libcity.model.traffic_od_prediction.CSTN

class libcity.model.traffic_od_prediction.CSTN.CNN(height, width, n_layers)

Bases: torch.nn.modules.module.Module

forward(x)

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_od_prediction.CSTN.CSTN(config, data_feature)

Bases: libcity.model.abstract_traffic_state_model.AbstractTrafficStateModel

calculate_loss(batch)

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

forward(batch)

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)

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_od_prediction.CSTN.ConvLSTM(input_dim, hidden_dim, kernel_size, num_layers, batch_first=False, bias=True, return_all_layers=False)

Bases: torch.nn.modules.module.Module

Parameters

• input_dim – Number of channels in input

• hidden_dim – Number of hidden channels

• kernel_size – Size of kernel in convolutions

• num_layers – Number of LSTM layers stacked on each other

• batch_first – Whether or not dimension 0 is the batch or not

• bias – Bias or no bias in Convolution

• return_all_layers – Return the list of computations for all layers

• Note – Will do same padding.

Input:

A tensor of size B, T, C, H, W or T, B, C, H, W

Output:

A tuple of two lists of length num_layers (or length 1 if return_all_layers is False).

0 - layer_output_list is the list of lists of length T of each output 1 - last_state_list is the list of last states

each element of the list is a tuple (h, c) for hidden state and memory

Example

>> x = torch.rand((32, 10, 64, 128, 128)) >> convlstm = ConvLSTM(64, 16, 3, 1, True, True, False) >> _, last_states = convlstm(x) >> h = last_states[0][0] # 0 for layer index, 0 for h index

forward(input_tensor, hidden_state=None)

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_od_prediction.CSTN.ConvLSTMCell(input_dim, hidden_dim, kernel_size, bias)

Bases: torch.nn.modules.module.Module

forward(input_tensor, cur_state)

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, image_size)

training: bool

class libcity.model.traffic_od_prediction.CSTN.GCC

Bases: torch.nn.modules.module.Module

forward(x: torch.Tensor)

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_od_prediction.CSTN.LSC(height, width, n_layers)

Bases: torch.nn.modules.module.Module

forward(x)

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_od_prediction.CSTN.MLP

Bases: torch.nn.modules.module.Module

forward(x)

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_od_prediction.CSTN.TEC(c_lt)

Bases: torch.nn.modules.module.Module

forward(x)

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