diff --git a/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml b/ppcls/configs/ImageNet/AlexNet/AlexNet.yaml index ea2e073cad8da4fdfea58c274b1d901be60a3fdc..25c19eedcb04c02bf1e711aab0343467d54977e5 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 4848cfc85ddb262ec623082f5aea869d0b6e77b8..29ca02e25613f34c13b8e4a61d5d9d3f886a80ea 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 a7697840ea8f1f29bed5d9e2c2226ea18f4e421d..5f116f11a7230822e33917bf7b7a59cb7e1b9ea3 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 a7100289c06b94f211dd3fea6cd0b8f2548b8244..d845d8f4dc06e6f5f3c311cfa0401c2166892b95 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 7c96343df5d00f8c7ceeba0f1dabec8ecccdbc57..9cadcc9013779729dcbd5495dee9326b0c1ea27d 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 4b682fec60569cd16e6cf81d093d016761f37615..1e01bb0bb44a16d3aabe744316a4dad7aae4469f 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 a191f4160fd2922974a14c379a8010f90a698b6c..2c182bb53c1168d5beadb5cb1fd305a3d66faf13 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 3a2be2837891639604cbea15b7179adfb5cda0c7..fa8986f2c60942e316760bd8580223335ee18821 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 a0630335b248ebf7c859e40ff17c6a1e60710ce7..591afe390dd9025ec227d3d219b62ce051ebf6dc 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 bbe9a9a89f1ef93c37f19ab2b353a1d06dc99753..0adec4be558de8ad5e72dc7c7b403893ec8a3cb7 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 e15cbf6f60c30f7bb157c16453272ad882f412db..6f5b23e100b1cfdecab9760dbdb8835f33571615 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 41e669885f21c0852a2198baab127facf8626c21..63a4aa1a0cca0a946b788916b517ef042dc2e301 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 b929023d5ce800f218e5c4e50f6f27fb261fbe8e..d6c0551df65d970d1df456f7ed1f951abc9cf300 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 fb6e3cbdbb2dc648e4ef0bd1cad59106efbf91db..4d705857bafa7967c1b46b045346a6e2d08c1ddf 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 1431ab28ddc62fe05445a62a5b09198a19aa4d57..b211c0cd11be4b733600d284885d4e85b8ad38bc 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 361709734fdab0dba321465dbdc760a9eb0f6bd0..14e2b9d9e93d2df4d0bbca62d98415dfc5f1077f 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 ea6791ba282cb7258871a712285543ef1ae543a0..8274a582e020bd120f90f04d4f9ef6ae0fbde008 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 4f7af8166c2ac7d396d8fde9c28358e1029e2cdf..4aa2e27ca0b205dde225f8750a8d214463d6a994 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 f5b33d21b084a1474fd45e0ff949367d163a4773..18b6d7ff6ef08dea4eabc42d4814327f81bbf7f1 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 b6033f70c09c227a0bbeafbb1ff48e19b549fc41..c876357631f0661919078a53b3b4a031b56c72b1 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 a1e2c09d2557c946fee22841c87cd2a1386f467e..580c8ce4c670e6ce1ca2a45ce9189b1e17eabcc5 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 8bd4c4646a6fe896ea77cbd6f4ff51da4dbf449a..0691a2afca273e4371b521e08f7a29f5b9d93b60 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 18c244d0bb12a7a5d6cdd4236492fa8364b1bba8..7731d361b334eacd72dc48788782f588d670a2f0 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 d9218df78a73630efc0bfba0e5ea4b40bb600503..55571603356b08c3d20d602a461c0732ca8ea704 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 8d20341357e33e9ca2b767063587b2d2c2c6a9dd..1fef5b86161ab9ce1c8ebbd37f1cf58f1227333e 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 e7f7d672e98b8dd5ea65fccf5b4022bad721713e..a88a940d2e5f16e27f9f14aeca8bb677c0889be8 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 a255f053b1002f5ab0405e16e5492f3206634983..0a82f7d2c171080d17411fd03adaeb48600e94e8 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 143b87f91d44c52ad2e40593768ffe27f5d6f4d0..19dc8ef3279fd2e74441427fbd0e67542e599cc4 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 779281980b13b12cfa20d2cb957aa27f3f6b8757..ebf247840e37007bf917ed6c5378ae7a9173e585 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 7df1256d478650a22c12bd6994c72e79377bf672..a18c341f6e854bdb046d9056e6a0e85b85299da3 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 88f1b57aa541be903a66991c5cb11520ba249117..68e9479ffa2f29bc94ac4c6e981e5cc1f836b538 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 c1e28081456bf139eb22badc50d5626087cd963d..33a0e416e3536e7dc8290876559c6ff41d816377 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 fb5b0ed5817c0c0c8a73a9a57e1748488a6be3aa..5830798457533384fad4f57d9477c95a1b3b3a3a 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 e394710ef8dd1c7b3c050ef7d3d334785121f7fb..f3bb994239e06eafacf6fe4cc382b7c8825a3107 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 2d853802f17d8e621c2ba0c393b28f0bb8f3f62d..7d4ffcb106f4c5483c56674b8336b917394dead3 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 25ee721047067aa176230a40d584f0dd0c66e7ee..e006104c9f77d1b934b44082e9831f27de9d43b7 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 b3ecec40a52e07189a9bf133e82f465f3cdf7abc..8846205825748667ed2ace3e758d549a546d917b 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 ec0f822ac43ef29ed75d76f669c7307db99a58d9..ccf05adaa4125ddd2e8d8db29748457511ba17ec 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 ab4c29c305dccbfde5b5f2889837195395a168f4..127cf91e8fafdd37471cf0e4ef17b47b2db0ca56 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 d75fede9e5bc51c9b03fd55f581b750da33b6123..542ce15f85df4018a96a19e0545699f0344d5e97 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 2fefb9f4bdf46ddeccf4f77c91377f370eccfe0c..21ec5f88be2bd24afad5674ad77b8ddc26c4ac68 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 4bf5306640642a3eb17fd75f7a2f59972a6c5470..5f9286b8260d97a1446500454f927abecad225e0 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 c0016aa00ce197863d8093c25c90a3ad514868a6..8e14546a3cadfd7e843f1d090951612a064de5c9 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 12e4ac8db243148d8856ccc47ab522fa9b505fc9..b8bdeba2c7b7d9ea8864339f665e5b728ff46fee 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 3434cab5acfc231c49c9ad78b22769f3c7bfd2ae..176acaf3e8acd375874fedcbb5c9eed3a9df77ef 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 153451e138cb24eee9b027634a6ca37efd8dfdf5..c1f8c14f6d4afc529d19262a1c6508792a7aef73 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 8e89c5ca199c65b1b72f38e35a55e658660b0ceb..1788e529d9c0780259214ea9484b11d910409a63 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 8c3cc4c3413729e2a5530de23105619ae76e0a15..39087d340caa59155dd1ddb8cf901dbeaff66b01 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 0b8c2e808c94f09f9a6283a6baa605bd7ac45d85..bf0ac2b7a63adb60d474a29714e67b6d58c39040 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 938916caa3937b2f1ef556e98c3adf9313b4c7b9..cf3f1dc5e174da9a868c7c9fbe645c3d32075534 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 4cbe6ffded134bbe52656879b5105f3676c44b64..8f4a9a9d2f130879afdccafa0eecc4a835e10f1b 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 d5ba0cee78de68ecccb35ffcbf099ddbdad3271d..0db9532e37c7be421435b6dbc7ff23a09c0340bf 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 a167c896e8be7746d9896cfcd9d3d3a8e7671908..5e91973b1d011d259b20d3559ccedc429bd6de3b 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 319e17025d758eadce16001863312f773410104c..3068ada56a8c6a5a51f252a35fc18daa458b67e3 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 1234d79b6ba68186466edd7c2d1ea4f6bc61eba9..3cd8cd06cd56a3c258ff068012403b5916cf2042 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 42c7e7843270de5ac258af58cda729f706803262..13e57d675ec563e01b4df1902a90f894819e9a61 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 3f9bbb6872e5132dfb8acbbd892c5b995ce04896..a3fa6b1cf72cb6278ec3ce0ed6fcd95173b838dd 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 3a046fb8f47579c1261558038db83b9617a72179..5eb27d4dd624fdb468621c29d7d6b04d53fa9b6f 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 ba6268213df5104831f58ebf0afa08e3a45256cb..6b7aad5c18c4656176703afce727c9b602518a9f 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 a0a8193b3332c2e73c10e491f26ec64aa2882d05..046e3a83c8a0ef4698a2843bddc3010292acd04c 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 a896c61ea156ab31ea5a1f9e218151a4df64cc75..7cc99b64ab8c6f529ece79a99ed7c1307d8d79bd 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 6fddbb7882cda8263c76576aeac4e49ff7f01774..6816cd25704459affce865ef6cf1b259ee58f46e 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 9bae5a3c1fde48afd103a6ccb302d723ea4a4c99..a689f61728185b29327c0ea559819facf5558455 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 fd1f35c11536a6e5aaa721652ff0b81203c4f91c..19c6845358bb99783154b91f16afcb027d1d2595 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 a4cd38a713b151248f2f78c6aca4f1857ee95421..d501f3dd5bf60f5bcff5398ee491eb1abe62a734 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 f3a87f961e87925c8af68432d966c2980305224c..1c87f03ec6ff6206906cd6a2faeb546ae826824b 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 669e6c0383ffab44c638f1d7bb697c8c7fafe892..d1100a1b78ccf951b0312b902db995507c144f67 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 7822a2bea425ac657dc9a10644aa2cdf1fd4273c..adabc5ae0968653999b53d320a01c5830e612e0a 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 b34ba075ca8d5a2bedd2a653901c63686062c776..a1ae01f2e0f89d8d1acc356e7a75230bf5b03371 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 0b82e0879a423a904f4fa061a80e8a7738787cf0..bf806d9cd07a043639ea4f4c63fee349b242626c 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 76623973f308d604ef5a515c8376bcb1a968d506..566560795fadeeb12877fa3376baf6164e72bfc3 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 583efd2eb9021ec49c4924c64ff69a3cb09f3b20..e0b32d5c536d45e1f108ed614b5800fb158a5d63 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 2d5b7d0797cfa8dd686190a7be6070bec49130e9..8f8ce18251b6fdb6488d143285e1a3fd4c7f65eb 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 b23030f436b16482e5f3c7c085e8a676a3d16e85..33cdcff94992fd2b847620432b6b68c30ff43e04 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 de48d03a8d7953772744336c7ec82ef6bc6f07e4..3d2e15f85f6a5b6d9d03d078dc40c43e8c138b21 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 3f0b559d465ba0bea4b46d4ba3e7009de73cce26..4dd71da17435f565a3a699731d3182b63f312af7 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 e3a009adf2a7908e73c6d490c4ecdff3565cb62c..a123c12678d7af6048b8ba6c6d9f7e82d29a8f5b 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 795dfa12ae948b71f95a6587bfae5160e0307bbb..8c163d64f33f959dabed0b5ed28f67f45f0cd5bb 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 f86dd04b33fe7f1222e868f867dd5bc6a6abb6b6..9897673a3a69db9928c0bc611351595c823d543a 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 d57d841d6f5797c23b8dde2cea2baa9472cab29f..83b36406d9117b5634dc094e8a6e93c3cafa277e 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 ba44691a167d94fded5b51a41b26e0760787f45e..fe56eb53d95ef2f3498911035a834bf819a9d283 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 a4e6e37a91801df6f6a1653e2a9170c96574a361..e063ec2b7e40fe9e1613303913b3dd7051f84e9c 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 69921bea45400259643263b53c7a8ec6d66fb1ce..40572d4ab5c7eb6c97ed5f5b1dcf72ad4f2fefed 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 935b0b58821b4c648a71161c247184c80e06ab10..76469db681a2dd80f0937ebf51264677c1774d31 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 5f7067cd4430f72be7dc66c4f444f772172c3fde..78fdce1b3b8b337b3d845c899147401dc72780b5 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 fcc6dc125ba56d7bcaac291adde3faf236f5546c..43a06712e30e0edcbfc2eb3b769e8a76e55d4bf2 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 a7096773d7ff42179fe0ee4debb55b53c5906768..a32ba1e6064379e0884d2d74d9a2c6ccd2c4865d 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 f530cc2db522618eb184d05cbbcf574a976a4d87..f8b715fd95ae16e2f9f5f510d495b78a15f4ae59 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 1c7ffc965fe82e52bf69b72745a28b7920163996..a91baa57777713a731d4fb87157c11f877138802 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 e72b0b3f63a7f269bc39c082082e25eb794afad2..f482fc6d5166d040621f4bdb411d2104b4741c38 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 2aa8e68163787c6c512322c4cf8c5dc0df8a9459..d15ba0c75b3b4b3cbbff0bea9b6fa79f1d14982b 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 2f0ef126aea2f526ac8f722f1a212f284c302a13..f0d8d8e2647930bc409de80ab7eb7a2ef535fe99 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 cf8f2edfcd1c8f1ed4935662739f9b6b7d312878..dc003a88fbf2ca40305dec96441c5e1aaf9b1959 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 8512859250cc70361adc4d5880d64b2d8e6520c8..f69bc650c98879a55081c94915b3d6370d909e88 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 5bc3c9e30f5ce4e77cb8e182eb7c6ba1fee70caa..6709433f9db0ef18c7e6627792c662eb57c5669e 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 3749ed86a058d207a710055af2aee2e8219a6444..fe6c66a34a3bdcb0c1481718ec6cde48a1fb1667 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 7df00cc1e47b614e03efd8a9682580a78b092148..9960494906ab48b7301b3033de7a74ef91afc4ae 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 a1a4f730345350a9392eab4edd9e32b7a1fbaecd..496155274c5a4c29071d64e061ae01f4f5658f97 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 bfc6eb4e4ae68e1e0dd3952cd96484d724c4894e..27798a673ae557aeae947f2f606a3a0888dd7955 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 9596e868912b4784df179e5311a1b59be728a805..7e045c65d0b693b87b669fdac5aa40cab0c15f37 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 fb42700126f581d585fe55f7d6e453b8e2295227..2b764daed49f9ca06e0be5fb82e859f1f297c512 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 8347c4aeea2f7a6ac9df89a0f521a3e0671dbf28..6751e066c456effde7ef596ff9d3822a47b93815 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 22bf240fb58e6345a2c68ac419aaa0360e371fe9..fe64441f83340d5f7339df3abc3f4e8845c5fe39 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 d68313036e2ce43e565a759f3f6b86df9d5c1fa7..694793e2a7c90fbfb95d8387edcbd27d2a06b4e3 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 6d8ad7c077d608227841ee146bae20f485377353..379445787ea17e5a36718f4df5bedf88b49fdae3 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 bae86627de2037d5ba1ae3683e5290ff97c84f8c..7a41c05ba1f5d856bb5ef72037d9e19d3bda387a 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 54bb18d81004bb036e55a2f561c1f0be86a7377d..fe75f2cc87c7f018f9bf4df9546f6422ee864fbb 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 2c2a18d0a288bb02e6761e7adc87d2390c841436..62c039bda7f187bee9ba1c90e92640dca529d32d 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 e0f5c6a8ea0f25b83b981b0072bf5496d1b54f9f..f56f40eb12fcff6aaf2df59808898c9b6b27087d 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 5b986ca8b311338f994f3dbf29839aa3867f8ae4..04772014a6e928d30a7c02d543c01ac79538a3d9 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 281015da3a7f7cfee555820adff74202f095e782..66fa53ec7f7e438f299759712a81a8aab8ba4dd8 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 86324cfec491f2c98c31c1467206c4f46aab9afe..364ed851a3388ef252fd9ec729d31238caa87149 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 1693e7876ce44279980c4d59e8d3cf17cdd6db9c..46fc988590f28e6431a0bd56468c6f43dd7ab4c9 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 b8b0477c994332069ac6372ee44151fc0a0fa74a..180e97c8cf57a3c9f945eb7d272249d8ee4002fe 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 2fe1f5cfd61e669ae833183f81f475d35af1ca77..12ba90772cad9657b414d30c3b694ecd8bf6cc26 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 d9f30fd7018c7e5eaea7de1809c86e50c5b97163..c8897d8fc0f6381f6069b52e5a96b0af213773bf 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 7abddd407772159b7c9e5d1affa304eab6a02acb..d6c761ba48ebc6093aae1252bc105ac881843727 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 e620d708bd828db8392b1c9526dcde0a03e8f6a8..ac33c49d447f924aa19cd49e203ccf777fb2b8b4 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 f9d6abc6334605b9efdf3c8d15d34ca6b1569812..98fc61db49e85bbc7a454c79ecebb042f725f068 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 fa5bf6802280ae88b373a2ac4b6641ab04dfbf98..1fd2734693f6308477a6ac5baed4ced4d92faf9d 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 0c81ebc98212f8dd10a58f407918b27830b3cff8..e8c400d1d92f7774cbcc8de6139a6458d985de72 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 76c70289a3395eaf33b89adb10af04d6f1bb402b..b357cc1ae654b19e3dabf2a2752d566d91ac5c38 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 a1e9126a7dd36978df1ca3fa88251dc3464805a7..b9bb092fa6d196b592db01916f5fc30d22ffc0b1 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 3e3ad709adbb4ed31d60b2bec1b37b2c1d19cc76..e3b54a66b12df0d03cffa2a287a6cdc2c193c356 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 097c41e796f57c00356f5cc81a4946d25f7e1be0..7eab8f9045fb24ed298b52762af570e158e41886 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 30ea2eba57de4b37a6c94623bb9e4d7c779f864b..8957dc785f08c93151f60c8766b727c4878fbb3f 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 3c13bbbb91cb4e6ab29fe893af899090ea08de7f..8d59196e0e8ef4c7158573993ebfb22c18c5265a 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 45608dfcae3173a3ac2654251ff45e0715d2b394..2ccb6faee9952c2c2f7518dfc9ffc7318d624f3c 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 02a3949c37b95642ef4211279262a47819680dc2..baac66968f550cc3e8969271aab314ad3e23c319 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 41e9dbc2b455fa18e2da69be46f7c15f60638651..f2df214257bb80eabd191cc5161bf2384c4dff34 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 eeae6907d1b61f60d467a882233940a0927ca45a..b401bd1680189db0da5b5228866cb16b9ae5beed 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 becfef460701681d8dfbd3fabe9ec6d24c02e769..bf39753e7eea65e7bba1e9f3308c889b55c76112 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 6cd152a134075455f88b00de1e15719d5e8903b0..c4b6804e57e550c530a343523d003e5bf82bff76 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 076b9f363a239588e1edc60cf744a70190fabedb..a3611f4e3bd5e6a1bfb9b6ceca85d9fea794c827 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 8b0924c942514011b9afb0076a9e02ec651bf61b..f700f6c6e3ceaa4eebefbfd95ceb21d9ceb51079 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 ed2501e65746629ed369e9a9d44c223ea9207dba..c83c504a7adae9591cf2a09e28a877c7ffd1a0ad 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 0f01d58a35855d832c694da68a37cf6f55854767..00eae55c488345aef2b8e9c5997700ebd44b3edb 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 7857882326311e290db688db39de8ce375aab4e1..96a43650b79213e78880dabb2da0c70c2972d2ca 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 f55a044f04cdea2e8e802f113b5c9b3bc6b302f8..97291077fb60917cc7b6514b548fa3f7494beab6 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 c53e3055eaf553c1fd067f3e868c22832bb2f17c..9f7abadd95a9528a1c19d0e8623e1cb48dcd6893 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 d654d42085207ea038bd11794b4e60d4870d4f63..367689197e0d3d3aaa9ee4773d63a4d0313598d4 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 50b19aa560b8e9828a3c0fec91ef8c7d5d29a2d9..87fb92eeeebf3855c9bbe449f548be3be8c4961d 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 4f677e5de82d32c9a7c1d57dad4e5e08f4f42676..762d21fed6f38dabff51ac3ab0b08f24f4c4bf92 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 640833938bd81d8dd24c8bdd0ae1de86d8697a10..4195efebce816fc9f37b850a8af522c79d515eac 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 6eef70190b8f01f6b45614b3c14935f1e8279d96..1551e673f9c514a468a08b9cd661207c08cc3c0f 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 a0dc6fb2a90acb5edef0a647eb3e11fd040656d9..bb937ba7860247fa3d1f450013413d4493dc880d 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 27fc20b99961b29e9ddbcb58363b495f199b8aec..447326ac7889d036268f1d4c1e45edafe5e1fe71 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 20fa39773f66d0ffff2786a031f86156c5fc5c41..35ab6507a5863342985e3abbde7d19dec514e4c2 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 cda94496e34747468ac3dfe0b474478c0d30cae6..ec93edc94e441d7934d188c2de3ef2bd1db9ed8a 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 2d48178f05c19bcca34da95087129893fc574bd0..77cfbad84b33adeb9d64eb71851e730ac19567e9 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 581a7060549607248f967e54b60956722bcb4be0..9d44434914b4918640d89825d352dd623295dd87 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 92da84d1ef00430a1aab66fb33f84e8016ab2c0c..78e08fa7a123744a3328df57eeb89d3f483289a8 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 4bb2449a40bdd605b7d36359437b39324e5b1772..b8f4da0d393dc715a73091392c6e3f418ae27f64 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 db2136a0a5090175b54a1391d668ae92e8407359..06b0059bcc12756db9a36f675dfb77d50ffd10e1 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 709d72fce4dbc879f67b73116fee8387e2578412..c4fa39e9b2bf796eca6a5c805902de56dc3f283d 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 18607c6de484c0a83ab8fda86701953019955697..8bfe5c3c04d00a8613e047e49aeabc7fc42ad855 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 99dca8b356020272f6682af9d5a58ffa6b49e0a9..66a8497cb7940261b6ca498b27f82faccabbd0c6 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 285b8dfbc5f91a17a851a39853cc7757526d1932..80a80c1798e2ee1d727eb2489673966338368990 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 a44294e3c22ba60770d3d7e9e6b0d69352f058d9..7b896d20f04b6465c0886dc561da5dd3e860c0ec 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 95ea5185b5cc68007a0a8fe17488b565c6e36390..fa793e4d475c6f242f63ccbc799061a4312d3f34 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 7d5cc03c40456d5fe9bbd74bbaaf8a921aa7ef9c..1b7bc86e9b405d66cea5dd744e5dcfee75b7d680 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 089db6f38aed42f6016b5eff53c7437eb170b6c7..d8e88eb909f3edca06fc514b102d099874570376 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 c2fb86349681fc42e84ad055fa3e4f5fd91d66ff..b1326d6474ce7db98765d50473e30257505cf4a0 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 02e045a2f3913188fb89e068a620af13f86f9cce..5e2a5cf06a593ebbba09b3121c982c74725d9779 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 30c5387bced1a2a2655fba0fd2f7d408079083a4..7d913542701b932bf4fdbb36476e6b58b727e9ef 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 fdcae13c31843a6caf82859f3e0e3284a84126bc..b14298f5eb7afd3a4fa32e223d14be11a79eb01a 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 b36dc08c1dc768eb1e2942814f16f061d4961d34..7de217298d958bd32e4c68358c8c212e3778c638 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 83c0a3a4764f36c327ea3c1dd0bf5eeff741d70f..1b87cabc09d1199786ef91ca7665eda2a95fa094 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 beb8aaf82c4b3987d51c8111e77b73fbc2327854..702296394f5bc17d40bcf00f3fbc91d591d4d76f 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 ab5f1c07b062642b495f898a7302cceacebe561f..0c770b0c355196433012f10462935d1ceb07a77b 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 148ee1e2ca647c959edc1bbcdea121bf077af97b..aad3bb23f714caa85ed99a2e87ae0a022f5efdbe 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 948c133cd71d16d833e4f2f5d37cbf33825c915a..54dbbcca43afbe477adf8666206782c2af633c1e 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 fd1a38d4d9957b4cbf1b0282e43f896bbddecbb4..eae2c6a4d8febb67a59d3069b6acadfece3bfcf9 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 dfd6a4c7b8cceead34018b208a1b94748fe139bc..8dd6d19db550a65ba9d49c59744bf52b4ae87a67 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 6353479ce3a3a1e7eb08de94132dec7a2d3be793..3cb65e9618d908a60d1fafc825d53dc19c2e92dd 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 dc7974b709ab7b4269e2463dbe9b564f2e56e084..ecec646440a2dcefc176af48f8308597dc00c5f4 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 f16d4e24dff19cd73bbbcb0889b73b919ce4b683..8ce5a8ad43f0c6bfdd4845124fe7251218a3b01c 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 b2b720aac6a856c598205c70710cd8d6153eaaca..7c8e4bc613b96d00938a09d4d657bb9ba0be088f 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 1257023d51fe81511d8e1221d3f3385362583739..5449d698d6e19427c7bd04f8f354b01a61675538 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 90fcfb11308f24bd87208da409bcfd3d86844d1c..37f92629955e6749b16434502b8acf39e0572a18 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 b3dd5f4213ce1125cd5b8346668aac6e61854802..a78d60d6d935b747482bd677ba1ec7e47e86d841 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 3890065de78fe01a58cc36858239b1e0882e9dd3..4b26784092c63ae288474a507491e18315dab97c 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 0c0935d8c69926d00624f16b4f9d965db9dd6c5c..d6c3f36a3adc0c24f771d979265456c3485813ca 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 cf9806b8bdc2925e99283e4440ea91d3e5787959..d596211bb88f18db1d324ee9e403bcae0243bc2e 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 ee11e7d043d0700ef6e03d22ec09dfb3e2094a2d..a15cbdc6cdc92bf0389d8ab994639533756cdecc 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 5288a5021c0d83872f65c627a236ea6ca554480b..d2921bdb503e3cf3e6ba77d32622162fa2e3c11d 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 0f863f2839d0ba76ba8eea0c6c192698387c9b5a..0b7f105f12644af28741bbb78dbdc699f247abe9 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 ed16b0366b9a2dfcc76d7cff8036d35cdc0e9ebd..155a5caee0432599a3385c5b03ceef4484425abd 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 af1f43825ae8d1e15ae0e5fd526226ba7f8cbf07..db0076cf3a7d66b6ac54dd6da351bae697ded340 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 7824052243deebc715ca65bdda6fe6f110df0999..e90812e03d2d626ad021bbcfa773b66bfafb3b26 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 60767baa788e280f28d850fe3106be66348a8dd2..eabf480411e380215f5c16b687fb0e450e48ba7b 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 977c14422c564d5f5ed996eec8f427a1dcb47666..4f72ecbb49a2693de96e25fc224a5a814207459f 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 ee2c5ec63a482e81d3704b93f8fb6df81e25a1f7..315b7c74e35f68d7a94ba22b6a626c5b489b5bcd 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 da56238c219a08470c3485c3e72e7db2ed98068f..cb37212644a556b40e33b444304858268854c250 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 d157d5314dd14c1b407da4a302c6958982eadb2b..985ff98e791eedeb07af0b8b0e1feb33cabf4e80 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 d822c8b2ef83bf213ddb1de8d898da0bdb973f5f..50529aecd6c692392f8f5a2991beb6ce4b432627 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 eb973afbae2737b9be33de50bf82272db741e7cd..c5db97b10c6eab82c9954c113f69483b32a71ac5 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 e0d0a5b32451fd90435f47443badd647fcdd75a6..08a2f7bf1974f8b1652b89f56b9caa9853633344 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 d68f5f7f08d962b181df0bf1eb097cdb6f8f78a8..be20d009dd0b453dc72b42dc4ab7c533a677a007 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 eadd9eede97bf371e79e6e265437131d46260456..3851038015113f6751823cc8a3ba954f8ef55736 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 5c59e5a8b72515b46e0740e2626c0d98406f604e..fa1bf5b14b51713d583d7fcee0db57492425043a 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 8bad3f6e39f4b9f16a0b34e6845699528737fcda..2c25ada953e355df752ad4d91b7028d923bd3d79 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 104f37a534ff98390a8e4d51d86bcf1fe979d217..6c065a95a3a11bb27cf333bd865652075ac85e19 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 638feef323e7a0dfe24b11e41c63a90de71d8b11..5ad91320ced5aa647d8a1879ea9aaf12ce3e8069 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 7c05197da17032b54a801014ce8018dfd52c258a..44a354cbc73541736b2becdce7fde19ba7560a8a 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 ef78f60b8e9f32df066a2cd6787acef3c43cbf80..ae3163dac7f6ce04b6b7eca87cb385927c132e05 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 b750357108fe7258be1676c17f87acd697d5730e..b1af78e0313eb5f59735d98c267a2d34941afa7e 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 baf38e36fbad1baa54eabd5dbc66ab2dd66ee639..be588107022b5b69a35d4a2a1df9378d85141172 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 dba5f8633fd072c1a59d8cc5be8eba76200f0aba..e040dd3784ec58cb93b23393b6bdc8518e36a9fd 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 71193aa9b439260696d66b71093403452e5bff07..e0d496e832074694897fb8d713923be4ccf98949 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 346d2ea25d503f797ab6e031d9f888820b664880..40f82b5fd07adde31708733878ac42fae7f2fc63 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 2db3bd6c22dec1960aeab9e54497d95c14134556..5974ca71911ab8f3b84260e0996c41b7db7843e9 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 bed3cc201b72944052cafc8cb5b87655ad08aa65..d2f4cd852de095047b9f8e7fb32e52572d8f20e2 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 2c98acf0c1d7f9ade35c45f049b905e3239e1437..afd6329b3b45f6a6355d2d8aafad0115889c34a6 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 d62b7bc387d760867e4465560d137cd3a6edd1c6..748dfc12ffcba58d7999841ab305fc342e8794c2 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 0dbbaf8e42ce2129bb7d16fd0e28de8be1e0d139..993d52e5d7b3e175b02d3fe2c3c9db0e66765dfd 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 735c84b0c58f1084555310b8036734b277bdbeda..8daf0f8060b8ac1507afae0d6033c6e95aaabd39 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 4e0e460acc31fe4517a84a995805e6688436a2e6..2c0c4c231e0ec28365b822d56ea32d8441128c39 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 af4785d13f1b4d8f0936845cc539de758ddcc1b3..35250d83a827f22d5865349ae551da64fdf0baec 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 015063347acdbe057f7a6ce36e3639eaffdf04d7..591ef505b269e0341cd27f7909c9e43f14b35e9b 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 c9209f1232b9464b66a718f23c42455d7cf1f9c6..135388ae7b1cf800df9485c1a31e1dd8f90ef939 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 5b90cf05f9c21cb719b28008932b76c7d0aed044..88af8fe0ea45c4a98d1ad1297868f565b1ef9b7e 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 a894ea4700a8f3d0bea4d1feda5a7d7b276063c5..feb6c4266b756932e2dbd9a85d6bc13f82759638 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 c2da23fb3426686d77fead4ec41940410bb752f9..a16ae3fb4d42dde64ad845cd128d974c20e42864 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 fd166940529c402e0505a9cda6a5ece36cfe7c16..9fb8da0d64e3b97334563a71de63786e77ba575a 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 be7b2d9db1f2f03a12a6ecea25554a9f5376e18f..5f30746b19890b2396c7fd0c80a3344cf60c243f 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 6545cbf9525378ee4b8ebce535c03b8a7b982f3f..33e72300aef5a298d08a321c583c18a1973579e7 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 f97430ef15d1f161d9dc639f73b2a70c0f182926..df3d9c5614cd026258dae570e2d6cb6c4b076259 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 b31250b02065df7a5250abd2e3097872b65003ef..192b5aafd23aed722b0179c821e009e815b45cf5 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 292b52d9c2f61f94fedae188ead6e0dc00b26eb1..e82bfe42cf6061073a103915f1ea22ff285a4c83 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 47d1754721b07c27810127dcd8ac3ba0004b306e..5cfed65d25673dd150ef3c79bc36396cf30b915b 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 174c181e3c97e206bec5de6e81a12bcafeafad79..857300d5162fabbe5a081552acfd75ba773e31c5 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 f503ea66c5f2873e28368384866c4d56ab77ba9d..e7b94138c95b1f58a75757297ef9a2a030d79f2a 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 e01891e9cb2af40a6d7a94c0e174d9424a37e660..4b0393cced32ba3a91e80faee210c4dbdebc98a6 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 c2e98055cc4dca15e58b206a271158882ecc44b4..a00c648cf386a45c437f493adb5ff7991a0921d0 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 dc7a5ef0eac22bf4645bcb28cc7f1777578963e9..e3b82cf90adbe56383c3887225f71b15bdd038e3 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 796fb7a3510b75898c765bc1d0d28fa3a6c990ec..c228b57a64dd1d177d21a56f7465c63fe07c9b16 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 809fb2a9e492ec1e9c3f7a7340dee3eed7184c02..f0d5d46f0cd645d6c6d344c96a138c5a9772f4a3 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 eb3e0132cdb47f7cc0bcfe42aea7c83b0c5e0fcb..202514c99a6813ff022896046cb2f75df2030f23 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 730cf437b249e0f536352d52d14148dfbb154a82..d633a754e6dc33aedb9294aa10a0695962e3503e 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 28eba49d0dcc262955c2316842c3d0fa7fc609b3..7224467c70cb1548f29ff998122306b65d7da796 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 b61a28c23b0d21a6d1e24cdfae4d64077131f856..ed5ef8ecb0fbf57ad201627054a1b8acb892a5e5 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 afc3fdcd263de26e4864ecafbe46db7afd575ddf..3503a423ca21a962fafa1397203ccbf72561a343 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 4920fae6c4bab15f16d908d112a618da42aa9b35..5bd262789d2edeeb3619bbe6bed5a2523244b2a2 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 a6dd74267eaab84d919ff47d979d4ed863520ff8..7e5c4f402711689fb76d20231c14345dab45afdc 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 564da72f1fd4dc88b7161d00259a346a25b38c42..0a523a825f47662676fb836c2b5f966e4f89aded 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 ba42f1efb8460581445e7b5a605971ec64bb0851..446c5e50e172313f28c205d01f4ee58a28bb3f15 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 26fa0ba61ed159ad458f9b0c21e03aa4fcd7f02e..2a3656e1b601edbfd4536ea64460b368d9bbec67 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 68e1b1b618f8676d7191e8071e6f49b171f67f11..966bb2cd11fba384639b6c684e321e784a538435 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 95372158a93c0351f3fd03a80937359a1a7ccba5..b71ad0ec4149e2ab0ec35df81eb18f2d597d369b 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 92fe7e595e9c77517941489137041d4d1c136b66..2337b5d8d9eca7d5afd482a78f0f8a0f91204890 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 f646bfa9127281bde1650ffb71d215184fd269f4..ed60a4b8293f22136647bad05042aa34a85fe264 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 0ed9414e3329ce8842c44c22c00b7d971e459c89..48972c48f9f07a8ae4c8ed8d1b43475cf62e8bbb 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 eb7a41d971501e5f71e06f5e7aeba9898f36135d..e8c4c7c4c71a3ddbe3c24f9e830f1f2c700c5cc9 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 041d8b99b2908c615a930a7dbaa932cb585ceada..c0e1e0c7ee38de8b760eb826a12855cc0b1accc7 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 c91efb6b2ebc4aa13b0926b40fc1a748a73297a8..18678441ee9922bd1c0cbc69f288a58f7807f1db 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 dc19f3e2686b321463b184b1110f2977143eeb17..a1b69b3d733d1492d482671f367a7a97269c41d1 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 d135a4c0b0f8b9905384fdcb1343d92c24cf99f6..c2c7766be04011e421cbaae3a4943abb3eb22591 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 229e69edf16f14b34391e1158cf83a35c4bdf411..2ab6cb60f581d7bfd60b1278037d7098281aa45b 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 f135f66539f81ab42dd6465b0c9efb346a1491f1..d4ba012ed57bd9c8880fe950f2ab91c1e0aedf2d 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 cdb9185a52ef61e10fe8238a3bc84f63f4ed49c3..8eac20eb15de4a6d4baeedbb5208e14a8cecec5f 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 7ca3e49299ee49a67f99ad51a558ea94b224f191..283f722614856c08eed9d0e25e4b30eabe95ced8 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 9a6695b48b98d1296d44a9a263255d407346587d..294ab685bbdea70c12e86705ad26a857a451be84 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 084f33acaed477e22c9094b153cb3bacacba818c..df286a79b7fbac3f4ba72b306f0f568dd5b61d15 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 36e5b086dc43376fe6424ca67eb02b93ac6ce9a4..216bda288b2dea58ced16baef066f78521457cd3 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 6e19d64618fa6d1dbb5608558592760f6ad61cb7..ff2e62520de48e56fbd1647747ec7929f48897b4 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 66235960a5c6e3fb4c255da86031214856ea761f..7de1133c155440c9440159c37ece15a9bf542088 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 96745495a0926bc2767a309e0c3bd71cf4201c0b..b3fbeca134f31e285601ee6c9b860a472fcecdf8 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 ca4baf942a4c0763ee03f4631030a9d2a0752e1d..4d91ea255e558dd53934f4595172b0500b65a380 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 a5e5f7e0564c9d6c96b51cbda0ff7312a899d6b0..97d5f1e1900380868ccc3f8580e792ad58ab7902 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 a374e8858da2e04a75ea817b9559bd7fff1f1feb..58ef6931f30db689a0277870993dd083db6708c9 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 0ab3ae6cf33deb2c31b566cf8561de4d7352fb3d..29a3d426130df0a758cd8a5d26d1ef0e21251c13 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 a46c1c48b3fabe4c2b3400585b52c6a7188f764b..5d1860d892a1f4e555312501269d856bf479c3dc 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 d43bd477a484c790f7e196a868a89518ea147e82..f08fb716a827e1eecdf04dbd92f9e1e06694e070 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 84b44610fa43be490b1023c4ca597d6274e19977..bff77c14057b3b1335330f2140523616a32b0944 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 d72f0faea3bd4f65e0c23b1a74e9075bd2e7d92c..f121dc57da06d6db47620a93fdc3a56b3f0d6b1c 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 b4edcfeac1b2348e5f4164c397ddfe3e189d5b73..fbc30f6f54ab24e2ad74a6ac9dbfe19dc7f347b1 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 7a3a2a22be99c4b422a46c4fd69de91b55cb0fe0..1dca03a2d9635b566551413d491705a6ab868ca6 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 ac1d204e49b01a4d5451c6e60b948ce4f39087a5..6bdef9a20a88b2d6a7a19e46888528b5699e8a6b 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 e55c4d0dadd6505f034e0874bb98deb74aa37e7a..25318c680f815bc3b394c5c7492a7f6fdc093f90 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 b4a0ee3413cf9e694235678c82d1a0a66b63ed18..3720cf8fe399514de7b2c45a53b439e7c6576522 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 154c4684ef2b5d282c983f1dfdc2aef62d831c7d..38f92e1064515bebfe6cf7ad69ce1e6c5e4ec624 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 0a7022e3a80bf0147db2c1a4c9be3bfdfd742835..77f6360a492c4a47400544abcc9a4c0bd8b9dba3 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 6d5857db5dc1feaf3eb6715edbcb14260b71c2d6..5b9c518a70bb8ec1ade90b61f7cfbedfa1196790 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 925d82770a191bb539158d5213f6bc96b9ed1bd7..a8792a036b1d349492a43fe77a5d73bf94b22ca9 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 fc4747b848ba4e24def391ff52b67fd0a89d3c13..477d9edd0d302e85fcd9a3783590aa8740021743 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 3882c55524358905f61b3faa60f43fce46adea28..7174f151fc13c750fa7a06c1ee49d0392b97ee7b 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 3bdb38719ff53d009b63de02db28a95c1e4e17d9..195cd22931428fb2db372263e144190e93a58efc 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 25212dd227c02f4bfd85eeeaa474c391c3fcd973..afa78dacfcff36a61b34adaf58ca62e61d57c6fd 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 0a956b498d85010cf71800dbed1727dda4a8ea87..7eafe2acd346946108daaa30808b7eb73d0e2e62 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 45e64a150884ba5b629e573404a575ab3e08a1c7..c622617f7e5a4a84ee88f4c3d66aed092ccf4ef8 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 daf0598e8a23e32542c5995df36a606fb6fac2ca..d03b6bc77b26c3479a6349b2a6dca5d338cc2594 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 c6bb5297b1a7cfb9d196a81ee9a0470c2ca25a37..c134331a04756e9a6fe11d32721ce066e099f983 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 1248a29d1a7da729ba35c96e04bd3ee776f9a26a..05e88336ea619d2e5322e0941192b58892077eae 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 7f714cc23da6c98b04a0c80d81011534eccf50b2..3fabd65956adb60ee0e823d525b49cb13dfb8077 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