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