日志符合benchmark规范

tools/program.py

@@ -185,12 +185,15 @@ def train(config,
     for epoch in range(start_epoch, epoch_num):
         if epoch > 0:
             train_dataloader = build_dataloader(config, 'Train', device, logger)
-
+        train_batch_cost = 0.0
+        train_reader_cost = 0.0
+        batch_sum = 0
+        batch_start = time.time()
         for idx, batch in enumerate(train_dataloader):
+            train_reader_cost += time.time() - batch_start
             if idx >= len(train_dataloader):
                 break
             lr = optimizer.get_lr()
-            t1 = time.time()
             images = batch[0]
             preds = model(images)
             loss = loss_class(preds, batch)
@@ -198,6 +201,10 @@ def train(config,
             avg_loss.backward()
             optimizer.step()
             optimizer.clear_grad()
+
+            train_batch_cost += time.time() - batch_start
+            batch_sum += len(images)
+
             if not isinstance(lr_scheduler, float):
                 lr_scheduler.step()
 
@@ -213,9 +220,6 @@ def train(config,
                 metirc = eval_class.get_metric()
                 train_stats.update(metirc)
 
-            t2 = time.time()
-            train_batch_elapse = t2 - t1
-
             if vdl_writer is not None and dist.get_rank() == 0:
                 for k, v in train_stats.get().items():
                     vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
@@ -224,9 +228,15 @@ def train(config,
             if dist.get_rank(
             ) == 0 and global_step > 0 and global_step % print_batch_step == 0:
                 logs = train_stats.log()
-                strs = 'epoch: [{}/{}], iter: {}, {}, time: {:.3f}'.format(
-                    epoch, epoch_num, global_step, logs, train_batch_elapse)
+                strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f}s, batch_cost: {:.5f}s, samples: {}, ips: {:.5f}'.format(
+                    epoch, epoch_num, global_step, logs, train_reader_cost /
+                    print_batch_step, train_batch_cost / print_batch_step,
+                    batch_sum, batch_sum / train_batch_cost)
                 logger.info(strs)
+                train_batch_cost = 0.0
+                train_reader_cost = 0.0
+                batch_sum = 0
+                batch_start = time.time()
             # eval
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0: