fix eval bug

ppcls/engine/evaluation/classification.py

@@ -56,15 +56,30 @@ def classification_eval(engine, epoch_id=0):
         batch[0] = paddle.to_tensor(batch[0]).astype("float32")
         if not engine.config["Global"].get("use_multilabel", False):
             batch[1] = batch[1].reshape([-1, 1]).astype("int64")
+            
         # image input
-        out = engine.model(batch[0])
-        # calc loss
-        if engine.eval_loss_func is not None:
-            loss_dict = engine.eval_loss_func(out, batch[1])
-            for key in loss_dict:
-                if key not in output_info:
-                    output_info[key] = AverageMeter(key, '7.5f')
-                output_info[key].update(loss_dict[key].numpy()[0], batch_size)
+        if engine.amp:
+            amp_level = 'O1'
+            if engine.config['AMP']['use_pure_fp16'] is True:
+                amp_level = 'O2'
+            with paddle.amp.auto_cast(custom_black_list={"flatten_contiguous_range", "greater_than"}, level=amp_level):
+                out = engine.model(batch[0])
+                # calc loss
+                if engine.eval_loss_func is not None:
+                    loss_dict = engine.eval_loss_func(out, batch[1])
+                    for key in loss_dict:
+                        if key not in output_info:
+                            output_info[key] = AverageMeter(key, '7.5f')
+                        output_info[key].update(loss_dict[key].numpy()[0], batch_size)
+        else:
+            out = engine.model(batch[0])
+            # calc loss
+            if engine.eval_loss_func is not None:
+                loss_dict = engine.eval_loss_func(out, batch[1])
+                for key in loss_dict:
+                    if key not in output_info:
+                        output_info[key] = AverageMeter(key, '7.5f')
+                    output_info[key].update(loss_dict[key].numpy()[0], batch_size)
 
         # just for DistributedBatchSampler issue: repeat sampling
         current_samples = batch_size * paddle.distributed.get_world_size()