README.md

@@ -2,38 +2,24 @@
 
 ![Diffusion Convolutional Recurrent Neural Network](figures/model_architecture.jpg "Model Architecture")
 
-This is a PyTorch implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
+This is a TensorFlow implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: \
 Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu, [Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting](https://arxiv.org/abs/1707.01926), ICLR 2018.
 
 
 ## Requirements
-* torch
+- scipy>=0.19.0
-* scipy>=0.19.0
+- numpy>=1.12.1
-* numpy>=1.12.1
+- pandas>=0.19.2
-* pandas>=0.19.2
+- pyaml
-* pyyaml
+- statsmodels
-* statsmodels
+- tensorflow>=1.3.0
-* tensorflow>=1.3.0
+
-* torch
-* tables
-* future
 
 Dependency can be installed using the following command:
 ```bash
 pip install -r requirements.txt
 ```
 
-### Comparison with Tensorflow implementation
-
-In MAE (For LA dataset, PEMS-BAY coming in a while)
-
-| Horizon | Tensorflow | Pytorch |
-|:--------|:--------:|:--------:|
-| 1 Hour |   3.69   |   3.12   |    
-| 30 Min |   3.15   |   2.82   |    
-| 15 Min |   2.77   |   2.56   |    
-
-
 ## Data Preparation
 The traffic data files for Los Angeles (METR-LA) and the Bay Area (PEMS-BAY), i.e., `metr-la.h5` and `pems-bay.h5`, are available at [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](https://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g), and should be
 put into the `data/` folder.
@@ -74,10 +60,10 @@ Besides, the locations of sensors in Los Angeles, i.e., METR-LA, are available a
 
 ```bash
 # METR-LA
-python run_demo_pytorch.py --config_filename=data/model/pretrained/METR-LA/config.yaml
+python run_demo.py --config_filename=data/model/pretrained/METR-LA/config.yaml
 
 # PEMS-BAY
-python run_demo_pytorch.py --config_filename=data/model/pretrained/PEMS-BAY/config.yaml
+python run_demo.py --config_filename=data/model/pretrained/PEMS-BAY/config.yaml
 ```
 The generated prediction of DCRNN is in `data/results/dcrnn_predictions`.
 
@@ -85,11 +71,12 @@ The generated prediction of DCRNN is in `data/results/dcrnn_predictions`.
 ## Model Training
 ```bash
 # METR-LA
-python dcrnn_train_pytorch.py --config_filename=data/model/dcrnn_la.yaml
+python dcrnn_train.py --config_filename=data/model/dcrnn_la.yaml
 
 # PEMS-BAY
-python dcrnn_train_pytorch.py --config_filename=data/model/dcrnn_bay.yaml
+python dcrnn_train.py --config_filename=data/model/dcrnn_bay.yaml
 ```
+Each epoch takes about 5min or 10 min on a single GTX 1080 Ti for METR-LA or PEMS-BAY respectively. 
 
 There is a chance that the training loss will explode, the temporary workaround is to restart from the last saved model before the explosion, or to decrease the learning rate earlier in the learning rate schedule. 
 
@@ -100,15 +87,7 @@ There is a chance that the training loss will explode, the temporary workaround
 python -m scripts.eval_baseline_methods --traffic_reading_filename=data/metr-la.h5
 ```
 
-### PyTorch Results
+More details are being added ...
-
-![PyTorch Results](figures/result1.png "PyTorch Results")
-
-![PyTorch Results](figures/result2.png "PyTorch Results")
-
-![PyTorch Results](figures/result3.png "PyTorch Results")
-
-![PyTorch Results](figures/result4.png "PyTorch Results")
 
 ## Citation
 

data/model/dcrnn_test_config.yaml

@@ -18,14 +18,14 @@ model:
   num_nodes: 207
   num_rnn_layers: 2
   output_dim: 1
-  rnn_units: 64
+  rnn_units: 16
   seq_len: 12
   use_curriculum_learning: true
 
 train:
   base_lr: 0.01
   dropout: 0
-  epoch: 51
+  epoch: 0
   epochs: 100
   epsilon: 1.0e-3
   global_step: 0

dcrnn_train_pytorch.py

@@ -3,12 +3,12 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
+import tensorflow as tf
 import yaml
 
 from lib.utils import load_graph_data
 from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
 
-
 def main(args):
     with open(args.config_filename) as f:
         supervisor_config = yaml.load(f)
@@ -16,6 +16,9 @@ def main(args):
         graph_pkl_filename = supervisor_config['data'].get('graph_pkl_filename')
         sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(graph_pkl_filename)
 
+        # if args.use_cpu_only:
+        #     tf_config = tf.ConfigProto(device_count={'GPU': 0})
+        # with tf.Session(config=tf_config) as sess:
         supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
 
         supervisor.train()

model/pytorch/dcrnn_supervisor.py

@@ -89,23 +89,11 @@ class DCRNNSupervisor:
         return 'models/epo%d.tar' % epoch
 
     def load_model(self):
-        self._setup_graph()
         assert os.path.exists('models/epo%d.tar' % self._epoch_num), 'Weights at epoch %d not found' % self._epoch_num
         checkpoint = torch.load('models/epo%d.tar' % self._epoch_num, map_location='cpu')
         self.dcrnn_model.load_state_dict(checkpoint['model_state_dict'])
         self._logger.info("Loaded model at {}".format(self._epoch_num))
 
-    def _setup_graph(self):
-        with torch.no_grad():
-            self.dcrnn_model = self.dcrnn_model.eval()
-
-            val_iterator = self._data['val_loader'].get_iterator()
-
-            for _, (x, y) in enumerate(val_iterator):
-                x, y = self._prepare_data(x, y)
-                output = self.dcrnn_model(x)
-                break
-
     def train(self, **kwargs):
         kwargs.update(self._train_kwargs)
         return self._train(**kwargs)
@@ -121,9 +109,6 @@ class DCRNNSupervisor:
             val_iterator = self._data['{}_loader'.format(dataset)].get_iterator()
             losses = []
 
-            y_truths = []
-            y_preds = []
-
             for _, (x, y) in enumerate(val_iterator):
                 x, y = self._prepare_data(x, y)
 
@@ -131,33 +116,19 @@ class DCRNNSupervisor:
                 loss = self._compute_loss(y, output)
                 losses.append(loss.item())
 
-                y_truths.append(y.cpu())
-                y_preds.append(output.cpu())
-
             mean_loss = np.mean(losses)
 
             self._writer.add_scalar('{} loss'.format(dataset), mean_loss, batches_seen)
 
-            y_preds = np.concatenate(y_preds, axis=1)
+            return mean_loss
-            y_truths = np.concatenate(y_truths, axis=1)  # concatenate on batch dimension
-
-            y_truths_scaled = []
-            y_preds_scaled = []
-            for t in range(y_preds.shape[0]):
-                y_truth = self.standard_scaler.inverse_transform(y_truths[t])
-                y_pred = self.standard_scaler.inverse_transform(y_preds[t])
-                y_truths_scaled.append(y_truth)
-                y_preds_scaled.append(y_pred)
-
-            return mean_loss, {'prediction': y_preds_scaled, 'truth': y_truths_scaled}
 
     def _train(self, base_lr,
                steps, patience=50, epochs=100, lr_decay_ratio=0.1, log_every=1, save_model=1,
-               test_every_n_epochs=10, epsilon=1e-8, **kwargs):
+               test_every_n_epochs=10, **kwargs):
         # steps is used in learning rate - will see if need to use it?
         min_val_loss = float('inf')
         wait = 0
-        optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr, eps=epsilon)
+        optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr)
 
         lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=steps,
                                                             gamma=lr_decay_ratio)
@@ -188,7 +159,7 @@ class DCRNNSupervisor:
 
                 if batches_seen == 0:
                     # this is a workaround to accommodate dynamically registered parameters in DCGRUCell
-                    optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr, eps=epsilon)
+                    optimizer = torch.optim.Adam(self.dcrnn_model.parameters(), lr=base_lr)
 
                 loss = self._compute_loss(y, output)
 
@@ -207,7 +178,7 @@ class DCRNNSupervisor:
             lr_scheduler.step()
             self._logger.info("evaluating now!")
 
-            val_loss, _ = self.evaluate(dataset='val', batches_seen=batches_seen)
+            val_loss = self.evaluate(dataset='val', batches_seen=batches_seen)
 
             end_time = time.time()
 
@@ -223,7 +194,7 @@ class DCRNNSupervisor:
                 self._logger.info(message)
 
             if (epoch_num % test_every_n_epochs) == test_every_n_epochs - 1:
-                test_loss, _ = self.evaluate(dataset='test', batches_seen=batches_seen)
+                test_loss = self.evaluate(dataset='test', batches_seen=batches_seen)
                 message = 'Epoch [{}/{}] ({}) train_mae: {:.4f}, test_mae: {:.4f},  lr: {:.6f}, ' \
                           '{:.1f}s'.format(epoch_num, epochs, batches_seen,
                                            np.mean(losses), test_loss, lr_scheduler.get_lr()[0],

run_demo.py

@@ -32,6 +32,6 @@ if __name__ == '__main__':
     parser.add_argument('--use_cpu_only', default=False, type=str, help='Whether to run tensorflow on cpu.')
     parser.add_argument('--config_filename', default='data/model/pretrained/METR-LA/config.yaml', type=str,
                         help='Config file for pretrained model.')
-    parser.add_argument('--output_filename', default='data/dcrnn_predictions_tf.npz')
+    parser.add_argument('--output_filename', default='data/dcrnn_predictions.npz')
     args = parser.parse_args()
     run_dcrnn(args)

run_demo_pytorch.py

@@ -1,33 +0,0 @@
-import argparse
-import numpy as np
-import os
-import sys
-import yaml
-
-from lib.utils import load_graph_data
-from model.pytorch.dcrnn_supervisor import DCRNNSupervisor
-
-
-def run_dcrnn(args):
-    with open(args.config_filename) as f:
-        supervisor_config = yaml.load(f)
-
-        graph_pkl_filename = supervisor_config['data'].get('graph_pkl_filename')
-        sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(graph_pkl_filename)
-
-        supervisor = DCRNNSupervisor(adj_mx=adj_mx, **supervisor_config)
-        mean_score, outputs = supervisor.evaluate('test')
-        np.savez_compressed(args.output_filename, **outputs)
-        print("MAE : {}".format(mean_score))
-        print('Predictions saved as {}.'.format(args.output_filename))
-
-
-if __name__ == '__main__':
-    sys.path.append(os.getcwd())
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--use_cpu_only', default=False, type=str, help='Whether to run tensorflow on cpu.')
-    parser.add_argument('--config_filename', default='data/model/pretrained/METR-LA/config.yaml', type=str,
-                        help='Config file for pretrained model.')
-    parser.add_argument('--output_filename', default='data/dcrnn_predictions.npz')
-    args = parser.parse_args()
-    run_dcrnn(args)