fix ptb_dy.py time print for benchmark

dygraph/ptb_lm/ptb_dy.py

@@ -37,6 +37,19 @@
     reload(sys)
     sys.setdefaultencoding("utf-8")
 
+class TimeCostAverage(object):
+    def __init__(self):
+        self.reset()
+    def reset(self):
+        self.cnt = 0
+        self.total_time = 0
+    def record(self, usetime):
+        self.cnt += 1
+        self.total_time += usetime
+    def get_average(self):
+        if self.cnt == 0:
+            return 0
+        return self.total_time / self.cnt
 
 class SimpleLSTMRNN(fluid.Layer):
     def __init__(self,
@@ -405,10 +418,17 @@ def eval(model, data):
             init_hidden = to_variable(init_hidden_data)
             init_cell = to_variable(init_cell_data)
 
+            batch_cost_avg = TimeCostAverage()
+            reader_cost_avg = TimeCostAverage()
+
             batch_start = time.time()
             for batch_id, batch in enumerate(train_data_loader):
                 if args.max_iter and total_batch_num == args.max_iter:
                     return
+
+                train_reader_cost = time.time() - batch_start
+                reader_cost_avg.record(train_reader_cost)
+
                 x, y = batch
 
                 dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
@@ -426,13 +446,17 @@ def eval(model, data):
                 total_batch_num = total_batch_num + 1  #this is for benchmark
 
                 train_batch_cost = time.time() - batch_start
+                batch_cost_avg.record(train_batch_cost)
+
                 if batch_id > 0 and batch_id % log_interval == 0:
                     ppl = np.exp(total_loss / iters)
                     print(
-                        "-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, lr: %.5f, loss: %.5f, batch_cost: %.5f s"
+                        "-- Epoch:[%d]; Batch:[%d]; ppl: %.5f, lr: %.5f, loss: %.5f, batch_cost: %.5f s, reader_cost: %.5f s"
                         % (epoch_id, batch_id, ppl[0],
                            sgd._global_learning_rate().numpy(), out_loss,
-                           train_batch_cost))
+                           batch_cost_avg.get_average(), reader_cost_avg.get_average()))
+                    batch_cost_avg.reset()
+                    reader_cost_avg.reset()
                 batch_start = time.time()
 
             ppl = np.exp(total_loss / iters)