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