From 0f86c55576cd3d9144c377ebd69455d159344ea9 Mon Sep 17 00:00:00 2001 From: gaotingquan Date: Fri, 26 May 2023 09:21:12 +0000 Subject: [PATCH] add amp args, use_amp=False --- ppcls/configs/ImageNet/AlexNet/AlexNet.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml | 12 ++++++++++++ .../CSWinTransformer/CSWinTransformer_base_224.yaml | 12 ++++++++++++ .../CSWinTransformer/CSWinTransformer_base_384.yaml | 12 ++++++++++++ .../CSWinTransformer/CSWinTransformer_large_224.yaml | 12 ++++++++++++ .../CSWinTransformer/CSWinTransformer_large_384.yaml | 12 ++++++++++++ .../CSWinTransformer/CSWinTransformer_small_224.yaml | 12 ++++++++++++ .../CSWinTransformer/CSWinTransformer_tiny_224.yaml | 12 ++++++++++++ .../configs/ImageNet/ConvNeXt/ConvNeXt_base_224.yaml | 12 ++++++++++++ .../configs/ImageNet/ConvNeXt/ConvNeXt_base_384.yaml | 12 ++++++++++++ .../ImageNet/ConvNeXt/ConvNeXt_large_224.yaml | 12 ++++++++++++ .../ImageNet/ConvNeXt/ConvNeXt_large_384.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_small.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_tiny.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/CvT/CvT_13_224.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/CvT/CvT_13_384.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/CvT/CvT_21_224.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/CvT/CvT_21_384.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/CvT/CvT_W24_384.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA102.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA102x.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA102x2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA169.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA34.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA46_c.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA46x_c.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA60.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA60x.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DLA/DLA60x_c.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DPN/DPN107.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DPN/DPN131.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DPN/DPN68.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DPN/DPN92.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DPN/DPN98.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DSNet/DSNet_base.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DSNet/DSNet_small.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DSNet/DSNet_tiny.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_AutoAugment.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_Baseline.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_Cutmix.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_Cutout.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_GridMask.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_HideAndSeek.yaml | 12 ++++++++++++ .../configs/ImageNet/DataAugment/ResNet50_Mixup.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_RandAugment.yaml | 12 ++++++++++++ .../ImageNet/DataAugment/ResNet50_RandomErasing.yaml | 12 ++++++++++++ .../DeiT/DeiT_base_distilled_patch16_224.yaml | 12 ++++++++++++ .../DeiT/DeiT_base_distilled_patch16_384.yaml | 12 ++++++++++++ .../configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml | 12 ++++++++++++ .../configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml | 12 ++++++++++++ .../DeiT/DeiT_small_distilled_patch16_224.yaml | 12 ++++++++++++ .../ImageNet/DeiT/DeiT_small_patch16_224.yaml | 12 ++++++++++++ .../DeiT/DeiT_tiny_distilled_patch16_224.yaml | 12 ++++++++++++ .../configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml | 12 ++++++++++++ .../mv3_large_x1_0_distill_mv3_small_x1_0.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_afd.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_dist.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_dkd.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_mgd.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_pefd.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_skd.yaml | 12 ++++++++++++ .../Distillation/resnet34_distill_resnet18_wsl.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB0.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB1.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB2.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB3.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB4.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB5.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB6.yaml | 12 ++++++++++++ .../ImageNet/EfficientNet/EfficientNetB7.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Inception/GoogLeNet.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Inception/InceptionV3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Inception/InceptionV4.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/LeViT/LeViT_128.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/LeViT/LeViT_192.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/LeViT/LeViT_256.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/LeViT/LeViT_384.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MixNet/MixNet_L.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MixNet/MixNet_M.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MixNet/MixNet_S.yaml | 12 ++++++++++++ .../configs/ImageNet/MobileNeXt/MobileNeXt_x1_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml | 12 ++++++++++++ .../MobileNetV3/MobileNetV3_large_x0_35.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml | 12 ++++++++++++ .../MobileNetV3/MobileNetV3_large_x0_75.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml | 12 ++++++++++++ .../MobileNetV3/MobileNetV3_large_x1_25.yaml | 12 ++++++++++++ .../MobileNetV3/MobileNetV3_small_x0_35.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml | 12 ++++++++++++ .../MobileNetV3/MobileNetV3_small_x0_75.yaml | 12 ++++++++++++ .../ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml | 12 ++++++++++++ .../MobileNetV3_small_x1_0_fp32_ultra.yaml | 12 ++++++++++++ .../MobileNetV3/MobileNetV3_small_x1_25.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml | 12 ++++++++++++ .../ImageNet/PPLCNet/PPLCNet_x1_0_fp32_ultra.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml | 12 ++++++++++++ .../configs/ImageNet/PPLCNetV2/PPLCNetV2_large.yaml | 12 ++++++++++++ .../configs/ImageNet/PPLCNetV2/PPLCNetV2_small.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RedNet/RedNet101.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RedNet/RedNet152.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RedNet/RedNet26.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RedNet/RedNet38.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RedNet/RedNet50.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_12GF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_1600MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_16GF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_200MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_3200MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_32GF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_400MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_600MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_6400MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_800MF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RegNet/RegNetX_8GF.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_A0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_A1.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_A2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B1.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B1g2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B1g4.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B2g4.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_B3g4.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/RepVGG/RepVGG_D2se.yaml | 12 ++++++++++++ .../ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml | 12 ++++++++++++ .../ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml | 12 ++++++++++++ .../ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeSt/ResNeSt200.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeSt/ResNeSt269.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml | 12 ++++++++++++ .../ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml | 12 ++++++++++++ .../ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml | 12 ++++++++++++ .../ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml | 12 ++++++++++++ .../ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml | 12 ++++++++++++ .../ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml | 12 ++++++++++++ .../configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml | 12 ++++++++++++ .../configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml | 12 ++++++++++++ .../ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml | 12 ++++++++++++ .../ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml | 12 ++++++++++++ .../ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml | 12 ++++++++++++ .../ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet101.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet152.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet18.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet18_dbb.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet34.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet50.yaml | 12 ++++++++++++ .../configs/ImageNet/ResNet/ResNet50_fp32_ultra.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SENet/SENet154_vd.yaml | 12 ++++++++++++ .../configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml | 12 ++++++++++++ .../ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml | 12 ++++++++++++ .../ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml | 12 ++++++++++++ .../SwinTransformer_base_patch4_window12_384.yaml | 12 ++++++++++++ .../SwinTransformer_base_patch4_window7_224.yaml | 12 ++++++++++++ .../SwinTransformer_large_patch4_window12_384.yaml | 12 ++++++++++++ .../SwinTransformer_large_patch4_window7_224.yaml | 12 ++++++++++++ .../SwinTransformer_small_patch4_window7_224.yaml | 12 ++++++++++++ .../SwinTransformer_tiny_patch4_window7_224.yaml | 12 ++++++++++++ .../SwinTransformerV2_base_patch4_window16_256.yaml | 12 ++++++++++++ .../SwinTransformerV2_base_patch4_window24_384.yaml | 12 ++++++++++++ .../SwinTransformerV2_base_patch4_window8_256.yaml | 12 ++++++++++++ .../SwinTransformerV2_large_patch4_window16_256.yaml | 12 ++++++++++++ .../SwinTransformerV2_large_patch4_window24_384.yaml | 12 ++++++++++++ .../SwinTransformerV2_small_patch4_window16_256.yaml | 12 ++++++++++++ .../SwinTransformerV2_small_patch4_window8_256.yaml | 12 ++++++++++++ .../SwinTransformerV2_tiny_patch4_window16_256.yaml | 12 ++++++++++++ .../SwinTransformerV2_tiny_patch4_window8_256.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TNT/TNT_base.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TNT/TNT_small.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TinyNet/TinyNet_A.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TinyNet/TinyNet_B.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TinyNet/TinyNet_C.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TinyNet/TinyNet_D.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/TinyNet/TinyNet_E.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Twins/pcpvt_base.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Twins/pcpvt_large.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Twins/pcpvt_small.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml | 12 ++++++++++++ .../ImageNet/UniFormer/UniFormer_base_ls.yaml | 12 ++++++++++++ .../configs/ImageNet/UniFormer/UniFormer_small.yaml | 12 ++++++++++++ .../ImageNet/UniFormer/UniFormer_small_plus.yaml | 12 ++++++++++++ .../UniFormer/UniFormer_small_plus_dim64.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VAN/VAN_B0.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VAN/VAN_B1.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VAN/VAN_B2.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VAN/VAN_B3.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VGG/VGG11.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VGG/VGG13.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VGG/VGG16.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/VGG/VGG19.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_base_patch16_224.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_base_patch16_384.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_base_patch32_384.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_large_patch16_224.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_large_patch16_384.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_large_patch32_384.yaml | 12 ++++++++++++ .../VisionTransformer/ViT_small_patch16_224.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Xception/Xception41.yaml | 12 ++++++++++++ .../ImageNet/Xception/Xception41_deeplab.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Xception/Xception65.yaml | 12 ++++++++++++ .../ImageNet/Xception/Xception65_deeplab.yaml | 12 ++++++++++++ ppcls/configs/ImageNet/Xception/Xception71.yaml | 12 ++++++++++++ 295 files changed, 3540 insertions(+) diff --git a/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml b/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml index ea2e073c..25c19eed 100644 --- a/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml b/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml index 4848cfc8..29ca02e2 100644 --- a/ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_224.yaml b/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_224.yaml index a7697840..5f116f11 100644 --- a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_384.yaml b/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_384.yaml index a7100289..d845d8f4 100644 --- a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_base_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_224.yaml b/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_224.yaml index 7c96343d..9cadcc90 100644 --- a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_384.yaml b/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_384.yaml index 4b682fec..1e01bb0b 100644 --- a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_large_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_small_224.yaml b/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_small_224.yaml index a191f416..2c182bb5 100644 --- a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_small_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.yaml b/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.yaml index 3a2be283..fa8986f2 100644 --- a/ppcls/configs/ImageNet/CSWinTransformer/CSWinTransformer_tiny_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_224.yaml b/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_224.yaml index a0630335..591afe39 100644 --- a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_384.yaml b/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_384.yaml index bbe9a9a8..0adec4be 100644 --- a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_base_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_224.yaml b/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_224.yaml index e15cbf6f..6f5b23e1 100644 --- a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_384.yaml b/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_384.yaml index 41e66988..63a4aa1a 100644 --- a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_large_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_small.yaml b/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_small.yaml index b929023d..d6c0551d 100644 --- a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_tiny.yaml b/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_tiny.yaml index fb6e3cbd..4d705857 100644 --- a/ppcls/configs/ImageNet/ConvNeXt/ConvNeXt_tiny.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CvT/CvT_13_224.yaml b/ppcls/configs/ImageNet/CvT/CvT_13_224.yaml index 1431ab28..b211c0cd 100644 --- a/ppcls/configs/ImageNet/CvT/CvT_13_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CvT/CvT_13_384.yaml b/ppcls/configs/ImageNet/CvT/CvT_13_384.yaml index 36170973..14e2b9d9 100644 --- a/ppcls/configs/ImageNet/CvT/CvT_13_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CvT/CvT_21_224.yaml b/ppcls/configs/ImageNet/CvT/CvT_21_224.yaml index ea6791ba..8274a582 100644 --- a/ppcls/configs/ImageNet/CvT/CvT_21_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CvT/CvT_21_384.yaml b/ppcls/configs/ImageNet/CvT/CvT_21_384.yaml index 4f7af816..4aa2e27c 100644 --- a/ppcls/configs/ImageNet/CvT/CvT_21_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/CvT/CvT_W24_384.yaml b/ppcls/configs/ImageNet/CvT/CvT_W24_384.yaml index f5b33d21..18b6d7ff 100644 --- a/ppcls/configs/ImageNet/CvT/CvT_W24_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA102.yaml b/ppcls/configs/ImageNet/DLA/DLA102.yaml index b6033f70..c8763576 100644 --- a/ppcls/configs/ImageNet/DLA/DLA102.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA102x.yaml b/ppcls/configs/ImageNet/DLA/DLA102x.yaml index a1e2c09d..580c8ce4 100644 --- a/ppcls/configs/ImageNet/DLA/DLA102x.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA102x2.yaml b/ppcls/configs/ImageNet/DLA/DLA102x2.yaml index 8bd4c464..0691a2af 100644 --- a/ppcls/configs/ImageNet/DLA/DLA102x2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA169.yaml b/ppcls/configs/ImageNet/DLA/DLA169.yaml index 18c244d0..7731d361 100644 --- a/ppcls/configs/ImageNet/DLA/DLA169.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA34.yaml b/ppcls/configs/ImageNet/DLA/DLA34.yaml index d9218df7..55571603 100644 --- a/ppcls/configs/ImageNet/DLA/DLA34.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA46_c.yaml b/ppcls/configs/ImageNet/DLA/DLA46_c.yaml index 8d203413..1fef5b86 100644 --- a/ppcls/configs/ImageNet/DLA/DLA46_c.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml b/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml index e7f7d672..a88a940d 100644 --- a/ppcls/configs/ImageNet/DLA/DLA46x_c.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA60.yaml b/ppcls/configs/ImageNet/DLA/DLA60.yaml index a255f053..0a82f7d2 100644 --- a/ppcls/configs/ImageNet/DLA/DLA60.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA60x.yaml b/ppcls/configs/ImageNet/DLA/DLA60x.yaml index 143b87f9..19dc8ef3 100644 --- a/ppcls/configs/ImageNet/DLA/DLA60x.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml b/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml index 77928198..ebf24784 100644 --- a/ppcls/configs/ImageNet/DLA/DLA60x_c.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DPN/DPN107.yaml b/ppcls/configs/ImageNet/DPN/DPN107.yaml index 7df1256d..a18c341f 100644 --- a/ppcls/configs/ImageNet/DPN/DPN107.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DPN/DPN131.yaml b/ppcls/configs/ImageNet/DPN/DPN131.yaml index 88f1b57a..68e9479f 100644 --- a/ppcls/configs/ImageNet/DPN/DPN131.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DPN/DPN68.yaml b/ppcls/configs/ImageNet/DPN/DPN68.yaml index c1e28081..33a0e416 100644 --- a/ppcls/configs/ImageNet/DPN/DPN68.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DPN/DPN92.yaml b/ppcls/configs/ImageNet/DPN/DPN92.yaml index fb5b0ed5..58307984 100644 --- a/ppcls/configs/ImageNet/DPN/DPN92.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DPN/DPN98.yaml b/ppcls/configs/ImageNet/DPN/DPN98.yaml index e394710e..f3bb9942 100644 --- a/ppcls/configs/ImageNet/DPN/DPN98.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DSNet/DSNet_base.yaml b/ppcls/configs/ImageNet/DSNet/DSNet_base.yaml index 2d853802..7d4ffcb1 100644 --- a/ppcls/configs/ImageNet/DSNet/DSNet_base.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DSNet/DSNet_small.yaml b/ppcls/configs/ImageNet/DSNet/DSNet_small.yaml index 25ee7210..e006104c 100644 --- a/ppcls/configs/ImageNet/DSNet/DSNet_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DSNet/DSNet_tiny.yaml b/ppcls/configs/ImageNet/DSNet/DSNet_tiny.yaml index b3ecec40..88462058 100644 --- a/ppcls/configs/ImageNet/DSNet/DSNet_tiny.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml b/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml index ec0f822a..ccf05ada 100644 --- a/ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml index ab4c29c3..127cf91e 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_AutoAugment.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml index d75fede9..542ce15f 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Baseline.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml index 2fefb9f4..21ec5f88 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutmix.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml index 4bf53066..5f9286b8 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Cutout.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml index c0016aa0..8e14546a 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_GridMask.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml index 12e4ac8d..b8bdeba2 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_HideAndSeek.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml index 3434cab5..176acaf3 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_Mixup.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml index 153451e1..c1f8c14f 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandAugment.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml b/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml index 8e89c5ca..1788e529 100644 --- a/ppcls/configs/ImageNet/DataAugment/ResNet50_RandomErasing.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml index 8c3cc4c3..39087d34 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml index 0b8c2e80..bf0ac2b7 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_base_distilled_patch16_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml index 938916ca..cf3f1dc5 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml index 4cbe6ffd..8f4a9a9d 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml index d5ba0cee..0db9532e 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_small_distilled_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml index a167c896..5e91973b 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml index 319e1702..3068ada5 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_distilled_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml b/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml index 1234d79b..3cd8cd06 100644 --- a/ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml index 42c7e784..13e57d67 100644 --- a/ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml index 3f9bbb68..a3fa6b1c 100644 --- a/ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml index 3a046fb8..5eb27d4d 100644 --- a/ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml index ba626821..6b7aad5c 100644 --- a/ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml b/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml index a0a8193b..046e3a83 100644 --- a/ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml b/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml index a896c61e..7cc99b64 100644 --- a/ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_afd.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_afd.yaml index 6fddbb78..6816cd25 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_afd.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dist.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dist.yaml index 9bae5a3c..a689f617 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dist.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.yaml index fd1f35c1..19c68453 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_mgd.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_mgd.yaml index a4cd38a7..d501f3dd 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_mgd.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_pefd.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_pefd.yaml index f3a87f96..1c87f03e 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_pefd.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_skd.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_skd.yaml index 669e6c03..d1100a1b 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_skd.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_wsl.yaml b/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_wsl.yaml index 7822a2be..adabc5ae 100644 --- a/ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_wsl.yaml +++ b/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" diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml index b34ba075..a1ae01f2 100644 --- a/ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml index 0b82e087..bf806d9c 100644 --- a/ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml index 76623973..56656079 100644 --- a/ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml b/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml index 583efd2e..e0b32d5c 100644 --- a/ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml index 2d5b7d07..8f8ce182 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml index b23030f4..33cdcff9 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml index de48d03a..3d2e15f8 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml index 3f0b559d..4dd71da1 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml index e3a009ad..a123c126 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml index 795dfa12..8c163d64 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml index f86dd04b..9897673a 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml b/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml index d57d841d..83b36406 100644 --- a/ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml b/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml index ba44691a..fe56eb53 100644 --- a/ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml b/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml index a4e6e37a..e063ec2b 100644 --- a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml b/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml index 69921bea..40572d4a 100644 --- a/ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml index 935b0b58..76469db6 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml index 5f7067cd..78fdce1b 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml index fcc6dc12..43a06712 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml index a7096773..a32ba1e6 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml index f530cc2d..f8b715fd 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml index 1c7ffc96..a91baa57 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml b/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml index e72b0b3f..f482fc6d 100644 --- a/ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml index 2aa8e681..d15ba0c7 100644 --- a/ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml index 2f0ef126..f0d8d8e2 100644 --- a/ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml index cf8f2edf..dc003a88 100644 --- a/ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml b/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml index 85128592..f69bc650 100644 --- a/ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml b/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml index 5bc3c9e3..6709433f 100644 --- a/ppcls/configs/ImageNet/Inception/GoogLeNet.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Inception/InceptionV3.yaml b/ppcls/configs/ImageNet/Inception/InceptionV3.yaml index 3749ed86..fe6c66a3 100644 --- a/ppcls/configs/ImageNet/Inception/InceptionV3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Inception/InceptionV4.yaml b/ppcls/configs/ImageNet/Inception/InceptionV4.yaml index 7df00cc1..99604949 100644 --- a/ppcls/configs/ImageNet/Inception/InceptionV4.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml index a1a4f730..49615527 100644 --- a/ppcls/configs/ImageNet/LeViT/LeViT_128.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml index bfc6eb4e..27798a67 100644 --- a/ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml index 9596e868..7e045c65 100644 --- a/ppcls/configs/ImageNet/LeViT/LeViT_192.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml index fb427001..2b764dae 100644 --- a/ppcls/configs/ImageNet/LeViT/LeViT_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml b/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml index 8347c4ae..6751e066 100644 --- a/ppcls/configs/ImageNet/LeViT/LeViT_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml b/ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml index 22bf240f..fe64441f 100644 --- a/ppcls/configs/ImageNet/MicroNet/MicroNet_M0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml b/ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml index d6831303..694793e2 100644 --- a/ppcls/configs/ImageNet/MicroNet/MicroNet_M1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml b/ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml index 6d8ad7c0..37944578 100644 --- a/ppcls/configs/ImageNet/MicroNet/MicroNet_M2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml b/ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml index bae86627..7a41c05b 100644 --- a/ppcls/configs/ImageNet/MicroNet/MicroNet_M3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml b/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml index 54bb18d8..fe75f2cc 100644 --- a/ppcls/configs/ImageNet/MixNet/MixNet_L.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml b/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml index 2c2a18d0..62c039bd 100644 --- a/ppcls/configs/ImageNet/MixNet/MixNet_M.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml b/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml index e0f5c6a8..f56f40eb 100644 --- a/ppcls/configs/ImageNet/MixNet/MixNet_S.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNeXt/MobileNeXt_x1_0.yaml b/ppcls/configs/ImageNet/MobileNeXt/MobileNeXt_x1_0.yaml index 5b986ca8..04772014 100644 --- a/ppcls/configs/ImageNet/MobileNeXt/MobileNeXt_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml index 281015da..66fa53ec 100644 --- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml index 86324cfe..364ed851 100644 --- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml index 1693e787..46fc9885 100644 --- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml index b8b0477c..180e97c8 100644 --- a/ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml index 2fe1f5cf..12ba9077 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml index d9f30fd7..c8897d8f 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml index 7abddd40..d6c761ba 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml index e620d708..ac33c49d 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml index f9d6abc6..98fc61db 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml b/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml index fa5bf680..1fd27346 100644 --- a/ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml index 0c81ebc9..e8c400d1 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml index 76c70289..b357cc1a 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml index a1e9126a..b9bb092f 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml index 3e3ad709..e3b54a66 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml index 097c41e7..7eab8f90 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml index 30ea2eba..8957dc78 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml index 3c13bbbb..8d59196e 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml index 45608dfc..2ccb6fae 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml index 02a3949c..baac6696 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0_fp32_ultra.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0_fp32_ultra.yaml index 41e9dbc2..f2df2142 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0_fp32_ultra.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml b/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml index eeae6907..b401bd16 100644 --- a/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml b/ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml index becfef46..bf39753e 100644 --- a/ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml b/ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml index 6cd152a1..c4b6804e 100644 --- a/ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml b/ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml index 076b9f36..a3611f4e 100644 --- a/ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml index 8b0924c9..f700f6c6 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml index ed2501e6..c83c504a 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml index 0f01d58a..00eae55c 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml index 78578823..96a43650 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml index f55a044f..97291077 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0_fp32_ultra.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0_fp32_ultra.yaml index c53e3055..9f7abadd 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0_fp32_ultra.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml index d654d420..36768919 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml index 50b19aa5..87fb92ee 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml index 4f677e5d..762d21fe 100644 --- a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml b/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml index 64083393..4195efeb 100644 --- a/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_large.yaml b/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_large.yaml index 6eef7019..1551e673 100644 --- a/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_large.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_small.yaml b/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_small.yaml index a0dc6fb2..bb937ba7 100644 --- a/ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml index 27fc20b9..447326ac 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml index 20fa3977..35ab6507 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml index cda94496..ec93edc9 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml index 2d48178f..77cfbad8 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B2_Linear.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml index 581a7060..9d444349 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml index 92da84d1..78e08fa7 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B4.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml b/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml index 4bb2449a..b8f4da0d 100644 --- a/ppcls/configs/ImageNet/PVTV2/PVT_V2_B5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml b/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml index db2136a0..06b0059b 100644 --- a/ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml index 709d72fc..c4fa39e9 100644 --- a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml index 18607c6d..8bfe5c3c 100644 --- a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml index 99dca8b3..66a8497c 100644 --- a/ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml index 285b8dfb..80a80c17 100644 --- a/ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml b/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml index a44294e3..7b896d20 100644 --- a/ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RedNet/RedNet101.yaml b/ppcls/configs/ImageNet/RedNet/RedNet101.yaml index 95ea5185..fa793e4d 100644 --- a/ppcls/configs/ImageNet/RedNet/RedNet101.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RedNet/RedNet152.yaml b/ppcls/configs/ImageNet/RedNet/RedNet152.yaml index 7d5cc03c..1b7bc86e 100644 --- a/ppcls/configs/ImageNet/RedNet/RedNet152.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RedNet/RedNet26.yaml b/ppcls/configs/ImageNet/RedNet/RedNet26.yaml index 089db6f3..d8e88eb9 100644 --- a/ppcls/configs/ImageNet/RedNet/RedNet26.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RedNet/RedNet38.yaml b/ppcls/configs/ImageNet/RedNet/RedNet38.yaml index c2fb8634..b1326d64 100644 --- a/ppcls/configs/ImageNet/RedNet/RedNet38.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RedNet/RedNet50.yaml b/ppcls/configs/ImageNet/RedNet/RedNet50.yaml index 02e045a2..5e2a5cf0 100644 --- a/ppcls/configs/ImageNet/RedNet/RedNet50.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_12GF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_12GF.yaml index 30c5387b..7d913542 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_12GF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_1600MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_1600MF.yaml index fdcae13c..b14298f5 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_1600MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_16GF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_16GF.yaml index b36dc08c..7de21729 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_16GF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_200MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_200MF.yaml index 83c0a3a4..1b87cabc 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_200MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_3200MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_3200MF.yaml index beb8aaf8..70229639 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_3200MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_32GF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_32GF.yaml index ab5f1c07..0c770b0c 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_32GF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_400MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_400MF.yaml index 148ee1e2..aad3bb23 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_400MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_600MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_600MF.yaml index 948c133c..54dbbcca 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_600MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_6400MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_6400MF.yaml index fd1a38d4..eae2c6a4 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_6400MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_800MF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_800MF.yaml index dfd6a4c7..8dd6d19d 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_800MF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RegNet/RegNetX_8GF.yaml b/ppcls/configs/ImageNet/RegNet/RegNetX_8GF.yaml index 6353479c..3cb65e96 100644 --- a/ppcls/configs/ImageNet/RegNet/RegNetX_8GF.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_A0.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_A0.yaml index dc7974b7..ecec6464 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_A0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_A1.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_A1.yaml index f16d4e24..8ce5a8ad 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_A1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_A2.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_A2.yaml index b2b720aa..7c8e4bc6 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_A2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B0.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B0.yaml index 1257023d..5449d698 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B1.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B1.yaml index 90fcfb11..37f92629 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B1g2.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B1g2.yaml index b3dd5f42..a78d60d6 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B1g2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B1g4.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B1g4.yaml index 3890065d..4b267840 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B1g4.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B2.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B2.yaml index 0c0935d8..d6c3f36a 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B2g4.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B2g4.yaml index cf9806b8..d596211b 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B2g4.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B3.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B3.yaml index ee11e7d0..a15cbdc6 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_B3g4.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_B3g4.yaml index 5288a502..d2921bdb 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_B3g4.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/RepVGG/RepVGG_D2se.yaml b/ppcls/configs/ImageNet/RepVGG/RepVGG_D2se.yaml index 0f863f28..0b7f105f 100644 --- a/ppcls/configs/ImageNet/RepVGG/RepVGG_D2se.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml index ed16b036..155a5cae 100644 --- a/ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml index af1f4382..db0076cf 100644 --- a/ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml index 78240522..e90812e0 100644 --- a/ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml index 60767baa..eabf4804 100644 --- a/ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml b/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml index 977c1442..4f72ecbb 100644 --- a/ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml index ee2c5ec6..315b7c74 100644 --- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt101.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt200.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt200.yaml index da56238c..cb372126 100644 --- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt200.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt269.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt269.yaml index d157d531..985ff98e 100644 --- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt269.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml index d822c8b2..50529aec 100644 --- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml b/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml index eb973afb..c5db97b1 100644 --- a/ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml index e0d0a5b3..08a2f7bf 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml index d68f5f7f..be20d009 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml index eadd9eed..38510380 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml index 5c59e5a8..fa1bf5b1 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml index 8bad3f6e..2c25ada9 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml index 104f37a5..6c065a95 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml index 638feef3..5ad91320 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml index 7c05197d..44a354cb 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml index ef78f60b..ae3163da 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml index b7503571..b1af78e0 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml index baf38e36..be588107 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml b/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml index dba5f863..e040dd37 100644 --- a/ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml index 71193aa9..e0d496e8 100644 --- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x16d_wsl.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml index 346d2ea2..40f82b5f 100644 --- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x32d_wsl.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml index 2db3bd6c..5974ca71 100644 --- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x48d_wsl.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml b/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml index bed3cc20..d2f4cd85 100644 --- a/ppcls/configs/ImageNet/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet101.yaml b/ppcls/configs/ImageNet/ResNet/ResNet101.yaml index 2c98acf0..afd6329b 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet101.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml index d62b7bc3..748dfc12 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet152.yaml b/ppcls/configs/ImageNet/ResNet/ResNet152.yaml index 0dbbaf8e..993d52e5 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet152.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml index 735c84b0..8daf0f80 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet18.yaml b/ppcls/configs/ImageNet/ResNet/ResNet18.yaml index 4e0e460a..2c0c4c23 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet18.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet18_dbb.yaml b/ppcls/configs/ImageNet/ResNet/ResNet18_dbb.yaml index af4785d1..35250d83 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet18_dbb.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml index 01506334..591ef505 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml index c9209f12..135388ae 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet34.yaml b/ppcls/configs/ImageNet/ResNet/ResNet34.yaml index 5b90cf05..88af8fe0 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet34.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml index a894ea47..feb6c426 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50.yaml index c2da23fb..a16ae3fb 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet50.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50_fp32_ultra.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50_fp32_ultra.yaml index fd166940..9fb8da0d 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet50_fp32_ultra.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml b/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml index be7b2d9d..5f30746b 100644 --- a/ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml b/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml index 6545cbf9..33e72300 100644 --- a/ppcls/configs/ImageNet/SENet/SENet154_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml index f97430ef..df3d9c56 100644 --- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml index b31250b0..192b5aaf 100644 --- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml index 292b52d9..e82bfe42 100644 --- a/ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml index 47d17547..5cfed65d 100644 --- a/ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml index 174c181e..857300d5 100644 --- a/ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml b/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml index f503ea66..e7b94138 100644 --- a/ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml index e01891e9..4b0393cc 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml index c2e98055..a00c648c 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml index dc7a5ef0..e3b82cf9 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml index 796fb7a3..c228b57a 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml index 809fb2a9..f0d5d46f 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml index eb3e0132..202514c9 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml b/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml index 730cf437..d633a754 100644 --- a/ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml b/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml index 28eba49d..7224467c 100644 --- a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml b/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml index b61a28c2..ed5ef8ec 100644 --- a/ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml index afc3fdcd..3503a423 100644 --- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml index 4920fae6..5bd26278 100644 --- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml index a6dd7426..7e5c4f40 100644 --- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml index 564da72f..0a523a82 100644 --- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml index ba42f1ef..446c5e50 100644 --- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml b/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml index 26fa0ba6..2a3656e1 100644 --- a/ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window16_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window16_256.yaml index 68e1b1b6..966bb2cd 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window16_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window24_384.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window24_384.yaml index 95372158..b71ad0ec 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window24_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window8_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window8_256.yaml index 92fe7e59..2337b5d8 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_base_patch4_window8_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window16_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window16_256.yaml index f646bfa9..ed60a4b8 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window16_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window24_384.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window24_384.yaml index 0ed9414e..48972c48 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_large_patch4_window24_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window16_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window16_256.yaml index eb7a41d9..e8c4c7c4 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window16_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window8_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window8_256.yaml index 041d8b99..c0e1e0c7 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_small_patch4_window8_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window16_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window16_256.yaml index c91efb6b..18678441 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window16_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window8_256.yaml b/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window8_256.yaml index dc19f3e2..a1b69b3d 100644 --- a/ppcls/configs/ImageNet/SwinTransformerV2/SwinTransformerV2_tiny_patch4_window8_256.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/TNT/TNT_base.yaml b/ppcls/configs/ImageNet/TNT/TNT_base.yaml index d135a4c0..c2c7766b 100644 --- a/ppcls/configs/ImageNet/TNT/TNT_base.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/TNT/TNT_small.yaml b/ppcls/configs/ImageNet/TNT/TNT_small.yaml index 229e69ed..2ab6cb60 100644 --- a/ppcls/configs/ImageNet/TNT/TNT_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/TinyNet/TinyNet_A.yaml b/ppcls/configs/ImageNet/TinyNet/TinyNet_A.yaml index f135f665..d4ba012e 100644 --- a/ppcls/configs/ImageNet/TinyNet/TinyNet_A.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/TinyNet/TinyNet_B.yaml b/ppcls/configs/ImageNet/TinyNet/TinyNet_B.yaml index cdb9185a..8eac20eb 100644 --- a/ppcls/configs/ImageNet/TinyNet/TinyNet_B.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/TinyNet/TinyNet_C.yaml b/ppcls/configs/ImageNet/TinyNet/TinyNet_C.yaml index 7ca3e492..283f7226 100644 --- a/ppcls/configs/ImageNet/TinyNet/TinyNet_C.yaml +++ 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 diff --git a/ppcls/configs/ImageNet/TinyNet/TinyNet_D.yaml b/ppcls/configs/ImageNet/TinyNet/TinyNet_D.yaml index 9a6695b4..294ab685 100644 --- a/ppcls/configs/ImageNet/TinyNet/TinyNet_D.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/TinyNet/TinyNet_E.yaml b/ppcls/configs/ImageNet/TinyNet/TinyNet_E.yaml index 084f33ac..df286a79 100644 --- a/ppcls/configs/ImageNet/TinyNet/TinyNet_E.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml b/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml index 36e5b086..216bda28 100644 --- a/ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml b/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml index 6e19d646..ff2e6252 100644 --- a/ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml b/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml index 66235960..7de1133c 100644 --- a/ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml b/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml index 96745495..b3fbeca1 100644 --- a/ppcls/configs/ImageNet/Twins/pcpvt_base.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml b/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml index ca4baf94..4d91ea25 100644 --- a/ppcls/configs/ImageNet/Twins/pcpvt_large.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml b/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml index a5e5f7e0..97d5f1e1 100644 --- a/ppcls/configs/ImageNet/Twins/pcpvt_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml b/ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml index a374e885..58ef6931 100644 --- a/ppcls/configs/ImageNet/UniFormer/UniFormer_base.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml b/ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml index 0ab3ae6c..29a3d426 100644 --- a/ppcls/configs/ImageNet/UniFormer/UniFormer_base_ls.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml b/ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml index a46c1c48..5d1860d8 100644 --- a/ppcls/configs/ImageNet/UniFormer/UniFormer_small.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml b/ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml index d43bd477..f08fb716 100644 --- a/ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml b/ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml index 84b44610..bff77c14 100644 --- a/ppcls/configs/ImageNet/UniFormer/UniFormer_small_plus_dim64.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VAN/VAN_B0.yaml b/ppcls/configs/ImageNet/VAN/VAN_B0.yaml index d72f0fae..f121dc57 100644 --- a/ppcls/configs/ImageNet/VAN/VAN_B0.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VAN/VAN_B1.yaml b/ppcls/configs/ImageNet/VAN/VAN_B1.yaml index b4edcfea..fbc30f6f 100644 --- a/ppcls/configs/ImageNet/VAN/VAN_B1.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VAN/VAN_B2.yaml b/ppcls/configs/ImageNet/VAN/VAN_B2.yaml index 7a3a2a22..1dca03a2 100644 --- a/ppcls/configs/ImageNet/VAN/VAN_B2.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VAN/VAN_B3.yaml b/ppcls/configs/ImageNet/VAN/VAN_B3.yaml index ac1d204e..6bdef9a2 100644 --- a/ppcls/configs/ImageNet/VAN/VAN_B3.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VGG/VGG11.yaml b/ppcls/configs/ImageNet/VGG/VGG11.yaml index e55c4d0d..25318c68 100644 --- a/ppcls/configs/ImageNet/VGG/VGG11.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VGG/VGG13.yaml b/ppcls/configs/ImageNet/VGG/VGG13.yaml index b4a0ee34..3720cf8f 100644 --- a/ppcls/configs/ImageNet/VGG/VGG13.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VGG/VGG16.yaml b/ppcls/configs/ImageNet/VGG/VGG16.yaml index 154c4684..38f92e10 100644 --- a/ppcls/configs/ImageNet/VGG/VGG16.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VGG/VGG19.yaml b/ppcls/configs/ImageNet/VGG/VGG19.yaml index 0a7022e3..77f6360a 100644 --- a/ppcls/configs/ImageNet/VGG/VGG19.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml index 6d5857db..5b9c518a 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml index 925d8277..a8792a03 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml index fc4747b8..477d9edd 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml index 3882c555..7174f151 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml index 3bdb3871..195cd229 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml index 25212dd2..afa78dac 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml b/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml index 0a956b49..7eafe2ac 100644 --- a/ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Xception/Xception41.yaml b/ppcls/configs/ImageNet/Xception/Xception41.yaml index 45e64a15..c622617f 100644 --- a/ppcls/configs/ImageNet/Xception/Xception41.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml b/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml index daf0598e..d03b6bc7 100644 --- a/ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Xception/Xception65.yaml b/ppcls/configs/ImageNet/Xception/Xception65.yaml index c6bb5297..c134331a 100644 --- a/ppcls/configs/ImageNet/Xception/Xception65.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml b/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml index 1248a29d..05e88336 100644 --- a/ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml +++ b/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 diff --git a/ppcls/configs/ImageNet/Xception/Xception71.yaml b/ppcls/configs/ImageNet/Xception/Xception71.yaml index 7f714cc2..3fabd659 100644 --- a/ppcls/configs/ImageNet/Xception/Xception71.yaml +++ b/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 -- GitLab