add validate_every in trainer

fastNLP/core/trainer.py

@@ -25,7 +25,7 @@ class Trainer(object):
     """Main Training Loop
 
     """
-   def __init__(self, train_data, model, n_epochs=3, batch_size=32, print_every=-1,
+    def __init__(self, train_data, model, n_epochs=3, batch_size=32, print_every=-1, validate_every=-1,
                  dev_data=None, use_cuda=False, save_path="./save",
                  optimizer=Optimizer("Adam", lr=0.001, weight_decay=0),
                  **kwargs):
@@ -39,6 +39,7 @@ class Trainer(object):
         self.use_cuda = bool(use_cuda)
         self.save_path = str(save_path)
         self.print_every = int(print_every)
+        self.validate_every = int(validate_every)
 
         model_name = model.__class__.__name__
         assert hasattr(self.model, 'get_loss'), "model {} has to have a 'get_loss' function.".format(model_name)
@@ -94,7 +95,8 @@ class Trainer(object):
 
                 self._train_epoch(data_iterator, self.model, epoch, self.dev_data, start)
 
-                if self.dev_data:
+                # validate_every override validation at end of epochs
+                if self.dev_data and self.validate_every <= 0:
                     self.do_validation()
                 self.save_model(self.model, 'training_model_' + self.start_time)
                 epoch += 1
@@ -128,10 +130,13 @@ class Trainer(object):
            if n_print > 0 and self.step % n_print == 0:
                 end = time.time()
                 diff = timedelta(seconds=round(end - start))
-                print_output = "[epoch: {:>3} step: {:>4}] train loss: {:>4.6} time: {}".format(
+                print_output = "[epoch: {:>3} step: {:>4}] train loss: {:>4.6} time:  {}".format(
                     epoch, self.step, loss.data, diff)
                 print(print_output)
 
+            if self.validate_every > 0 and self.step % self.validate_every == 0:
+                self.do_validation()
+
             self.step += 1
 
     def do_validation(self):