221 lines
6.9 KiB
Python
221 lines
6.9 KiB
Python
from collections import namedtuple
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Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
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PRIMITIVES = [
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'none',
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'max_pool_3x3',
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'avg_pool_3x3',
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'skip_connect',
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'sep_conv_3x3',
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'sep_conv_5x5',
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'dil_conv_3x3',
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'dil_conv_5x5'
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]
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NASNet = Genotype(
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normal = [
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('sep_conv_5x5', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('sep_conv_5x5', 0, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('avg_pool_3x3', 1, 1.0),
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('skip_connect', 0, 1.0),
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('avg_pool_3x3', 0, 1.0),
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('avg_pool_3x3', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('skip_connect', 1, 1.0),
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],
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normal_concat = [2, 3, 4, 5, 6],
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reduce = [
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('sep_conv_5x5', 1, 1.0),
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('sep_conv_7x7', 0, 1.0),
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('max_pool_3x3', 1, 1.0),
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('sep_conv_7x7', 0, 1.0),
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('avg_pool_3x3', 1, 1.0),
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('sep_conv_5x5', 0, 1.0),
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('skip_connect', 3, 1.0),
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('avg_pool_3x3', 2, 1.0),
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('sep_conv_3x3', 2, 1.0),
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('max_pool_3x3', 1, 1.0),
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],
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reduce_concat = [4, 5, 6],
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)
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AmoebaNet = Genotype(
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normal = [
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('avg_pool_3x3', 0, 1.0),
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('max_pool_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('sep_conv_5x5', 2, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('avg_pool_3x3', 3, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('skip_connect', 1, 1.0),
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('skip_connect', 0, 1.0),
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('avg_pool_3x3', 1, 1.0),
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],
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normal_concat = [4, 5, 6],
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reduce = [
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('avg_pool_3x3', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('max_pool_3x3', 0, 1.0),
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('sep_conv_7x7', 2, 1.0),
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('sep_conv_7x7', 0, 1.0),
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('avg_pool_3x3', 1, 1.0),
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('max_pool_3x3', 0, 1.0),
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('max_pool_3x3', 1, 1.0),
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('conv_7x1_1x7', 0, 1.0),
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('sep_conv_3x3', 5, 1.0),
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],
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reduce_concat = [3, 4, 6]
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)
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DARTS_V1 = Genotype(
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normal=[
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('skip_connect', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('skip_connect', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('skip_connect', 2, 1.0)],
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normal_concat=[2, 3, 4, 5],
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reduce=[
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('max_pool_3x3', 0, 1.0),
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('max_pool_3x3', 1, 1.0),
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('skip_connect', 2, 1.0),
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('max_pool_3x3', 0, 1.0),
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('max_pool_3x3', 0, 1.0),
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('skip_connect', 2, 1.0),
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('skip_connect', 2, 1.0),
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('avg_pool_3x3', 0, 1.0)],
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reduce_concat=[2, 3, 4, 5]
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)
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DARTS_V2 = Genotype(
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normal=[
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('sep_conv_3x3', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('skip_connect', 0, 1.0),
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('skip_connect', 0, 1.0),
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('dil_conv_3x3', 2, 1.0)],
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normal_concat=[2, 3, 4, 5],
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reduce=[
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('max_pool_3x3', 0, 1.0),
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('max_pool_3x3', 1, 1.0),
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('skip_connect', 2, 1.0),
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('max_pool_3x3', 1, 1.0),
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('max_pool_3x3', 0, 1.0),
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('skip_connect', 2, 1.0),
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('skip_connect', 2, 1.0),
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('max_pool_3x3', 1, 1.0)],
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reduce_concat=[2, 3, 4, 5]
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)
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PNASNet = Genotype(
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normal = [
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('sep_conv_5x5', 0, 1.0),
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('max_pool_3x3', 0, 1.0),
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('sep_conv_7x7', 1, 1.0),
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('max_pool_3x3', 1, 1.0),
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('sep_conv_5x5', 1, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 4, 1.0),
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('max_pool_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('skip_connect', 1, 1.0),
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],
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normal_concat = [2, 3, 4, 5, 6],
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reduce = [
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('sep_conv_5x5', 0, 1.0),
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('max_pool_3x3', 0, 1.0),
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('sep_conv_7x7', 1, 1.0),
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('max_pool_3x3', 1, 1.0),
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('sep_conv_5x5', 1, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 4, 1.0),
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('max_pool_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('skip_connect', 1, 1.0),
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],
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reduce_concat = [2, 3, 4, 5, 6],
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)
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# https://arxiv.org/pdf/1802.03268.pdf
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ENASNet = Genotype(
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normal = [
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('sep_conv_3x3', 1, 1.0),
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('skip_connect', 1, 1.0),
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('sep_conv_5x5', 1, 1.0),
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('skip_connect', 0, 1.0),
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('avg_pool_3x3', 0, 1.0),
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('sep_conv_3x3', 1, 1.0),
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('sep_conv_3x3', 0, 1.0),
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('avg_pool_3x3', 1, 1.0),
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('sep_conv_5x5', 1, 1.0),
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('avg_pool_3x3', 0, 1.0),
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],
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normal_concat = [2, 3, 4, 5, 6],
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reduce = [
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('sep_conv_5x5', 0, 1.0),
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('sep_conv_3x3', 1, 1.0), # 2
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('sep_conv_3x3', 1, 1.0),
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('avg_pool_3x3', 1, 1.0), # 3
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('sep_conv_3x3', 1, 1.0),
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('avg_pool_3x3', 1, 1.0), # 4
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('avg_pool_3x3', 1, 1.0),
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('sep_conv_5x5', 4, 1.0), # 5
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('sep_conv_3x3', 5, 1.0),
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('sep_conv_5x5', 0, 1.0),
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],
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reduce_concat = [2, 3, 4, 5, 6],
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)
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DARTS = DARTS_V2
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# Search by normal and reduce
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GDAS_V1 = Genotype(
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normal=[('skip_connect', 0, 0.13017432391643524), ('skip_connect', 1, 0.12947972118854523), ('skip_connect', 0, 0.13062666356563568), ('sep_conv_5x5', 2, 0.12980839610099792), ('sep_conv_3x3', 3, 0.12923765182495117), ('skip_connect', 0, 0.12901571393013), ('sep_conv_5x5', 4, 0.12938997149467468), ('sep_conv_3x3', 3, 0.1289220005273819)],
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normal_concat=range(2, 6),
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reduce=[('sep_conv_5x5', 0, 0.12862831354141235), ('sep_conv_3x3', 1, 0.12783904373645782), ('sep_conv_5x5', 2, 0.12725995481014252), ('sep_conv_5x5', 1, 0.12705285847187042), ('dil_conv_5x5', 2, 0.12797553837299347), ('sep_conv_3x3', 1, 0.12737272679805756), ('sep_conv_5x5', 0, 0.12833961844444275), ('sep_conv_5x5', 1, 0.12758426368236542)],
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reduce_concat=range(2, 6)
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)
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# Search by normal and fixing reduction
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GDAS_F1 = Genotype(
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normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)],
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normal_concat=[2, 3, 4, 5],
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reduce=None,
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reduce_concat=[2, 3, 4, 5],
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)
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# Combine DMS_V1 and DMS_F1
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GDAS_GF = Genotype(
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normal=[('skip_connect', 0, 0.13017432391643524), ('skip_connect', 1, 0.12947972118854523), ('skip_connect', 0, 0.13062666356563568), ('sep_conv_5x5', 2, 0.12980839610099792), ('sep_conv_3x3', 3, 0.12923765182495117), ('skip_connect', 0, 0.12901571393013), ('sep_conv_5x5', 4, 0.12938997149467468), ('sep_conv_3x3', 3, 0.1289220005273819)],
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normal_concat=range(2, 6),
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reduce=None,
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reduce_concat=range(2, 6)
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)
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GDAS_FG = Genotype(
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normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)],
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normal_concat=range(2, 6),
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reduce=[('sep_conv_5x5', 0, 0.12862831354141235), ('sep_conv_3x3', 1, 0.12783904373645782), ('sep_conv_5x5', 2, 0.12725995481014252), ('sep_conv_5x5', 1, 0.12705285847187042), ('dil_conv_5x5', 2, 0.12797553837299347), ('sep_conv_3x3', 1, 0.12737272679805756), ('sep_conv_5x5', 0, 0.12833961844444275), ('sep_conv_5x5', 1, 0.12758426368236542)],
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reduce_concat=range(2, 6)
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)
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model_types = {'DARTS_V1': DARTS_V1,
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'DARTS_V2': DARTS_V2,
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'NASNet' : NASNet,
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'PNASNet' : PNASNet,
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'AmoebaNet': AmoebaNet,
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'ENASNet' : ENASNet,
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'GDAS_V1' : GDAS_V1,
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'GDAS_F1' : GDAS_F1,
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'GDAS_GF' : GDAS_GF,
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'GDAS_FG' : GDAS_FG}
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