use fp32 to eval

ppcls/engine/evaluation/classification.py

@@ -40,6 +40,17 @@ def classification_eval(engine, epoch_id=0):
         dataset) if not engine.use_dali else engine.eval_dataloader.size
     max_iter = len(engine.eval_dataloader) - 1 if platform.system(
     ) == "Windows" else len(engine.eval_dataloader)
+
+    # print("========================fp16 layer")
+    # for layer in engine.model.sublayers(include_self=True):
+    #     print(type(layer), layer._dtype)
+
+    # 用fp32做eval
+    engine.model.to(dtype='float32')
+    # print("========================to fp32 layer")
+    # for layer in engine.model.sublayers(include_self=True):
+    #     print(type(layer), layer._dtype)
+
     for iter_id, batch in enumerate(engine.eval_dataloader):
         if iter_id >= max_iter:
             break
@@ -56,7 +67,8 @@ 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
         if engine.amp:
             amp_level = 'O1'
@@ -80,6 +92,19 @@ def classification_eval(engine, epoch_id=0):
                     if key not in output_info:
                         output_info[key] = AverageMeter(key, '7.5f')
                     output_info[key].update(loss_dict[key].numpy()[0], batch_size)
+        '''
+
+        #========================================================
+        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()
@@ -151,6 +176,16 @@ def classification_eval(engine, epoch_id=0):
                 len(engine.eval_dataloader), metric_msg, time_msg, ips_msg))
 
         tic = time.time()
+    
+    #如果是amp-o2做eval后再将模型转回amp-o2的模式
+    if engine.amp:
+        if engine.config['AMP']['use_pure_fp16'] is True:
+            paddle.fluid.dygraph.amp.auto_cast.pure_fp16_initialize([engine.model])
+            # print("========================to fp16 layer")
+            # for layer in engine.model.sublayers(include_self=True):
+            #     print(type(layer), layer._dtype)
+    # import sys
+    # sys.exit()
     if engine.use_dali:
         engine.eval_dataloader.reset()
     metric_msg = ", ".join([