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mhz
2024-08-25 18:02:31 +02:00
parent 192f286cfb
commit a0a25f291c
431 changed files with 50646 additions and 8 deletions
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13
AutoDL-Projects/configs/NeurIPS-2019/C010-ResNet110.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "110"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "14", "16", "14", "16", "16", "16", "16", "16", "14", "16", "16", "16", "12", "16", "16", "16", "9", "16", "8", "16", "4", "16", "4", "4", "4", "16", "4", "4", "4", "4", "6", "6", "4", "6", "11", "4", "32", "32", "32", "32", "32", "32", "32", "32", "28", "32", "32", "28", "22", "22", "22", "32", "32", "25", "28", "9", "9", "28", "12", "9", "12", "32", "9", "9", "22", "12", "16", "9", "12", "9", "9", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "38", "64", "25", "19", "19", "19", "19", "19", "25", "32", "19", "19", "25", "25", "19", "19", "38", "38", "19", "19", "51"]],
"xblocks" : ["int" , ["11", "11", "9"]],
"estimated_FLOP" : ["float" , "117.498238"]
}

13
AutoDL-Projects/configs/NeurIPS-2019/C010-ResNet164.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "164"],
"module" : ["str" , "ResNetBottleneck"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "8", "16", "64", "6", "16", "64", "14", "16", "64", "8", "16", "64", "4", "16", "64", "6", "16", "64", "6", "16", "64", "11", "11", "64", "4", "14", "64", "4", "4", "57", "4", "16", "64", "9", "12", "57", "4", "16", "64", "4", "8", "57", "6", "6", "51", "6", "4", "44", "6", "4", "57", "6", "6", "19", "32", "32", "128", "32", "32", "128", "32", "32", "128", "9", "32", "128", "32", "32", "128", "25", "28", "115", "12", "32", "128", "32", "32", "128", "32", "32", "128", "32", "32", "102", "28", "32", "128", "16", "32", "128", "28", "19", "128", "32", "9", "51", "16", "12", "102", "12", "22", "115", "9", "12", "51", "12", "16", "38", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "57", "204", "64", "25", "179", "19", "25", "204", "44", "19", "153", "38", "25", "76", "19", "32", "128", "19", "51", "76", "57", "32", "76"]],
"xblocks" : ["int" , ["13", "15", "13"]],
"estimated_FLOP" : ["float" , "173.023672"]
}

13
AutoDL-Projects/configs/NeurIPS-2019/C010-ResNet20.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "20"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "6", "4", "4", "4", "4", "4", "32", "32", "12", "19", "32", "28", "64", "64", "64", "64", "64", "44"]],
"xblocks" : ["int" , ["3", "3", "3"]],
"estimated_FLOP" : ["float" , "22.444472"]
}

13
AutoDL-Projects/configs/NeurIPS-2019/C010-ResNet32.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "32"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "6", "4", "12", "4", "4", "16", "9", "9", "6", "14", "32", "32", "9", "19", "28", "9", "32", "19", "32", "9", "64", "64", "64", "64", "64", "64", "64", "32", "38", "32"]],
"xblocks" : ["int" , ["5", "5", "5"]],
"estimated_FLOP" : ["float" , "34.945344"]
}

13
AutoDL-Projects/configs/NeurIPS-2019/C010-ResNet56.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "56"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "16", "16", "14", "11", "9", "16", "12", "16", "6", "16", "4", "8", "4", "14", "6", "4", "4", "4", "32", "32", "32", "32", "32", "32", "22", "28", "32", "32", "19", "9", "19", "16", "9", "25", "16", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "51", "19", "19", "32", "19", "19", "32", "19", "25"]],
"xblocks" : ["int" , ["5", "5", "5"]],
"estimated_FLOP" : ["float" , "57.93305"]
}

13
AutoDL-Projects/configs/NeurIPS-2019/C100-ResNet110.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "110"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "14", "16", "11", "14", "16", "16", "11", "16", "9", "14", "12", "16", "16", "16", "8", "16", "14", "16", "12", "4", "11", "16", "4", "4", "4", "16", "12", "4", "8", "4", "9", "4", "6", "14", "4", "4", "32", "32", "32", "32", "28", "28", "32", "32", "32", "32", "32", "28", "32", "28", "25", "32", "32", "32", "9", "9", "32", "32", "9", "25", "28", "32", "28", "9", "9", "32", "12", "12", "9", "22", "12", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "44", "64", "57", "19", "19", "19", "19", "25", "19", "25", "19", "25", "19", "19", "25", "19", "19", "19", "25", "25", "19"]],
"xblocks" : ["int" , ["13", "9", "11"]],
"estimated_FLOP" : ["float" , "117.653164"]
}

13
AutoDL-Projects/configs/NeurIPS-2019/C100-ResNet164.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "164"],
"module" : ["str" , "ResNetBottleneck"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "16", "16", "57", "6", "11", "64", "4", "6", "51", "6", "9", "64", "4", "8", "64", "4", "14", "64", "4", "8", "64", "4", "8", "64", "6", "12", "64", "6", "16", "64", "8", "16", "64", "14", "12", "64", "4", "16", "64", "4", "14", "64", "11", "16", "64", "4", "14", "64", "11", "4", "64", "4", "4", "19", "25", "32", "128", "28", "32", "115", "28", "32", "128", "25", "32", "128", "32", "32", "128", "25", "32", "128", "12", "32", "128", "25", "32", "128", "28", "32", "128", "25", "28", "128", "32", "32", "128", "28", "19", "128", "32", "32", "128", "19", "28", "128", "9", "19", "128", "28", "9", "89", "28", "19", "128", "9", "16", "38", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "256", "64", "64", "204", "64", "64", "179", "64", "64", "102", "64", "64", "102", "44", "19", "76", "19", "19", "76", "19", "38", "76", "25", "38", "153", "44", "25", "230"]],
"xblocks" : ["int" , ["15", "15", "15"]],
"estimated_FLOP" : ["float" , "165.583512"]
}

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AutoDL-Projects/configs/NeurIPS-2019/C100-ResNet20.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "20"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "4", "4", "4", "4", "6", "4", "32", "32", "9", "19", "32", "28", "64", "64", "64", "64", "64", "64"]],
"xblocks" : ["int" , ["3", "3", "3"]],
"estimated_FLOP" : ["float" , "22.433792"]
}

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AutoDL-Projects/configs/NeurIPS-2019/C100-ResNet32.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "32"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"xchannels" : ["int" , ["3", "16", "4", "4", "6", "11", "6", "4", "8", "4", "4", "4", "32", "32", "9", "28", "28", "28", "28", "28", "32", "32", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64"]],
"xblocks" : ["int" , ["5", "5", "5"]],
"estimated_FLOP" : ["float" , "42.47"]
}

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AutoDL-Projects/configs/NeurIPS-2019/C100-ResNet56.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "56"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "16", "9", "14", "16", "14", "16", "8", "16", "8", "14", "4", "4", "4", "8", "4", "6", "4", "4", "32", "32", "32", "28", "32", "32", "32", "22", "32", "32", "32", "9", "25", "19", "25", "12", "9", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "51", "19", "19", "19", "19", "25", "38", "19", "19"]],
"xblocks" : ["int" , ["5", "5", "7"]],
"estimated_FLOP" : ["float" , "59.472556"]
}

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AutoDL-Projects/configs/NeurIPS-2019/ImageNet-ResNet18V1.config Normal file
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{
"dataset" : ["str" , "imagenet"],
"arch" : ["str" , "resnet"],
"block_name" : ["str" , "BasicBlock"],
"layers" : ["int" , ["2", "2", "2", "2"]],
"deep_stem" : ["bool" , "0"],
"zero_init_residual" : ["bool" , "1"],
"class_num" : ["int" , "1000"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "64", "25", "64", "38", "19", "128", "128", "38", "38", "256", "256", "256", "256", "512", "512", "512", "512"]],
"xblocks" : ["int" , ["1", "1", "2", "2"]],
"super_type" : ["str" , "infer-shape"],
"estimated_FLOP" : ["float" , "1120.44032"]
}

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AutoDL-Projects/configs/NeurIPS-2019/ImageNet-ResNet50V1.config Normal file
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{
"dataset" : ["str" , "imagenet"],
"arch" : ["str" , "resnet"],
"block_name" : ["str" , "Bottleneck"],
"layers" : ["int" , ["3", "4", "6", "3"]],
"deep_stem" : ["bool" , "0"],
"zero_init_residual" : ["bool" , "1"],
"class_num" : ["int" , "1000"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "45", "45", "30", "102", "33", "60", "154", "68", "70", "180", "38", "38", "307", "38", "38", "410", "64", "128", "358", "38", "51", "256", "76", "76", "512", "76", "76", "512", "179", "256", "614", "100", "102", "307", "179", "230", "614", "204", "102", "307", "153", "153", "1228", "512", "512", "1434", "512", "512", "1844"]],
"xblocks" : ["int" , ["3", "4", "5", "3"]],
"super_type" : ["str" , "infer-shape"],
"estimated_FLOP" : ["float" , "2291.316289"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet08.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 8],
"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet1001.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 1001],
"module" : ["str", "ResNetBottleneck"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet110.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 110],
"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet164.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 164],
"module" : ["str", "ResNetBottleneck"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet20.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 20],
"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet32.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 32],
"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-ResNet56.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
"depth" : ["int", 56],
"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-SIM05.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "simres"],
"depth" : ["int", 5],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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AutoDL-Projects/configs/archs/CIFAR-WRN28-10.config Normal file
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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "wideresnet"],
"depth" : ["int", 28],
"wide_factor":["int", 10],
"dropout" : ["bool", 0],
"super_type": ["str" , "basic"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet101V1.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,4,23,3]],
"deep_stem" : ["bool", 0],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet101V2.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,4,23,3]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet152V1.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,8,36,3]],
"deep_stem" : ["bool", 0],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet152V2.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,8,36,3]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet18V1.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "BasicBlock"],
"layers" : ["int", [2,2,2,2]],
"deep_stem" : ["bool", 0],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet18V2.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "BasicBlock"],
"layers" : ["int", [2,2,2,2]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet200V1.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,24,36,3]],
"deep_stem" : ["bool", 0],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet200V2.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,24,36,3]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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AutoDL-Projects/configs/archs/ImageNet-ResNet34V1.config Normal file
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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "BasicBlock"],
"layers" : ["int", [3,4,6,3]],
"deep_stem" : ["bool", 0],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "BasicBlock"],
"layers" : ["int", [3,4,6,3]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,4,6,3]],
"deep_stem" : ["bool", 0],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,4,6,3]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 1],
"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
"block_name" : ["str", "Bottleneck"],
"layers" : ["int", [3,4,6,3]],
"deep_stem" : ["bool", 1],
"zero_init_residual" : ["bool", "1"],
"groups" : ["int", 32],
"width_per_group" : ["int", 4],
"norm_layer" : ["none", "None"]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "DARTS"],
"dataset" : ["str", "cifar"],
"ichannel" : ["int", 36],
"layers" : ["int", 6],
"stem_multi": ["int", 3],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "GDAS_V1"],
"dataset" : ["str", "cifar"],
"ichannel" : ["int", 36],
"layers" : ["int", 6],
"stem_multi": ["int", 3],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "NASNet"],
"dataset" : ["str", "cifar"],
"ichannel" : ["int", 33],
"layers" : ["int", 6],
"stem_multi": ["int", 3],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
"dataset" : ["str", "cifar"],
"ichannel" : ["int", 36],
"layers" : ["int", 6],
"stem_multi": ["int", 3],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"super_type": ["str", "infer-nasnet.cifar"],
"genotype" : ["none", "none"],
"dataset" : ["str", "cifar"],
"ichannel" : ["int", 33],
"layers" : ["int", 6],
"stem_multi": ["int", 3],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "DARTS_V2"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 48],
"layers" : ["int", 4],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "GDAS_V1"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 50],
"layers" : ["int", 4],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 58],
"layers" : ["int", 2],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 73],
"layers" : ["int", 1],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 58],
"layers" : ["int", 2],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 49],
"layers" : ["int", 3],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 44],
"layers" : ["int", 4],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

9
AutoDL-Projects/configs/archs/NAS-IMAGENET-none.config Normal file
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{
"super_type": ["str", "infer-nasnet.imagenet"],
"genotype" : ["none", "none"],
"dataset" : ["str", "imagenet"],
"ichannel" : ["int", 50],
"layers" : ["int", 4],
"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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AutoDL-Projects/configs/compares/CIFAR010-TAS-R110.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "110"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "14", "16", "14", "16", "16", "16", "16", "16", "14", "16", "16", "16", "12", "16", "16", "16", "9", "16", "8", "16", "4", "16", "4", "4", "4", "16", "4", "4", "4", "4", "6", "6", "4", "6", "11", "4", "32", "32", "32", "32", "32", "32", "32", "32", "28", "32", "32", "28", "22", "22", "22", "32", "32", "25", "28", "9", "9", "28", "12", "9", "12", "32", "9", "9", "22", "12", "16", "9", "12", "9", "9", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "38", "64", "25", "19", "19", "19", "19", "19", "25", "32", "19", "19", "25", "25", "19", "19", "38", "38", "19", "19", "51"]],
"xblocks" : ["int" , ["11", "11", "9"]],
"estimated_FLOP" : ["float" , "117.498238"]
}

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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "56"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "16", "16", "14", "14", "14", "16", "12", "16", "6", "16", "4", "4", "4", "14", "4", "4", "6", "4", "32", "32", "32", "32", "32", "32", "22", "25", "16", "32", "19", "9", "9", "16", "25", "12", "16", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "51", "19", "19", "32", "51", "25", "32", "19", "19"]],
"xblocks" : ["int" , ["5", "5", "5"]],
"estimated_FLOP" : ["float" , "57.52595"]
}

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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "32"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-width"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"xchannels" : ["int" , ["3", "12", "12", "12", "12", "12", "12", "12", "12", "12", "12", "12", "25", "25", "25", "25", "25", "25", "25", "25", "25", "25", "50", "50", "50", "50", "50", "50", "50", "50", "50", "50"]],
"estimated_FLOP" : ["float" , "41.095816"]
}

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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "32"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-width"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"xchannels" : ["int" , ["3", "4", "11", "11", "11", "12", "14", "16", "8", "9", "6", "12", "28", "32", "28", "32", "12", "25", "28", "22", "28", "25", "57", "19", "38", "64", "64", "51", "57", "64", "64", "57"]],
"estimated_FLOP" : ["float" , "42.908996"]
}

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AutoDL-Projects/configs/compares/CIFAR100-TAS-R110.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "110"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "14", "16", "11", "14", "16", "16", "11", "16", "9", "14", "12", "16", "16", "16", "8", "16", "14", "16", "12", "4", "11", "16", "4", "4", "4", "16", "12", "4", "8", "4", "9", "4", "6", "14", "4", "4", "32", "32", "32", "32", "28", "28", "32", "32", "32", "32", "32", "28", "32", "28", "25", "32", "32", "32", "9", "9", "32", "32", "9", "25", "28", "32", "28", "9", "9", "32", "12", "12", "9", "22", "12", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "44", "64", "57", "19", "19", "19", "19", "25", "19", "25", "19", "25", "19", "19", "25", "19", "19", "19", "25", "25", "19"]],
"xblocks" : ["int" , ["13", "9", "11"]],
"estimated_FLOP" : ["float" , "117.653164"]
}

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AutoDL-Projects/configs/compares/CIFAR100-TAS-R32.config Normal file
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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "32"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-width"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"xchannels" : ["int" , ["3", "16", "4", "4", "4", "14", "6", "4", "8", "4", "4", "4", "32", "32", "9", "28", "28", "28", "28", "28", "32", "32", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64"]],
"estimated_FLOP" : ["float" , "42.493184"]
}

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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "56"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "100"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "16", "9", "14", "16", "14", "16", "8", "16", "8", "14", "4", "4", "4", "8", "4", "6", "4", "4", "32", "32", "32", "28", "32", "32", "32", "22", "32", "32", "32", "9", "25", "19", "25", "12", "9", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "51", "19", "19", "19", "19", "25", "38", "19", "19"]],
"xblocks" : ["int" , ["5", "5", "7"]],
"estimated_FLOP" : ["float" , "59.472556"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "200"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "200"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "12"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

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{
"scheduler": ["str", "cos"],
"LR" : ["float", "0.025"],
"eta_min" : ["float", "0.001"],
"epochs" : ["int", "50"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "64"]
}

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{
"scheduler": ["str", "cos"],
"LR" : ["float", "0.05"],
"eta_min" : ["float", "0.0005"],
"epochs" : ["int", "250"],
"T_max" : ["int", "10"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"decay" : ["float", "0.00025"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"controller_lr" : ["float", "0.001"],
"controller_betas": ["float", [0, 0.999]],
"controller_eps" : ["float", 0.001],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "128"]
}

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{
"scheduler": ["str", "cos"],
"LR" : ["float", "0.025"],
"eta_min" : ["float", "0.001"],
"epochs" : ["int", "250"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "64"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.001"],
"epochs" : ["int", "25"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "64"]
}

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{
"scheduler": ["str", "cos"],
"LR" : ["float", "0.025"],
"eta_min" : ["float", "0.001"],
"epochs" : ["int", "250"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "64"],
"test_batch_size": ["int", "512"]
}

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{
"scheduler": ["str", "cos"],
"LR" : ["float", "0.025"],
"eta_min" : ["float", "0.001"],
"epochs" : ["int", "250"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "64"],
"test_batch_size": ["int", "512"]
}

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AutoDL-Projects/configs/nas-benchmark/algos/weight-sharing.config Normal file
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{
"scheduler": ["str", "cos"],
"LR" : ["float", "0.025"],
"eta_min" : ["float", "0.001"],
"epochs" : ["int", "100"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "64"],
"test_batch_size": ["int", "512"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "1"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "12"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

13
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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "200"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "90"],
"warmup" : ["int", "0"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"batch_size": ["int", "256"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.4"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "600"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "100"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "595"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "0.4"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"] ,
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool" , "1"] ,
"criterion": ["str" , "Softmax"],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "SmoothSoftmax"],
"label_smooth": ["float", 0.1],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "multistep"],
"epochs" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"milestones":["int", [30, 60, 90]],
"gammas" : ["float", [0.1, 0.1, 0.1]],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "595"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0003"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "0.4"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "295"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "0.4"]
}

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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "245"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.00003"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "SmoothSoftmax"],
"label_smooth": ["float", 0.1],
"auxiliary" : ["float", "0.4"]
}

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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: TabnetModel
module_path: qlib.contrib.model.pytorch_tabnet
kwargs:
d_feat: 360
pretrain: True
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
pretrain: [2008-01-01, 2014-12-31]
pretrain_validation: [2015-01-01, 2016-12-31]
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: ALSTM
module_path: qlib.contrib.model.pytorch_alstm
kwargs:
d_feat: 6
hidden_size: 64
num_layers: 2
dropout: 0.0
n_epochs: 200
lr: 1e-3
early_stop: 20
batch_size: 800
metric: loss
loss: mse
GPU: 0
rnn_type: GRU
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

100
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors: []
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: DEnsembleModel
module_path: qlib.contrib.model.double_ensemble
kwargs:
base_model: "gbm"
loss: mse
num_models: 6
enable_sr: True
enable_fs: True
alpha1: 1
alpha2: 1
bins_sr: 10
bins_fs: 5
decay: 0.5
sample_ratios:
- 0.8
- 0.7
- 0.6
- 0.5
- 0.4
sub_weights:
- 1
- 0.2
- 0.2
- 0.2
- 0.2
- 0.2
epochs: 136
colsample_bytree: 0.8879
learning_rate: 0.0421
subsample: 0.8789
lambda_l1: 205.6999
lambda_l2: 580.9768
max_depth: 8
num_leaves: 210
num_threads: 20
verbosity: -1
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

85
AutoDL-Projects/configs/qlib/workflow_config_gru_Alpha360.yaml Normal file
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: GRU
module_path: qlib.contrib.model.pytorch_gru
kwargs:
d_feat: 6
hidden_size: 64
num_layers: 2
dropout: 0.0
n_epochs: 200
lr: 1e-3
early_stop: 20
batch_size: 800
metric: loss
loss: mse
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

83
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: LGBModel
module_path: qlib.contrib.model.gbdt
kwargs:
loss: mse
colsample_bytree: 0.8879
learning_rate: 0.0421
subsample: 0.8789
lambda_l1: 205.6999
lambda_l2: 580.9768
max_depth: 8
num_leaves: 210
num_threads: 20
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

85
AutoDL-Projects/configs/qlib/workflow_config_lstm_Alpha360.yaml Normal file
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@@ -0,0 +1,85 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: LSTM
module_path: qlib.contrib.model.pytorch_lstm
kwargs:
d_feat: 6
hidden_size: 64
num_layers: 2
dropout: 0.0
n_epochs: 200
lr: 1e-3
early_stop: 20
batch_size: 800
metric: loss
loss: mse
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

85
AutoDL-Projects/configs/qlib/workflow_config_mlp_Alpha360.yaml Normal file
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@@ -0,0 +1,85 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: DNNModelPytorch
module_path: qlib.contrib.model.pytorch_nn
kwargs:
loss: mse
input_dim: 360
output_dim: 1
lr: 0.002
lr_decay: 0.96
lr_decay_steps: 100
optimizer: adam
max_steps: 8000
batch_size: 4096
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

64
AutoDL-Projects/configs/qlib/workflow_config_naive_v1_Alpha360.yaml Normal file
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@@ -0,0 +1,64 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors: []
learn_processors: []
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: NAIVE_V1
module_path: trade_models.naive_v1_model
kwargs:
d_feat: 6
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

64
AutoDL-Projects/configs/qlib/workflow_config_naive_v2_Alpha360.yaml Normal file
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@@ -0,0 +1,64 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors: []
learn_processors: []
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: NAIVE_V2
module_path: trade_models.naive_v2_model
kwargs:
d_feat: 6
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

88
AutoDL-Projects/configs/qlib/workflow_config_sfm_Alpha360.yaml Normal file
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@@ -0,0 +1,88 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: SFM
module_path: qlib.contrib.model.pytorch_sfm
kwargs:
d_feat: 6
hidden_size: 64
output_dim: 32
freq_dim: 25
dropout_W: 0.5
dropout_U: 0.5
n_epochs: 20
lr: 1e-3
batch_size: 1600
early_stop: 20
eval_steps: 5
loss: mse
optimizer: adam
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

78
AutoDL-Projects/configs/qlib/workflow_config_transformer_Alpha360.yaml Normal file
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@@ -0,0 +1,78 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: QuantTransformer
module_path: trade_models.quant_transformer
kwargs:
net_config:
opt_config:
loss: mse
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

86
AutoDL-Projects/configs/qlib/workflow_config_transformer_basic_Alpha360.yaml Normal file
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@@ -0,0 +1,86 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: QuantTransformer
module_path: trade_models.quant_transformer
kwargs:
net_config:
name: basic
d_feat: 6
embed_dim: 32
num_heads: [4, 4, 4, 4, 4]
mlp_hidden_multipliers: [4, 4, 4, 4, 4]
qkv_bias: True
pos_drop: 0.1
other_drop: 0
opt_config:
loss: mse
GPU: 0
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

81
AutoDL-Projects/configs/qlib/workflow_config_xgboost_Alpha360.yaml Normal file
View File

@@ -0,0 +1,81 @@
qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: XGBModel
module_path: qlib.contrib.model.xgboost
kwargs:
eval_metric: rmse
colsample_bytree: 0.8879
eta: 0.0421
max_depth: 8
n_estimators: 647
subsample: 0.8789
nthread: 20
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

14
AutoDL-Projects/configs/search-KD-opts/ImageNet-E120-Cos-Soft.config Normal file
View File

@@ -0,0 +1,14 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "125"],
"T_max" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

15
AutoDL-Projects/configs/search-KD-opts/ImageNet-E150-MobileFast.config Normal file
View File

@@ -0,0 +1,15 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "155"],
"T_max" : ["int", "150"],
"warmup" : ["int", "0"],
"gamma" : ["float", "0.98"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.05"],
"decay" : ["float", "0.00004"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "0"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

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