add ips for dygraph mobilenet and resnet models (#4883)

test=develop

dygraph/mobilenet/train.py

@@ -38,20 +38,25 @@ args = parse_args()
 if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
     print_arguments(args)
 
+
 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
 
+
 def eval(net, test_data_loader, eop):
     total_loss = 0.0
     total_acc1 = 0.0
@@ -232,13 +237,14 @@ def train_mobilenet():
 
                 total_batch_num = total_batch_num + 1
                 if batch_id % args.print_step == 0:
+                    ips = float(args.batch_size) / batch_cost_avg.get_average()
                     print(
-                        "[Epoch %d, batch %d], avg_loss %.5f, acc_top1 %.5f, acc_top5 %.5f, batch_cost: %.5f s, net_t: %.5f s, backward_t: %.5f s, reader_t: %.5f s"
+                        "[Epoch %d, batch %d], avg_loss %.5f, acc_top1 %.5f, acc_top5 %.5f, batch_cost: %.5f s, net_t: %.5f s, backward_t: %.5f s, reader_t: %.5f s, ips: %.5f images/sec"
                         % (eop, batch_id, avg_loss.numpy(), acc_top1.numpy(),
                            acc_top5.numpy(), batch_cost_avg.get_average(),
                            batch_net_avg.get_average(),
                            batch_backward_avg.get_average(),
-                           batch_reader_avg.get_average()))
+                           batch_reader_avg.get_average(), ips))
                     sys.stdout.flush()
                     batch_cost_avg.reset()
                     batch_net_avg.reset()

dygraph/resnet/train.py

@@ -494,12 +494,14 @@ def train_resnet():
 
                 total_batch_num = total_batch_num + 1  #this is for benchmark
                 if batch_id % 10 == 0:
+                    ips = float(
+                        args.batch_size) / train_batch_cost_avg.get_average()
                     print(
-                        "[Epoch %d, batch %d] loss %.5f, acc1 %.5f, acc5 %.5f, batch_cost: %.5f s, reader_cost: %.5f s"
+                        "[Epoch %d, batch %d] loss %.5f, acc1 %.5f, acc5 %.5f, batch_cost: %.5f s, reader_cost: %.5f s, ips: %.5f images/sec"
                         % (eop, batch_id, total_loss / total_sample,
                            total_acc1 / total_sample, total_acc5 / total_sample,
                            train_batch_cost_avg.get_average(),
-                           train_reader_cost_avg.get_average()))
+                           train_reader_cost_avg.get_average(), ips))
                     train_batch_cost_avg.reset()
                     train_reader_cost_avg.reset()
                 batch_start = time.time()