add configs for the searched shape (NeurIPS19)
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@ -57,6 +57,9 @@ CUDA_VISIBLE_DEVICES=0,1 bash ./scripts-search/search-cifar.sh cifar10 ResNet56
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args: `cifar10` indicates the dataset name, `ResNet56` indicates the basemodel name, `CIFARX` indicates the searching hyper-parameters, `0.47/0.57` indicates the expected FLOP ratio, `-1` indicates the random seed.
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### Model Configuration
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The searched shapes for ResNet-20/32/56/110/164 in Table 3 in the original paper are listed in [`configs/NeurIPS-2019`](https://github.com/D-X-Y/NAS-Projects/tree/master/configs/NeurIPS-2019).
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## [One-Shot Neural Architecture Search via Self-Evaluated Template Network](https://arxiv.org/abs/1910.05733)
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configs/NeurIPS-2019/C010-ResNet20.config
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configs/NeurIPS-2019/C010-ResNet20.config
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{
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"dataset" : ["str" , "cifar"],
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"arch" : ["str" , "resnet"],
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"depth" : ["int" , "20"],
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"module" : ["str" , "ResNetBasicblock"],
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"super_type" : ["str" , "infer-shape"],
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"zero_init_residual" : ["bool" , "0"],
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"class_num" : ["int" , "10"],
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"search_mode" : ["str" , "shape"],
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"xchannels" : ["int" , ["3", "16", "6", "4", "4", "4", "4", "4", "32", "32", "12", "19", "32", "28", "64", "64", "64", "64", "64", "44"]],
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"xblocks" : ["int" , ["3", "3", "3"]],
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"estimated_FLOP" : ["float" , "22.444472"]
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}
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configs/NeurIPS-2019/C010-ResNet32.config
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configs/NeurIPS-2019/C010-ResNet32.config
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{
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"dataset" : ["str" , "cifar"],
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"arch" : ["str" , "resnet"],
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"depth" : ["int" , "32"],
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"module" : ["str" , "ResNetBasicblock"],
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"super_type" : ["str" , "infer-shape"],
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"zero_init_residual" : ["bool" , "0"],
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"class_num" : ["int" , "10"],
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"search_mode" : ["str" , "shape"],
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"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"]],
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"xblocks" : ["int" , ["5", "5", "5"]],
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"estimated_FLOP" : ["float" , "34.945344"]
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}
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configs/NeurIPS-2019/C100-ResNet20.config
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configs/NeurIPS-2019/C100-ResNet20.config
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{
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"dataset" : ["str" , "cifar"],
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"arch" : ["str" , "resnet"],
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"depth" : ["int" , "20"],
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"module" : ["str" , "ResNetBasicblock"],
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"super_type" : ["str" , "infer-shape"],
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"zero_init_residual" : ["bool" , "0"],
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"class_num" : ["int" , "100"],
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"search_mode" : ["str" , "shape"],
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"xchannels" : ["int" , ["3", "16", "4", "4", "4", "4", "6", "4", "32", "32", "9", "19", "32", "28", "64", "64", "64", "64", "64", "64"]],
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"xblocks" : ["int" , ["3", "3", "3"]],
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"estimated_FLOP" : ["float" , "22.433792"]
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}
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configs/NeurIPS-2019/C100-ResNet32.config
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configs/NeurIPS-2019/C100-ResNet32.config
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{
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"dataset" : ["str" , "cifar"],
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"arch" : ["str" , "resnet"],
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"depth" : ["int" , "32"],
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"module" : ["str" , "ResNetBasicblock"],
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"super_type" : ["str" , "infer"],
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"zero_init_residual" : ["bool" , "0"],
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"class_num" : ["int" , "100"],
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"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"]],
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"estimated_FLOP" : ["float" , "42.493184"]
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}
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