updates for beta

configs/nas-benchmark/algos/SETN.config

@@ -9,5 +9,6 @@
   "momentum" : ["float", "0.9"],
   "nesterov" : ["bool",  "1"],
   "criterion": ["str",   "Softmax"],
-  "batch_size": ["int",  "64"]
+  "batch_size": ["int",  "64"],
+  "test_batch_size": ["int",  "512"]
 }

exps/AA-NAS-Bench-main.py

@@ -0,0 +1,274 @@
+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+import os, sys, time, torch, random, argparse
+from PIL     import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+from copy    import deepcopy
+from pathlib import Path
+
+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
+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 import evaluate_for_seed
+
+
+def evaluate_all_datasets(arch, datasets, xpaths, splits, seed, arch_config, workers, logger):
+  machine_info, arch_config = get_machine_info(), deepcopy(arch_config)
+  all_infos = {'info': machine_info}
+  all_dataset_keys = []
+  # look all the datasets
+  for dataset, xpath, split in zip(datasets, xpaths, splits):
+    # train valid data
+    train_data, valid_data, xshape, class_num = get_datasets(dataset, xpath, -1)
+    # load the configurature
+    if dataset == 'cifar10' or dataset == 'cifar100':
+      config_path = 'configs/nas-benchmark/CIFAR.config'
+      split_info  = load_config('configs/nas-benchmark/cifar-split.txt', None, None)
+    elif dataset.startswith('ImageNet16'):
+      config_path = 'configs/nas-benchmark/ImageNet-16.config'
+      split_info  = load_config('configs/nas-benchmark/{:}-split.txt'.format(dataset), None, None)
+    else:
+      raise ValueError('invalid dataset : {:}'.format(dataset))
+    config = load_config(config_path, \
+                            {'class_num': class_num,
+                             'xshape'   : xshape}, \
+                            logger)
+    # check whether use splited validation set
+    if bool(split):
+      assert len(train_data) == len(split_info.train) + len(split_info.valid), 'invalid length : {:} vs {:} + {:}'.format(len(train_data), len(split_info.train), len(split_info.valid))
+      train_data_v2 = deepcopy(train_data)
+      train_data_v2.transform = valid_data.transform
+      valid_data = train_data_v2
+      # data loader
+      train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.train), num_workers=workers, pin_memory=True)
+      valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(split_info.valid), num_workers=workers, pin_memory=True)
+    else:
+      # data loader
+      train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
+      valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
+    
+    dataset_key = '{:}'.format(dataset)
+    if bool(split): dataset_key = dataset_key + '-valid'
+    logger.log('Evaluate ||||||| {:10s} ||||||| Train-Num={:}, Valid-Num={:}, Train-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(dataset_key, len(train_data), len(valid_data), len(train_loader), len(valid_loader), config.batch_size))
+    logger.log('Evaluate ||||||| {:10s} ||||||| Config={:}'.format(dataset_key, config))
+    results = evaluate_for_seed(arch_config, config, arch, train_loader, valid_loader, seed, logger)
+    all_infos[dataset_key] = results
+    all_dataset_keys.append( dataset_key )
+  all_infos['all_dataset_keys'] = all_dataset_keys
+  return all_infos
+
+
+def main(save_dir, workers, datasets, xpaths, splits, srange, arch_index, seeds, cover_mode, meta_info, arch_config):
+  assert torch.cuda.is_available(), 'CUDA is not available.'
+  torch.backends.cudnn.enabled   = True
+  #torch.backends.cudnn.benchmark = True
+  torch.backends.cudnn.deterministic = True
+  torch.set_num_threads( workers )
+
+  assert len(srange) == 2 and 0 <= srange[0] <= srange[1], 'invalid srange : {:}'.format(srange)
+  
+  sub_dir = Path(save_dir) / '{:06d}-{:06d}-C{:}-N{:}'.format(srange[0], srange[1], arch_config['channel'], arch_config['num_cells'])
+  logger  = Logger(str(sub_dir), 0, False)
+
+  all_archs = meta_info['archs']
+  assert srange[1] < meta_info['total'], 'invalid range : {:}-{:} vs. {:}'.format(srange[0], srange[1], meta_info['total'])
+  assert arch_index == -1 or srange[0] <= arch_index <= srange[1], 'invalid range : {:} vs. {:} vs. {:}'.format(srange[0], arch_index, srange[1])
+  if arch_index == -1:
+    to_evaluate_indexes = list(range(srange[0], srange[1]+1))
+  else:
+    to_evaluate_indexes = [arch_index]
+  logger.log('xargs : seeds      = {:}'.format(seeds))
+  logger.log('xargs : arch_index = {:}'.format(arch_index))
+  logger.log('xargs : cover_mode = {:}'.format(cover_mode))
+  logger.log('-'*100)
+
+  logger.log('Start evaluating range =: {:06d} vs. {:06d} vs. {:06d} / {:06d} with cover-mode={:}'.format(srange[0], arch_index, srange[1], meta_info['total'], cover_mode))
+  for i, (dataset, xpath, split) in enumerate(zip(datasets, xpaths, splits)):
+    logger.log('--->>> Evaluate {:}/{:} : dataset={:9s}, path={:}, split={:}'.format(i, len(datasets), dataset, xpath, split))
+  logger.log('--->>> architecture config : {:}'.format(arch_config))
+  
+
+  start_time, epoch_time = time.time(), AverageMeter()
+  for i, index in enumerate(to_evaluate_indexes):
+    arch = all_archs[index]
+    logger.log('\n{:} evaluate {:06d}/{:06d} ({:06d}/{:06d})-th architecture [seeds={:}] {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seeds, '-'*15))
+    #logger.log('{:} {:} {:}'.format('-'*15, arch.tostr(), '-'*15))
+    logger.log('{:} {:} {:}'.format('-'*15, arch, '-'*15))
+  
+    # test this arch on different datasets with different seeds
+    has_continue = False
+    for seed in seeds:
+      to_save_name = sub_dir / 'arch-{:06d}-seed-{:04d}.pth'.format(index, seed)
+      if to_save_name.exists():
+        if cover_mode:
+          logger.log('Find existing file : {:}, remove it before evaluation'.format(to_save_name))
+          os.remove(str(to_save_name))
+        else         :
+          logger.log('Find existing file : {:}, skip this evaluation'.format(to_save_name))
+          has_continue = True
+          continue
+      results = evaluate_all_datasets(CellStructure.str2structure(arch), \
+                                        datasets, xpaths, splits, seed, \
+                                        arch_config, workers, logger)
+      torch.save(results, to_save_name)
+      logger.log('{:} valuate {:06d}/{:06d} ({:06d}/{:06d})-th seed={:} done, save into {:}'.format('-'*15, i, len(to_evaluate_indexes), index, meta_info['total'], seed, to_save_name))
+    # measure elapsed time
+    if not has_continue: epoch_time.update(time.time() - start_time)
+    start_time = time.time()
+    need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.avg * (len(to_evaluate_indexes)-i-1), True) )
+    logger.log('This arch costs : {:}'.format( convert_secs2time(epoch_time.val, True) ))
+    logger.log('{:}'.format('*'*100))
+    logger.log('{:}   {:74s}   {:}'.format('*'*10, '{:06d}/{:06d} ({:06d}/{:06d})-th done, left {:}'.format(i, len(to_evaluate_indexes), index, meta_info['total'], need_time), '*'*10))
+    logger.log('{:}'.format('*'*100))
+
+  logger.close()
+
+
+def train_single_model(save_dir, workers, datasets, xpaths, splits, 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'])
+  logger   = Logger(str(save_dir), 0, False)
+  if model_str in CellArchitectures:
+    arch   = CellArchitectures[model_str]
+    logger.log('The model string is found in pre-defined architecture dict : {:}'.format(model_str))
+  else:
+    try:
+      arch = CellStructure.str2structure(model_str)
+    except:
+      raise ValueError('Invalid model string : {:}. It can not be found or parsed.'.format(model_str))
+  assert arch.check_valid_op(get_search_spaces('cell', 'full')), '{:} has the invalid op.'.format(arch)
+  logger.log('Start train-evaluate {:}'.format(arch.tostr()))
+  logger.log('arch_config : {:}'.format(arch_config))
+
+  start_time, seed_time = time.time(), AverageMeter()
+  for _is, seed in enumerate(seeds):
+    logger.log('\nThe {:02d}/{:02d}-th seed is {:} ----------------------<.>----------------------'.format(_is, len(seeds), seed))
+    to_save_name = save_dir / 'seed-{:04d}.pth'.format(seed)
+    if to_save_name.exists():
+      logger.log('Find the existing file {:}, directly load!'.format(to_save_name))
+      checkpoint = torch.load(to_save_name)
+    else:
+      logger.log('Does not find the existing file {:}, train and evaluate!'.format(to_save_name))
+      checkpoint = evaluate_all_datasets(arch, datasets, xpaths, splits, seed, arch_config, workers, logger)
+      torch.save(checkpoint, to_save_name)
+    # log information
+    logger.log('{:}'.format(checkpoint['info']))
+    all_dataset_keys = checkpoint['all_dataset_keys']
+    for dataset_key in all_dataset_keys:
+      logger.log('\n{:} dataset : {:} {:}'.format('-'*15, dataset_key, '-'*15))
+      dataset_info = checkpoint[dataset_key]
+      #logger.log('Network ==>\n{:}'.format( dataset_info['net_string'] ))
+      logger.log('Flops = {:} MB, Params = {:} MB'.format(dataset_info['flop'], dataset_info['param']))
+      logger.log('config : {:}'.format(dataset_info['config']))
+      logger.log('Training State (finish) = {:}'.format(dataset_info['finish-train']))
+      last_epoch = dataset_info['total_epoch'] - 1
+      train_acc1es, train_acc5es = dataset_info['train_acc1es'], dataset_info['train_acc5es']
+      valid_acc1es, valid_acc5es = dataset_info['valid_acc1es'], dataset_info['valid_acc5es']
+      logger.log('Last Info : Train = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%, Test = Acc@1 {:.2f}% Acc@5 {:.2f}% Error@1 {:.2f}%'.format(train_acc1es[last_epoch], train_acc5es[last_epoch], 100-train_acc1es[last_epoch], valid_acc1es[last_epoch], valid_acc5es[last_epoch], 100-valid_acc1es[last_epoch]))
+    # measure elapsed time
+    seed_time.update(time.time() - start_time)
+    start_time = time.time()
+    need_time = 'Time Left: {:}'.format( convert_secs2time(seed_time.avg * (len(seeds)-_is-1), True) )
+    logger.log('\n<<<***>>> The {:02d}/{:02d}-th seed is {:} <finish> other procedures need {:}'.format(_is, len(seeds), seed, need_time))
+  logger.close()
+
+
+def generate_meta_info(save_dir, max_node, divide=40):
+  aa_nas_bench_ss = get_search_spaces('cell', 'aa-nas')
+  archs = CellStructure.gen_all(aa_nas_bench_ss, max_node, False)
+  print ('There are {:} archs vs {:}.'.format(len(archs), len(aa_nas_bench_ss) ** ((max_node-1)*max_node/2)))
+
+  random.seed( 88 ) # please do not change this line for reproducibility
+  random.shuffle( archs )
+  # to test fixed-random shuffle 
+  #print ('arch [0] : {:}\n---->>>>   {:}'.format( archs[0], archs[0].tostr() ))
+  #print ('arch [9] : {:}\n---->>>>   {:}'.format( archs[9], archs[9].tostr() ))
+  assert archs[0  ].tostr() == '|avg_pool_3x3~0|+|nor_conv_1x1~0|skip_connect~1|+|nor_conv_1x1~0|skip_connect~1|skip_connect~2|', 'please check the 0-th architecture : {:}'.format(archs[0])
+  assert archs[9  ].tostr() == '|avg_pool_3x3~0|+|none~0|none~1|+|skip_connect~0|none~1|nor_conv_3x3~2|', 'please check the 9-th architecture : {:}'.format(archs[9])
+  assert archs[123].tostr() == '|avg_pool_3x3~0|+|avg_pool_3x3~0|nor_conv_1x1~1|+|none~0|avg_pool_3x3~1|nor_conv_3x3~2|', 'please check the 123-th architecture : {:}'.format(archs[123])
+  total_arch = len(archs)
+  
+  num = 50000
+  indexes_5W = list(range(num))
+  random.seed( 1021 )
+  random.shuffle( indexes_5W )
+  train_split = sorted( list(set(indexes_5W[:num//2])) )
+  valid_split = sorted( list(set(indexes_5W[num//2:])) )
+  assert len(train_split) + len(valid_split) == num
+  assert train_split[0] == 0 and train_split[10] == 26 and train_split[111] == 203 and valid_split[0] == 1 and valid_split[10] == 18 and valid_split[111] == 242, '{:} {:} {:} - {:} {:} {:}'.format(train_split[0], train_split[10], train_split[111], valid_split[0], valid_split[10], valid_split[111])
+  splits = {num: {'train': train_split, 'valid': valid_split} }
+
+  info = {'archs' : [x.tostr() for x in archs],
+          'total' : total_arch,
+          'max_node' : max_node,
+          'splits': splits}
+
+  save_dir = Path(save_dir)
+  save_dir.mkdir(parents=True, exist_ok=True)
+  save_name = save_dir / 'meta-node-{:}.pth'.format(max_node)
+  assert not save_name.exists(), '{:} already exist'.format(save_name)
+  torch.save(info, save_name)
+  print ('save the meta file into {:}'.format(save_name))
+
+  script_name = save_dir / 'meta-node-{:}.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))
+
+
+
+if __name__ == '__main__':
+  #mode_choices = ['meta', 'new', 'cover'] + ['specific-{:}'.format(_) for _ in CellArchitectures.keys()]
+  parser = argparse.ArgumentParser(description='Algorithm-Agnostic NAS Benchmark', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--mode'   ,     type=str,   required=True,  help='The script mode.')
+  parser.add_argument('--save_dir',    type=str,                   help='Folder to save checkpoints and log.')
+  parser.add_argument('--max_node',    type=int,                   help='The maximum node in a cell.')
+  # use for train the model
+  parser.add_argument('--workers',     type=int,   default=8,      help='number of data loading workers (default: 2)')
+  parser.add_argument('--srange' ,     type=int,   nargs='+',      help='The range of models to be evaluated')
+  parser.add_argument('--arch_index',  type=int,   default=-1,     help='The architecture index to be evaluated (cover mode).')
+  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('--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.')
+  args = parser.parse_args()
+
+  assert args.mode in ['meta', 'new', 'cover'] or args.mode.startswith('specific-'), 'invalid mode : {:}'.format(args.mode)
+
+  if args.mode == 'meta':
+    generate_meta_info(args.save_dir, args.max_node)
+  elif args.mode.startswith('specific'):
+    assert len(args.mode.split('-')) == 2, 'invalid mode : {:}'.format(args.mode)
+    model_str = args.mode.split('-')[1]
+    train_single_model(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, \
+                         tuple(args.seeds), model_str, {'channel': args.channel, 'num_cells': args.num_cells})
+  else:
+    meta_path = Path(args.save_dir) / 'meta-node-{:}.pth'.format(args.max_node)
+    assert meta_path.exists(), '{:} does not exist.'.format(meta_path)
+    meta_info = torch.load( meta_path )
+    # check whether args is ok
+    assert len(args.srange) == 2 and args.srange[0] <= args.srange[1], 'invalid length of srange args: {:}'.format(args.srange)
+    assert len(args.seeds) > 0, 'invalid length of seeds args: {:}'.format(args.seeds)
+    assert len(args.datasets) == len(args.xpaths) == len(args.splits), 'invalid infos : {:} vs {:} vs {:}'.format(len(args.datasets), len(args.xpaths), len(args.splits))
+    assert args.workers > 0, 'invalid number of workers : {:}'.format(args.workers)
+  
+    main(args.save_dir, args.workers, args.datasets, args.xpaths, args.splits, \
+           tuple(args.srange), args.arch_index, tuple(args.seeds), \
+           args.mode == 'cover', meta_info, \
+           {'channel': args.channel, 'num_cells': args.num_cells})

exps/algos/ENAS.py

@@ -62,7 +62,7 @@ def train_controller(xloader, shared_cnn, controller, criterion, optimizer, conf
   # config. (containing some necessary arg)
   #   baseline: The baseline score (i.e. average val_acc) from the previous epoch
   data_time, batch_time = AverageMeter(), AverageMeter()
-  GradnormMeter, LossMeter, ValAccMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
+  GradnormMeter, LossMeter, ValAccMeter, EntropyMeter, BaselineMeter, RewardMeter, xend = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), time.time()
   
   shared_cnn.eval()
   controller.train()
@@ -96,8 +96,9 @@ def train_controller(xloader, shared_cnn, controller, criterion, optimizer, conf
     # account
     RewardMeter.update(reward.item())
     BaselineMeter.update(baseline.item())
-    ValAccMeter.update(val_top1.item())
+    ValAccMeter.update(val_top1.item()*100)
     LossMeter.update(loss.item())
+    EntropyMeter.update(entropy.item())
   
     # Average gradient over controller_num_aggregate samples
     loss = loss / config.ctl_num_aggre
@@ -116,7 +117,8 @@ def train_controller(xloader, shared_cnn, controller, criterion, optimizer, conf
       Sstr = '*Train-Controller* ' + time_string() + ' [{:}][{:03d}/{:03d}]'.format(epoch_str, step, config.ctl_train_steps * config.ctl_num_aggre)
       Tstr = 'Time {batch_time.val:.2f} ({batch_time.avg:.2f}) Data {data_time.val:.2f} ({data_time.avg:.2f})'.format(batch_time=batch_time, data_time=data_time)
       Wstr = '[Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Reward {reward.val:.2f} ({reward.avg:.2f})] Baseline {basel.val:.2f} ({basel.avg:.2f})'.format(loss=LossMeter, top1=ValAccMeter, reward=RewardMeter, basel=BaselineMeter)
-      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr)
+      Estr = 'Entropy={:.4f} ({:.4f})'.format(EntropyMeter.val, EntropyMeter.avg)
+      logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Estr)
 
   return LossMeter.avg, ValAccMeter.avg, BaselineMeter.avg, RewardMeter.avg, baseline.item()
 
@@ -250,7 +252,7 @@ def main(xargs):
     w_scheduler.update(epoch, 0.0)
     need_time = 'Time Left: {:}'.format( convert_secs2time(epoch_time.val * (total_epoch-epoch), True) )
     epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
-    logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
+    logger.log('\n[Search the {:}-th epoch] {:}, LR={:}, baseline={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr()), baseline))
 
     cnn_loss, cnn_top1, cnn_top5 = train_shared_cnn(train_loader, shared_cnn, controller, criterion, w_scheduler, w_optimizer, epoch_str, xargs.print_freq, logger)
     logger.log('[{:}] shared-cnn : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, cnn_loss, cnn_top1, cnn_top5))
@@ -264,7 +266,7 @@ def main(xargs):
     logger.log('[{:}] controller : loss={:.2f}, accuracy={:.2f}%, baseline={:.2f}, reward={:.2f}, current-baseline={:.4f}'.format(epoch_str, ctl_loss, ctl_acc, ctl_baseline, ctl_reward, baseline))
     best_arch, _ = get_best_arch(controller, shared_cnn, valid_loader)
     shared_cnn.module.update_arch(best_arch)
-    best_valid_acc = valid_func(valid_loader, shared_cnn, criterion)
+    _, best_valid_acc, _ = valid_func(valid_loader, shared_cnn, criterion)
 
     genotypes[epoch] = best_arch
     # check the best accuracy
@@ -301,6 +303,14 @@ def main(xargs):
     start_time = time.time()
 
   logger.log('\n' + '-'*100)
+  logger.log('During searching, the best architecture is {:}'.format(genotypes['best']))
+  logger.log('Its accuracy is {:.2f}%'.format(valid_accuracies['best']))
+  logger.log('Randomly select {:} architectures and select the best.'.format(xargs.controller_num_samples))
+  final_arch, _ = get_best_arch(controller, shared_cnn, valid_loader, xargs.controller_num_samples)
+  shared_cnn.module.update_arch(final_arch)
+  final_loss, final_top1, final_top5 = valid_func(valid_loader, shared_cnn, criterion)
+  logger.log('The Selected Final Architecture : {:}'.format(final_arch))
+  logger.log('Loss={:.3f}, Accuracy@1={:.2f}%, Accuracy@5={:.2f}%'.format(final_loss, final_top1, final_top5))
   # check the performance from the architecture dataset
   #if xargs.arch_nas_dataset is None or not os.path.isfile(xargs.arch_nas_dataset):
   #  logger.log('Can not find the architecture dataset : {:}.'.format(xargs.arch_nas_dataset))

exps/algos/SETN.py

@@ -23,7 +23,6 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
   data_time, batch_time = AverageMeter(), AverageMeter()
   base_losses, base_top1, base_top5 = AverageMeter(), AverageMeter(), AverageMeter()
   arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
-  network.train()
   end = time.time()
   for step, (base_inputs, base_targets, arch_inputs, arch_targets) in enumerate(xloader):
     scheduler.update(None, 1.0 * step / len(xloader))
@@ -33,9 +32,13 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
     data_time.update(time.time() - end)
     
     # update the weights
-    network.module.set_cal_mode( 'urs' )
+    network.train()
-    w_optimizer.zero_grad()
+    sampled_arch = network.module.dync_genotype(True)
-    _, logits = network(base_inputs)
+    network.module.set_cal_mode('dynamic', sampled_arch)
+    #network.module.set_cal_mode( 'urs' )
+    network.zero_grad()
+    _, logits = network( torch.cat((base_inputs, arch_inputs), dim=0) )
+    logits    = logits[:base_inputs.size(0)]
     base_loss = criterion(logits, base_targets)
     base_loss.backward()
     w_optimizer.step()
@@ -46,8 +49,9 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
     base_top5.update  (base_prec5.item(), base_inputs.size(0))
 
     # update the architecture-weight
+    network.eval()
     network.module.set_cal_mode( 'joint' )
-    a_optimizer.zero_grad()
+    network.zero_grad()
     _, logits = network(arch_inputs)
     arch_loss = criterion(logits, arch_targets)
     arch_loss.backward()
@@ -68,15 +72,42 @@ def search_func(xloader, network, criterion, scheduler, w_optimizer, a_optimizer
       Wstr = 'Base [Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=base_losses, top1=base_top1, top5=base_top5)
       Astr = 'Arch [Loss {loss.val:.3f} ({loss.avg:.3f})  Prec@1 {top1.val:.2f} ({top1.avg:.2f}) Prec@5 {top5.val:.2f} ({top5.avg:.2f})]'.format(loss=arch_losses, top1=arch_top1, top5=arch_top5)
       logger.log(Sstr + ' ' + Tstr + ' ' + Wstr + ' ' + Astr)
-  return base_losses.avg, base_top1.avg, base_top5.avg
+      #print (nn.functional.softmax(network.module.arch_parameters, dim=-1))
+      #print (network.module.arch_parameters)
+  return base_losses.avg, base_top1.avg, base_top5.avg, arch_losses.avg, arch_top1.avg, arch_top5.avg
+
+
+def get_best_arch(xloader, network, n_samples):
+  with torch.no_grad():
+    network.eval()
+    archs, valid_accs = [], []
+    loader_iter = iter(xloader)
+    for i in range(n_samples):
+      try:
+        inputs, targets = next(loader_iter)
+      except:
+        loader_iter = iter(xloader)
+        inputs, targets = next(loader_iter)
+
+      sampled_arch = network.module.dync_genotype(False)
+      network.module.set_cal_mode('dynamic', sampled_arch)
+      _, logits = network(inputs)
+      val_top1, val_top5 = obtain_accuracy(logits.cpu().data, targets.data, topk=(1, 5))
+
+      archs.append( sampled_arch )
+      valid_accs.append( val_top1.item() )
+
+    best_idx = np.argmax(valid_accs)
+    best_arch, best_valid_acc = archs[best_idx], valid_accs[best_idx]
+    return best_arch, best_valid_acc
 
 
 def valid_func(xloader, network, criterion):
   data_time, batch_time = AverageMeter(), AverageMeter()
   arch_losses, arch_top1, arch_top5 = AverageMeter(), AverageMeter(), AverageMeter()
-  network.train()
   end = time.time()
   with torch.no_grad():
+    network.eval()
     for step, (arch_inputs, arch_targets) in enumerate(xloader):
       arch_targets = arch_targets.cuda(non_blocking=True)
       # measure data loading time
@@ -117,8 +148,8 @@ def main(xargs):
     logger.log('Load split file from {:}'.format(split_Fpath))
   else:
     raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
-  config_path = 'configs/nas-benchmark/algos/SETN.config'
+  #config_path = 'configs/nas-benchmark/algos/SETN.config'
-  config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
+  config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
   # To split data
   train_data_v2 = deepcopy(train_data)
   train_data_v2.transform = valid_data.transform
@@ -126,7 +157,7 @@ def main(xargs):
   search_data   = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
   # data loader
   search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
-  valid_loader  = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
+  valid_loader  = torch.utils.data.DataLoader(valid_data,  batch_size=config.test_batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
   logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
   logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
 
@@ -134,6 +165,7 @@ def main(xargs):
   model_config = dict2config({'name': 'SETN', 'C': xargs.channel, 'N': xargs.num_cells,
                               'max_nodes': xargs.max_nodes, 'num_classes': class_num,
                               'space'    : search_space}, None)
+  logger.log('search space : {:}'.format(search_space))
   search_model = get_cell_based_tiny_net(model_config)
   
   w_optimizer, w_scheduler, criterion = get_optim_scheduler(search_model.get_weights(), config)
@@ -173,17 +205,24 @@ def main(xargs):
     epoch_str = '{:03d}-{:03d}'.format(epoch, total_epoch)
     logger.log('\n[Search the {:}-th epoch] {:}, LR={:}'.format(epoch_str, need_time, min(w_scheduler.get_lr())))
 
-    search_w_loss, search_w_top1, search_w_top5 = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
+    search_w_loss, search_w_top1, search_w_top5, search_a_loss, search_a_top1, search_a_top5 \
-    logger.log('[{:}] searching : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
+                = search_func(search_loader, network, criterion, w_scheduler, w_optimizer, a_optimizer, epoch_str, xargs.print_freq, logger)
-    search_model.set_cal_mode('urs')
+    logger.log('[{:}] search [base] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_w_loss, search_w_top1, search_w_top5))
+    logger.log('[{:}] search [arch] : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, search_a_loss, search_a_top1, search_a_top5))
+
+    genotype, temp_accuracy = get_best_arch(valid_loader, network, xargs.select_num)
+    network.module.set_cal_mode('dynamic', genotype)
     valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
-    logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+    logger.log('[{:}] evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}% | {:}'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5, genotype))
-    search_model.set_cal_mode('joint')
+    #search_model.set_cal_mode('urs')
-    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    #valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
-    logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+    #logger.log('[{:}] URS---evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
-    search_model.set_cal_mode('select')
+    #search_model.set_cal_mode('joint')
-    valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    #valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
-    logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+    #logger.log('[{:}] JOINT-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
+    #search_model.set_cal_mode('select')
+    #valid_a_loss , valid_a_top1 , valid_a_top5  = valid_func(valid_loader, network, criterion)
+    #logger.log('[{:}] Selec-evaluate : loss={:.2f}, accuracy@1={:.2f}%, accuracy@5={:.2f}%'.format(epoch_str, valid_a_loss, valid_a_top1, valid_a_top5))
     # check the best accuracy
     valid_accuracies[epoch] = valid_a_top1
     if valid_a_top1 > valid_accuracies['best']:
@@ -192,7 +231,7 @@ def main(xargs):
       find_best = True
     else: find_best = False
 
-    genotypes[epoch] = search_model.genotype()
+    genotypes[epoch] = genotype
     logger.log('<<<--->>> The {:}-th epoch : {:}'.format(epoch_str, genotypes[epoch]))
     # save checkpoint
     save_path = save_checkpoint({'epoch' : epoch + 1,
@@ -219,6 +258,7 @@ def main(xargs):
     start_time = time.time()
 
   # sampling
+  """
   with torch.no_grad():
     logger.log('arch-parameters :\n{:}'.format( nn.functional.softmax(search_model.arch_parameters, dim=-1).cpu() ))
   selected_archs = set()
@@ -238,6 +278,7 @@ def main(xargs):
     if best_arch is None or best_acc < valid_a_top1:
       best_arch, best_acc = arch, valid_a_top1
   logger.log('Find the best one : {:} with accuracy={:.2f}%'.format(best_arch, best_acc))
+  """
 
   logger.log('\n' + '-'*100)
   # check the performance from the architecture dataset
@@ -267,6 +308,7 @@ if __name__ == '__main__':
   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.')
   parser.add_argument('--select_num',         type=int,   help='The number of selected architectures to evaluate.')
+  parser.add_argument('--config_path',        type=str,   help='.')
   # architecture leraning rate
   parser.add_argument('--arch_learning_rate', type=float, default=3e-4, help='learning rate for arch encoding')
   parser.add_argument('--arch_weight_decay',  type=float, default=1e-3, help='weight decay for arch encoding')

lib/models/cell_searchs/search_cells.py

@@ -83,7 +83,8 @@ class SearchCell(nn.Module):
       for j in range(i):
         node_str = '{:}<-{:}'.format(i, j)
         weights  = weightss[ self.edge2index[node_str] ]
-        aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel()
+        #aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) ) / weights.numel()
+        aggregation = sum( layer(nodes[j]) * w for layer, w in zip(self.edges[node_str], weights) )
         inter_nodes.append( aggregation )
       nodes.append( sum(inter_nodes) )
     return nodes[-1]

lib/models/cell_searchs/search_model_setn.py

@@ -3,7 +3,7 @@
 ######################################################################################
 # One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
 ######################################################################################
-import torch
+import torch, random
 import torch.nn as nn
 from copy import deepcopy
 from ..cell_operations import ResNetBasicblock
@@ -87,7 +87,7 @@ class TinyNetworkSETN(nn.Module):
     return Structure( genotypes )
 
 
-  def dync_genotype(self):
+  def dync_genotype(self, use_random=False):
     genotypes = []
     with torch.no_grad():
       alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
@@ -95,9 +95,12 @@ class TinyNetworkSETN(nn.Module):
       xlist = []
       for j in range(i):
         node_str = '{:}<-{:}'.format(i, j)
-        weights  = alphas_cpu[ self.edge2index[node_str] ]
+        if use_random:
-        op_index = torch.multinomial(weights, 1).item()
+          op_name  = random.choice(self.op_names)
-        op_name  = self.op_names[ op_index ]
+        else:
+          weights  = alphas_cpu[ self.edge2index[node_str] ]
+          op_index = torch.multinomial(weights, 1).item()
+          op_name  = self.op_names[ op_index ]
         xlist.append((op_name, j))
       genotypes.append( tuple(xlist) )
     return Structure( genotypes )

lib/procedures/optimizers.py

@@ -69,12 +69,15 @@ class CosineAnnealingLR(_LRScheduler):
   def get_lr(self):
     lrs = []
     for base_lr in self.base_lrs:
-      if self.current_epoch >= self.warmup_epochs:
+      if self.current_epoch >= self.warmup_epochs and self.current_epoch < self.max_epochs:
         last_epoch = self.current_epoch - self.warmup_epochs
-        if last_epoch < self.T_max:
+        #if last_epoch < self.T_max:
-          lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * last_epoch / self.T_max)) / 2
+        #if last_epoch < self.max_epochs:
-        else:
+        lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * last_epoch / self.T_max)) / 2
-          lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * (self.T_max-1.0) / self.T_max)) / 2
+        #else:
+        #  lr = self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * (self.T_max-1.0) / self.T_max)) / 2
+      elif self.current_epoch >= self.max_epochs:
+        lr = self.eta_min
       else:
         lr = (self.current_epoch / self.warmup_epochs + self.current_iter / self.warmup_epochs) * base_lr
       lrs.append( lr )

scripts-search/algos/ENAS.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+# Efficient Neural Architecture Search via Parameter Sharing, ICML 2018
+# bash ./scripts-search/scripts/algos/ENAS.sh cifar10 -1
+echo script name: $0
+echo $# arguments
+if [ "$#" -ne 2 ] ;then
+  echo "Input illegal number of parameters " $#
+  echo "Need 2 parameters for dataset and seed"
+  exit 1
+fi
+if [ "$TORCH_HOME" = "" ]; then
+  echo "Must set TORCH_HOME envoriment variable for data dir saving"
+  exit 1
+else
+  echo "TORCH_HOME : $TORCH_HOME"
+fi
+
+dataset=$1
+seed=$2
+channel=16
+num_cells=5
+max_nodes=4
+
+if [ "$dataset" == "cifar10" ] || [ "$dataset" == "cifar100" ]; then
+  data_path="$TORCH_HOME/cifar.python"
+else
+  data_path="$TORCH_HOME/cifar.python/ImageNet16"
+fi
+
+save_dir=./output/cell-search-tiny/ENAS-${dataset}
+
+OMP_NUM_THREADS=4 python ./exps/algos/ENAS.py \
+	--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
+	--dataset ${dataset} --data_path ${data_path} \
+	--search_space_name aa-nas \
+	--config_path ./configs/nas-benchmark/algos/ENAS.config \
+	--controller_entropy_weight 0.0001 \
+	--controller_bl_dec 0.99 \
+	--controller_train_steps 50 \
+	--controller_num_aggregate 20 \
+	--controller_num_samples 100 \
+	--workers 4 --print_freq 200 --rand_seed ${seed}

scripts-search/algos/SETN.sh

@@ -33,6 +33,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/SETN.py \
 	--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
 	--dataset ${dataset} --data_path ${data_path} \
 	--search_space_name aa-nas \
+	--config_path configs/nas-benchmark/algos/SETN.config \
 	--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
 	--select_num 100 \
 	--workers 4 --print_freq 200 --rand_seed ${seed}