fix: support AMP infer

ppcls/configs/ImageNet/ResNet/ResNet50_amp_O2_ultra.yaml

@@ -105,7 +105,6 @@ DataLoader:
             mean: [0.485, 0.456, 0.406]
             std: [0.229, 0.224, 0.225]
             order: ''
-            output_fp16: True
             channel_num: *image_channel
     sampler:
       name: DistributedBatchSampler
@@ -132,7 +131,6 @@ Infer:
         mean: [0.485, 0.456, 0.406]
         std: [0.229, 0.224, 0.225]
         order: ''
-        output_fp16: True
         channel_num: *image_channel
     - ToCHWImage:
   PostProcess:

ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d_amp_O2_ultra.yaml

@@ -99,7 +99,6 @@ DataLoader:
             mean: [0.485, 0.456, 0.406]
             std: [0.229, 0.224, 0.225]
             order: ''
-            output_fp16: True
             channel_num: *image_channel
     sampler:
       name: DistributedBatchSampler
@@ -126,7 +125,6 @@ Infer:
         mean: [0.485, 0.456, 0.406]
         std: [0.229, 0.224, 0.225]
         order: ''
-        output_fp16: True
         channel_num: *image_channel
     - ToCHWImage:
   PostProcess:

ppcls/engine/engine.py

@@ -239,7 +239,7 @@ class Engine(object):
 
             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(
+            if self.mode == "train" and 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. "
@@ -269,10 +269,11 @@ class Engine(object):
                             save_dtype='float32')
             # paddle version >= 2.3.0 or develop
             else:
-                self.model = paddle.amp.decorate(
-                    models=self.model,
-                    level=self.amp_level,
-                    save_dtype='float32')
+                if self.mode == "train" or self.amp_eval:
+                    self.model = paddle.amp.decorate(
+                        models=self.model,
+                        level=self.amp_level,
+                        save_dtype='float32')
 
             if self.mode == "train" and len(self.train_loss_func.parameters(
             )) > 0:
@@ -431,7 +432,17 @@ class Engine(object):
             image_file_list.append(image_file)
             if len(batch_data) >= batch_size or idx == len(image_list) - 1:
                 batch_tensor = paddle.to_tensor(batch_data)
-                out = self.model(batch_tensor)
+
+                if self.amp and self.amp_eval:
+                    with paddle.amp.auto_cast(
+                            custom_black_list={
+                                "flatten_contiguous_range", "greater_than"
+                            },
+                            level=self.amp_level):
+                        out = self.model(batch_tensor)
+                else:
+                    out = self.model(batch_tensor)
+
                 if isinstance(out, list):
                     out = out[0]
                 if isinstance(out, dict) and "logits" in out: