Upgrade API of NAS-Bench-201
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D-X-Y
@@ -1,14 +1,17 @@
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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import time, torch
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import os, time, copy, torch, pathlib
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import datasets
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from config_utils import load_config
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from procedures import prepare_seed, get_optim_scheduler
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from utils import get_model_infos, obtain_accuracy
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from log_utils import AverageMeter, time_string, convert_secs2time
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from models import get_cell_based_tiny_net
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__all__ = ['evaluate_for_seed', 'pure_evaluate']
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__all__ = ['evaluate_for_seed', 'pure_evaluate', 'get_nas_bench_loaders']
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def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
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@@ -127,3 +130,72 @@ def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed
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'finish-train': True
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}
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return info_seed
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def get_nas_bench_loaders(workers):
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torch.set_num_threads(workers)
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root_dir = (pathlib.Path(__file__).parent / '..' / '..').resolve()
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torch_dir = pathlib.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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get_datasets = datasets.get_datasets # a function to return the dataset
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break_line = '-' * 150
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print ('{:} Create data-loader for all datasets'.format(time_string()))
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print (break_line)
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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 = copy.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 (break_line)
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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 (break_line)
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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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