add save_dtype

ppcls/engine/engine.py

@@ -218,7 +218,7 @@ class Engine(object):
                 init_loss_scaling=self.scale_loss,
                 use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
             if self.config['AMP']['use_pure_fp16'] is True:
-                self.model = paddle.amp.decorate(models=self.model, level='O2')
+                self.model = paddle.amp.decorate(models=self.model, level='O2', save_dtype='float32')
 
         # for distributed
         self.config["Global"][

ppcls/engine/evaluation/classification.py

@@ -40,17 +40,6 @@ 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
@@ -68,7 +57,6 @@ def classification_eval(engine, epoch_id=0):
         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'
@@ -92,19 +80,6 @@ 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()
@@ -176,16 +151,6 @@ 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([