74 lines
3.6 KiB
Python
74 lines
3.6 KiB
Python
import torch
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import torch.nn as nn
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class DCRNNModel(nn.Module):
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def __init__(self, is_training, scale_factor, adj_mx, **model_kwargs):
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super().__init__()
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self.adj_mx = adj_mx
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self.is_training = is_training
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self.scale_factor = scale_factor
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self.max_diffusion_step = int(model_kwargs.get('max_diffusion_step', 2))
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self.cl_decay_steps = int(model_kwargs.get('cl_decay_steps', 1000))
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self.filter_type = model_kwargs.get('filter_type', 'laplacian')
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self.horizon = int(model_kwargs.get('horizon', 1)) # for the decoder
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# self.max_grad_norm = float(model_kwargs.get('max_grad_norm', 5.0))
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self.num_nodes = int(model_kwargs.get('num_nodes', 1))
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self.num_rnn_layers = int(model_kwargs.get('num_rnn_layers', 1))
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self.rnn_units = int(model_kwargs.get('rnn_units'))
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self.seq_len = int(model_kwargs.get('seq_len')) # for the encoder
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self.use_curriculum_learning = bool(model_kwargs.get('use_curriculum_learning', False))
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self.input_dim = int(model_kwargs.get('input_dim', 1))
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self.output_dim = int(model_kwargs.get('output_dim', 1))
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class EncoderModel(DCRNNModel):
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def __init__(self, is_training, scaler, adj_mx, **model_kwargs):
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super().__init__(is_training, scaler, adj_mx, **model_kwargs)
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# https://pytorch.org/docs/stable/nn.html#gru
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# input shape is supposed to be Input (batch_size, timesteps, num_sensor*input_dim)
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# first layer takes input shape and subsequent layer take input from the first layer
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self.dcgru_layers = [nn.GRUCell(input_size=self.num_nodes * self.input_dim,
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hidden_size=self.rnn_units,
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bias=True)] + [nn.GRUCell(input_size=self.rnn_units,
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hidden_size=self.rnn_units,
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bias=True) for _ in
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range(self.num_rnn_layers - 1)]
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def forward(self, inputs, hidden_state=None):
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"""
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Encoder forward pass.
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:param inputs: shape (batch_size, timesteps, num_sensor*input_dim)
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:param hidden_state: (num_layers, batch_size, rnn_units) -> optional, zeros if not provided
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:return: output, hidden_state
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"""
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layer_input = inputs.permute(1, 0, 2) # first axis is now timesteps
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if hidden_state is None:
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batch_size = inputs.size()[0]
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hidden_state = torch.zeros((self.num_rnn_layers, batch_size, self.rnn_units),
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device=device)
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hidden = torch.empty_like(hidden_state)
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for layer_num, dcgru_layer in enumerate(self.dcgru_layers):
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layer_states = self._forward_layer(layer_input, dcgru_layer, hidden_state[layer_num])
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# append last time step's hidden state
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hidden[layer_num] = layer_states[-1]
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layer_input = layer_states
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output = layer_input # last layer's output
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return output, hidden
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@staticmethod
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def _forward_layer(inputs, dcgru_layer, hidden_state):
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# inputs shape = (timesteps, batch_size, input_size)
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outputs = [] # shape (timesteps, batch_size, self.rnn_units)
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for cell_input in inputs[:, ]:
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hidden_state = dcgru_layer(cell_input, hidden_state)
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outputs.append(hidden_state)
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return torch.cat(outputs, dim=1) # runs in O(timesteps) not too slow
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