Merge pull request #4 from PaddlePaddle/develop

update

fluid/PaddleCV/gan/c_gan/c_gan.py

@@ -165,7 +165,8 @@ def train(args):
                           'conditions': conditions_data},
                     fetch_list={dg_loss})[0][0]
                 losses[1].append(dg_loss_n)
-            t_time += (time.time() - s_time)
+            batch_time = time.time() - s_time
+            t_time += batch_time
 
             
 
@@ -180,8 +181,9 @@ def train(args):
                     fetch_list={g_img})[0]
                 total_images = np.concatenate([real_image, generated_images])
                 fig = plot(total_images)
-                msg = "Epoch ID={0}\n Batch ID={1}\n D-Loss={2}\n DG-Loss={3}\n gen={4}".format(
-                    pass_id, batch_id, d_loss_n, dg_loss_n, check(generated_images))
+                msg = "Epoch ID={0}\n Batch ID={1}\n D-Loss={2}\n DG-Loss={3}\n gen={4}\n " \
+                      "Batch_time_cost={5:.2f}".format(
+                    pass_id, batch_id, d_loss_n, dg_loss_n, check(generated_images), batch_time)
                 print(msg)
                 plt.title(msg)
                 plt.savefig(

fluid/PaddleCV/gan/cycle_gan/train.py

@@ -187,10 +187,12 @@ def train(args):
                 fetch_list=[d_A_trainer.d_loss_A],
                 feed={"input_A": tensor_A,
                       "fake_pool_A": fake_pool_A})[0]
-            t_time += (time.time() - s_time)
-            print("epoch{}; batch{}; g_A_loss: {}; d_B_loss: {}; g_B_loss: {}; d_A_loss: {};".format(
+            batch_time = time.time() - s_time
+            t_time += batch_time
+            print("epoch{}; batch{}; g_A_loss: {}; d_B_loss: {}; g_B_loss: {}; d_A_loss: {}; "
+                  "Batch_time_cost: {:.2f}".format(
                 epoch, batch_id, g_A_loss[0], d_B_loss[0], g_B_loss[0],
-                d_A_loss[0]))
+                d_A_loss[0], batch_time))
             losses[0].append(g_A_loss[0])
             losses[1].append(d_A_loss[0])
             sys.stdout.flush()

fluid/PaddleNLP/deep_attention_matching_net/train_and_evaluate.py

@@ -390,6 +390,8 @@ def train(args):
         else:
             global_step, last_cost = train_with_feed(global_step)
         train_time += time.time() - begin_time
+        print("Pass {0}, pass_time_cost {1}"
+          .format(epoch, "%2.2f sec" % time.time() -begin_time ))
     # For internal continuous evaluation
     if "CE_MODE_X" in os.environ:
         print("kpis	train_cost	%f" % last_cost)

fluid/PaddleNLP/machine_reading_comprehension/run.py

@@ -446,7 +446,9 @@ def train(logger, args):
                             logger.info('Dev eval result: {}'.format(
                                 bleu_rouge))
                 pass_end_time = time.time()
-
+                time_consumed = pass_end_time - pass_start_time
+                logger.info('epoch: {0}, epoch_time_cost: {1:.2f}'.format(
+                    pass_id, time_consumed))
                 logger.info('Evaluating the model after epoch {}'.format(
                     pass_id))
                 if brc_data.dev_set is not None:
@@ -459,7 +461,7 @@ def train(logger, args):
                 else:
                     logger.warning(
                         'No dev set is loaded for evaluation in the dataset!')
-                time_consumed = pass_end_time - pass_start_time
+
                 logger.info('Average train loss for epoch {} is {}'.format(
                     pass_id, "%.10f" % (1.0 * total_loss / total_num)))