Merge pull request #6726 from andyjpaddle/fix_amp_vqa

fix amp train vqa

tools/program.py

@@ -255,6 +255,8 @@ def train(config,
                 with paddle.amp.auto_cast():
                     if model_type == 'table' or extra_input:
                         preds = model(images, data=batch[1:])
+                    elif model_type in ["kie", 'vqa']:
+                        preds = model(batch)
                     else:
                         preds = model(images)
             else:
@@ -307,7 +309,8 @@ def train(config,
             train_stats.update(stats)
 
             if log_writer is not None and dist.get_rank() == 0:
-                log_writer.log_metrics(metrics=train_stats.get(), prefix="TRAIN", step=global_step)
+                log_writer.log_metrics(
+                    metrics=train_stats.get(), prefix="TRAIN", step=global_step)
 
             if dist.get_rank() == 0 and (
                 (global_step > 0 and global_step % print_batch_step == 0) or
@@ -354,7 +357,8 @@ def train(config,
 
                 # logger metric
                 if log_writer is not None:
-                    log_writer.log_metrics(metrics=cur_metric, prefix="EVAL", step=global_step)
+                    log_writer.log_metrics(
+                        metrics=cur_metric, prefix="EVAL", step=global_step)
 
                 if cur_metric[main_indicator] >= best_model_dict[
                         main_indicator]:
@@ -377,11 +381,18 @@ def train(config,
                 logger.info(best_str)
                 # logger best metric
                 if log_writer is not None:
-                    log_writer.log_metrics(metrics={
-                        "best_{}".format(main_indicator): best_model_dict[main_indicator]
-                        }, prefix="EVAL", step=global_step)
-                    
-                    log_writer.log_model(is_best=True, prefix="best_accuracy", metadata=best_model_dict)
+                    log_writer.log_metrics(
+                        metrics={
+                            "best_{}".format(main_indicator):
+                            best_model_dict[main_indicator]
+                        },
+                        prefix="EVAL",
+                        step=global_step)
+
+                    log_writer.log_model(
+                        is_best=True,
+                        prefix="best_accuracy",
+                        metadata=best_model_dict)
 
             reader_start = time.time()
         if dist.get_rank() == 0:
@@ -413,7 +424,8 @@ def train(config,
                 epoch=epoch,
                 global_step=global_step)
             if log_writer is not None:
-                log_writer.log_model(is_best=False, prefix='iter_epoch_{}'.format(epoch))
+                log_writer.log_model(
+                    is_best=False, prefix='iter_epoch_{}'.format(epoch))
 
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
@@ -585,7 +597,8 @@ def preprocess(is_train=False):
         vdl_writer_path = '{}/vdl/'.format(save_model_dir)
         log_writer = VDLLogger(save_model_dir)
         loggers.append(log_writer)
-    if ('use_wandb' in config['Global'] and config['Global']['use_wandb']) or 'wandb' in config:
+    if ('use_wandb' in config['Global'] and
+            config['Global']['use_wandb']) or 'wandb' in config:
         save_dir = config['Global']['save_model_dir']
         wandb_writer_path = "{}/wandb".format(save_dir)
         if "wandb" in config: