fix: compatible with Paddle 2.2, 2.3, and develop.

ppcls/engine/engine.py

@@ -98,23 +98,6 @@ class Engine(object):
         logger.info('train with paddle {} and device {}'.format(
             paddle.__version__, self.device))
 
-        # AMP training and evaluating
-        self.amp = "AMP" in self.config and self.config["AMP"] is not None
-        if self.amp:
-            self.scale_loss = self.config["AMP"].get("scale_loss", 1.0)
-            self.use_dynamic_loss_scaling = self.config["AMP"].get(
-                "use_dynamic_loss_scaling", False)
-        else:
-            self.scale_loss = 1.0
-            self.use_dynamic_loss_scaling = False
-        if self.amp:
-            AMP_RELATED_FLAGS_SETTING = {'FLAGS_max_inplace_grad_add': 8, }
-            if paddle.is_compiled_with_cuda():
-                AMP_RELATED_FLAGS_SETTING.update({
-                    'FLAGS_cudnn_batchnorm_spatial_persistent': 1
-                })
-            paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
-
         if "class_num" in config["Global"]:
             global_class_num = config["Global"]["class_num"]
             if "class_num" not in config["Arch"]:
@@ -228,27 +211,77 @@ class Engine(object):
                 len(self.train_dataloader),
                 [self.model, self.train_loss_func])
 
+        # AMP training and evaluating
+        self.amp = "AMP" in self.config and self.config["AMP"] is not None
+        self.amp_eval = False
         # for amp
         if self.amp:
+            AMP_RELATED_FLAGS_SETTING = {'FLAGS_max_inplace_grad_add': 8, }
+            if paddle.is_compiled_with_cuda():
+                AMP_RELATED_FLAGS_SETTING.update({
+                    'FLAGS_cudnn_batchnorm_spatial_persistent': 1
+                })
+            paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
+
+            self.scale_loss = self.config["AMP"].get("scale_loss", 1.0)
+            self.use_dynamic_loss_scaling = self.config["AMP"].get(
+                "use_dynamic_loss_scaling", False)
             self.scaler = paddle.amp.GradScaler(
                 init_loss_scaling=self.scale_loss,
                 use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
-            amp_level = self.config['AMP'].get("level", "O1")
-            if amp_level not in ["O1", "O2"]:
+
+            self.amp_level = self.config['AMP'].get("level", "O1")
+            if self.amp_level not in ["O1", "O2"]:
                 msg = "[Parameter Error]: The optimize level of AMP only support 'O1' and 'O2'. The level has been set 'O1'."
                 logger.warning(msg)
                 self.config['AMP']["level"] = "O1"
-                amp_level = "O1"
+                self.amp_level = "O1"
+
+            self.amp_eval = self.config["AMP"].get("use_fp16_test", False)
+            # TODO(gaotingquan): Paddle not yet support FP32 evaluation when training with AMPO2
+            if self.config["Global"].get(
+                    "eval_during_train",
+                    True) and self.amp_level == "O2" and self.amp_eval == False:
+                msg = "PaddlePaddle only support FP16 evaluation when training with AMP O2 now. "
+                logger.warning(msg)
+                self.config["AMP"]["use_fp16_test"] = True
+                self.amp_eval = True
+
+            # TODO(gaotingquan): to compatible with Paddle 2.2, 2.3, develop and so on.
+            paddle_version = sum([
+                int(x) * 10**(2 - i)
+                for i, x in enumerate(paddle.__version__.split(".")[:3])
+            ])
+            # paddle version < 2.3.0 and not develop
+            if paddle_version < 230 and paddle_version != 0:
+                if self.mode == "train":
+                    self.model, self.optimizer = paddle.amp.decorate(
+                        models=self.model,
+                        optimizers=self.optimizer,
+                        level=self.amp_level,
+                        save_dtype='float32')
+                elif self.amp_eval:
+                    if self.amp_level == "O2":
+                        msg = "The PaddlePaddle that installed not support FP16 evaluation in AMP O2. Please use PaddlePaddle version >= 2.3.0. Use FP32 evaluation instead and please notice the Eval Dataset output_fp16 should be 'False'."
+                        logger.warning(msg)
+                        self.amp_eval = False
+                    else:
+                        self.model, self.optimizer = paddle.amp.decorate(
+                            models=self.model,
+                            level=self.amp_level,
+                            save_dtype='float32')
+            # paddle version >= 2.3.0 or develop
+            else:
                 self.model = paddle.amp.decorate(
-                models=self.model, level=amp_level, save_dtype='float32')
-            # TODO(gaotingquan): to compatible with Paddle develop and 2.2
-            if isinstance(self.model, tuple):
-                self.model = self.model[0]
+                    models=self.model,
+                    level=self.amp_level,
+                    save_dtype='float32')
+
             if self.mode == "train" and len(self.train_loss_func.parameters(
             )) > 0:
                 self.train_loss_func = paddle.amp.decorate(
                     models=self.train_loss_func,
-                    level=amp_level,
+                    level=self.amp_level,
                     save_dtype='float32')
 
         # for distributed

ppcls/engine/evaluation/classification.py

@@ -32,15 +32,6 @@ def classification_eval(engine, epoch_id=0):
     }
     print_batch_step = engine.config["Global"]["print_batch_step"]
 
-    if engine.amp:
-        amp_level = engine.config['AMP'].get("level", "O1").upper()
-        if amp_level == "O2" and engine.config["AMP"].get("use_fp16_test",
-                                                          False):
-            engine.config["AMP"]["use_fp16_test"] = True
-            msg = "Only support FP16 evaluation when AMP O2 is enabled."
-            logger.warning(msg)
-        amp_eval = engine.config["AMP"].get("use_fp16_test", False)
-
     metric_key = None
     tic = time.time()
     accum_samples = 0
@@ -67,12 +58,12 @@ def classification_eval(engine, epoch_id=0):
             batch[1] = batch[1].reshape([-1, 1]).astype("int64")
 
         # image input
-        if engine.amp and amp_eval:
+        if engine.amp and engine.amp_eval:
             with paddle.amp.auto_cast(
                     custom_black_list={
                         "flatten_contiguous_range", "greater_than"
                     },
-                    level=amp_level):
+                    level=engine.amp_level):
                 out = engine.model(batch[0])
         else:
             out = engine.model(batch[0])
@@ -120,12 +111,12 @@ def classification_eval(engine, epoch_id=0):
 
         # calc loss
         if engine.eval_loss_func is not None:
-            if engine.amp and amp_eval:
+            if engine.amp and engine.amp_eval:
                 with paddle.amp.auto_cast(
                         custom_black_list={
                             "flatten_contiguous_range", "greater_than"
                         },
-                        level=amp_level):
+                        level=engine.amp_level):
                     loss_dict = engine.eval_loss_func(preds, labels)
             else:
                 loss_dict = engine.eval_loss_func(preds, labels)