naswot/models/__init__.py
2020-06-03 12:59:01 +01:00

186 lines
9.1 KiB
Python

##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
from os import path as osp
from typing import List, Text
import torch
__all__ = ['change_key', 'get_cell_based_tiny_net', 'get_search_spaces', 'get_cifar_models', 'get_imagenet_models', \
'obtain_model', 'obtain_search_model', 'load_net_from_checkpoint', \
'CellStructure', 'CellArchitectures'
]
# useful modules
from config_utils import dict2config
from .SharedUtils import change_key
from .cell_searchs import CellStructure, CellArchitectures
# Cell-based NAS Models
def get_cell_based_tiny_net(config):
if isinstance(config, dict): config = dict2config(config, None) # to support the argument being a dict
super_type = getattr(config, 'super_type', 'basic')
group_names = ['DARTS-V1', 'DARTS-V2', 'GDAS', 'SETN', 'ENAS', 'RANDOM']
if super_type == 'basic' and config.name in group_names:
from .cell_searchs import nas201_super_nets as nas_super_nets
try:
return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space, config.affine, config.track_running_stats)
except:
return nas_super_nets[config.name](config.C, config.N, config.max_nodes, config.num_classes, config.space)
elif super_type == 'nasnet-super':
from .cell_searchs import nasnet_super_nets as nas_super_nets
return nas_super_nets[config.name](config.C, config.N, config.steps, config.multiplier, \
config.stem_multiplier, config.num_classes, config.space, config.affine, config.track_running_stats)
elif config.name == 'infer.tiny':
from .cell_infers import TinyNetwork
if hasattr(config, 'genotype'):
genotype = config.genotype
elif hasattr(config, 'arch_str'):
genotype = CellStructure.str2structure(config.arch_str)
else: raise ValueError('Can not find genotype from this config : {:}'.format(config))
return TinyNetwork(config.C, config.N, genotype, config.num_classes)
elif config.name == 'infer.shape.tiny':
from .shape_infers import DynamicShapeTinyNet
if isinstance(config.channels, str):
channels = tuple([int(x) for x in config.channels.split(':')])
else: channels = config.channels
genotype = CellStructure.str2structure(config.genotype)
return DynamicShapeTinyNet(channels, genotype, config.num_classes)
elif config.name == 'infer.nasnet-cifar':
from .cell_infers import NASNetonCIFAR
raise NotImplementedError
else:
raise ValueError('invalid network name : {:}'.format(config.name))
# obtain the search space, i.e., a dict mapping the operation name into a python-function for this op
def get_search_spaces(xtype, name) -> List[Text]:
if xtype == 'cell':
from .cell_operations import SearchSpaceNames
assert name in SearchSpaceNames, 'invalid name [{:}] in {:}'.format(name, SearchSpaceNames.keys())
return SearchSpaceNames[name]
else:
raise ValueError('invalid search-space type is {:}'.format(xtype))
def get_cifar_models(config, extra_path=None):
super_type = getattr(config, 'super_type', 'basic')
if super_type == 'basic':
from .CifarResNet import CifarResNet
from .CifarDenseNet import DenseNet
from .CifarWideResNet import CifarWideResNet
if config.arch == 'resnet':
return CifarResNet(config.module, config.depth, config.class_num, config.zero_init_residual)
elif config.arch == 'densenet':
return DenseNet(config.growthRate, config.depth, config.reduction, config.class_num, config.bottleneck)
elif config.arch == 'wideresnet':
return CifarWideResNet(config.depth, config.wide_factor, config.class_num, config.dropout)
else:
raise ValueError('invalid module type : {:}'.format(config.arch))
elif super_type.startswith('infer'):
from .shape_infers import InferWidthCifarResNet
from .shape_infers import InferDepthCifarResNet
from .shape_infers import InferCifarResNet
from .cell_infers import NASNetonCIFAR
assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
infer_mode = super_type.split('-')[1]
if infer_mode == 'width':
return InferWidthCifarResNet(config.module, config.depth, config.xchannels, config.class_num, config.zero_init_residual)
elif infer_mode == 'depth':
return InferDepthCifarResNet(config.module, config.depth, config.xblocks, config.class_num, config.zero_init_residual)
elif infer_mode == 'shape':
return InferCifarResNet(config.module, config.depth, config.xblocks, config.xchannels, config.class_num, config.zero_init_residual)
elif infer_mode == 'nasnet.cifar':
genotype = config.genotype
if extra_path is not None: # reload genotype by extra_path
if not osp.isfile(extra_path): raise ValueError('invalid extra_path : {:}'.format(extra_path))
xdata = torch.load(extra_path)
current_epoch = xdata['epoch']
genotype = xdata['genotypes'][current_epoch-1]
C = config.C if hasattr(config, 'C') else config.ichannel
N = config.N if hasattr(config, 'N') else config.layers
return NASNetonCIFAR(C, N, config.stem_multi, config.class_num, genotype, config.auxiliary)
else:
raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
else:
raise ValueError('invalid super-type : {:}'.format(super_type))
def get_imagenet_models(config):
super_type = getattr(config, 'super_type', 'basic')
if super_type == 'basic':
from .ImageNet_ResNet import ResNet
from .ImageNet_MobileNetV2 import MobileNetV2
if config.arch == 'resnet':
return ResNet(config.block_name, config.layers, config.deep_stem, config.class_num, config.zero_init_residual, config.groups, config.width_per_group)
elif config.arch == 'mobilenet_v2':
return MobileNetV2(config.class_num, config.width_multi, config.input_channel, config.last_channel, 'InvertedResidual', config.dropout)
else:
raise ValueError('invalid arch : {:}'.format( config.arch ))
elif super_type.startswith('infer'): # NAS searched architecture
assert len(super_type.split('-')) == 2, 'invalid super_type : {:}'.format(super_type)
infer_mode = super_type.split('-')[1]
if infer_mode == 'shape':
from .shape_infers import InferImagenetResNet
from .shape_infers import InferMobileNetV2
if config.arch == 'resnet':
return InferImagenetResNet(config.block_name, config.layers, config.xblocks, config.xchannels, config.deep_stem, config.class_num, config.zero_init_residual)
elif config.arch == "MobileNetV2":
return InferMobileNetV2(config.class_num, config.xchannels, config.xblocks, config.dropout)
else:
raise ValueError('invalid arch-mode : {:}'.format(config.arch))
else:
raise ValueError('invalid infer-mode : {:}'.format(infer_mode))
else:
raise ValueError('invalid super-type : {:}'.format(super_type))
# Try to obtain the network by config.
def obtain_model(config, extra_path=None):
if config.dataset == 'cifar':
return get_cifar_models(config, extra_path)
elif config.dataset == 'imagenet':
return get_imagenet_models(config)
else:
raise ValueError('invalid dataset in the model config : {:}'.format(config))
def obtain_search_model(config):
if config.dataset == 'cifar':
if config.arch == 'resnet':
from .shape_searchs import SearchWidthCifarResNet
from .shape_searchs import SearchDepthCifarResNet
from .shape_searchs import SearchShapeCifarResNet
if config.search_mode == 'width':
return SearchWidthCifarResNet(config.module, config.depth, config.class_num)
elif config.search_mode == 'depth':
return SearchDepthCifarResNet(config.module, config.depth, config.class_num)
elif config.search_mode == 'shape':
return SearchShapeCifarResNet(config.module, config.depth, config.class_num)
else: raise ValueError('invalid search mode : {:}'.format(config.search_mode))
elif config.arch == 'simres':
from .shape_searchs import SearchWidthSimResNet
if config.search_mode == 'width':
return SearchWidthSimResNet(config.depth, config.class_num)
else: raise ValueError('invalid search mode : {:}'.format(config.search_mode))
else:
raise ValueError('invalid arch : {:} for dataset [{:}]'.format(config.arch, config.dataset))
elif config.dataset == 'imagenet':
from .shape_searchs import SearchShapeImagenetResNet
assert config.search_mode == 'shape', 'invalid search-mode : {:}'.format( config.search_mode )
if config.arch == 'resnet':
return SearchShapeImagenetResNet(config.block_name, config.layers, config.deep_stem, config.class_num)
else:
raise ValueError('invalid model config : {:}'.format(config))
else:
raise ValueError('invalid dataset in the model config : {:}'.format(config))
def load_net_from_checkpoint(checkpoint):
assert osp.isfile(checkpoint), 'checkpoint {:} does not exist'.format(checkpoint)
checkpoint = torch.load(checkpoint)
model_config = dict2config(checkpoint['model-config'], None)
model = obtain_model(model_config)
model.load_state_dict(checkpoint['base-model'])
return model