add amp args, use_amp=False

ppcls/configs/ImageNet/AlexNet/AlexNet.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: AlexNet

ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSPDarkNet53

ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_224.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSWinTransformer_base_224

ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_384.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSWinTransformer_base_384

ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_224.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSWinTransformer_large_224

ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_384.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSWinTransformer_large_384

ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_small_224.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSWinTransformer_small_224

ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CSWinTransformer_tiny_224

ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_224.yaml

@@ -22,6 +22,18 @@ EMA:
   decay: 0.9999
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ConvNeXt_base_224

ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_384.yaml

@@ -22,6 +22,18 @@ EMA:
   decay: 0.9999
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ConvNeXt_base_384

ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_224.yaml

@@ -22,6 +22,18 @@ EMA:
   decay: 0.9999
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ConvNeXt_large_224

ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_384.yaml

@@ -22,6 +22,18 @@ EMA:
   decay: 0.9999
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ConvNeXt_large_384

ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_small.yaml

@@ -22,6 +22,18 @@ EMA:
   decay: 0.9999
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ConvNeXt_small

ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_tiny.yaml

@@ -22,6 +22,18 @@ EMA:
   decay: 0.9999
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ConvNeXt_tiny

ppcls/configs/ImageNet/CvT/CvT_13_224.yaml

@@ -17,6 +17,18 @@ Global:
   to_static: False
   update_freq: 2  # for 8 cards
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CvT_13_224

ppcls/configs/ImageNet/CvT/CvT_13_384.yaml

@@ -17,6 +17,18 @@ Global:
   to_static: False
   update_freq: 2  # for 8 cards
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CvT_13_384

ppcls/configs/ImageNet/CvT/CvT_21_224.yaml

@@ -17,6 +17,18 @@ Global:
   to_static: False
   update_freq: 2  # for 8 cards
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CvT_21_224

ppcls/configs/ImageNet/CvT/CvT_21_384.yaml

@@ -17,6 +17,18 @@ Global:
   to_static: False
   update_freq: 2  # for 8 cards
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CvT_21_384

ppcls/configs/ImageNet/CvT/CvT_W24_384.yaml

@@ -17,6 +17,18 @@ Global:
   to_static: False
   update_freq: 2  # for 8 cards
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: CvT_W24_384

ppcls/configs/ImageNet/DLA/DLA102.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA102

ppcls/configs/ImageNet/DLA/DLA102x.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA102x

ppcls/configs/ImageNet/DLA/DLA102x2.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA102x2

ppcls/configs/ImageNet/DLA/DLA169.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA169

ppcls/configs/ImageNet/DLA/DLA34.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA34

ppcls/configs/ImageNet/DLA/DLA46_c.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA46_c

ppcls/configs/ImageNet/DLA/DLA46x_c.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA46x_c

ppcls/configs/ImageNet/DLA/DLA60.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA60

ppcls/configs/ImageNet/DLA/DLA60x.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA60x

ppcls/configs/ImageNet/DLA/DLA60x_c.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DLA60x_c

ppcls/configs/ImageNet/DPN/DPN107.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DPN107

ppcls/configs/ImageNet/DPN/DPN131.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DPN131

ppcls/configs/ImageNet/DPN/DPN68.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DPN68

ppcls/configs/ImageNet/DPN/DPN92.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DPN92

ppcls/configs/ImageNet/DPN/DPN98.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DPN98

ppcls/configs/ImageNet/DSNet/DSNet_base.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DSNet_base

ppcls/configs/ImageNet/DSNet/DSNet_small.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DSNet_small

ppcls/configs/ImageNet/DSNet/DSNet_tiny.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DSNet_tiny

ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DarkNet53

ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_base_distilled_patch16_224

ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 384, 384]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_base_distilled_patch16_384

ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_base_patch16_224

ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 384, 384]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_base_patch16_384

ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_small_distilled_patch16_224

ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_small_patch16_224

ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_tiny_distilled_patch16_224

ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DeiT_tiny_patch16_224

ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DenseNet121

ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DenseNet161

ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DenseNet169

ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DenseNet201

ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: DenseNet264

ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml

@@ -15,6 +15,18 @@ Global:
   save_inference_dir: "./inference"
   use_dali: false
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_afd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: "./inference"
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dist.yaml

@@ -15,6 +15,18 @@ Global:
   save_inference_dir: ./inference
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: "./inference"
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_mgd.yaml

@@ -15,6 +15,18 @@ Global:
   save_inference_dir: ./inference
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_pefd.yaml

@@ -15,6 +15,18 @@ Global:
   save_inference_dir: ./inference
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_skd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: "./inference"
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_wsl.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: "./inference"
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: "DistillationModel"

ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ESNet_x0_25

ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ESNet_x0_5

ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ESNet_x0_75

ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ESNet_x1_0

ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB0

ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 240, 240]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB1

ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 260, 260]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB2

ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 300, 300]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB3

ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 380, 380]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB4

ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 456, 456]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB5

ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 528, 528]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB6

ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 600, 600]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: EfficientNetB7

ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: GhostNet_x0_5

ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: GhostNet_x1_0

ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: GhostNet_x1_3

ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W18_C

ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W30_C

ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W32_C

ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W40_C

ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W44_C

ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W48_C

ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HRNet_W64_C

ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HarDNet39_ds

ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HarDNet68

ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HarDNet68_ds

ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: HarDNet85

ppcls/configs/ImageNet/Inception/GoogLeNet.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: GoogLeNet

ppcls/configs/ImageNet/Inception/InceptionV3.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: InceptionV3

ppcls/configs/ImageNet/Inception/InceptionV4.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: InceptionV4

ppcls/configs/ImageNet/LeViT/LeViT_128.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: LeViT_128

ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: LeViT_128S

ppcls/configs/ImageNet/LeViT/LeViT_192.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: LeViT_192

ppcls/configs/ImageNet/LeViT/LeViT_256.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: LeViT_256

ppcls/configs/ImageNet/LeViT/LeViT_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: LeViT_384

ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MicroNet_M0

ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MicroNet_M1

ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MicroNet_M2

ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MicroNet_M3

ppcls/configs/ImageNet/MixNet/MixNet_L.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MixNet_L

ppcls/configs/ImageNet/MixNet/MixNet_M.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MixNet_M

ppcls/configs/ImageNet/MixNet/MixNet_S.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MixNet_S

ppcls/configs/ImageNet/MobileNeXt/MobileNeXt_x1_0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNeXt_x1_0

ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV1

ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV1_x0_25

ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV1_x0_5

ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV1_x0_75

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV2

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV2_x0_25

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV2_x0_5

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV2_x0_75

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV2_x1_5

ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV2_x2_0

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_large_x0_35

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_large_x0_5

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_large_x0_75

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_large_x1_0

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_large_x1_25

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_small_x0_35

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_small_x0_5

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_small_x0_75

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_small_x1_0

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0_fp32_ultra.yaml

@@ -15,6 +15,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_small_x1_0

ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileNetV3_small_x1_25

ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileViT_S

ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileViT_XS

ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
   use_dali: False
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: MobileViT_XXS

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x0_25

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x0_35

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x0_5

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x0_75

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x1_0

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0_fp32_ultra.yaml

@@ -15,6 +15,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x1_0

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x1_5

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x2_0

ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml

@@ -13,6 +13,18 @@ Global:
   # used for static mode and model export
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNet_x2_5

ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNetV2_base

ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_large.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNetV2_large

ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_small.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PPLCNetV2_small

ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B0

ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B1

ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B2

ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B2_Linear

ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B3

ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B4

ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PVT_V2_B5

ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: PeleeNet

ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ReXNet_1_0

ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ReXNet_1_3

ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ReXNet_1_5

ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ReXNet_2_0

ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ReXNet_3_0

ppcls/configs/ImageNet/RedNet/RedNet101.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RedNet101

ppcls/configs/ImageNet/RedNet/RedNet152.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RedNet152

ppcls/configs/ImageNet/RedNet/RedNet26.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RedNet26

ppcls/configs/ImageNet/RedNet/RedNet38.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RedNet38

ppcls/configs/ImageNet/RedNet/RedNet50.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RedNet50

ppcls/configs/ImageNet/RegNet/RegNetX_12GF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_12GF

ppcls/configs/ImageNet/RegNet/RegNetX_1600MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_1600MF

ppcls/configs/ImageNet/RegNet/RegNetX_16GF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_16GF

ppcls/configs/ImageNet/RegNet/RegNetX_200MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_200MF

ppcls/configs/ImageNet/RegNet/RegNetX_3200MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_3200MF

ppcls/configs/ImageNet/RegNet/RegNetX_32GF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_32GF

ppcls/configs/ImageNet/RegNet/RegNetX_400MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_400MF

ppcls/configs/ImageNet/RegNet/RegNetX_600MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_600MF

ppcls/configs/ImageNet/RegNet/RegNetX_6400MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_6400MF

ppcls/configs/ImageNet/RegNet/RegNetX_800MF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_800MF

ppcls/configs/ImageNet/RegNet/RegNetX_8GF.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RegNetX_8GF

ppcls/configs/ImageNet/RepVGG/RepVGG_A0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_A0

ppcls/configs/ImageNet/RepVGG/RepVGG_A1.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_A1

ppcls/configs/ImageNet/RepVGG/RepVGG_A2.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_A2

ppcls/configs/ImageNet/RepVGG/RepVGG_B0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B0

ppcls/configs/ImageNet/RepVGG/RepVGG_B1.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B1

ppcls/configs/ImageNet/RepVGG/RepVGG_B1g2.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B1g2

ppcls/configs/ImageNet/RepVGG/RepVGG_B1g4.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B1g4

ppcls/configs/ImageNet/RepVGG/RepVGG_B2.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B2

ppcls/configs/ImageNet/RepVGG/RepVGG_B2g4.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B2g4

ppcls/configs/ImageNet/RepVGG/RepVGG_B3.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B3

ppcls/configs/ImageNet/RepVGG/RepVGG_B3g4.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_B3g4

ppcls/configs/ImageNet/RepVGG/RepVGG_D2se.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 320, 320]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: RepVGG_D2se

ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Res2Net101_vd_26w_4s

ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Res2Net200_vd_26w_4s

ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Res2Net50_14w_8s

ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Res2Net50_26w_4s

ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Res2Net50_vd_26w_4s

ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 256, 256]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeSt101

ppcls/configs/ImageNet/ResNeSt/ResNeSt200.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 320, 320]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeSt200

ppcls/configs/ImageNet/ResNeSt/ResNeSt269.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 416, 416]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeSt269

ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeSt50

ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeSt50_fast_1s1x64d

ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_32x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_64x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_vd_32x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_vd_64x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt152_32x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt152_64x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt152_vd_32x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt152_vd_64x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt50_32x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt50_64x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt50_vd_32x4d

ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt50_vd_64x4d

ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_32x16d_wsl

ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_32x32d_wsl

ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_32x48d_wsl

ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNeXt101_32x8d_wsl

ppcls/configs/ImageNet/ResNet/ResNet101.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet101

ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet101_vd

ppcls/configs/ImageNet/ResNet/ResNet152.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet152

ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet152_vd

ppcls/configs/ImageNet/ResNet/ResNet18.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet18

ppcls/configs/ImageNet/ResNet/ResNet18_dbb.yaml

@@ -15,6 +15,18 @@ Global:
   save_inference_dir: ./inference
 
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet18

ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet18_vd

ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet200_vd

ppcls/configs/ImageNet/ResNet/ResNet34.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet34

ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet34_vd

ppcls/configs/ImageNet/ResNet/ResNet50.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/ResNet/ResNet50_fp32_ultra.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50

ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ResNet50_vd

ppcls/configs/ImageNet/SENet/SENet154_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SENet154_vd

ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SE_ResNeXt101_32x4d

ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SE_ResNeXt50_32x4d

ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SE_ResNeXt50_vd_32x4d

ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SE_ResNet18_vd

ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SE_ResNet34_vd

ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SE_ResNet50_vd

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_swish

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_x0_25

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_x0_33

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_x0_5

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_x1_0

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_x1_5

ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ShuffleNetV2_x2_0

ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SqueezeNet1_0

ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SqueezeNet1_1

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SwinTransformer_base_patch4_window12_384

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SwinTransformer_base_patch4_window7_224

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SwinTransformer_large_patch4_window12_384

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SwinTransformer_large_patch4_window7_224

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SwinTransformer_small_patch4_window7_224

ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: SwinTransformer_tiny_patch4_window7_224

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window16_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_base_patch4_window16_256

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window24_384.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_base_patch4_window24_384

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window8_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_base_patch4_window8_256

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window16_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_large_patch4_window16_256

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window24_384.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_large_patch4_window24_384

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window16_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_small_patch4_window16_256

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window8_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_small_patch4_window8_256

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window16_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_tiny_patch4_window16_256

ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window8_256.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name:  SwinTransformerV2_tiny_patch4_window8_256

ppcls/configs/ImageNet/TNT/TNT_base.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TNT_base

ppcls/configs/ImageNet/TNT/TNT_small.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TNT_small

ppcls/configs/ImageNet/TinyNet/TinyNet_A.yaml

@@ -18,6 +18,18 @@ Global:
 EMA:
   decay: 0.9999
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TinyNet_A

ppcls/configs/ImageNet/TinyNet/TinyNet_B.yaml

@@ -18,6 +18,18 @@ Global:
 EMA:
   decay: 0.9999
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TinyNet_B

ppcls/configs/ImageNet/TinyNet/TinyNet_C.yaml

@@ -18,6 +18,18 @@ Global:
 EMA:
   decay: 0.9999
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TinyNet_C

ppcls/configs/ImageNet/TinyNet/TinyNet_D.yaml

@@ -18,6 +18,18 @@ Global:
 EMA:
   decay: 0.9999
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TinyNet_D

ppcls/configs/ImageNet/TinyNet/TinyNet_E.yaml

@@ -18,6 +18,18 @@ Global:
 EMA:
   decay: 0.9999
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: TinyNet_E

ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: alt_gvt_base

ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: alt_gvt_large

ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: alt_gvt_small

ppcls/configs/ImageNet/Twins/pcpvt_base.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: pcpvt_base

ppcls/configs/ImageNet/Twins/pcpvt_large.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: pcpvt_large

ppcls/configs/ImageNet/Twins/pcpvt_small.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: pcpvt_small

ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: UniFormer_base

ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: UniFormer_base_ls

ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: UniFormer_small

ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: UniFormer_small_plus

ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml

@@ -17,6 +17,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: UniFormer_small_plus_dim64

ppcls/configs/ImageNet/VAN/VAN_B0.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VAN_B0

ppcls/configs/ImageNet/VAN/VAN_B1.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VAN_B1

ppcls/configs/ImageNet/VAN/VAN_B2.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VAN_B2

ppcls/configs/ImageNet/VAN/VAN_B3.yaml

@@ -16,6 +16,18 @@ Global:
   # training model under @to_static
   to_static: False
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VAN_B3

ppcls/configs/ImageNet/VGG/VGG11.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VGG11

ppcls/configs/ImageNet/VGG/VGG13.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VGG13

ppcls/configs/ImageNet/VGG/VGG16.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VGG16

ppcls/configs/ImageNet/VGG/VGG19.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: VGG19

ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_base_patch16_224

ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 384, 384]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_base_patch16_384

ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 384, 384]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_base_patch32_384

ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_large_patch16_224

ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 384, 384]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_large_patch16_384

ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 384, 384]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_large_patch32_384

ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 224, 224]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: ViT_small_patch16_224

ppcls/configs/ImageNet/Xception/Xception41.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Xception41

ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Xception41_deeplab

ppcls/configs/ImageNet/Xception/Xception65.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Xception65

ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Xception65_deeplab

ppcls/configs/ImageNet/Xception/Xception71.yaml

@@ -14,6 +14,18 @@ Global:
   image_shape: [3, 299, 299]
   save_inference_dir: ./inference
 
+
+# mixed precision
+AMP:
+  use_amp: False
+  use_fp16_test: False
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  use_promote: False
+  # O1: mixed fp16, O2: pure fp16
+  level: O1
+
+
 # model architecture
 Arch:
   name: Xception71