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cifar_exp_config.py
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cifar_exp_config.py
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from __future__ import (absolute_import, division, print_function,
unicode_literals)
import json
import os
def get_config(dataset, model):
# Use one of the pre-set config.
if model == "base":
config = BaselineConfig()
elif model == "base-drop":
config = BaselineDropoutConfig()
elif model == "base-l1":
config = BaselineConfig()
config.model = "base-l1"
if dataset == "cifar-10":
config.l1_reg = 0.01
else:
config.l1_reg = 0.01
elif model == "bn":
config = BNConfig()
elif model == "bnms":
config = BNMSConfig()
elif model == "bn-s":
config = BNSConfig()
if dataset == "cifar-10":
config.sigma_init = 1.0
else:
config.sigma_init = 0.25
elif model == "bn-l1":
config = BNL1Config()
if dataset == "cifar-10":
config.l1_reg = 0.005
else:
config.l1_reg = 0.01
elif model == "bn-star":
config = BNStarConfig()
if dataset == "cifar-10":
config.sigma_init = 1.0
config.l1_reg = 0.005
else:
config.sigma_init = 0.25
config.l1_reg = 0.01
elif model == "ln":
config = LNConfig()
elif model == "lnms":
config = LNMSConfig()
elif model == "ln-s":
config = LNSConfig()
if dataset == "cifar-10":
config.sigma_init = 0.5
else:
config.sigma_init = 0.5
elif model == "ln-l1":
config = LNL1Config()
if dataset == "cifar-10":
config.l1_reg = 0.005
else:
config.l1_reg = 0.005
elif model == "ln-star":
config = LNStarConfig()
if dataset == "cifar-10":
config.sigma_init = 0.5
config.l1_reg = 0.005
else:
config.sigma_init = 0.5
config.l1_reg = 0.005
elif model == "dn":
config = DNConfig()
if dataset == "cifar-10":
config.sigma_init = 0.5
else:
config.sigma_init = 0.5
elif model == "dnms":
config = DNMSConfig()
elif model == "dn-star":
config = DNStarConfig()
if dataset == "cifar-10":
config.sigma_init = 0.5
config.l1_reg = 0.005
else:
config.sigma_init = 0.5
config.l1_reg = 0.001
elif model == "resnet-32":
config = ResNet32Config()
elif model == "resnet-32-no":
config = ResNet32NoNormConfig()
elif model == "resnet-32-bn":
config = ResNet32BNConfig()
elif model == "resnet-32-dn":
config = ResNet32DNConfig()
if dataset == "cifar-10":
config.sigma_init = 0.5
else:
config.sigma_init = 2.0
elif model == "resnet-32-dn-star":
config = ResNet32DNConfig()
if dataset == "cifar-10":
config.sigma_init = 0.5
config.l1_reg = 1e-3
else:
config.sigma_init = 2.0
config.l1_reg = 1e-2
elif model == "resnet-32-ln":
config = ResNet32LNConfig()
elif model == "resnet-110":
config = ResNet110Config()
elif model == "resnet-164":
config = ResNet164Config()
else:
raise Exception("Unknown model \"{}\"".format(model))
if dataset == "cifar-10":
config.mlp_dims = [1024, 64, 10]
config.num_classes = 10
elif dataset == "cifar-100":
config.mlp_dims = [1024, 64, 100]
config.num_classes = 100
else:
raise Exception("Unknown dataset")
return config
class BaselineConfig(object):
"""Standard CNN on CIFAR-10"""
def __init__(self):
self.model = "base"
self.batch_size = 100
self.height = 32
self.width = 32
self.num_channel = 3
self.disp_iter = 100
self.save_iter = 5000
self.valid_iter = 500
self.max_train_iter = 50000
self.momentum = 0.9
self.base_learn_rate = 1e-3
self.label_size = 10
self.filter_size = [[5, 5, 3, 32], [5, 5, 32, 32], [5, 5, 32, 64]]
self.strides = [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
self.pool_fn = ["max_pool", "avg_pool", "avg_pool"]
self.pool_size = [[1, 3, 3, 1], [1, 3, 3, 1], [1, 3, 3, 1]]
self.pool_strides = [[1, 2, 2, 1], [1, 2, 2, 1], [1, 2, 2, 1]]
self.conv_act_fn = ["relu"] * 3
self.conv_init_method = None
self.conv_init_std = [1.0e-4, 1.0e-2, 1.0e-2]
self.mlp_init_method = None
self.mlp_init_std = [1.0e-1, 1.0e-1]
self.mlp_act_fn = [None] * 2
self.mlp_dims = [1024, 64, 10]
self.lr_decay_steps = [5000, 30000]
self.wd = 0.0
self.mlp_dropout = [False, False]
self.norm_field = None
self.stagewise_norm = False
self.sigma_init = 1e-2
self.learn_sigma = False
self.norm_affine = False
self.l1_reg = 0.0
self.prefetch = False
self.data_aug = False
self.whiten = False
self.div255 = False
def set_name(self, val):
self.model = val
return self
def set_whiten(self, val):
self.whiten = val
return self
def set_div255(self, val):
self.div255 = val
return self
def set_mlp_dropout(self, val):
self.mlp_dropout = [val] * 2
return self
def set_max_train_iter(self, val):
self.max_train_iter = val
return self
def set_lr_decay_steps(self, val):
self.lr_decay_steps = val
return self
def set_wd(self, val):
self.wd = val
return self
def set_l1_reg(self, val):
self.l1_reg = val
return self
def set_sigma_init(self, val):
self.sigma_init = val
return self
def to_json(self):
return json.dumps(self, default=lambda o: o.__dict__)
@classmethod
def from_json(cls, s):
dic = json.loads(s)
config = cls()
config.__dict__ = dic
return config
class BaselineDropoutConfig(BaselineConfig):
def __init__(self):
super(BaselineDropoutConfig, self).__init__()
self.mlp_dropout = [True, True]
self.model = "base-drop"
class BaselineWDConfig(BaselineConfig):
def __init__(self):
super(BaselineWDConfig, self).__init__()
self.wd = 0.1
self.model = "base-wd"
class BaselineWDDropoutConfig(BaselineWDConfig):
def __init__(self):
super(BaselineWDDropoutConfig, self).__init__()
self.mlp_dropout = [True, True]
self.model = "base-wd-drop"
class BNConfig(BaselineConfig):
def __init__(self):
super(BNConfig, self).__init__()
self.norm_field = "batch"
self.sigma_init = 1e-2
self.norm_affine = True
self.max_train_iter = 80000
self.lr_decay_steps = [30000, 50000]
self.model = "bn"
self.bn_mask = [True] * 3
class BNMSConfig(BNConfig):
def __init__(self):
super(BNMSConfig, self).__init__()
self.norm_field = "batch_ms"
self.model = "bnms"
class BNSConfig(BNConfig):
def __init__(self):
super(BNSConfig, self).__init__()
self.sigma_init = 1.0
self.model = "bn-s"
class BNL1Config(BNConfig):
def __init__(self):
super(BNL1Config, self).__init__()
self.l1_reg = 0.01
self.model = "bn-l1"
class BNStarConfig(BNConfig):
def __init__(self):
super(BNStarConfig, self).__init__()
self.l1_reg = 0.01
self.sigma_init = 1.0
self.model = "bn-star"
class LNConfig(BNConfig):
def __init__(self):
super(LNConfig, self).__init__()
self.norm_field = "layer"
self.model = "ln"
class LNMSConfig(LNConfig):
def __init__(self):
super(LNMSConfig, self).__init__()
self.norm_field = "layer_ms"
self.model = "lnms"
class LNSConfig(BNSConfig):
def __init__(self):
super(LNSConfig, self).__init__()
self.norm_field = "layer"
self.norm_affine = False
self.model = "ln-s"
class LNL1Config(BNL1Config):
def __init__(self):
super(LNL1Config, self).__init__()
self.norm_field = "layer"
self.model = "ln-l1"
class LNStarConfig(BNStarConfig):
def __init__(self):
super(LNStarConfig, self).__init__()
self.norm_field = "layer"
self.norm_affine = False
self.model = "ln-star"
class DNConfig(BNConfig):
def __init__(self):
super(DNConfig, self).__init__()
self.norm_field = "div"
self.model = "dn"
self.sum_window = [[5, 5], [3, 3], [3, 3]]
self.sup_window = [[5, 5], [3, 3], [3, 3]]
self.norm_affine = False
class DNMSConfig(DNConfig):
def __init__(self):
super(DNMSConfig, self).__init__()
self.norm_field = "div_ms"
self.model = "dnms"
self.sum_window = [[5, 5], [3, 3], [3, 3]]
self.sup_window = None
self.norm_affine = False
class DNStarConfig(BNStarConfig):
def __init__(self):
super(DNStarConfig, self).__init__()
self.norm_field = "div"
self.norm_affine = False
self.sum_window = [[5, 5], [3, 3], [3, 3]]
self.sup_window = [[5, 5], [3, 3], [3, 3]]
self.model = "dn-star"
class ResNet32Config(object):
def __init__(self):
self.batch_size = 100
self.height = 32
self.width = 32
self.num_channel = 3
self.min_lrn_rate = 0.0001
self.base_learn_rate = 0.1
self.num_residual_units = [5, 5, 5] # ResNet-32
self.seed = 1234
self.strides = [1, 2, 2]
self.activate_before_residual = [True, False, False]
self.init_stride = 1
self.init_max_pool = False
self.init_filter = 3
self.use_bottleneck = False
self.filters = [16, 16, 32, 64]
self.wd = 0.0002
# self.relu_leakiness = 0.1 # Original TF model has leaky relu.
self.relu_leakiness = 0.0
self.optimizer = "mom"
self.max_train_iter = 80000
self.lr_decay_steps = [40000, 60000]
self.model = "resnet-32"
self.disp_iter = 100
self.save_iter = 5000
self.valid_iter = 500
self.norm_field = None
self.sigma_init = 1e-2
self.learn_sigma = False
self.norm_affine = False
self.stagewise_norm = False
self.l1_reg = 0.0
self.prefetch = True
self.data_aug = True
self.whiten = False # Original TF has whiten.
self.div255 = True
def set_name(self, model):
self.model = model
return self
def set_l1_reg(self, val):
self.l1_reg = val
return self
def set_wd(self, val):
self.wd = val
return self
def set_sigma_init(self, val):
self.sigma_init = val
return self
def set_max_train_iter(self, val):
self.max_train_iter = val
return self
def set_lr_decay_steps(self, val):
self.lr_decay_steps = val
return self
def to_json(self):
return json.dumps(self, default=lambda o: o.__dict__)
@classmethod
def from_json(cls, s):
dic = json.loads(s)
config = cls()
config.__dict__ = dic
return config
class ResNet32NoNormConfig(ResNet32Config):
def __init__(self):
super(ResNet32NoNormConfig, self).__init__()
self.model = "resnet-32-no"
self.norm_field = "no"
self.base_learn_rate = 0.005
class ResNet32DNConfig(ResNet32Config):
def __init__(self):
super(ResNet32DNConfig, self).__init__()
self.model = "resnet-32-dn"
self.sum_window = [[7, 7], [5, 5], [5, 5], [5, 5]]
self.sup_window = [[7, 7], [5, 5], [5, 5], [5, 5]]
self.norm_field = "div"
self.norm_affine = False
self.learn_sigma = True
self.sigma_init = 0.5
class ResNet32LNConfig(ResNet32Config):
def __init__(self):
super(ResNet32LNConfig, self).__init__()
self.model = "resnet-32-ln"
self.norm_field = "layer"
self.norm_affine = False
self.sigma_init = 0.5
class ResNet110Config(ResNet32Config):
def __init__(self):
super(ResNet110Config, self).__init__()
self.num_residual_units = [18, 18, 18] # ResNet-110
self.model = "resnet-110"
class ResNet164Config(ResNet32Config):
def __init__(self):
super(ResNet164Config, self).__init__()
self.num_residual_units = [18, 18, 18] # ResNet-164
self.use_bottleneck = True
self.model = "resnet-164"