refine benchmark log (#4888)

* refine benchmark log

test=develop

* refine log

test=develop

dygraph/mobilenet/train.py

@@ -239,7 +239,7 @@ def train_mobilenet():
                 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, ips: %.5f images/sec"
+                        "[Epoch %d, batch %d], avg_loss %.5f, acc_top1 %.5f, acc_top5 %.5f, batch_cost: %.5f sec, net_cost: %.5f sec, backward_cost: %.5f sec, reader_cost: %.5f sec, 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(),

dygraph/resnet/train.py

@@ -497,7 +497,7 @@ def train_resnet():
                     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, ips: %.5f images/sec"
+                        "[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f, batch_cost: %.5f sec, reader_cost: %.5f sec, 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(),