179 lines
7.4 KiB
Python
179 lines
7.4 KiB
Python
import os
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import time
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import numpy as np
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import torch
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from lib import utils
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from model.pytorch.dcrnn_model import EncoderModel, DecoderModel
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class DCRNNSupervisor:
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def __init__(self, adj_mx, **kwargs):
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self._kwargs = kwargs
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self._data_kwargs = kwargs.get('data')
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self._model_kwargs = kwargs.get('model')
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self._train_kwargs = kwargs.get('train')
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self.max_grad_norm = self._train_kwargs.get('max_grad_norm', 1.)
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# logging.
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self._log_dir = self._get_log_dir(kwargs)
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log_level = self._kwargs.get('log_level', 'INFO')
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self._logger = utils.get_logger(self._log_dir, __name__, 'info.log', level=log_level)
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# data set
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self._data = utils.load_dataset(**self._data_kwargs)
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self.standard_scaler = self._data['scaler']
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self.num_nodes = int(self._model_kwargs.get('num_nodes', 1))
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self.input_dim = int(self._model_kwargs.get('input_dim', 1))
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self.seq_len = int(self._model_kwargs.get('seq_len')) # for the encoder
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self.output_dim = int(self._model_kwargs.get('output_dim', 1))
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self.cl_decay_steps = int(self._model_kwargs.get('cl_decay_steps', 1000))
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self.use_curriculum_learning = bool(
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self._model_kwargs.get('use_curriculum_learning', False))
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self.horizon = int(self._model_kwargs.get('horizon', 1)) # for the decoder
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# setup model
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self.encoder_model = EncoderModel(True, adj_mx, **self._model_kwargs)
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self.decoder_model = DecoderModel(True, adj_mx, **self._model_kwargs)
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@staticmethod
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def _get_log_dir(kwargs):
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log_dir = kwargs['train'].get('log_dir')
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if log_dir is None:
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batch_size = kwargs['data'].get('batch_size')
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learning_rate = kwargs['train'].get('base_lr')
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max_diffusion_step = kwargs['model'].get('max_diffusion_step')
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num_rnn_layers = kwargs['model'].get('num_rnn_layers')
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rnn_units = kwargs['model'].get('rnn_units')
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structure = '-'.join(
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['%d' % rnn_units for _ in range(num_rnn_layers)])
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horizon = kwargs['model'].get('horizon')
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filter_type = kwargs['model'].get('filter_type')
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filter_type_abbr = 'L'
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if filter_type == 'random_walk':
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filter_type_abbr = 'R'
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elif filter_type == 'dual_random_walk':
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filter_type_abbr = 'DR'
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run_id = 'dcrnn_%s_%d_h_%d_%s_lr_%g_bs_%d_%s/' % (
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filter_type_abbr, max_diffusion_step, horizon,
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structure, learning_rate, batch_size,
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time.strftime('%m%d%H%M%S'))
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base_dir = kwargs.get('base_dir')
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log_dir = os.path.join(base_dir, run_id)
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if not os.path.exists(log_dir):
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os.makedirs(log_dir)
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return log_dir
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def train(self, **kwargs):
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kwargs.update(self._train_kwargs)
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return self._train(**kwargs)
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def _train_one_batch(self, inputs, labels, batches_seen, encoder_optimizer,
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decoder_optimizer, criterion):
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"""
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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, input_dim)
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:param encoder_optimizer:
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:param decoder_optimizer:
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:param criterion: minimize this criterion
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:return: loss?
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"""
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encoder_optimizer.zero_grad()
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decoder_optimizer.zero_grad()
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batch_size = inputs.size(1)
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inputs = inputs.view(self.seq_len, batch_size, self.num_nodes * self.input_dim)
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labels = labels[..., :self.output_dim].view(self.horizon, batch_size,
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self.num_nodes * self.output_dim)
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loss = 0
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encoder_hidden_state = None
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for t in range(self.seq_len):
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_, encoder_hidden_state = self.encoder_model.forward(inputs[t], encoder_hidden_state)
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self._logger.info("Encoder complete, starting decoder")
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go_symbol = torch.zeros((batch_size, self.num_nodes * self.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.horizon):
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decoder_output, decoder_hidden_state = self.decoder_model.forward(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.use_curriculum_learning: # todo check for is_training (pytorch way?)
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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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loss += criterion(self.standard_scaler.inverse_transform(decoder_output),
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self.standard_scaler.inverse_transform(labels[t]))
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self._logger.info("Decoder complete, starting backprop")
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loss.backward()
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# gradient clipping - this does it in place
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torch.nn.utils.clip_grad_norm_(self.encoder_model.parameters(), self.max_grad_norm)
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torch.nn.utils.clip_grad_norm_(self.decoder_model.parameters(), self.max_grad_norm)
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encoder_optimizer.step()
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decoder_optimizer.step()
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outputs = torch.stack(outputs)
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return outputs.view(self.horizon, batch_size, self.num_nodes, self.output_dim), loss.item()
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def _train(self, base_lr,
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steps, patience=50, epochs=100,
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min_learning_rate=2e-6, lr_decay_ratio=0.1, log_every=10, save_model=1,
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test_every_n_epochs=10, **kwargs):
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# steps is used in learning rate - will see if need to use it?
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encoder_optimizer = torch.optim.Adam(self.encoder_model.parameters(), lr=base_lr)
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decoder_optimizer = torch.optim.Adam(self.encoder_model.parameters(), lr=base_lr)
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criterion = torch.nn.L1Loss() # mae loss
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batches_seen = 0
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self._logger.info('Start training ...')
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for epoch_num in range(epochs):
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train_iterator = self._data['train_loader'].get_iterator()
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losses = []
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start_time = time.time()
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for _, (x, y) in enumerate(train_iterator):
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x = torch.from_numpy(x).float()
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y = torch.from_numpy(y).float()
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self._logger.debug("X: {}".format(x.size()))
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self._logger.debug("y: {}".format(y.size()))
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x = x.permute(1, 0, 2, 3)
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y = y.permute(1, 0, 2, 3)
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output, loss = self._train_one_batch(x, y, batches_seen, encoder_optimizer,
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decoder_optimizer, criterion)
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losses.append(loss)
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batches_seen += 1
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end_time = time.time()
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if epoch_num % log_every == 0:
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message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} ' \
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'lr:{:.6f} {:.1f}s'.format(epoch_num, epochs, batches_seen,
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np.mean(losses), 0.0,
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0.0, (end_time - start_time))
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self._logger.info(message)
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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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