162 lines
7.8 KiB
Python
162 lines
7.8 KiB
Python
import numpy as np
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import torch
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import torch.nn as nn
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class Seq2SeqAttrs:
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def __init__(self, adj_mx, **model_kwargs):
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self.adj_mx = adj_mx
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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.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.hidden_state_size = self.num_nodes * self.rnn_units
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class EncoderModel(nn.Module, Seq2SeqAttrs):
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def __init__(self, adj_mx, **model_kwargs):
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# super().__init__(is_training, adj_mx, **model_kwargs)
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# https://pytorch.org/docs/stable/nn.html#gru
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nn.Module.__init__(self)
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Seq2SeqAttrs.__init__(self, adj_mx, **model_kwargs)
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self.input_dim = int(model_kwargs.get('input_dim', 1))
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self.seq_len = int(model_kwargs.get('seq_len')) # for the encoder
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self.dcgru_layers = nn.ModuleList([nn.GRUCell(input_size=self.num_nodes * self.input_dim,
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hidden_size=self.hidden_state_size,
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bias=True)] + [
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nn.GRUCell(input_size=self.hidden_state_size,
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hidden_size=self.hidden_state_size,
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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, self.num_nodes * self.input_dim)
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:param hidden_state: (num_layers, batch_size, self.hidden_state_size)
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optional, zeros if not provided
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:return: output: # shape (batch_size, self.hidden_state_size)
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hidden_state # shape (num_layers, batch_size, self.hidden_state_size)
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(lower indices mean lower layers)
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"""
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batch_size, _ = inputs.size()
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if hidden_state is None:
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hidden_state = torch.zeros((self.num_rnn_layers, batch_size, self.hidden_state_size))
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hidden_states = []
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output = inputs
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for layer_num, dcgru_layer in enumerate(self.dcgru_layers):
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next_hidden_state = dcgru_layer(output, hidden_state[layer_num])
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hidden_states.append(next_hidden_state)
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output = next_hidden_state
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return output, torch.stack(hidden_states) # runs in O(num_layers) so not too slow
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class DecoderModel(nn.Module, Seq2SeqAttrs):
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def __init__(self, adj_mx, **model_kwargs):
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# super().__init__(is_training, adj_mx, **model_kwargs)
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nn.Module.__init__(self)
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Seq2SeqAttrs.__init__(self, adj_mx, **model_kwargs)
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self.output_dim = int(model_kwargs.get('output_dim', 1))
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self.horizon = int(model_kwargs.get('horizon', 1)) # for the decoder
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self.projection_layer = nn.Linear(self.hidden_state_size, self.num_nodes * self.output_dim)
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self.dcgru_layers = nn.ModuleList([nn.GRUCell(input_size=self.num_nodes * self.output_dim,
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hidden_size=self.hidden_state_size,
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bias=True)] + [
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nn.GRUCell(input_size=self.hidden_state_size,
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hidden_size=self.hidden_state_size,
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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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Decoder forward pass.
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:param inputs: shape (batch_size, self.num_nodes * self.output_dim)
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:param hidden_state: (num_layers, batch_size, self.hidden_state_size)
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optional, zeros if not provided
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:return: output: # shape (batch_size, self.num_nodes * self.output_dim)
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hidden_state # shape (num_layers, batch_size, self.hidden_state_size)
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(lower indices mean lower layers)
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"""
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hidden_states = []
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output = inputs
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for layer_num, dcgru_layer in enumerate(self.dcgru_layers):
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next_hidden_state = dcgru_layer(output, hidden_state[layer_num])
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hidden_states.append(next_hidden_state)
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output = next_hidden_state
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return self.projection_layer(output), torch.stack(hidden_states)
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class DCRNNModel(nn.Module, Seq2SeqAttrs):
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def __init__(self, adj_mx, logger, **model_kwargs):
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super().__init__()
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Seq2SeqAttrs.__init__(self, adj_mx, **model_kwargs)
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self.encoder_model = EncoderModel(adj_mx, **model_kwargs)
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self.decoder_model = DecoderModel(adj_mx, **model_kwargs)
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self.cl_decay_steps = int(model_kwargs.get('cl_decay_steps', 1000))
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self.use_curriculum_learning = bool(model_kwargs.get('use_curriculum_learning', False))
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self._logger = logger
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def _compute_sampling_threshold(self, batches_seen):
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return self.cl_decay_steps / (
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self.cl_decay_steps + np.exp(batches_seen / self.cl_decay_steps))
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def encoder(self, inputs):
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"""
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encoder forward pass on t time steps
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:param inputs: shape (seq_len, batch_size, num_sensor * input_dim)
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:return: encoder_hidden_state: (num_layers, batch_size, self.hidden_state_size)
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"""
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encoder_hidden_state = None
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for t in range(self.encoder_model.seq_len):
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_, encoder_hidden_state = self.encoder_model(inputs[t], encoder_hidden_state)
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return encoder_hidden_state
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def decoder(self, encoder_hidden_state, labels=None, batches_seen=None):
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"""
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Decoder forward pass
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:param encoder_hidden_state: (num_layers, batch_size, self.hidden_state_size)
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:param labels: (self.horizon, batch_size, self.num_nodes * self.output_dim) [optional, not exist for inference]
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:param batches_seen: global step [optional, not exist for inference]
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:return: output: (self.horizon, batch_size, self.num_nodes * self.output_dim)
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"""
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batch_size = encoder_hidden_state.size(1)
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go_symbol = torch.zeros((batch_size, self.num_nodes * self.decoder_model.output_dim))
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decoder_hidden_state = encoder_hidden_state
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decoder_input = go_symbol
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outputs = []
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for t in range(self.decoder_model.horizon):
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decoder_output, decoder_hidden_state = self.decoder_model(decoder_input,
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decoder_hidden_state)
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decoder_input = decoder_output
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outputs.append(decoder_output)
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if self.training and self.use_curriculum_learning:
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c = np.random.uniform(0, 1)
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if c < self._compute_sampling_threshold(batches_seen):
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decoder_input = labels[t]
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outputs = torch.stack(outputs)
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return outputs
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def forward(self, inputs, labels=None, batches_seen=None):
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"""
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seq2seq forward pass
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:param inputs: shape (seq_len, batch_size, num_sensor * input_dim)
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:param labels: shape (horizon, batch_size, num_sensor * output)
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:param batches_seen: batches seen till date
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:return: output: (self.horizon, batch_size, self.num_nodes * self.output_dim)
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"""
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encoder_hidden_state = self.encoder(inputs)
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self._logger.info("Encoder complete, starting decoder")
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outputs = self.decoder(encoder_hidden_state, labels, batches_seen=batches_seen)
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self._logger.info("Decoder complete")
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return outputs
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