##################################################### # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 # ##################################################### import os, time, copy, torch, pathlib import datasets from config_utils import load_config from autodl.procedures import prepare_seed, get_optim_scheduler from autodl.utils import get_model_infos, obtain_accuracy from autodl.log_utils import AverageMeter, time_string, convert_secs2time from models import get_cell_based_tiny_net __all__ = ['evaluate_for_seed', 'pure_evaluate', 'get_nas_bench_loaders'] def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()): data_time, batch_time, batch = AverageMeter(), AverageMeter(), None losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter() latencies, device = [], torch.cuda.current_device() network.eval() with torch.no_grad(): end = time.time() for i, (inputs, targets) in enumerate(xloader): targets = targets.cuda(device=device, non_blocking=True) inputs = inputs.cuda(device=device, 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 def procedure(xloader, network, criterion, scheduler, optimizer, mode: str): losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter() if mode == 'train' : network.train() elif mode == 'valid': network.eval() else: raise ValueError("The mode is not right : {:}".format(mode)) device = torch.cuda.current_device() 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)) targets = targets.cuda(device=device, non_blocking=True) if mode == 'train': optimizer.zero_grad() # forward features, logits = network(inputs) loss = criterion(logits, targets) # backward if mode == 'train': loss.backward() optimizer.step() # 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)) # count time batch_time.update(time.time() - end) end = time.time() return losses.avg, top1.avg, top5.avg, batch_time.sum def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed: int, logger): prepare_seed(seed) # random seed net = get_cell_based_tiny_net(arch_config) #net = TinyNetwork(arch_config['channel'], arch_config['num_cells'], arch, config.class_num) flop, param = get_model_infos(net, opt_config.xshape) logger.log('Network : {:}'.format(net.get_message()), False) 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(), opt_config) default_device = torch.cuda.current_device() network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(device=default_device) criterion = criterion.cuda(device=default_device) # start training start_time, epoch_time, total_epoch = time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {} train_times , valid_times, lrs = {}, {}, {} for epoch in range(total_epoch): scheduler.update(epoch, 0.0) lr = min(scheduler.get_lr()) train_loss, train_acc1, train_acc5, train_tm = procedure(train_loader, network, criterion, scheduler, optimizer, 'train') train_losses[epoch] = train_loss train_acc1es[epoch] = train_acc1 train_acc5es[epoch] = train_acc5 train_times [epoch] = train_tm lrs[epoch] = lr 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) start_time = time.time() need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (total_epoch-epoch-1), True) ) 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)) info_seed = {'flop' : flop, 'param': param, 'arch_config' : arch_config._asdict(), 'opt_config' : opt_config._asdict(), 'total_epoch' : total_epoch , 'train_losses': train_losses, 'train_acc1es': train_acc1es, 'train_acc5es': train_acc5es, 'train_times' : train_times, 'valid_losses': valid_losses, 'valid_acc1es': valid_acc1es, 'valid_acc5es': valid_acc5es, 'valid_times' : valid_times, 'learning_rates': lrs, 'net_state_dict': net.state_dict(), 'net_string' : '{:}'.format(net), 'finish-train': True } return info_seed def get_nas_bench_loaders(workers): torch.set_num_threads(workers) root_dir = (pathlib.Path(__file__).parent / '..' / '..').resolve() torch_dir = pathlib.Path(os.environ['TORCH_HOME']) # cifar cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config' cifar_config = load_config(cifar_config_path, None, None) get_datasets = datasets.get_datasets # a function to return the dataset break_line = '-' * 150 print ('{:} Create data-loader for all datasets'.format(time_string())) print (break_line) TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1) print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num)) cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None) 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] temp_dataset = copy.deepcopy(TRAIN_CIFAR10) temp_dataset.transform = VALID_CIFAR10.transform # data loader trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True) 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) 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) test__cifar10_loader = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True) print ('CIFAR-10 : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size)) print ('CIFAR-10 : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size)) print ('CIFAR-10 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size)) print ('CIFAR-10 : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size)) print (break_line) # CIFAR-100 TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1) print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num)) cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None) 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] train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True) 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) 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) print ('CIFAR-100 : train-loader has {:3d} batch'.format(len(train_cifar100_loader))) print ('CIFAR-100 : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader))) print ('CIFAR-100 : test--loader has {:3d} batch'.format(len(test__cifar100_loader))) print (break_line) imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config' imagenet16_config = load_config(imagenet16_config_path, None, None) TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1) 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)) imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None) 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] train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True) 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) 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) print ('ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size)) print ('ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size)) print ('ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size)) # 'cifar10', 'cifar100', 'ImageNet16-120' loaders = {'cifar10@trainval': trainval_cifar10_loader, 'cifar10@train' : train_cifar10_loader, 'cifar10@valid' : valid_cifar10_loader, 'cifar10@test' : test__cifar10_loader, 'cifar100@train' : train_cifar100_loader, 'cifar100@valid' : valid_cifar100_loader, 'cifar100@test' : test__cifar100_loader, 'ImageNet16-120@train': train_imagenet_loader, 'ImageNet16-120@valid': valid_imagenet_loader, 'ImageNet16-120@test' : test__imagenet_loader} return loaders