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DCRNN
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Setup training loop and logging

This commit is contained in:
Chintan Shah 2019-10-02 17:34:07 -04:00
parent 86c4c5704d
commit c876cbfba3
1 changed files with 81 additions and 17 deletions
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96
model/pytorch/dcrnn_supervisor.py
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@ -1,37 +1,78 @@
import os
import time
import numpy as np
import torch
from lib import utils
from model.pytorch.dcrnn_model import EncoderModel, DecoderModel
class DCRNNSupervisor:
def __init__(self, adj_mx, **kwargs):
def __init__(self, adj_mx, encoder_model: EncoderModel, decoder_model: DecoderModel, **kwargs):
self.decoder_model = decoder_model
self.encoder_model = encoder_model
self._kwargs = kwargs
self._data_kwargs = kwargs.get('data')
self._model_kwargs = kwargs.get('model')
self._train_kwargs = kwargs.get('train')
# logging.
self._log_dir = self._get_log_dir(kwargs)
log_level = self._kwargs.get('log_level', 'INFO')
self._logger = utils.get_logger(self._log_dir, __name__, 'info.log', level=log_level)
# data set
self._data = utils.load_dataset(**self._data_kwargs)
self.standard_scaler = self._data['scaler']
self.num_nodes = int(self._model_kwargs.get('num_nodes', 1))
self.input_dim = int(self._model_kwargs.get('input_dim', 1))
self.seq_len = int(self._model_kwargs.get('seq_len')) # for the encoder
self.output_dim = int(self._model_kwargs.get('output_dim', 1))
self.cl_decay_steps = int(self._model_kwargs.get('cl_decay_steps', 1000))
self.use_curriculum_learning = bool(
self._model_kwargs.get('use_curriculum_learning', False))
self.horizon = int(self._model_kwargs.get('horizon', 1)) # for the decoder
def train(self, encoder_model: EncoderModel, decoder_model: DecoderModel, **kwargs):
@staticmethod
def _get_log_dir(kwargs):
log_dir = kwargs['train'].get('log_dir')
if log_dir is None:
batch_size = kwargs['data'].get('batch_size')
learning_rate = kwargs['train'].get('base_lr')
max_diffusion_step = kwargs['model'].get('max_diffusion_step')
num_rnn_layers = kwargs['model'].get('num_rnn_layers')
rnn_units = kwargs['model'].get('rnn_units')
structure = '-'.join(
['%d' % rnn_units for _ in range(num_rnn_layers)])
horizon = kwargs['model'].get('horizon')
filter_type = kwargs['model'].get('filter_type')
filter_type_abbr = 'L'
if filter_type == 'random_walk':
filter_type_abbr = 'R'
elif filter_type == 'dual_random_walk':
filter_type_abbr = 'DR'
run_id = 'dcrnn_%s_%d_h_%d_%s_lr_%g_bs_%d_%s/' % (
filter_type_abbr, max_diffusion_step, horizon,
structure, learning_rate, batch_size,
time.strftime('%m%d%H%M%S'))
base_dir = kwargs.get('base_dir')
log_dir = os.path.join(base_dir, run_id)
if not os.path.exists(log_dir):
os.makedirs(log_dir)
return log_dir
def train(self, **kwargs):
kwargs.update(self._train_kwargs)
return self._train(**kwargs)
def _train_one_batch(self, inputs, labels, encoder_model: EncoderModel,
decoder_model: DecoderModel, encoder_optimizer,
def _train_one_batch(self, inputs, labels, batches_seen, encoder_optimizer,
decoder_optimizer, criterion):
"""
:param inputs: shape (seq_len, batch_size, num_sensor, input_dim)
:param labels: shape (horizon, batch_size, num_sensor, input_dim)
:param encoder_model:
:param decoder_model:
:param encoder_optimizer:
:param decoder_optimizer:
:param criterion: minimize this criterion
@ -50,7 +91,7 @@ class DCRNNSupervisor:
encoder_hidden_state = None
for t in range(self.seq_len):
_, encoder_hidden_state = encoder_model.forward(inputs[t], encoder_hidden_state)
_, encoder_hidden_state = self.encoder_model.forward(inputs[t], encoder_hidden_state)
go_symbol = torch.zeros((batch_size, self.num_nodes * self.output_dim))
@ -58,27 +99,50 @@ class DCRNNSupervisor:
decoder_input = go_symbol
for t in range(self.horizon):
decoder_output, decoder_hidden_state = decoder_model.forward(decoder_input,
decoder_output, decoder_hidden_state = self.decoder_model.forward(decoder_input,
decoder_hidden_state)
decoder_input = decoder_output
if self.use_curriculum_learning: # todo check for is_training (pytorch way?)
c = np.random.uniform(0, 1)
if c < self._compute_sampling_threshold():
if c < self._compute_sampling_threshold(batches_seen):
decoder_input = labels[t]
loss += criterion(decoder_output, labels[t])
loss += criterion(self.standard_scaler.inverse_transform(decoder_output),
self.standard_scaler.inverse_transform(labels[t]))
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
return loss.item()
def _train(self, encoder_model: EncoderModel, decoder_model: DecoderModel, base_lr, epoch,
def _train(self, base_lr,
steps, patience=50, epochs=100,
min_learning_rate=2e-6, lr_decay_ratio=0.1, save_model=1,
test_every_n_epochs=10):
pass
min_learning_rate=2e-6, lr_decay_ratio=0.1, log_every=10, save_model=1,
test_every_n_epochs=10, **kwargs):
# steps is used in learning rate - will see if need to use it?
encoder_optimizer = torch.optim.Adam(self.encoder_model.parameters(), lr=base_lr)
decoder_optimizer = torch.optim.Adam(self.encoder_model.parameters(), lr=base_lr)
criterion = torch.nn.L1Loss() # mae loss
def _compute_sampling_threshold(self):
return 1.0 # todo
batches_seen = 0
self._logger.info('Start training ...')
for epoch_num in range(epochs):
train_iterator = self._data['train_loader'].get_iterator()
losses = []
start_time = time.time()
for x, y in train_iterator:
loss = self._train_one_batch(x, y, batches_seen, encoder_optimizer, decoder_optimizer, criterion)
losses.append(loss)
batches_seen += 1
end_time = time.time()
if epoch_num % log_every == 0:
message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} lr:{:.6f} {:.1f}s'.format(
epoch_num, epochs, batches_seen, np.mean(losses), 0.0, 0.0, (end_time - start_time))
self._logger.info(message)
def _compute_sampling_threshold(self, batches_seen):
return self.cl_decay_steps / (self.cl_decay_steps + np.exp(batches_seen / self.cl_decay_steps))