Refactor DCRNN Model.

model/dcrnn_model.py

@@ -2,12 +2,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import numpy as np
 import tensorflow as tf
 
 from tensorflow.contrib import legacy_seq2seq
 
-from lib.metrics import masked_mse_loss, masked_mae_loss, masked_rmse_loss
+from lib.metrics import masked_mae_loss
 from model.dcrnn_cell import DCGRUCell
 
 
@@ -21,12 +20,6 @@ class DCRNNModel(object):
         self._mae = None
         self._train_op = None
 
-        # Learning rate.
-        self._lr = tf.get_variable('learning_rate', shape=(), initializer=tf.constant_initializer(0.01),
-                                   trainable=False)
-        self._new_lr = tf.placeholder(tf.float32, shape=(), name='new_learning_rate')
-        self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
-
         max_diffusion_step = int(model_kwargs.get('max_diffusion_step', 2))
         cl_decay_steps = int(model_kwargs.get('cl_decay_steps', 1000))
         filter_type = model_kwargs.get('filter_type', 'laplacian')
@@ -46,7 +39,8 @@ class DCRNNModel(object):
         # Labels: (batch_size, timesteps, num_sensor, input_dim), same format with input except the temporal dimension.
         self._labels = tf.placeholder(tf.float32, shape=(batch_size, horizon, num_nodes, input_dim), name='labels')
 
-        GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * input_dim))
+        # GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * input_dim))
+        GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * output_dim))
 
         cell = DCGRUCell(rnn_units, adj_mx, max_diffusion_step=max_diffusion_step, num_nodes=num_nodes,
                          filter_type=filter_type)
@@ -80,7 +74,7 @@ class DCRNNModel(object):
                 else:
                     # Return the prediction of the model in testing.
                     result = prev
-                if aux_dim > 0:
+                if False and aux_dim > 0:
                     result = tf.reshape(result, (batch_size, num_nodes, output_dim))
                     result = tf.concat([result, aux_info[i]], axis=-1)
                     result = tf.reshape(result, (batch_size, num_nodes * input_dim))
@@ -93,20 +87,6 @@ class DCRNNModel(object):
         # Project the output to output_dim.
         outputs = tf.stack(outputs[:-1], axis=1)
         self._outputs = tf.reshape(outputs, (batch_size, horizon, num_nodes, output_dim), name='outputs')
-
-        preds = self._outputs
-        labels = self._labels[..., :output_dim]
-
-        null_val = 0.
-        self._mae = masked_mae_loss(self._scaler, null_val)(preds=preds, labels=labels)
-        self._loss = masked_mae_loss(self._scaler, null_val)(preds=preds, labels=labels)
-        if is_training:
-            optimizer = tf.train.AdamOptimizer(self._lr)
-            tvars = tf.trainable_variables()
-            grads = tf.gradients(self._loss, tvars)
-            grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
-            self._train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step, name='train_op')
-
         self._merged = tf.summary.merge_all()
 
     @staticmethod
@@ -119,61 +99,6 @@ class DCRNNModel(object):
         """
         return tf.cast(k / (k + tf.exp(global_step / k)), tf.float32)
 
-    @staticmethod
-    def run_epoch_generator(sess, model, data_generator, return_output=False, train_op=None, writer=None):
-        losses = []
-        maes = []
-        outputs = []
-
-        fetches = {
-            'mae': model.mae,
-            'loss': model.loss,
-            'global_step': tf.train.get_or_create_global_step()
-        }
-        if train_op:
-            fetches.update({
-                'train_op': train_op,
-            })
-            merged = model.merged
-            if merged is not None:
-                fetches.update({'merged': merged})
-
-        if return_output:
-            fetches.update({
-                'outputs': model.outputs
-            })
-
-        for _, (x, y) in enumerate(data_generator):
-            feed_dict = {
-                model.inputs: x,
-                model.labels: y,
-            }
-
-            vals = sess.run(fetches, feed_dict=feed_dict)
-
-            losses.append(vals['loss'])
-            maes.append(vals['mae'])
-            if writer is not None and 'merged' in vals:
-                writer.add_summary(vals['merged'], global_step=vals['global_step'])
-            if return_output:
-                outputs.append(vals['outputs'])
-
-        results = {
-            'loss': np.mean(losses),
-            'mae': np.mean(maes)
-        }
-        if return_output:
-            results['outputs'] = outputs
-        return results
-
-    def get_lr(self, sess):
-        return np.asscalar(sess.run(self._lr))
-
-    def set_lr(self, sess, lr):
-        sess.run(self._lr_update, feed_dict={
-            self._new_lr: lr
-        })
-
     @property
     def inputs(self):
         return self._inputs
@@ -186,10 +111,6 @@ class DCRNNModel(object):
     def loss(self):
         return self._loss
 
-    @property
-    def lr(self):
-        return self._lr
-
     @property
     def mae(self):
         return self._mae
@@ -201,7 +122,3 @@ class DCRNNModel(object):
     @property
     def outputs(self):
         return self._outputs
-
-    @property
-    def train_op(self):
-        return self._train_op

model/dcrnn_supervisor.py

@@ -10,6 +10,8 @@ import time
 import yaml
 
 from lib import utils, metrics
+from lib.AMSGrad import AMSGrad
+from lib.metrics import masked_mae_loss
 from lib.utils import StandardScaler, DataLoader
 
 from model.dcrnn_model import DCRNNModel
@@ -29,7 +31,7 @@ class DCRNNSupervisor(object):
 
         # logging.
         self._log_dir = self._get_log_dir(kwargs)
-        self._logger = utils.get_logger(self.log_dir, __name__, 'info.log')
+        self._logger = utils.get_logger(self._log_dir, __name__, 'info.log')
         self._writer = tf.summary.FileWriter(self._log_dir)
         self._logger.info(kwargs)
 
@@ -61,6 +63,37 @@ class DCRNNSupervisor(object):
                                               batch_size=self._data_kwargs['test_batch_size'],
                                               adj_mx=adj_mx, **self._model_kwargs)
 
+        # Calculate loss
+        output_dim = self._model_kwargs.get('output_dim')
+        preds = self._train_model.outputs
+        labels = self._train_model.labels[..., :output_dim]
+
+        null_val = 0.
+        self._loss_fn = masked_mae_loss(scaler, null_val)
+        self._train_loss = self._loss_fn(preds=preds, labels=labels)
+
+        # Learning rate.
+        self._lr = tf.get_variable('learning_rate', shape=(), initializer=tf.constant_initializer(0.01),
+                                   trainable=False)
+        self._new_lr = tf.placeholder(tf.float32, shape=(), name='new_learning_rate')
+        self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
+
+        # Configure optimizer
+        optimizer_name = self._train_kwargs.get('optimizer', 'adam').lower()
+        epsilon = float(self._train_kwargs.get('epsilon', 1e-3))
+        optimizer = tf.train.AdamOptimizer(self._lr, epsilon=epsilon)
+        if optimizer_name == 'sgd':
+            optimizer = tf.train.GradientDescentOptimizer(self._lr, )
+        elif optimizer_name == 'amsgrad':
+            optimizer = AMSGrad(self._lr, epsilon=epsilon)
+
+        tvars = tf.trainable_variables()
+        grads = tf.gradients(self._train_loss, tvars)
+        max_grad_norm = kwargs['train'].get('max_grad_norm', 1.)
+        grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
+        global_step = tf.train.get_or_create_global_step()
+        self._train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step, name='train_op')
+
         # Log model statistics.
         total_trainable_parameter = utils.get_total_trainable_parameter_size()
         self._logger.info('Total number of trainable parameters: %d' % total_trainable_parameter)
@@ -114,6 +147,63 @@ class DCRNNSupervisor(object):
 
         return data
 
+    def run_epoch_generator(self, sess, model, data_generator, return_output=False, training=False, writer=None):
+        losses = []
+        maes = []
+        outputs = []
+        output_dim = self._model_kwargs.get('output_dim')
+        preds = model.outputs
+        labels = model.labels[..., :output_dim]
+        loss = self._loss_fn(preds=preds, labels=labels)
+        fetches = {
+            'loss': loss,
+            'mae': loss,
+            'global_step': tf.train.get_or_create_global_step()
+        }
+        if training:
+            fetches.update({
+                'train_op': self._train_op
+            })
+            merged = model.merged
+            if merged is not None:
+                fetches.update({'merged': merged})
+
+        if return_output:
+            fetches.update({
+                'outputs': model.outputs
+            })
+
+        for _, (x, y) in enumerate(data_generator):
+            feed_dict = {
+                model.inputs: x,
+                model.labels: y,
+            }
+
+            vals = sess.run(fetches, feed_dict=feed_dict)
+
+            losses.append(vals['loss'])
+            maes.append(vals['mae'])
+            if writer is not None and 'merged' in vals:
+                writer.add_summary(vals['merged'], global_step=vals['global_step'])
+            if return_output:
+                outputs.append(vals['outputs'])
+
+        results = {
+            'loss': np.mean(losses),
+            'mae': np.mean(maes)
+        }
+        if return_output:
+            results['outputs'] = outputs
+        return results
+
+    def get_lr(self, sess):
+        return np.asscalar(sess.run(self._lr))
+
+    def set_lr(self, sess, lr):
+        sess.run(self._lr_update, feed_dict={
+            self._new_lr: lr
+        })
+
     def train(self, sess, **kwargs):
         kwargs.update(self._train_kwargs)
         return self._train(sess, **kwargs)
@@ -133,16 +223,17 @@ class DCRNNSupervisor(object):
             self._epoch = epoch + 1
         else:
             sess.run(tf.global_variables_initializer())
+        self._logger.info('Start training ...')
+
         while self._epoch <= epochs:
             # Learning rate schedule.
             new_lr = max(min_learning_rate, base_lr * (lr_decay_ratio ** np.sum(self._epoch >= np.array(steps))))
-            self._train_model.set_lr(sess=sess, lr=new_lr)
+            self.set_lr(sess=sess, lr=new_lr)
-            sys.stdout.flush()
 
             start_time = time.time()
-            train_results = self._train_model.run_epoch_generator(sess, self._train_model,
+            train_results = self.run_epoch_generator(sess, self._train_model,
                                                      self._data['train_loader'].get_iterator(),
-                                                                  train_op=self._train_model.train_op,
+                                                     training=True,
                                                      writer=self._writer)
             train_loss, train_mae = train_results['loss'], train_results['mae']
             if train_loss > 1e5:
@@ -151,17 +242,17 @@ class DCRNNSupervisor(object):
 
             global_step = sess.run(tf.train.get_or_create_global_step())
             # Compute validation error.
-            val_results = self._val_model.run_epoch_generator(sess, self._val_model,
+            val_results = self.run_epoch_generator(sess, self._val_model,
                                                    self._data['val_loader'].get_iterator(),
-                                                              train_op=None)
+                                                   training=False)
             val_loss, val_mae = val_results['loss'], val_results['mae']
 
             utils.add_simple_summary(self._writer,
                                      ['loss/train_loss', 'metric/train_mae', 'loss/val_loss', 'metric/val_mae'],
                                      [train_loss, train_mae, val_loss, val_mae], global_step=global_step)
             end_time = time.time()
-            message = 'Epoch [{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} lr:{:.6f} {:.1f}s'.format(
+            message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, val_mae: {:.4f} lr:{:.6f} {:.1f}s'.format(
-                self._epoch, global_step, train_mae, val_mae, new_lr, (end_time - start_time))
+                self._epoch, epochs, global_step, train_mae, val_mae, new_lr, (end_time - start_time))
             self._logger.info(message)
             if self._epoch % test_every_n_epochs == test_every_n_epochs - 1:
                 self.test_and_write_result(sess, global_step)
@@ -186,9 +277,10 @@ class DCRNNSupervisor(object):
         return np.min(history)
 
     def test_and_write_result(self, sess, global_step, **kwargs):
-        test_results = self._test_model.run_epoch_generator(sess, self._test_model,
+        test_results = self.run_epoch_generator(sess, self._test_model,
                                                 self._data['test_loader'].get_iterator(),
-                                                            return_output=True, train_op=None)
+                                                return_output=True,
+                                                training=False)
 
         # y_preds:  a list of (batch_size, horizon, num_nodes, output_dim)
         test_loss, y_preds = test_results['loss'], test_results['outputs']
@@ -196,14 +288,17 @@ class DCRNNSupervisor(object):
 
         y_preds = np.concatenate(y_preds, axis=0)
         scaler = self._data['scaler']
-        outputs = []
+        predictions = []
+        y_truths = []
         for horizon_i in range(self._data['y_test'].shape[1]):
-            y_truth = np.concatenate(self._data['y_test'][:, horizon_i, :, 0], axis=0)
+            y_truth = self._data['y_test'][:, horizon_i, :, 0]
             y_truth = scaler.inverse_transform(y_truth)
-            y_pred = np.concatenate(y_preds[:, horizon_i, :, 0], axis=0)
+            y_truths.append(y_truth)
-            y_pred = y_pred[:y_truth.shape[0], ...]  # Only take the batch number
+
+            y_pred = y_preds[:y_truth.shape[0], horizon_i, :, 0]
             y_pred = scaler.inverse_transform(y_pred)
-            outputs.append(y_pred)
+            predictions.append(y_pred)
+
             mae = metrics.masked_mae_np(y_pred, y_truth, null_val=0)
             mape = metrics.masked_mape_np(y_pred, y_truth, null_val=0)
             rmse = metrics.masked_rmse_np(y_pred, y_truth, null_val=0)
@@ -217,7 +312,11 @@ class DCRNNSupervisor(object):
                                       ['metric/rmse', 'metric/mape', 'metric/mae']],
                                      [rmse, mape, mae],
                                      global_step=global_step)
-        return y_preds
+        outputs = {
+            'predictions': predictions,
+            'groundtruth': y_truths
+        }
+        return outputs
 
     @staticmethod
     def restore(sess, config):
@@ -245,7 +344,3 @@ class DCRNNSupervisor(object):
         with open(os.path.join(self._log_dir, config_filename), 'w') as f:
             yaml.dump(config, f, default_flow_style=False)
         return config['train']['model_filename']
-
-    @property
-    def log_dir(self):
-        return self._log_dir