204 lines
12 KiB
Python
204 lines
12 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
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#####################################################
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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 autodl.procedures import prepare_seed, get_optim_scheduler
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from autodl.utils import get_model_infos, obtain_accuracy
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from autodl.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', 'get_nas_bench_loaders']
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def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
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data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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latencies, device = [], torch.cuda.current_device()
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network.eval()
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with torch.no_grad():
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end = time.time()
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for i, (inputs, targets) in enumerate(xloader):
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targets = targets.cuda(device=device, non_blocking=True)
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inputs = inputs.cuda(device=device, non_blocking=True)
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data_time.update(time.time() - end)
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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batch_time.update(time.time() - end)
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if batch is None or batch == inputs.size(0):
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batch = inputs.size(0)
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latencies.append( batch_time.val - data_time.val )
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# record loss and accuracy
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1.update (prec1.item(), inputs.size(0))
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top5.update (prec5.item(), inputs.size(0))
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end = time.time()
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if len(latencies) > 2: latencies = latencies[1:]
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return losses.avg, top1.avg, top5.avg, latencies
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def procedure(xloader, network, criterion, scheduler, optimizer, mode: str):
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losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
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if mode == 'train' : network.train()
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elif mode == 'valid': network.eval()
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else: raise ValueError("The mode is not right : {:}".format(mode))
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device = torch.cuda.current_device()
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data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
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for i, (inputs, targets) in enumerate(xloader):
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if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
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targets = targets.cuda(device=device, non_blocking=True)
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if mode == 'train': optimizer.zero_grad()
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# forward
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features, logits = network(inputs)
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loss = criterion(logits, targets)
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# backward
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if mode == 'train':
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loss.backward()
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optimizer.step()
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# record loss and accuracy
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prec1, prec5 = obtain_accuracy(logits.data, targets.data, topk=(1, 5))
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losses.update(loss.item(), inputs.size(0))
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top1.update (prec1.item(), inputs.size(0))
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top5.update (prec5.item(), inputs.size(0))
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# count time
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batch_time.update(time.time() - end)
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end = time.time()
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return losses.avg, top1.avg, top5.avg, batch_time.sum
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def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed: int, logger):
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prepare_seed(seed) # random seed
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net = get_cell_based_tiny_net(arch_config)
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#net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num)
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flop, param = get_model_infos(net, opt_config.xshape)
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logger.log('Network : {:}'.format(net.get_message()), False)
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logger.log('{:} Seed-------------------------- {:} --------------------------'.format(time_string(), seed))
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logger.log('FLOP = {:} MB, Param = {:} MB'.format(flop, param))
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# train and valid
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optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), opt_config)
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default_device = torch.cuda.current_device()
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network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(device=default_device)
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criterion = criterion.cuda(device=default_device)
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# start training
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start_time, epoch_time, total_epoch = time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup
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train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}
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train_times , valid_times, lrs = {}, {}, {}
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for epoch in range(total_epoch):
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scheduler.update(epoch, 0.0)
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lr = min(scheduler.get_lr())
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train_loss, train_acc1, train_acc5, train_tm = procedure(train_loader, network, criterion, scheduler, optimizer, 'train')
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train_losses[epoch] = train_loss
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train_acc1es[epoch] = train_acc1
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train_acc5es[epoch] = train_acc5
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train_times [epoch] = train_tm
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lrs[epoch] = lr
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with torch.no_grad():
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for key, xloder in valid_loaders.items():
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valid_loss, valid_acc1, valid_acc5, valid_tm = procedure(xloder , network, criterion, None, None, 'valid')
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valid_losses['{:}@{:}'.format(key,epoch)] = valid_loss
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valid_acc1es['{:}@{:}'.format(key,epoch)] = valid_acc1
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valid_acc5es['{:}@{:}'.format(key,epoch)] = valid_acc5
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valid_times ['{:}@{:}'.format(key,epoch)] = valid_tm
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# measure elapsed time
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epoch_time.update(time.time() - start_time)
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start_time = time.time()
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need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (total_epoch-epoch-1), True) )
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logger.log('{:} {:} epoch={:03d}/{:03d} :: Train [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%] Valid [loss={:.5f}, acc@1={:.2f}%, acc@5={:.2f}%], lr={:}'.format(time_string(), need_time, epoch, total_epoch, train_loss, train_acc1, train_acc5, valid_loss, valid_acc1, valid_acc5, lr))
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info_seed = {'flop' : flop,
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'param': param,
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'arch_config' : arch_config._asdict(),
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'opt_config' : opt_config._asdict(),
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'total_epoch' : total_epoch ,
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'train_losses': train_losses,
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'train_acc1es': train_acc1es,
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'train_acc5es': train_acc5es,
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'train_times' : train_times,
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'valid_losses': valid_losses,
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'valid_acc1es': valid_acc1es,
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'valid_acc5es': valid_acc5es,
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'valid_times' : valid_times,
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'learning_rates': lrs,
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'net_state_dict': net.state_dict(),
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'net_string' : '{:}'.format(net),
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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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