update configs
This commit is contained in:
D-X-Y
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time, argparse, collections
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from copy import deepcopy
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import torch
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import torch.nn as nn
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from pathlib import Path
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from collections import defaultdict
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lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from log_utils import AverageMeter, time_string, convert_secs2time
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from config_utils import load_config, dict2config
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from datasets import get_datasets
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# AA-NAS-Bench related module or function
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from models import CellStructure, get_cell_based_tiny_net
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from aa_nas_api import ArchResults, ResultsCount
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from AA_functions import pure_evaluate
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def account_one_arch(arch_index, arch_str, checkpoints, datasets, dataloader_dict):
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information = ArchResults(arch_index, arch_str)
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for checkpoint_path in checkpoints:
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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used_seed = checkpoint_path.name.split('-')[-1].split('.')[0]
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for dataset in datasets:
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assert dataset in checkpoint, 'Can not find {:} in arch-{:} from {:}'.format(dataset, arch_index, checkpoint_path)
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results = checkpoint[dataset]
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assert results['finish-train'], 'This {:} arch seed={:} does not finish train on {:} ::: {:}'.format(arch_index, used_seed, dataset, checkpoint_path)
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arch_config = {'channel': results['channel'], 'num_cells': results['num_cells'], 'arch_str': arch_str, 'class_num': results['config']['class_num']}
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xresult = ResultsCount(dataset, results['net_state_dict'], results['train_acc1es'], results['train_losses'], \
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results['param'], results['flop'], arch_config, used_seed, results['total_epoch'], None)
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if dataset == 'cifar10-valid':
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xresult.update_eval('x-valid' , results['valid_acc1es'], results['valid_losses'])
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elif dataset == 'cifar10':
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xresult.update_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
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elif dataset == 'cifar100' or dataset == 'ImageNet16-120':
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xresult.update_eval('ori-test', results['valid_acc1es'], results['valid_losses'])
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net_config = dict2config({'name': 'infer.tiny', 'C': arch_config['channel'], 'N': arch_config['num_cells'],
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'genotype': CellStructure.str2structure(arch_config['arch_str']), 'num_classes':arch_config['class_num']}, None)
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network = get_cell_based_tiny_net(net_config)
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network.load_state_dict(xresult.get_net_param())
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network = network.cuda()
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'valid')], network)
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xresult.update_eval('x-valid', {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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loss, top1, top5, latencies = pure_evaluate(dataloader_dict['{:}@{:}'.format(dataset, 'test')], network)
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xresult.update_eval('x-test' , {results['total_epoch']-1: top1}, {results['total_epoch']-1: loss})
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xresult.update_latency(latencies)
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else:
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raise ValueError('invalid dataset name : {:}'.format(dataset))
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information.update(dataset, int(used_seed), xresult)
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return information
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def GET_DataLoaders(workers):
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torch.set_num_threads(workers)
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root_dir = (Path(__file__).parent / '..').resolve()
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torch_dir = Path(os.environ['TORCH_HOME'])
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# cifar
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cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
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cifar_config = load_config(cifar_config_path, None, None)
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print ('{:} Create data-loader for all datasets'.format(time_string()))
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print ('-'*200)
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TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
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print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
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cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
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assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
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temp_dataset = deepcopy(TRAIN_CIFAR10)
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temp_dataset.transform = VALID_CIFAR10.transform
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# data loader
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trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
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train_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
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valid_cifar10_loader = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
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test__cifar10_loader = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
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print ('CIFAR-10 : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
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print ('CIFAR-10 : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
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print ('CIFAR-10 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
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print ('CIFAR-10 : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
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print ('-'*200)
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# CIFAR-100
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TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
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print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
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cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
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assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
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train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
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valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
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test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
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print ('CIFAR-100 : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
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print ('CIFAR-100 : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
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print ('CIFAR-100 : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
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print ('-'*200)
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imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
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imagenet16_config = load_config(imagenet16_config_path, None, None)
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TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
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print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
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imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
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assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
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train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
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valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
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test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
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print ('ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
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print ('ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
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print ('ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))
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# 'cifar10', 'cifar100', 'ImageNet16-120'
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loaders = {'cifar10@trainval': trainval_cifar10_loader,
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'cifar10@train' : train_cifar10_loader,
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'cifar10@valid' : valid_cifar10_loader,
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'cifar10@test' : test__cifar10_loader,
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'cifar100@train' : train_cifar100_loader,
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'cifar100@valid' : valid_cifar100_loader,
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'cifar100@test' : test__cifar100_loader,
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'ImageNet16-120@train': train_imagenet_loader,
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'ImageNet16-120@valid': valid_imagenet_loader,
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'ImageNet16-120@test' : test__imagenet_loader}
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return loaders
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def simplify(save_dir, meta_file, basestr, target_dir):
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meta_infos = torch.load(meta_file, map_location='cpu')
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meta_archs = meta_infos['archs'] # a list of architecture strings
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meta_num_archs = meta_infos['total']
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meta_max_node = meta_infos['max_node']
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assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
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sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
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print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
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subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
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num_seeds = defaultdict(lambda: 0)
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for index, sub_dir in enumerate(sub_model_dirs):
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xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
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arch_indexes = set()
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for checkpoint in xcheckpoints:
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temp_names = checkpoint.name.split('-')
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assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
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arch_indexes.add( temp_names[1] )
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subdir2archs[sub_dir] = sorted(list(arch_indexes))
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num_evaluated_arch += len(arch_indexes)
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# count number of seeds for each architecture
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for arch_index in arch_indexes:
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num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
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print('{:} There are {:5d} architectures that have been evaluated ({:} in total).'.format(time_string(), num_evaluated_arch, meta_num_archs))
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for key in sorted( list( num_seeds.keys() ) ): print ('{:} There are {:5d} architectures that are evaluated {:} times.'.format(time_string(), num_seeds[key], key))
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dataloader_dict = GET_DataLoaders( 6 )
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to_save_simply = save_dir / 'simplifies'
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to_save_allarc = save_dir / 'simplifies' / 'architectures'
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if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
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if not to_save_allarc.exists(): to_save_allarc.mkdir(parents=True, exist_ok=True)
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assert (save_dir / target_dir) in subdir2archs, 'can not find {:}'.format(target_dir)
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arch2infos, datasets = {}, ('cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120')
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evaluated_indexes = set()
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target_directory = save_dir / target_dir
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arch_indexes = subdir2archs[ target_directory ]
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num_seeds = defaultdict(lambda: 0)
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end_time = time.time()
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arch_time = AverageMeter()
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for idx, arch_index in enumerate(arch_indexes):
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checkpoints = list(target_directory.glob('arch-{:}-seed-*.pth'.format(arch_index)))
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try:
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arch_info = account_one_arch(arch_index, meta_archs[int(arch_index)], checkpoints, datasets, dataloader_dict)
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num_seeds[ len(checkpoints) ] += 1
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except:
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print('Loading {:} failed, : {:}'.format(arch_index, checkpoints))
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continue
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assert int(arch_index) not in evaluated_indexes, 'conflict arch-index : {:}'.format(arch_index)
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assert 0 <= int(arch_index) < len(meta_archs), 'invalid arch-index {:} (not found in meta_archs)'.format(arch_index)
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evaluated_indexes.add( int(arch_index) )
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arch2infos[int(arch_index)] = arch_info
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torch.save(arch_info.state_dict(), to_save_allarc / '{:}-FULL.pth'.format(arch_index))
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#torch.save(arch_info, to_save_allarc / '{:}-FULL.pth'.format(arch_index))
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arch_info.clear_params()
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torch.save(arch_info.state_dict(), to_save_allarc / '{:}-SIMPLE.pth'.format(arch_index))
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# measure elapsed time
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arch_time.update(time.time() - end_time)
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end_time = time.time()
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need_time = '{:}'.format( convert_secs2time(arch_time.avg * (len(arch_indexes)-idx-1), True) )
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print('{:} {:} [{:03d}/{:03d}] : {:} still need {:}'.format(time_string(), target_dir, idx, len(arch_indexes), arch_index, need_time))
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# measure time
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xstrs = ['{:}:{:03d}'.format(key, num_seeds[key]) for key in sorted( list( num_seeds.keys() ) ) ]
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print('{:} {:} done : {:}'.format(time_string(), target_dir, xstrs))
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final_infos = {'meta_archs' : meta_archs,
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'total_archs': meta_num_archs,
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'basestr' : basestr,
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'arch2infos' : arch2infos,
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'evaluated_indexes': evaluated_indexes}
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save_file_name = to_save_simply / '{:}.pth'.format(target_dir)
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torch.save(final_infos, save_file_name)
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print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
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def merge_all(save_dir, meta_file, basestr):
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meta_infos = torch.load(meta_file, map_location='cpu')
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meta_archs = meta_infos['archs']
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meta_num_archs = meta_infos['total']
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meta_max_node = meta_infos['max_node']
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assert meta_num_archs == len(meta_archs), 'invalid number of archs : {:} vs {:}'.format(meta_num_archs, len(meta_archs))
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sub_model_dirs = sorted(list(save_dir.glob('*-*-{:}'.format(basestr))))
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print ('{:} find {:} directories used to save checkpoints'.format(time_string(), len(sub_model_dirs)))
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for index, sub_dir in enumerate(sub_model_dirs):
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arch_info_files = sorted( list(sub_dir.glob('arch-*-seed-*.pth') ) )
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print ('The {:02d}/{:02d}-th directory : {:} : {:} runs.'.format(index, len(sub_model_dirs), sub_dir, len(arch_info_files)))
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subdir2archs, num_evaluated_arch = collections.OrderedDict(), 0
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num_seeds = defaultdict(lambda: 0)
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for index, sub_dir in enumerate(sub_model_dirs):
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xcheckpoints = list(sub_dir.glob('arch-*-seed-*.pth'))
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arch_indexes = set()
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for checkpoint in xcheckpoints:
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temp_names = checkpoint.name.split('-')
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assert len(temp_names) == 4 and temp_names[0] == 'arch' and temp_names[2] == 'seed', 'invalid checkpoint name : {:}'.format(checkpoint.name)
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arch_indexes.add( temp_names[1] )
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subdir2archs[sub_dir] = sorted(list(arch_indexes))
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num_evaluated_arch += len(arch_indexes)
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# count number of seeds for each architecture
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for arch_index in arch_indexes:
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num_seeds[ len(list(sub_dir.glob('arch-{:}-seed-*.pth'.format(arch_index)))) ] += 1
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print('There are {:5d} architectures that have been evaluated ({:} in total).'.format(num_evaluated_arch, meta_num_archs))
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for key in sorted( list( num_seeds.keys() ) ): print ('There are {:5d} architectures that are evaluated {:} times.'.format(num_seeds[key], key))
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arch2infos, evaluated_indexes = dict(), set()
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for IDX, (sub_dir, arch_indexes) in enumerate(subdir2archs.items()):
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ckp_path = sub_dir.parent / 'simplifies' / '{:}.pth'.format(sub_dir.name)
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if ckp_path.exists():
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sub_ckps = torch.load(ckp_path, map_location='cpu')
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assert sub_ckps['total_archs'] == meta_num_archs and sub_ckps['basestr'] == basestr
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xarch2infos = sub_ckps['arch2infos']
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xevalindexs = sub_ckps['evaluated_indexes']
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for eval_index in xevalindexs:
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assert eval_index not in evaluated_indexes and eval_index not in arch2infos
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arch2infos[eval_index] = xarch2infos[eval_index].state_dict()
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evaluated_indexes.add( eval_index )
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print ('{:} [{:03d}/{:03d}] merge data from {:} with {:} models.'.format(time_string(), IDX, len(subdir2archs), ckp_path, len(xevalindexs)))
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else:
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print ('{:} [{:03d}/{:03d}] can not find {:}, skip.'.format(time_string(), IDX, len(subdir2archs), ckp_path))
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evaluated_indexes = sorted( list( evaluated_indexes ) )
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print ('Finally, there are {:} models.'.format(len(evaluated_indexes)))
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to_save_simply = save_dir / 'simplifies'
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if not to_save_simply.exists(): to_save_simply.mkdir(parents=True, exist_ok=True)
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final_infos = {'meta_archs' : meta_archs,
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'total_archs': meta_num_archs,
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'arch2infos' : arch2infos,
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'evaluated_indexes': evaluated_indexes}
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save_file_name = to_save_simply / '{:}-final-infos.pth'.format(basestr)
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torch.save(final_infos, save_file_name)
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print ('Save {:} / {:} architecture results into {:}.'.format(len(evaluated_indexes), meta_num_archs, save_file_name))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='An Algorithm-Agnostic (AA) NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('--mode' , type=str, choices=['cal', 'merge'], help='The running mode for this script.')
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parser.add_argument('--base_save_dir', type=str, default='./output/AA-NAS-BENCH-4', help='The base-name of folder to save checkpoints and log.')
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parser.add_argument('--target_dir' , type=str, help='The target directory.')
|
||||
parser.add_argument('--max_node' , type=int, default=4, help='The maximum node in a cell.')
|
||||
parser.add_argument('--channel' , type=int, default=16, help='The number of channels.')
|
||||
parser.add_argument('--num_cells' , type=int, default=5, help='The number of cells in one stage.')
|
||||
args = parser.parse_args()
|
||||
|
||||
save_dir = Path( args.base_save_dir )
|
||||
meta_path = save_dir / 'meta-node-{:}.pth'.format(args.max_node)
|
||||
assert save_dir.exists(), 'invalid save dir path : {:}'.format(save_dir)
|
||||
assert meta_path.exists(), 'invalid saved meta path : {:}'.format(meta_path)
|
||||
print ('start the statistics of our nas-benchmark from {:} using {:}.'.format(save_dir, args.target_dir))
|
||||
basestr = 'C{:}-N{:}'.format(args.channel, args.num_cells)
|
||||
|
||||
if args.mode == 'cal':
|
||||
simplify(save_dir, meta_path, basestr, args.target_dir)
|
||||
elif args.mode == 'merge':
|
||||
merge_all(save_dir, meta_path, basestr)
|
||||
else:
|
||||
raise ValueError('invalid mode : {:}'.format(args.mode))
|
||||
@@ -9,35 +9,8 @@ from log_utils import AverageMeter, time_string, convert_secs2time
|
||||
from models import get_cell_based_tiny_net
|
||||
|
||||
|
||||
__all__ = ['evaluate_for_seed', 'pure_evaluate']
|
||||
|
||||
|
||||
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
|
||||
data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
|
||||
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
|
||||
latencies = []
|
||||
network.eval()
|
||||
with torch.no_grad():
|
||||
end = time.time()
|
||||
for i, (inputs, targets) in enumerate(xloader):
|
||||
targets = targets.cuda(non_blocking=True)
|
||||
inputs = inputs.cuda(non_blocking=True)
|
||||
data_time.update(time.time() - end)
|
||||
# forward
|
||||
features, logits = network(inputs)
|
||||
loss = criterion(logits, targets)
|
||||
batch_time.update(time.time() - end)
|
||||
if batch is None or batch == inputs.size(0):
|
||||
batch = inputs.size(0)
|
||||
latencies.append( batch_time.val - data_time.val )
|
||||
# record loss and accuracy
|
||||
prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
|
||||
losses.update(loss.item(), inputs.size(0))
|
||||
top1.update (prec1.item(), inputs.size(0))
|
||||
top5.update (prec5.item(), inputs.size(0))
|
||||
end = time.time()
|
||||
if len(latencies) > 2: latencies = latencies[1:]
|
||||
return losses.avg, top1.avg, top5.avg, latencies
|
||||
__all__ = ['evaluate_for_seed']
|
||||
|
||||
|
||||
|
||||
@@ -47,7 +20,7 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode):
|
||||
elif mode == 'valid': network.eval()
|
||||
else: raise ValueError("The mode is not right : {:}".format(mode))
|
||||
|
||||
batch_time, end = AverageMeter(), time.time()
|
||||
data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
|
||||
for i, (inputs, targets) in enumerate(xloader):
|
||||
if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
|
||||
|
||||
@@ -72,7 +45,7 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode):
|
||||
|
||||
|
||||
|
||||
def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loader, seed, logger):
|
||||
def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loaders, seed, logger):
|
||||
|
||||
prepare_seed(seed) # random seed
|
||||
net = get_cell_based_tiny_net(dict2config({'name': 'infer.tiny',
|
||||
@@ -83,7 +56,7 @@ def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loader, see
|
||||
#net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
|
||||
flop, param = get_model_infos(net, config.xshape)
|
||||
logger.log('Network : {:}'.format(net.get_message()), False)
|
||||
logger.log('Seed-------------------------- {:} --------------------------'.format(seed))
|
||||
logger.log('{:} Seed-------------------------- {:} --------------------------'.format(time_string(), seed))
|
||||
logger.log('FLOP = {:} MB, Param = {:} MB'.format(flop, param))
|
||||
# train and valid
|
||||
optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), config)
|
||||
@@ -96,16 +69,17 @@ def evaluate_for_seed(arch_config, config, arch, train_loader, valid_loader, see
|
||||
scheduler.update(epoch, 0.0)
|
||||
|
||||
train_loss, train_acc1, train_acc5, train_tm = procedure(train_loader, network, criterion, scheduler, optimizer, 'train')
|
||||
with torch.no_grad():
|
||||
valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(valid_loader, network, criterion, None, None, 'valid')
|
||||
train_losses[epoch] = train_loss
|
||||
train_acc1es[epoch] = train_acc1
|
||||
train_acc5es[epoch] = train_acc5
|
||||
valid_losses[epoch] = valid_loss
|
||||
valid_acc1es[epoch] = valid_acc1
|
||||
valid_acc5es[epoch] = valid_acc5
|
||||
train_times [epoch] = train_tm
|
||||
valid_times [epoch] = valid_tm
|
||||
with torch.no_grad():
|
||||
for key, xloder in valid_loaders.items():
|
||||
valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(xloder , network, criterion, None, None, 'valid')
|
||||
valid_losses['{:}@{:}'.format(key,epoch)] = valid_loss
|
||||
valid_acc1es['{:}@{:}'.format(key,epoch)] = valid_acc1
|
||||
valid_acc5es['{:}@{:}'.format(key,epoch)] = valid_acc5
|
||||
valid_times ['{:}@{:}'.format(key,epoch)] = valid_tm
|
||||
|
||||
# measure elapsed time
|
||||
epoch_time.update(time.time() - start_time)
|
||||
@@ -7,7 +7,7 @@ ImageFile.LOAD_TRUNCATED_IMAGES = True
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
|
||||
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
|
||||
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
|
||||
from config_utils import load_config
|
||||
from procedures import save_checkpoint, copy_checkpoint
|
||||
@@ -15,7 +15,7 @@ from procedures import get_machine_info
|
||||
from datasets import get_datasets
|
||||
from log_utils import Logger, AverageMeter, time_string, convert_secs2time
|
||||
from models import CellStructure, CellArchitectures, get_search_spaces
|
||||
from AA_functions_v2 import evaluate_for_seed
|
||||
from functions import evaluate_for_seed
|
||||
|
||||
|
||||
def evaluate_all_datasets(arch, datasets, xpaths, splits, use_less, seed, arch_config, workers, logger):
|
||||
@@ -156,14 +156,14 @@ def main(save_dir, workers, datasets, xpaths, splits, use_less, srange, arch_ind
|
||||
logger.close()
|
||||
|
||||
|
||||
def train_single_model(save_dir, workers, datasets, xpaths, use_less, splits, seeds, model_str, arch_config):
|
||||
def train_single_model(save_dir, workers, datasets, xpaths, splits, use_less, seeds, model_str, arch_config):
|
||||
assert torch.cuda.is_available(), 'CUDA is not available.'
|
||||
torch.backends.cudnn.enabled = True
|
||||
torch.backends.cudnn.deterministic = True
|
||||
#torch.backends.cudnn.benchmark = True
|
||||
torch.set_num_threads( workers )
|
||||
|
||||
save_dir = Path(save_dir) / 'specifics' / '{:}-{:}-{:}'.format(model_str, arch_config['channel'], arch_config['num_cells'])
|
||||
save_dir = Path(save_dir) / 'specifics' / '{:}-{:}-{:}-{:}'.format('LESS' if use_less else 'FULL', model_str, arch_config['channel'], arch_config['num_cells'])
|
||||
logger = Logger(str(save_dir), 0, False)
|
||||
if model_str in CellArchitectures:
|
||||
arch = CellArchitectures[model_str]
|
||||
@@ -247,18 +247,22 @@ def generate_meta_info(save_dir, max_node, divide=40):
|
||||
torch.save(info, save_name)
|
||||
print ('save the meta file into {:}'.format(save_name))
|
||||
|
||||
script_name = save_dir / 'meta-node-{:}.opt-script.txt'.format(max_node)
|
||||
with open(str(script_name), 'w') as cfile:
|
||||
gaps = total_arch // divide
|
||||
for start in range(0, total_arch, gaps):
|
||||
xend = min(start+gaps, total_arch)
|
||||
cfile.write('bash ./scripts-search/AA-NAS-train-archs.sh {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
|
||||
print ('save the training script into {:}'.format(script_name))
|
||||
script_name_full = save_dir / 'BENCH-102-N{:}.opt-full.script'.format(max_node)
|
||||
script_name_less = save_dir / 'BENCH-102-N{:}.opt-less.script'.format(max_node)
|
||||
full_file = open(str(script_name_full), 'w')
|
||||
less_file = open(str(script_name_less), 'w')
|
||||
gaps = total_arch // divide
|
||||
for start in range(0, total_arch, gaps):
|
||||
xend = min(start+gaps, total_arch)
|
||||
full_file.write('bash ./scripts-search/NAS-Bench-102/train-models.sh 0 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
|
||||
less_file.write('bash ./scripts-search/NAS-Bench-102/train-models.sh 1 {:5d} {:5d} -1 \'777 888 999\'\n'.format(start, xend-1))
|
||||
print ('save the training script into {:} and {:}'.format(script_name_full, script_name_less))
|
||||
full_file.close()
|
||||
less_file.close()
|
||||
|
||||
script_name = save_dir / 'meta-node-{:}.cal-script.txt'.format(max_node)
|
||||
macro = 'OMP_NUM_THREADS=6 CUDA_VISIBLE_DEVICES=0'
|
||||
with open(str(script_name), 'w') as cfile:
|
||||
gaps = total_arch // divide
|
||||
for start in range(0, total_arch, gaps):
|
||||
xend = min(start+gaps, total_arch)
|
||||
cfile.write('{:} python exps/AA-NAS-statistics.py --mode cal --target_dir {:06d}-{:06d}-C16-N5\n'.format(macro, start, xend-1))
|
||||
@@ -278,7 +282,7 @@ if __name__ == '__main__':
|
||||
parser.add_argument('--datasets', type=str, nargs='+', help='The applied datasets.')
|
||||
parser.add_argument('--xpaths', type=str, nargs='+', help='The root path for this dataset.')
|
||||
parser.add_argument('--splits', type=int, nargs='+', help='The root path for this dataset.')
|
||||
parser.add_argument('--use_less', type=int, default=0, help='Using the less-training-epoch config.')
|
||||
parser.add_argument('--use_less', type=int, default=0, choices=[0,1], help='Using the less-training-epoch config.')
|
||||
parser.add_argument('--seeds' , type=int, nargs='+', help='The range of models to be evaluated')
|
||||
parser.add_argument('--channel', type=int, help='The number of channels.')
|
||||
parser.add_argument('--num_cells', type=int, help='The number of cells in one stage.')
|
||||
Reference in New Issue
Block a user