42 lines
1.9 KiB
Python
42 lines
1.9 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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# since input shape is Input (batch_size, timesteps, num_sensor*input_dim),batch_first=True
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self.dcgru = nn.GRU(input_size=self.num_nodes * self.input_dim,
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hidden_size=self.rnn_units,
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num_layers=self.num_rnn_layers,
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batch_first=True)
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def forward(self, inputs, hidden_state=None):
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# is None okay?
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return self.dcgru(inputs, hidden_state)
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