add common loss and metrics (#32)

tests/test_ignite.py

@@ -4,7 +4,6 @@ if sys.version_info.major >= 3:
     import os
     import unittest
     import torch
-    from ignite.metrics import RootMeanSquaredError
     from ignite.engine import create_supervised_trainer, \
         create_supervised_evaluator
     import torchani
@@ -39,15 +38,13 @@ if sys.version_info.major >= 3:
             nnp = Flatten(nnp)
             batch_nnp = torchani.models.BatchModel(nnp)
             container = torchani.ignite.Container({'energies': batch_nnp})
-            loss = torchani.ignite.DictLoss('energies', torch.nn.MSELoss())
             optimizer = torch.optim.SGD(container.parameters(),
                                         lr=0.001, momentum=0.8)
-            trainer = create_supervised_trainer(container, optimizer, loss)
+            trainer = create_supervised_trainer(
+                container, optimizer, torchani.ignite.energy_mse_loss)
             trainer.run(loader, max_epochs=10)
-            metric = torchani.ignite.DictMetric('energies',
-                                                RootMeanSquaredError())
             evaluator = create_supervised_evaluator(container, metrics={
-                'RMSE': metric
+                'RMSE': torchani.ignite.energy_rmse_metric
             })
             evaluator.run(loader)
 

torchani/ignite/__init__.py

 from .container import Container
-from .loss_metrics import DictLoss, DictMetric
+from .loss_metrics import DictLoss, DictMetric, energy_mse_loss, \
+    energy_rmse_metric
 
-__all__ = ['Container', 'DictLoss', 'DictMetric']
+__all__ = ['Container', 'DictLoss', 'DictMetric', 'energy_mse_loss',
+           'energy_rmse_metric']

torchani/ignite/loss_metrics.py

 from torch.nn.modules.loss import _Loss
 from ignite.metrics.metric import Metric
+from ignite.metrics import RootMeanSquaredError
+import torch
 
 
 class DictLoss(_Loss):
@@ -29,3 +31,7 @@ class DictMetric(Metric):
 
     def compute(self):
         self.metric.compute()
+
+
+energy_mse_loss = DictLoss('energies', torch.nn.MSELoss())
+energy_rmse_metric = DictMetric('energies', RootMeanSquaredError())