[source]¶

输入一个batch的数据，返回训练过程的loss，也就是需要定义一个loss函数

Parameters: batch (Batch) – a batch of input
Returns: return training loss
Return type: torch.tensor

evaluate_temp_att(encoder, decoder, batch, n_pred, device)[source]¶

forward(batch)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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.

predict(batch)[source]¶

输入一个batch的数据，返回对应的预测值，一般应该是**多步预测**的结果，一般会调用nn.Moudle的forward()方法

Parameters: batch (Batch) – a batch of input
Returns: predict result of this batch
Return type: torch.tensor

training: bool¶

class libcity.model.traffic_flow_prediction.CRANN.ConvBlock(in_features, out_features, num_conv)[source]¶

Bases: torch.nn.modules.module.Module

Description |

Convolutional blocks of num_conv convolutions with out_features channels

Attributes |

in_featuresint: Number of input channels
out_featuresint: Number of middle and output channels
num_convint: Number of convolutions

Methods |

forward(x): Forward pass of the network

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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.traffic_flow_prediction.CRANN.EncoderLSTM(input_size, hidden_size, n_layers=1, drop_prob=0, device='cuda')[source]¶

Bases: torch.nn.modules.module.Module

Description |

Encoder for temporal module

Attributes |

input_sizeint: Number of input features
hidden_sizeint: Dimension of hidden space
n_layersint: Number of layers for the encoder
drop_probfloat: Dropout for the encoder
deviceint/str: Device in which hiddens are stored

Methods |

forward(x): Forward pass of the network

forward(inputs, hidden)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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.

init_hidden(batch_size)[source]¶

training: bool¶

class libcity.model.traffic_flow_prediction.CRANN.MLP(n_inputs, n_outputs, n_layers=1, n_hidden=0, dropout=0)[source]¶

Bases: torch.nn.modules.module.Module

Description |

Dense module

Attributes |

n_inputsint: Number of input features
n_outputsint: Number of output features
n_layersint: Number of layers
n_hiddenint: Dimension of hidden layers

Methods |

forward(x): Forward pass of the network

forward(x)[source]¶

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

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¶